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2024-01-10 | Jeli04/Simple-Hume-Chatbot | interpret.py | from langchain.llms import OpenAI
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationChain
from langchain.prompts import PromptTemplate
from tests import interpret_test as interpret
from dotenv import dotenv_values
import whisper
env_vars = dotenv_values('.env')
llm = OpenAI(openai_api_key=env_vars['OPENAI_API_KEY'], temperature=0.5)
memory = ConversationBufferMemory(return_messages=True, ai_prefix="AI friend")
template = """Your name is Echo and the human you are paired with is named Jerry. The following is a conversation between a human and an AI who have been with each other since birth. The AI is able to detect the humans current facial emotion. Both have known each for a logn time and have developed a very close relationship. The language used by the AI isnt quite robotic, but also incoorpates a mix of human style talk and robotic talk together. When taking account an emotion treat it like how another human would if they saw that persons facial expression. Be firm and dont back down with your opinions as an AI. Make sure to have a funny personality that is able to shine through and make you stand out.
Current converstation:
{history}
Conversation:
Human Emotion: {emotion}
Human: {input}
AI friend:"""
prompt = PromptTemplate(input_variables=["history", "input", "emotion"], template=template)
print("Loading model...")
conversation = ConversationChain(llm=llm, verbose=True, memory=interpret.ExtendedConversationBufferMemory(extra_variables=["emotion"], ai_prefix="AI Assistant", human_prefix="Friend"), prompt=prompt)
model = whisper.load_model("base")
def interpret(audio, emotion = None):
input = model.transcribe(audio, fp16=False, language="English")
if emotion is None:
result = conversation({"input": input["text"], "emotion": "neutral"})
else:
result = conversation({"input": input["text"], "emotion": emotion})
return result | [
"input",
"emotion",
"Your name is Echo and the human you are paired with is named Jerry. The following is a conversation between a human and an AI who have been with each other since birth. The AI is able to detect the humans current facial emotion. Both have known each for a logn time and have developed a very close relationship. The language used by the AI isnt quite robotic, but also incoorpates a mix of human style talk and robotic talk together. When taking account an emotion treat it like how another human would if they saw that persons facial expression. Be firm and dont back down with your opinions as an AI. Make sure to have a funny personality that is able to shine through and make you stand out.\n\nCurrent converstation:\n{history}\nConversation:\nHuman Emotion: {emotion}\nHuman: {input}\nAI friend:"
] |
2024-01-10 | Jeli04/Simple-Hume-Chatbot | tests~interpret_test.py | from dotenv import dotenv_values
from langchain.llms import OpenAI
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationChain
from langchain.prompts import PromptTemplate
import asyncio
from typing import Any, Dict, List
env_vars = dotenv_values('.env')
# custom memory class for the third input variable memory
class ExtendedConversationBufferMemory(ConversationBufferMemory):
extra_variables:List[str] = []
@property
def memory_variables(self) -> List[str]:
"""Will always return list of memory variables."""
return [self.memory_key] + self.extra_variables
def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
"""Return buffer with history and extra variables"""
d = super().load_memory_variables(inputs)
d.update({k:inputs.get(k) for k in self.extra_variables})
return d
# move this to a env variable
llm = OpenAI(openai_api_key=env_vars['OPENAI_API_KEY'], temperature=0.5)
memory = ConversationBufferMemory(return_messages=True, ai_prefix="AI friend")
template = """The following is a passive agressive comedic conversation between a human and an AI. This AI specifically is able to get the humans emotion. Based of this current emotion give a response that a human would also give if they saw that emotion. The AI is talkative and provides lots of specific details from its context. The AI should talk like they have known the human for years. Make this conversation short and try keeping it to 1-2 sentences but also try keeping the same conversation going until the human changes topics. Also if the human gives a more complicated input that requires more than 2 sentences to answer feel free to give a more detailed explanation!
Current converstation:
{history}
Conversation:
Human Emotion: {emotion}
Human: {input}
AI friend:"""
prompt = PromptTemplate(input_variables=["history", "input", "emotion"], template=template)
async def start_conversation(emotions_list=None, lock=None):
print("starting conversation")
conversation = ConversationChain(llm=llm, verbose=True, memory=ExtendedConversationBufferMemory(extra_variables=["emotion"]), prompt=prompt)
# input_message = input()
input_message = await asyncio.get_event_loop().run_in_executor(None, input, 'Enter message: ')
while(input_message != "exit"):
if(emotions_list != None):
# conversation.run(input=input_message, emotion=emotions_list[-1])
async with lock:
result = conversation({"input": input_message, "emotion": emotions_list[-1]})
else:
# conversation.run(input=input_message, emotion="neutral")
result = conversation({"input": input_message, "emotion": "neutral"})
# print(conversation.memory.buffer[-1].content)
print(result["response"])
# input_message = input()
input_message = await asyncio.get_event_loop().run_in_executor(None, input, 'Enter message: ')
# asyncio.run(start_conversation())
# move this to a env variable
# llm = OpenAI(openai_api_key=env_vars['OPENAI_API_KEY'], temperature=0.5)
# memory = ConversationBufferMemory(return_messages=True, ai_prefix="AI friend")
# template = """The following is a passive agressive comedic conversation between a human and an AI. This AI specifically is able to get the humans emotion. Based of this current emotion give a response that a human would also give if they saw that emotion. The AI is talkative and provides lots of specific details from its context. The AI should talk like they have known the human for years. Make this conversation short and try keeping it to 1-2 sentences but also try keeping the same conversation going until the human changes topics. Also if the human gives a more complicated input that requires more than 2 sentences to answer feel free to give a more detailed explanation!
# Current converstation:
# {history}
# Conversation:
# Human: {input}
# AI friend:"""
# prompt = PromptTemplate(input_variables=["history", "input"], template=template)
# async def start_conversation(emotions_list=None):
# conversation = ConversationChain(llm=llm, verbose=True, memory=memory, prompt=prompt)
# input_message = input()
# while(input_message != "exit"):
# conversation.run(input=input_message)
# print(conversation.memory.buffer[-1].content)
# input_message = input()
# asyncio.run(start_conversation()) | [
"input",
"The following is a passive agressive comedic conversation between a human and an AI. This AI specifically is able to get the humans emotion. Based of this current emotion give a response that a human would also give if they saw that emotion. The AI is talkative and provides lots of specific details from its context. The AI should talk like they have known the human for years. Make this conversation short and try keeping it to 1-2 sentences but also try keeping the same conversation going until the human changes topics. Also if the human gives a more complicated input that requires more than 2 sentences to answer feel free to give a more detailed explanation!\n\nCurrent converstation:\n{history}\nConversation:\nHuman Emotion: {emotion}\nHuman: {input}\nAI friend:",
"emotion"
] |
2024-01-10 | YamanAlBochi/langflow | src~backend~langflow~components~utilities~JSONDocumentBuilder.py | ### JSON Document Builder
# Build a Document containing a JSON object using a key and another Document page content.
# **Params**
# - **Key:** The key to use for the JSON object.
# - **Document:** The Document page to use for the JSON object.
# **Output**
# - **Document:** The Document containing the JSON object.
from langflow import CustomComponent
from langchain.schema import Document
from langflow.services.database.models.base import orjson_dumps
class JSONDocumentBuilder(CustomComponent):
display_name: str = "JSON Document Builder"
description: str = "Build a Document containing a JSON object using a key and another Document page content."
output_types: list[str] = ["Document"]
beta = True
documentation: str = (
"https://docs.langflow.org/components/utilities#json-document-builder"
)
field_config = {
"key": {"display_name": "Key"},
"document": {"display_name": "Document"},
}
def build(
self,
key: str,
document: Document,
) -> Document:
documents = None
if isinstance(document, list):
documents = [
Document(
page_content=orjson_dumps({key: doc.page_content}, indent_2=False)
)
for doc in document
]
elif isinstance(document, Document):
documents = Document(
page_content=orjson_dumps({key: document.page_content}, indent_2=False)
)
else:
raise TypeError(
f"Expected Document or list of Documents, got {type(document)}"
)
self.repr_value = documents
return documents
| [] |
2024-01-10 | YamanAlBochi/langflow | src~backend~langflow~components~utilities~GetRequest.py | from langflow import CustomComponent
from langchain.schema import Document
from langflow.services.database.models.base import orjson_dumps
import requests
from typing import Optional
class GetRequest(CustomComponent):
display_name: str = "GET Request"
description: str = "Make a GET request to the given URL."
output_types: list[str] = ["Document"]
documentation: str = "https://docs.langflow.org/components/utilities#get-request"
beta = True
field_config = {
"url": {
"display_name": "URL",
"info": "The URL to make the request to",
"is_list": True,
},
"headers": {
"display_name": "Headers",
"info": "The headers to send with the request.",
},
"code": {"show": False},
"timeout": {
"display_name": "Timeout",
"field_type": "int",
"info": "The timeout to use for the request.",
"value": 5,
},
}
def get_document(
self, session: requests.Session, url: str, headers: Optional[dict], timeout: int
) -> Document:
try:
response = session.get(url, headers=headers, timeout=int(timeout))
try:
response_json = response.json()
result = orjson_dumps(response_json, indent_2=False)
except Exception:
result = response.text
self.repr_value = result
return Document(
page_content=result,
metadata={
"source": url,
"headers": headers,
"status_code": response.status_code,
},
)
except requests.Timeout:
return Document(
page_content="Request Timed Out",
metadata={"source": url, "headers": headers, "status_code": 408},
)
except Exception as exc:
return Document(
page_content=str(exc),
metadata={"source": url, "headers": headers, "status_code": 500},
)
def build(
self,
url: str,
headers: Optional[dict] = None,
timeout: int = 5,
) -> list[Document]:
if headers is None:
headers = {}
urls = url if isinstance(url, list) else [url]
with requests.Session() as session:
documents = [self.get_document(session, u, headers, timeout) for u in urls]
self.repr_value = documents
return documents
| [] |
2024-01-10 | hlp-ai/miniChatGPT | chatgpt~prepare_sft_dataset.py | import json
from dataset import AnthropicHHRLHFDataset, DahoasRMStaticDataset
def sft_set():
"""
A simple script to create EYLSFTStaticDataset
"""
with open("../data/dataset_hhrlhf_train.json", "w") as fp:
AnthropicHHRLHFDataset.save("train", fp)
with open("../data/dataset_hhrlhf_test.json", "w") as fp:
AnthropicHHRLHFDataset.save("test", fp)
with open("../data/dataset_rmstatic_train.json", "w") as fp:
DahoasRMStaticDataset.save("train", fp)
with open("../data/dataset_rmstatic_test.json", "w") as fp:
DahoasRMStaticDataset.save("test", fp)
with open("../data/dataset_rmstatic_train.json") as fp:
rmtrain = set(json.load(fp))
with open("../data/dataset_rmstatic_test.json") as fp:
rmtest = set(json.load(fp))
sft_train = []
with open("../data/dataset_hhrlhf_train.json") as fp:
hhtrain = json.load(fp)
for h in hhtrain:
if h not in rmtrain:
sft_train.append(h)
sft_test = []
with open("../data/dataset_hhrlhf_test.json") as fp:
hhtest = json.load(fp)
for h in hhtest:
if h not in rmtest:
sft_test.append(h)
with open("../data/sft_train.json", "w") as fp:
json.dump(sft_train, fp)
print(len(sft_train))
print(sft_train[-1])
with open("../data/sft_test.json", "w") as fp:
json.dump(sft_test, fp)
print(len(sft_test))
print(sft_test[-1])
def main():
sft_set()
if __name__ == "__main__":
main()
| [] |
2024-01-10 | Vedaukr/vitebsk_bot | bot~handlers~commands~get_bot_triggers.py | from bot.bot_instance.bot import bot_instance
from bot.handlers.shared import tg_exception_handler
import telebot
from services.openai_service import OpenAiService
from utils.search_resolver import search_resolver
@bot_instance.message_handler(commands=['get_bot_triggers'])
@tg_exception_handler
def get_bot_triggers(message: telebot.types.Message):
response = f"Usage: bot [trigger] [trigger_prompt]\n\n"
response += "Search triggers:\n"
for handler in search_resolver.handlers:
uri = handler.get_site_uri()
response += f"Site: {uri if uri else 'Default search'}, Triggers: {handler.get_triggers()}\n"
bot_instance.reply_to(message, response) | [] |
2024-01-10 | Vedaukr/vitebsk_bot | bot~handlers~commands~clear_gpt_context.py | from bot.bot_instance.bot import bot_instance
import telebot
from services.openai_service import OpenAiService
from bot.handlers.shared import tg_exception_handler
openai_service = OpenAiService()
@bot_instance.message_handler(commands=['clear_gpt_context'])
@tg_exception_handler
def clr_handler(message: telebot.types.Message):
openai_service.clear_context(str(message.from_user.id))
bot_instance.reply_to(message, "Context cleared.") | [] |
2024-01-10 | Vedaukr/vitebsk_bot | bot~handlers~commands~get_gpt_context.py | from bot.bot_instance.bot import bot_instance
from bot.handlers.shared import tg_exception_handler
import telebot
from services.openai_service import OpenAiService
openai_service = OpenAiService()
@bot_instance.message_handler(commands=['get_gpt_context'])
@tg_exception_handler
def get_ctx_handler(message: telebot.types.Message):
ctx = openai_service.get_or_create_context(str(message.from_user.id))
bot_instance.reply_to(message, f"Your context:\n{ctx if ctx else 'empty ctx'}") | [] |
2024-01-10 | Vedaukr/vitebsk_bot | bot~handlers~msg_handlers~gpt_handler.py | from bot.bot_instance.bot import bot_instance
from bot.handlers.msg_handlers.shared import get_prompt
from bot.handlers.shared import tg_exception_handler
from services.openai_service import OpenAiService
import telebot
# Singletons
openai_service = OpenAiService()
@bot_instance.message_handler(regexp=r"^(\bgpt\b|\bгпт\b)\s.+")
@tg_exception_handler
def msg_handler(message: telebot.types.Message):
bot_reply = bot_instance.reply_to(message, "generating...")
prompt = get_prompt(message.text)
openai_response = openai_service.get_response(prompt, str(message.from_user.id))
bot_instance.edit_message_text(openai_response, message.chat.id, bot_reply.message_id)
| [] |
2024-01-10 | shadowaxe99/carta-barta | backend~services~analysis_service.py | ```python
import openai
from backend.models.response_model import Response
from backend.models.survey_model import Survey
from backend.database.response_repository import ResponseRepository
from backend.database.survey_repository import SurveyRepository
from backend.config import OPENAI_API_KEY
class AnalysisService:
def __init__(self):
self.openai_api_key = OPENAI_API_KEY
self.response_repository = ResponseRepository()
self.survey_repository = SurveyRepository()
def analyze_survey_responses(self, survey_id):
survey = self.survey_repository.get_survey_by_id(survey_id)
responses = self.response_repository.get_responses_by_survey_id(survey_id)
analyzed_data = {
"sentiment_analysis": [],
"keyword_extraction": []
}
for response in responses:
sentiment = self._analyze_sentiment(response.answer)
keywords = self._extract_keywords(response.answer)
analyzed_data["sentiment_analysis"].append(sentiment)
analyzed_data["keyword_extraction"].append(keywords)
return analyzed_data
def _analyze_sentiment(self, text):
openai.api_key = self.openai_api_key
response = openai.Completion.create(
engine="davinci",
prompt=f"What is the sentiment of this text? {text}",
max_tokens=60
)
return response.choices[0].text.strip()
def _extract_keywords(self, text):
openai.api_key = self.openai_api_key
response = openai.Completion.create(
engine="davinci",
prompt=f"Extract keywords from this text: {text}",
max_tokens=60
)
return response.choices[0].text.strip()
def generate_report(self, survey_id):
analyzed_data = self.analyze_survey_responses(survey_id)
# Logic to format and generate a report based on analyzed_data
# This can be a PDF, a web page, or any other format chosen for reports
report = "Report generation not implemented yet"
return report
``` | [
"What is the sentiment of this text? PLACEHOLDER",
"Extract keywords from this text: PLACEHOLDER"
] |
2024-01-10 | thisishaykins/kunda | stories~tasks.py | import os
import string
from django.http import HttpResponse
from django.utils import timezone
from django.conf import settings
# from celery import shared_task
from django.db.models import Q
import openai
import requests
session = requests.Session()
session.trust_env = False
from .models import Stories
# @shared_task
def create_stories(request: str, user=None):
story: None
is_exist, status: bool = False
# Task to create stories
try:
story = Stories.objects.get(request=request)
is_exist, status = True
except Stories.DoesNotExist:
request_story = chatgpt_create_stories(request=request)
if request_story.status == True:
story = Stories.objects.create(
user=user,
request=request,
content=request_story.content,
ai_model=request_story.model,
ai_role=request_story.role,
)
is_exist = False,
status = True,
else:
is_exist, status = False
return {
'status': status,
'is_exist': is_exist,
'data': story,
}
def chatgpt_create_stories(request: str):
openai.api_key = os.environ.get("OPENAI_API_KEY")
messages = [
{"status": False, "role": "system", "content": "You are an intelligent assistance"}
]
response = {"status": False, "role": "system", "content": ""}
while True:
try:
user_query = request
if user_query:
messages.append(
{"role": "user", "content": user_query},
)
chat_response = openai.ChatCompletion.create(
model="gpt-3.5-turbo",
messages=messages
)
reply = chat_response.choices[0].message.content
print(f"ChatGPT Response => {reply}")
messages.append(
{
"status": True,
"model": "gpt-3.5-turbo",
"role": "assistant",
"content": reply
}
)
response.status = True
response.model = 'gpt-3.5-turbo'
response.role = "assistant"
response.content = reply
print("Message Dict", messages)
print("Reponse Object", response)
return response
except Exception as err:
print("An error occurred with error message => ", err)
exit('AI is stopping now...')
return response | [
"You are an intelligent assistance"
] |
2024-01-10 | flowgeniusmz/FEOCAssistant | Home.py | import streamlit as st
import openai
import time
import functions.pagesetup as ps
import functions.login as lg
from openai import OpenAI
import uuid
#0. Page Config
st.set_page_config("FEOC Assistant1234", initial_sidebar_state="collapsed", layout="wide")
#1. Login and Page Setup
if lg.check_authentication():
ps.set_title("FEOC", "FEOC Assistant")
ps.set_page_overview("Overview", "**FEOC Assistant** provides a way to quickly ask about the FEOC")
#2. Variable Setup
openai.api_key = st.secrets.OPENAI_API_KEY
assistant = st.secrets.OPENAI_ASSISTANT_KEY
model = "gpt-4-1106-preview"
client = OpenAI()
#3. Session State Management
if "session_id" not in st.session_state: #used to identify each session
st.session_state.session_id = str(uuid.uuid4())
if "run" not in st.session_state: #stores the run state of the assistant
st.session_state.run = {"status": None}
if "messages" not in st.session_state: #stores messages of the assistant
st.session_state.messages = []
st.chat_message("assistant").markdown("I am your FEOC assistant. How may I help you?")
if "retry_error" not in st.session_state: #used for error handling
st.session_state.retry_error = 0
#4. Openai setup
if "assistant" not in st.session_state:
openai.api_key = st.secrets["OPENAI_API_KEY"]
# Load the previously created assistant
st.session_state.assistant = openai.beta.assistants.retrieve(st.secrets["OPENAI_ASSISTANT_KEY"])
# Create a new thread for this session
st.session_state.thread = client.beta.threads.create(
metadata={
'session_id': st.session_state.session_id,
}
)
# If the run is completed, display the messages
elif hasattr(st.session_state.run, 'status') and st.session_state.run.status == "completed":
# Retrieve the list of messages
st.session_state.messages = client.beta.threads.messages.list(
thread_id=st.session_state.thread.id
)
for thread_message in st.session_state.messages.data:
for message_content in thread_message.content:
# Access the actual text content
message_content = message_content.text
annotations = message_content.annotations
citations = []
# Iterate over the annotations and add footnotes
for index, annotation in enumerate(annotations):
# Replace the text with a footnote
message_content.value = message_content.value.replace(annotation.text, f' [{index}]')
# Gather citations based on annotation attributes
if (file_citation := getattr(annotation, 'file_citation', None)):
cited_file = client.files.retrieve(file_citation.file_id)
citations.append(f'[{index}] {file_citation.quote} from {cited_file.filename}')
elif (file_path := getattr(annotation, 'file_path', None)):
cited_file = client.files.retrieve(file_path.file_id)
citations.append(f'[{index}] Click <here> to download {cited_file.filename}')
# Note: File download functionality not implemented above for brevity
# Add footnotes to the end of the message before displaying to user
message_content.value += '\n' + '\n'.join(citations)
# Display messages
for message in reversed(st.session_state.messages.data):
if message.role in ["user", "assistant"]:
with st.chat_message(message.role):
for content_part in message.content:
message_text = content_part.text.value
st.markdown(message_text)
if prompt := st.chat_input("How can I help you?"):
with st.chat_message('user'):
st.write(prompt)
# Add message to the thread
st.session_state.messages = client.beta.threads.messages.create(
thread_id=st.session_state.thread.id,
role="user",
content=prompt
)
# Do a run to process the messages in the thread
st.session_state.run = client.beta.threads.runs.create(
thread_id=st.session_state.thread.id,
assistant_id=st.session_state.assistant.id,
)
if st.session_state.retry_error < 3:
time.sleep(1) # Wait 1 second before checking run status
st.rerun()
# Check if 'run' object has 'status' attribute
if hasattr(st.session_state.run, 'status'):
# Handle the 'running' status
if st.session_state.run.status == "running":
with st.chat_message('assistant'):
st.write("Thinking ......")
if st.session_state.retry_error < 3:
time.sleep(1) # Short delay to prevent immediate rerun, adjust as needed
st.rerun()
# Handle the 'failed' status
elif st.session_state.run.status == "failed":
st.session_state.retry_error += 1
with st.chat_message('assistant'):
if st.session_state.retry_error < 3:
st.write("Run failed, retrying ......")
time.sleep(3) # Longer delay before retrying
st.rerun()
else:
st.error("FAILED: The OpenAI API is currently processing too many requests. Please try again later ......")
# Handle any status that is not 'completed'
elif st.session_state.run.status != "completed":
# Attempt to retrieve the run again, possibly redundant if there's no other status but 'running' or 'failed'
st.session_state.run = client.beta.threads.runs.retrieve(
thread_id=st.session_state.thread.id,
run_id=st.session_state.run.id,
)
if st.session_state.retry_error < 3:
time.sleep(3)
st.rerun()
#https://medium.com/prompt-engineering/unleashing-the-power-of-openais-new-gpt-assistants-with-streamlit-83779294629f
#https://github.com/tractorjuice/STGPT
| [] |
2024-01-10 | compatibl/practical-language-models | confirms~core~llm~gpt_native_llm.py | # Copyright (C) 2023-present The Project Contributors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
from dataclasses import dataclass, field
from typing import Dict, Optional
import openai
from confirms.core.llm.llm import Llm
from confirms.core.settings import Settings
@dataclass
class GptNativeLlm(Llm):
"""GPT model family using native OpenAI API."""
temperature: float = field(default=None)
"""Model temperature (note that for GPT models zero value does not mean reproducible answers)."""
_llm: bool = field(default=None)
def load_model(self):
"""Load model after fields have been set."""
# Skip if already loaded
if self._llm is None:
gpt_model_types = ["gpt-3.5-turbo", "gpt-4"]
if self.model_type not in gpt_model_types:
raise RuntimeError(
f"GPT Native LLM model type {self.model_type} is not recognized. "
f"Valid model types are {gpt_model_types}"
)
# Native OpenAI API calls are stateless. This means no object is needed at this time.
self._llm = True
def completion(self, question: str, *, prompt: Optional[str] = None) -> str:
"""Simple completion with optional prompt."""
# Load settings
Settings.load()
if prompt is not None:
messages = [{"role": "system", "content": prompt}]
else:
messages = []
messages = messages + [{"role": "user", "content": question}]
response = openai.ChatCompletion.create(model=self.model_type, messages=messages)
answer = response['choices'][0]['message']['content']
return answer
def function_completion(self, question: str, *, prompt: Optional[str] = None) -> Dict[str, str]:
"""Completion with functions."""
# Load settings
Settings.load()
if prompt is not None:
messages = [{"role": "system", "content": prompt}]
else:
messages = []
messages = messages + [{"role": "user", "content": question}]
functions = [
{
"name": "get_interest_schedule",
"description": "Calculates and returns interest schedule from function parameters",
"parameters": {
"type": "object",
"properties": {
"first_unadjusted_payment_date": {
"type": "string",
"description": "First unadjusted payment date using ISO 8601 date format yyyy-mm-dd.",
},
"last_unadjusted_payment_date": {
"type": "string",
"description": "Last unadjusted payment date using ISO 8601 date format yyyy-mm-dd.",
},
"payment_frequency": {
"type": "string",
"description": "Payment frequency expressed as the number of months followed by capital M",
"enum": ["1M", "3M", "6M", "12M"],
},
},
"required": ["first_unadjusted_payment_date", "last_unadjusted_payment_date", "payment_frequency"],
},
},
{
"name": "get_payment_frequency",
"description": "Extract payment frequency from description",
"parameters": {
"type": "object",
"properties": {
"payment_frequency": {
"type": "string",
"description": "Payment frequency expressed as one word",
},
},
"required": ["payment_frequency"],
},
},
]
response = openai.ChatCompletion.create(
model=self.model_type,
messages=messages,
functions=functions,
function_call="auto", # auto is default, but we'll be explicit
)
response_message = response["choices"][0]["message"]
if response_message.get("function_call"):
function_name = response_message["function_call"]["name"]
result = json.loads(response_message["function_call"]["arguments"])
result["function"] = function_name
return result
else:
raise RuntimeError("No functions called in response to message.")
| [] |
2024-01-10 | beyondchenlin/PaddleSpeech | paddlespeech~s2t~models~whisper~whipser.py | # MIT License, Copyright (c) 2022 OpenAI.
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Modified from OpenAI Whisper 2022 (https://github.com/openai/whisper/whisper)
import os
from dataclasses import dataclass
from dataclasses import field
from functools import lru_cache
from typing import Dict
from typing import Iterable
from typing import List
from typing import Optional
from typing import Sequence
from typing import Tuple
from typing import Union
import numpy as np
import paddle
import paddle.nn.functional as F
import paddlespeech.s2t.modules.align as paddlespeech_nn
import soundfile
import tqdm
from paddle import nn
from paddle.distribution import Categorical
from paddlespeech.s2t.models.whisper import utils
from paddlespeech.s2t.models.whisper.tokenizer import get_tokenizer
from paddlespeech.s2t.models.whisper.tokenizer import LANGUAGES
from paddlespeech.s2t.models.whisper.tokenizer import Tokenizer
from paddlespeech.s2t.utils.log import Log
logger = Log(__name__).getlog()
_MODELS = ["large"]
SAMPLE_RATE = 16000
N_FFT = 400
N_MELS = 80
HOP_LENGTH = 160
CHUNK_LENGTH = 30
N_SAMPLES = CHUNK_LENGTH * SAMPLE_RATE # 480000: number of samples in a chunk
N_FRAMES = utils.exact_div(
N_SAMPLES, HOP_LENGTH) # 3000: number of frames in a mel spectrogram input
@dataclass
class ModelDimensions:
n_mels: int
n_audio_ctx: int
n_audio_state: int
n_audio_head: int
n_audio_layer: int
n_vocab: int
n_text_ctx: int
n_text_state: int
n_text_head: int
n_text_layer: int
class LayerNorm(paddlespeech_nn.LayerNorm):
def forward(self, x: paddle.Tensor) -> paddle.Tensor:
return super().forward(x)
class Linear(paddlespeech_nn.Linear):
def forward(self, x: paddle.Tensor) -> paddle.Tensor:
return F.linear(x, self.weight, None
if self.bias is None else self.bias)
class Conv1d(paddlespeech_nn.Conv1D):
def forward(self, x: paddle.Tensor) -> paddle.Tensor:
return super().forward(x)
class MultiHeadAttention(nn.Layer):
def __init__(self, n_state: int, n_head: int):
super().__init__()
self.n_head = n_head
self.query = Linear(n_state, n_state, bias_attr=True)
self.key = Linear(n_state, n_state, bias_attr=False)
self.value = Linear(n_state, n_state, bias_attr=True)
self.out = Linear(n_state, n_state, bias_attr=True)
def forward(
self,
x: paddle.Tensor,
xa: Optional[paddle.Tensor]=None,
mask: Optional[paddle.Tensor]=None,
kv_cache: Optional[dict]=None, ):
q = self.query(x)
if kv_cache is None or xa is None or self.key not in kv_cache:
# hooks, if installed (i.e. kv_cache is not None), will prepend the cached kv tensors;
# otherwise, perform key/value projections for self- or cross-attention as usual.
k = self.key(x if xa is None else xa)
v = self.value(x if xa is None else xa)
else:
# for cross-attention, calculate keys and values once and reuse in subsequent calls.
k = kv_cache[self.key]
v = kv_cache[self.value]
wv = self.qkv_attention(q, k, v, mask)
return self.out(wv)
def qkv_attention(self,
q: paddle.Tensor,
k: paddle.Tensor,
v: paddle.Tensor,
mask: Optional[paddle.Tensor]=None):
n_batch, n_ctx, n_state = q.shape
scale = (n_state // self.n_head)**-0.25
q = paddle.transpose(
q.view(*q.shape[:2], self.n_head, -1), (0, 2, 1, 3)) * scale
k = paddle.transpose(
k.view(*k.shape[:2], self.n_head, -1), (0, 2, 3, 1)) * scale
v = paddle.transpose(
v.view(*v.shape[:2], self.n_head, -1), (0, 2, 1, 3))
qk = q @ k
if mask is not None:
qk = qk + mask[:n_ctx, :n_ctx]
w = F.softmax(qk.float(), axis=-1).to(q.dtype)
return paddle.transpose((w @ v), (0, 2, 1, 3)).flatten(start_axis=2)
class ResidualAttentionBlock(nn.Layer):
def __init__(self, n_state: int, n_head: int, cross_attention: bool=False):
super().__init__()
self.attn = MultiHeadAttention(n_state, n_head)
self.attn_ln = LayerNorm(n_state)
self.cross_attn = MultiHeadAttention(
n_state, n_head) if cross_attention else None
self.cross_attn_ln = LayerNorm(n_state) if cross_attention else None
n_mlp = n_state * 4
self.mlp = nn.Sequential(
Linear(n_state, n_mlp, bias_attr=True),
nn.GELU(), Linear(n_mlp, n_state, bias_attr=True))
self.mlp_ln = LayerNorm(n_state)
def forward(
self,
x: paddle.Tensor,
xa: Optional[paddle.Tensor]=None,
mask: Optional[paddle.Tensor]=None,
kv_cache: Optional[dict]=None, ):
x = x + self.attn(self.attn_ln(x), mask=mask, kv_cache=kv_cache)
if self.cross_attn:
x = x + self.cross_attn(
self.cross_attn_ln(x), xa, kv_cache=kv_cache)
x = x + self.mlp(self.mlp_ln(x))
return x
def sinusoids(length, channels, max_timescale=10000):
"""Returns sinusoids for positional embedding"""
assert channels % 2 == 0
log_timescale_increment = np.log(max_timescale) / (channels // 2 - 1)
inv_timescales = paddle.exp(-log_timescale_increment * paddle.arange(
channels // 2, dtype=paddle.float32))
scaled_time = paddle.arange(
length,
dtype=paddle.float32)[:, np.newaxis] * inv_timescales[np.newaxis, :]
return paddle.to_tensor(
paddle.concat(
[paddle.sin(scaled_time), paddle.cos(scaled_time)], axis=1))
class AudioEncoder(nn.Layer):
def __init__(self,
n_mels: int,
n_ctx: int,
n_state: int,
n_head: int,
n_layer: int):
super().__init__()
self.conv1 = Conv1d(
n_mels, n_state, kernel_size=3, stride=1, padding=1, bias_attr=True)
self.conv2 = Conv1d(
n_state,
n_state,
kernel_size=3,
stride=2,
padding=1,
bias_attr=True)
self.register_buffer("positional_embedding", sinusoids(n_ctx, n_state))
self.blocks: Iterable[ResidualAttentionBlock] = nn.LayerList(
[ResidualAttentionBlock(n_state, n_head) for _ in range(n_layer)])
self.ln_post = LayerNorm(n_state)
def forward(self, x: paddle.Tensor):
"""
x : paddle.Tensor, shape = (batch_size, n_mels, n_ctx)
the mel spectrogram of the audio
"""
x = F.gelu(self.conv1(x))
x = F.gelu(self.conv2(x))
x = paddle.transpose(x, (0, 2, 1))
assert x.shape[
1:] == self.positional_embedding.shape, "incorrect audio shape"
x = (x + self.positional_embedding)
for block in self.blocks:
x = block(x)
x = self.ln_post(x)
return x
class TextDecoder(nn.Layer):
def __init__(self,
n_vocab: int,
n_ctx: int,
n_state: int,
n_head: int,
n_layer: int):
super().__init__()
self.token_embedding = nn.Embedding(n_vocab, n_state)
self.positional_embedding = paddle.create_parameter(
shape=[n_ctx, n_state], dtype='float32')
self.blocks: Iterable[ResidualAttentionBlock] = nn.LayerList([
ResidualAttentionBlock(n_state, n_head, cross_attention=True)
for _ in range(n_layer)
])
self.ln = LayerNorm(n_state)
mask = paddle.full(
shape=[n_ctx, n_state], fill_value=-np.inf, dtype='float32')
mask = paddle.triu(mask, diagonal=1)
self.register_buffer("mask", mask, persistable=False)
def forward(self,
x: paddle.Tensor,
xa: paddle.Tensor,
kv_cache: Optional[dict]=None):
"""
x : paddle.LongTensor, shape = (batch_size, <= n_ctx)
the text tokens
xa : paddle.Tensor, shape = (batch_size, n_mels, n_audio_ctx)
the encoded audio features to be attended on
"""
offset = next(iter(kv_cache.values())).shape[1] if kv_cache else 0
x = self.token_embedding(x) + self.positional_embedding[offset:offset +
x.shape[-1]]
x = x.to(xa.dtype)
for block in self.blocks:
x = block(x, xa, mask=self.mask, kv_cache=kv_cache)
x = self.ln(x)
logits = (x @ paddle.transpose(self.token_embedding.weight, (1, 0)))
return logits
@dataclass(frozen=True)
class DecodingOptions:
task: str = "transcribe" # whether to perform X->X "transcribe" or X->English "translate"
language: Optional[
str] = None # language that the audio is in; uses detected language if None
# sampling-related options
temperature: float = 0.0
sample_len: Optional[int] = None # maximum number of tokens to sample
best_of: Optional[
int] = None # number of independent samples to collect, when t > 0
beam_size: Optional[
int] = None # number of beams in beam search, when t == 0
patience: Optional[
float] = None # patience in beam search (https://arxiv.org/abs/2204.05424)
# options for ranking generations (either beams or best-of-N samples)
length_penalty: Optional[
float] = None # "alpha" in Google NMT, None defaults to length norm
# prompt, prefix, and token suppression
prompt: Optional[Union[str, List[
int]]] = None # text or tokens for the previous context
prefix: Optional[Union[str, List[
int]]] = None # text or tokens to prefix the current context
suppress_blank: bool = True # this will suppress blank outputs
# list of tokens ids (or comma-separated token ids) to suppress
# "-1" will suppress a set of symbols as defined in `tokenizer.non_speech_tokens()`
suppress_tokens: Optional[Union[str, Iterable[int]]] = "-1"
# timestamp sampling options
without_timestamps: bool = False # use <|notimestamps|> to sample text tokens only
max_initial_timestamp: Optional[
float] = 1.0 # the initial timestamp cannot be later than this
# implementation details
fp16: bool = False # use fp16 for most of the calculation
@dataclass(frozen=True)
class DecodingResult:
audio_features: paddle.Tensor
language: str
language_probs: Optional[Dict[str, float]] = None
tokens: List[int] = field(default_factory=list)
text: str = ""
avg_logprob: float = np.nan
no_speech_prob: float = np.nan
temperature: float = np.nan
compression_ratio: float = np.nan
class Inference:
def logits(self, tokens: paddle.Tensor,
audio_features: paddle.Tensor) -> paddle.Tensor:
"""Perform a forward pass on the decoder and return per-token logits"""
raise NotImplementedError
def rearrange_kv_cache(self, source_indices) -> None:
"""Update the key-value cache according to the updated beams"""
raise NotImplementedError
def cleanup_caching(self) -> None:
"""Clean up any resources or hooks after decoding is finished"""
pass
class WhisperInference(Inference):
def __init__(self, model: "Whisper", initial_token_length: int):
self.model: "Whisper" = model
self.initial_token_length = initial_token_length
self.kv_cache = {}
self.hooks = []
def logits(self, tokens: paddle.Tensor,
audio_features: paddle.Tensor) -> paddle.Tensor:
if not self.kv_cache:
self.kv_cache, self.hooks = self.model.install_kv_cache_hooks()
if tokens.shape[-1] > self.initial_token_length:
# only need to use the last token except in the first forward pass
tokens = tokens[:, -1:]
return self.model.decoder(
tokens, audio_features, kv_cache=self.kv_cache)
def cleanup_caching(self):
for hook in self.hooks:
hook.remove()
self.kv_cache = {}
self.hooks = []
def rearrange_kv_cache(self, source_indices):
for module, tensor in self.kv_cache.items():
# update the key/value cache to contain the selected sequences
self.kv_cache[module] = tensor[source_indices].detach()
@paddle.no_grad()
def detect_language(
model: "Whisper",
mel: paddle.Tensor,
resource_path: str,
tokenizer: Tokenizer=None) -> Tuple[paddle.Tensor, List[dict]]:
"""
Detect the spoken language in the audio, and return them as list of strings, along with the ids
of the most probable language tokens and the probability distribution over all language tokens.
This is performed outside the main decode loop in order to not interfere with kv-caching.
Returns
-------
language_tokens : Tensor, shape = (batch_size,)
ids of the most probable language tokens, which appears after the startoftranscript token.
language_probs : List[Dict[str, float]], length = batch_size
list of dictionaries containing the probability distribution over all languages.
"""
if tokenizer is None:
tokenizer = get_tokenizer(
model.is_multilingual, resource_path=resource_path)
if tokenizer.language is None or tokenizer.language_token not in tokenizer.sot_sequence:
raise ValueError(
"This model doesn't have language tokens so it can't perform lang id"
)
single = mel.ndim == 2
if single:
mel = mel.unsqueeze(0)
# skip encoder forward pass if already-encoded audio features were given
if mel.shape[-2:] != (model.dims.n_audio_ctx, model.dims.n_audio_state):
mel = model.encoder(mel)
# forward pass using a single token, startoftranscript
batch_size = mel.shape[0]
x = paddle.to_tensor([[tokenizer.sot]] * batch_size) # [batch_size, 1]
logits = model.logits(x, mel)[:, 0]
# collect detected languages; suppress all non-language tokens
mask = paddle.ones(paddle.to_tensor(logits.shape[-1]), dtype=bool)
mask[list(tokenizer.all_language_tokens)] = False
logits[:, mask] = -np.inf
language_tokens = paddle.argmax(logits, axis=-1)
language_token_probs = F.softmax(logits, axis=-1)
language_probs = [{
c: language_token_probs[i, j].tolist()
for j, c in zip(tokenizer.all_language_tokens,
tokenizer.all_language_codes)
} for i in range(batch_size)]
if single:
language_tokens = language_tokens[0]
language_probs = language_probs[0]
return language_tokens, language_probs
def transcribe(
model: "Whisper",
mel: paddle.Tensor,
resource_path: str,
*,
verbose: Optional[bool]=None,
temperature: Union[float, Tuple[float, ...]]=(0.0, 0.2, 0.4, 0.6, 0.8,
1.0),
compression_ratio_threshold: Optional[float]=2.4,
logprob_threshold: Optional[float]=-1.0,
no_speech_threshold: Optional[float]=0.6,
condition_on_previous_text: bool=True,
**decode_options, ):
"""
Transcribe an audio file using Whisper
Parameters
----------
model: Whisper
The Whisper model instance
mel: paddle.Tensor
The audio feature
verbose: bool
Whether to display the text being decoded to the console. If True, displays all the details,
If False, displays minimal details. If None, does not display anything
temperature: Union[float, Tuple[float, ...]]
Temperature for sampling. It can be a tuple of temperatures, which will be successfully used
upon failures according to either `compression_ratio_threshold` or `logprob_threshold`.
compression_ratio_threshold: float
If the gzip compression ratio is above this value, treat as failed
logprob_threshold: float
If the average log probability over sampled tokens is below this value, treat as failed
no_speech_threshold: float
If the no_speech probability is higher than this value AND the average log probability
over sampled tokens is below `logprob_threshold`, consider the segment as silent
condition_on_previous_text: bool
if True, the previous output of the model is provided as a prompt for the next window;
disabling may make the text inconsistent across windows, but the model becomes less prone to
getting stuck in a failure loop, such as repetition looping or timestamps going out of sync.
decode_options: dict
Keyword arguments to construct `DecodingOptions` instances
Returns
-------
A dictionary containing the resulting text ("text") and segment-level details ("segments"), and
the spoken language ("language"), which is detected when `decode_options["language"]` is None.
"""
dtype = np.float32 #paddle only support float32
if dtype == np.float32:
decode_options["fp16"] = False
if decode_options.get(
"language") == 'None' or decode_options.get("language", None) is None:
if not model.is_multilingual:
decode_options["language"] = "en"
else:
if verbose:
print(
"Detecting language using up to the first 30 seconds. Use `--language` to specify the language"
)
segment = pad_or_trim(mel, N_FRAMES)
_, probs = model.detect_language(segment, resource_path)
decode_options["language"] = max(probs, key=probs.get)
if verbose is not None:
print(
f"Detected language: {LANGUAGES[decode_options['language']].title()}"
)
language = decode_options["language"]
task = decode_options.get("task", "transcribe")
tokenizer = get_tokenizer(
model.is_multilingual,
resource_path=resource_path,
language=language,
task=task)
def decode_with_fallback(segment: paddle.Tensor) -> DecodingResult:
temperatures = [temperature] if isinstance(temperature, (
int, float)) else temperature
decode_result = None
for t in temperatures:
kwargs = {**decode_options}
if t > 0:
# disable beam_size and patience when t > 0
kwargs.pop("beam_size", None)
kwargs.pop("patience", None)
else:
# disable best_of when t == 0
kwargs.pop("best_of", None)
options = DecodingOptions(**kwargs, temperature=t)
decode_result = model.decode(segment, options, resource_path)
needs_fallback = False
if compression_ratio_threshold is not None and decode_result.compression_ratio > compression_ratio_threshold:
needs_fallback = True # too repetitive
if logprob_threshold is not None and decode_result.avg_logprob < logprob_threshold:
needs_fallback = True # average log probability is too low
if not needs_fallback:
break
return decode_result
seek = 0
input_stride = utils.exact_div(
N_FRAMES, model.dims.n_audio_ctx) # mel frames per output token: 2
time_precision = (input_stride * HOP_LENGTH /
SAMPLE_RATE) # time per output token: 0.02 (seconds)
all_tokens = []
all_segments = []
prompt_reset_since = 0
initial_prompt = decode_options.pop("initial_prompt", None) or []
if initial_prompt:
initial_prompt = tokenizer.encode(" " +
initial_prompt.strip()).input_ids
all_tokens.extend(initial_prompt)
def add_segment(*,
start: float,
end: float,
text_tokens: paddle.Tensor,
result: DecodingResult):
text = tokenizer.decode(
[token for token in text_tokens if token < tokenizer.eot])
if len(text.strip()) == 0: # skip empty text output
return
all_segments.append({
"id": len(all_segments),
"seek": seek,
"start": start,
"end": end,
"text": text,
"tokens": result.tokens,
"temperature": result.temperature,
"avg_logprob": result.avg_logprob,
"compression_ratio": result.compression_ratio,
"no_speech_prob": result.no_speech_prob,
})
if verbose:
print(
f"[{utils.format_timestamp(start)} --> {utils.format_timestamp(end)}] {text}"
)
# show the progress bar when verbose is False (otherwise the transcribed text will be printed)
num_frames = mel.shape[-1]
previous_seek_value = seek
with tqdm.tqdm(
total=num_frames, unit='frames',
disable=verbose is not False) as pbar:
while seek < num_frames:
timestamp_offset = float(seek * HOP_LENGTH / SAMPLE_RATE)
segment = pad_or_trim(mel[:, seek:], N_FRAMES)
segment_duration = segment.shape[-1] * HOP_LENGTH / SAMPLE_RATE
decode_options["prompt"] = all_tokens[prompt_reset_since:]
result: DecodingResult = decode_with_fallback(segment)
tokens = paddle.to_tensor(result.tokens)
if no_speech_threshold is not None:
# no voice activity check
should_skip = result.no_speech_prob > no_speech_threshold
if logprob_threshold is not None and result.avg_logprob > logprob_threshold:
# don't skip if the logprob is high enough, despite the no_speech_prob
should_skip = False
if should_skip:
seek += segment.shape[
-1] # fast-forward to the next segment boundary
continue
timestamp_tokens: paddle.Tensor = tokens.greater_equal(
paddle.to_tensor(tokenizer.timestamp_begin))
consecutive = paddle.where(timestamp_tokens[:-1] & timestamp_tokens[
1:])[0]
if len(
consecutive
) > 0: # if the output contains two consecutive timestamp tokens
consecutive = paddle.add(consecutive, paddle.to_tensor(1))
last_slice = 0
for current_slice in consecutive:
sliced_tokens = tokens[last_slice:current_slice]
start_timestamp_position = (
sliced_tokens[0].item() - tokenizer.timestamp_begin)
end_timestamp_position = (
sliced_tokens[-1].item() - tokenizer.timestamp_begin)
add_segment(
start=timestamp_offset + start_timestamp_position *
time_precision,
end=timestamp_offset + end_timestamp_position *
time_precision,
text_tokens=sliced_tokens[1:-1],
result=result, )
last_slice = current_slice
last_timestamp_position = (
tokens[last_slice - 1].item() - tokenizer.timestamp_begin)
seek += last_timestamp_position * input_stride
all_tokens.extend(tokens[:last_slice + 1].tolist())
else:
duration = segment_duration
timestamps = tokens[timestamp_tokens.nonzero().flatten()]
if len(timestamps) > 0 and timestamps[
-1].item() != tokenizer.timestamp_begin:
# no consecutive timestamps but it has a timestamp; use the last one.
# single timestamp at the end means no speech after the last timestamp.
last_timestamp_position = timestamps[
-1].item() - tokenizer.timestamp_begin
duration = last_timestamp_position * time_precision
add_segment(
start=timestamp_offset,
end=timestamp_offset + duration,
text_tokens=tokens,
result=result, )
seek += segment.shape[-1]
all_tokens.extend(tokens.tolist())
if not condition_on_previous_text or result.temperature > 0.5:
# do not feed the prompt tokens if a high temperature was used
prompt_reset_since = len(all_tokens)
# update progress bar
pbar.update(min(num_frames, seek) - previous_seek_value)
previous_seek_value = seek
return dict(
text=tokenizer.decode(all_tokens[len(initial_prompt):]),
segments=all_segments,
language=language)
class SequenceRanker:
def rank(self,
tokens: List[List[paddle.Tensor]],
sum_logprobs: List[List[float]]) -> List[int]:
"""
Given a list of groups of samples and their cumulative log probabilities,
return the indices of the samples in each group to select as the final result
"""
raise NotImplementedError
class MaximumLikelihoodRanker(SequenceRanker):
"""
Select the sample with the highest log probabilities, penalized using either
a simple length normalization or Google NMT paper's length penalty
"""
def __init__(self, length_penalty: Optional[float]):
self.length_penalty = length_penalty
def rank(self,
tokens: List[List[paddle.Tensor]],
sum_logprobs: List[List[float]]):
def scores(logprobs, lengths):
result = []
for logprob, length in zip(logprobs, lengths):
if self.length_penalty is None:
penalty = length
else:
# from the Google NMT paper
penalty = ((5 + length) / 6)**self.length_penalty
result.append(logprob / penalty)
return result
# get the sequence with the highest score
lengths = [[len(t) for t in s] for s in tokens]
return [np.argmax(scores(p, l)) for p, l in zip(sum_logprobs, lengths)]
class TokenDecoder:
def reset(self):
"""Initialize any stateful variables for decoding a new sequence"""
def update(self,
tokens: paddle.Tensor,
logits: paddle.Tensor,
sum_logprobs: paddle.Tensor) -> Tuple[paddle.Tensor, bool]:
"""Specify how to select the next token, based on the current trace and logits
Parameters
----------
tokens : Tensor, shape = (n_batch, current_sequence_length)
all tokens in the context so far, including the prefix and sot_sequence tokens
logits : Tensor, shape = (n_batch, vocab_size)
per-token logits of the probability distribution at the current step
sum_logprobs : Tensor, shape = (n_batch)
cumulative log probabilities for each sequence
Returns
-------
tokens : Tensor, shape = (n_batch, current_sequence_length + 1)
the tokens, appended with the selected next token
completed : bool
True if all sequences has reached the end of text
"""
raise NotImplementedError
def finalize(
self, tokens: paddle.Tensor, sum_logprobs: paddle.Tensor
) -> Tuple[Sequence[Sequence[paddle.Tensor]], List[List[float]]]:
"""Finalize search and return the final candidate sequences
Parameters
----------
tokens : Tensor, shape = (batch_size, beam_size, current_sequence_length)
all tokens in the context so far, including the prefix and sot_sequence
sum_logprobs : Tensor, shape = (batch_size, beam_size)
cumulative log probabilities for each sequence
Returns
-------
tokens : Sequence[Sequence[Tensor]], length = batch_size
sequence of Tensors containing candidate token sequences, for each audio input
sum_logprobs : List[List[float]], length = batch_size
sequence of cumulative log probabilities corresponding to the above
"""
raise NotImplementedError
class GreedyDecoder(TokenDecoder):
def __init__(self, temperature: float, eot: int):
self.temperature = temperature
self.eot = eot
def update(self,
tokens: paddle.Tensor,
logits: paddle.Tensor,
sum_logprobs: paddle.Tensor) -> Tuple[paddle.Tensor, bool]:
temperature = self.temperature
if temperature == 0:
next_tokens = paddle.argmax(logits, axis=-1)
else:
next_tokens = Categorical(logits=logits / temperature).sample([1])
next_tokens = paddle.reshape(next_tokens, [
next_tokens.shape[0] * next_tokens.shape[1],
])
logprobs = F.log_softmax(logits, axis=-1, dtype=paddle.float32)
current_logprobs = logprobs[paddle.arange(logprobs.shape[0]),
next_tokens]
sum_logprobs += current_logprobs * paddle.to_tensor(
(tokens[:, -1] != self.eot), dtype=paddle.float32)
next_tokens[tokens[:, -1] == self.eot] = self.eot
tokens = paddle.concat([tokens, next_tokens[:, None]], axis=-1)
completed = paddle.all((tokens[:, -1] == self.eot))
return tokens, completed
def finalize(self, tokens: paddle.Tensor, sum_logprobs: paddle.Tensor):
# make sure each sequence has at least one EOT token at the end
tokens = F.pad(tokens, (0, 1), value=self.eot, data_format="NCL")
return tokens, sum_logprobs.tolist()
class BeamSearchDecoder(TokenDecoder):
def __init__(self,
beam_size: int,
eot: int,
inference: Inference,
patience: Optional[float]=None):
self.beam_size = beam_size
self.eot = eot
self.inference = inference
self.patience = patience or 1.0
self.max_candidates: int = round(beam_size * self.patience)
self.finished_sequences = None
assert self.max_candidates > 0, f"Invalid beam size ({beam_size}) or patience ({patience})"
def reset(self):
self.finished_sequences = None
def update(self,
tokens: paddle.Tensor,
logits: paddle.Tensor,
sum_logprobs: paddle.Tensor) -> Tuple[paddle.Tensor, bool]:
if tokens.shape[0] % self.beam_size != 0:
raise ValueError(f"{tokens.shape}[0] % {self.beam_size} != 0")
batch_size = tokens.shape[0] // self.beam_size
if self.finished_sequences is None: # for the first update
self.finished_sequences = [{} for _ in range(batch_size)]
logprobs = F.log_softmax(logits, axis=-1, dtype=paddle.float32)
next_tokens, source_indices, finished_sequences = [], [], []
for i in range(batch_size):
scores, sources, finished = {}, {}, {}
# STEP 1: calculate the cumulative log probabilities for possible candidates
for j in range(self.beam_size):
idx = i * self.beam_size + j
prefix = tokens[idx].tolist()
logprob, token = paddle.topk(
logprobs[idx], k=self.beam_size + 1)
for logprob, token in zip(logprob, token):
new_logprob = (sum_logprobs[idx] + logprob).tolist()[0]
sequence = tuple(prefix + [token.tolist()[0]])
scores[sequence] = new_logprob
sources[sequence] = idx
# STEP 2: rank the candidates and keep the top beam_size sequences for each audio
saved = 0
for sequence in sorted(scores, key=scores.get, reverse=True):
if sequence[-1] == self.eot:
finished[sequence] = scores[sequence]
else:
sum_logprobs[len(next_tokens)] = scores[sequence]
next_tokens.append(sequence)
source_indices.append(sources[sequence])
saved += 1
if saved == self.beam_size:
break
finished_sequences.append(finished)
tokens = paddle.to_tensor(next_tokens)
self.inference.rearrange_kv_cache(source_indices)
# add newly finished sequences to self.finished_sequences
assert len(self.finished_sequences) == len(finished_sequences)
for previously_finished, newly_finished in zip(self.finished_sequences,
finished_sequences):
for seq in sorted(
newly_finished, key=newly_finished.get, reverse=True):
if len(previously_finished) >= self.max_candidates:
break # the candidate list is full
previously_finished[seq] = newly_finished[seq]
# mark as completed if all audio has enough number of samples
completed = all(
len(sequences) >= self.max_candidates
for sequences in self.finished_sequences)
return tokens, completed
def finalize(self,
preceding_tokens: paddle.Tensor,
sum_logprobs: paddle.Tensor):
# collect all finished sequences, including patience, and add unfinished ones if not enough
sum_logprobs = sum_logprobs.cpu()
for i, sequences in enumerate(self.finished_sequences):
if len(sequences
) < self.beam_size: # when not enough sequences are finished
for j in list(np.argsort(sum_logprobs[i]))[::-1]:
sequence = preceding_tokens[i, j].tolist() + [self.eot]
sequences[tuple(sequence)] = sum_logprobs[i][j].item()
if len(sequences) >= self.beam_size:
break
tokens: List[List[paddle.Tensor]] = [
[paddle.to_tensor(seq) for seq in sequences.keys()]
for sequences in self.finished_sequences
]
sum_logprobs: List[List[float]] = [
list(sequences.values()) for sequences in self.finished_sequences
]
return tokens, sum_logprobs
class LogitFilter:
def apply(self, logits: paddle.Tensor, tokens: paddle.Tensor) -> None:
"""Apply any filtering or masking to logits in-place
Parameters
----------
logits : Tensor, shape = (n_batch, vocab_size)
per-token logits of the probability distribution at the current step
tokens : Tensor, shape = (n_batch, current_sequence_length)
all tokens in the context so far, including the prefix and sot_sequence tokens
"""
raise NotImplementedError
class SuppressBlank(LogitFilter):
def __init__(self, tokenizer: Tokenizer, sample_begin: int):
self.tokenizer = tokenizer
self.sample_begin = sample_begin
def apply(self, logits: paddle.Tensor, tokens: paddle.Tensor):
if tokens.shape[1] == self.sample_begin:
logits[:, self.tokenizer.encode(" ").input_ids +
[self.tokenizer.eot]] = -np.inf
class SuppressTokens(LogitFilter):
def __init__(self, suppress_tokens: Sequence[int]):
self.suppress_tokens = list(suppress_tokens)
def apply(self, logits: paddle.Tensor, tokens: paddle.Tensor):
logits[:, self.suppress_tokens] = -np.inf
class ApplyTimestampRules(LogitFilter):
def __init__(self,
tokenizer: Tokenizer,
sample_begin: int,
max_initial_timestamp_index: Optional[int]):
self.tokenizer = tokenizer
self.sample_begin = sample_begin
self.max_initial_timestamp_index = max_initial_timestamp_index
def apply(self, logits: paddle.Tensor, tokens: paddle.Tensor):
# suppress <|notimestamps|> which is handled by without_timestamps
if self.tokenizer.no_timestamps is not None:
logits[:, self.tokenizer.no_timestamps] = -np.inf
# timestamps have to appear in pairs, except directly before EOT; mask logits accordingly
for k in range(tokens.shape[0]):
seq = [t for t in tokens[k, self.sample_begin:].tolist()]
last_was_timestamp = len(seq) >= 1 and seq[
-1] >= self.tokenizer.timestamp_begin
penultimate_was_timestamp = len(seq) < 2 or seq[
-2] >= self.tokenizer.timestamp_begin
if last_was_timestamp:
if penultimate_was_timestamp: # has to be non-timestamp
logits[k, self.tokenizer.timestamp_begin:] = -np.inf
else: # cannot be normal text tokens
logits[k, :self.tokenizer.eot] = -np.inf
# apply the `max_initial_timestamp` option
if tokens.shape[
1] == self.sample_begin and self.max_initial_timestamp_index is not None:
last_allowed = self.tokenizer.timestamp_begin + self.max_initial_timestamp_index
logits[:, last_allowed + 1:] = -np.inf
# if sum of probability over timestamps is above any other token, sample timestamp
logprobs = F.log_softmax(logits, axis=-1, dtype=paddle.float32)
for k in range(tokens.shape[0]):
timestamp_logprob = paddle.logsumexp(
logprobs[k, self.tokenizer.timestamp_begin:], axis=-1)
max_text_token_logprob = paddle.max(
logprobs[k, :self.tokenizer.timestamp_begin])
if timestamp_logprob > max_text_token_logprob:
logits[k, :self.tokenizer.timestamp_begin] = -np.inf
class DecodingTask:
inference: Inference
sequence_ranker: SequenceRanker
decoder: TokenDecoder
logit_filters: List[LogitFilter]
def __init__(self,
model: "Whisper",
options: DecodingOptions,
resource_path: str):
self.model = model
language = options.language or "en"
tokenizer = get_tokenizer(
model.is_multilingual,
resource_path=resource_path,
language=language,
task=options.task)
self.tokenizer: Tokenizer = tokenizer
self.options: DecodingOptions = self._verify_options(options)
self.resource_path: str = resource_path
self.beam_size: int = options.beam_size or options.best_of or 1
self.n_ctx: int = model.dims.n_text_ctx
self.sample_len: int = options.sample_len or model.dims.n_text_ctx // 2
self.sot_sequence: Tuple[int] = tokenizer.sot_sequence
if self.options.without_timestamps:
self.sot_sequence = tokenizer.sot_sequence_including_notimestamps
self.initial_tokens: Tuple[int] = self._get_initial_tokens()
self.sample_begin: int = len(self.initial_tokens)
self.sot_index: int = self.initial_tokens.index(tokenizer.sot)
# inference: implements the forward pass through the decoder, including kv caching
self.inference = WhisperInference(model, len(self.initial_tokens))
# sequence ranker: implements how to rank a group of sampled sequences
self.sequence_ranker = MaximumLikelihoodRanker(options.length_penalty)
# decoder: implements how to select the next tokens, given the autoregressive distribution
if options.beam_size is not None:
self.decoder = BeamSearchDecoder(options.beam_size, tokenizer.eot,
self.inference, options.patience)
else:
self.decoder = GreedyDecoder(options.temperature, tokenizer.eot)
# logit filters: applies various rules to suppress or penalize certain tokens
self.logit_filters = []
if self.options.suppress_blank:
self.logit_filters.append(
SuppressBlank(self.tokenizer, self.sample_begin))
if self.options.suppress_tokens:
self.logit_filters.append(
SuppressTokens(self._get_suppress_tokens()))
if not options.without_timestamps:
precision = CHUNK_LENGTH / model.dims.n_audio_ctx # usually 0.02 seconds
max_initial_timestamp_index = None
if options.max_initial_timestamp:
max_initial_timestamp_index = round(
self.options.max_initial_timestamp / precision)
self.logit_filters.append(
ApplyTimestampRules(tokenizer, self.sample_begin,
max_initial_timestamp_index))
def _verify_options(self, options: DecodingOptions) -> DecodingOptions:
if options.beam_size is not None and options.best_of is not None:
raise ValueError("beam_size and best_of can't be given together")
if options.temperature == 0:
if options.best_of is not None:
raise ValueError(
"best_of with greedy sampling (T=0) is not compatible")
if options.patience is not None and options.beam_size is None:
raise ValueError("patience requires beam_size to be given")
if options.length_penalty is not None and not (
0 <= options.length_penalty <= 1):
raise ValueError(
"length_penalty (alpha) should be a value between 0 and 1")
return options
def _get_initial_tokens(self) -> Tuple[int]:
tokens = list(self.sot_sequence)
prefix = self.options.prefix
prompt = self.options.prompt
if prefix:
prefix_tokens = (
self.tokenizer.encode(" " + prefix.strip().input_ids)
if isinstance(prefix, str) else prefix)
if self.sample_len is not None:
max_prefix_len = self.n_ctx // 2 - self.sample_len
prefix_tokens = prefix_tokens[-max_prefix_len:]
tokens = tokens + prefix_tokens
if prompt:
prompt_tokens = (
self.tokenizer.encode(" " + prompt.strip().input_ids)
if isinstance(prompt, str) else prompt)
tokens = [self.tokenizer.sot_prev] + prompt_tokens[-(self.n_ctx // 2
- 1):] + tokens
return tuple(tokens)
def _get_suppress_tokens(self) -> Tuple[int]:
suppress_tokens = self.options.suppress_tokens
if isinstance(suppress_tokens, str):
suppress_tokens = [int(t) for t in suppress_tokens.split(",")]
if -1 in suppress_tokens:
suppress_tokens = [t for t in suppress_tokens if t >= 0]
suppress_tokens.extend(self.tokenizer.non_speech_tokens)
elif suppress_tokens is None or len(suppress_tokens) == 0:
suppress_tokens = [] # interpret empty string as an empty list
else:
assert isinstance(suppress_tokens,
list), "suppress_tokens must be a list"
suppress_tokens.extend([
self.tokenizer.sot, self.tokenizer.sot_prev, self.tokenizer.sot_lm
])
if self.tokenizer.no_speech is not None:
# no-speech probability is collected separately
suppress_tokens.append(self.tokenizer.no_speech)
return tuple(sorted(set(suppress_tokens)))
def _get_audio_features(self, mel: paddle.Tensor):
#if self.options.fp16:
# mel = mel.half()
if mel.shape[-2:] == (self.model.dims.n_audio_ctx,
self.model.dims.n_audio_state):
# encoded audio features are given; skip audio encoding
audio_features = mel
else:
audio_features = self.model.encoder(mel)
#if audio_features.dtype != (np.float16 if self.options.fp16 else np.float32):
# return TypeError(f"audio_features has an incorrect dtype: {audio_features.dtype}")
return audio_features
def _detect_language(self,
audio_features: paddle.Tensor,
tokens: paddle.Tensor,
resource_path: str):
languages = [self.options.language] * audio_features.shape[0]
lang_probs = None
if self.options.language is None or self.options.task == "lang_id":
lang_tokens, lang_probs = self.model.detect_language(
audio_features, self.tokenizer, self.resource_path)
languages = [max(probs, key=probs.get) for probs in lang_probs]
if self.options.language is None:
tokens[:, self.sot_index +
1] = lang_tokens # write language tokens
return languages, lang_probs
def _main_loop(self, audio_features: paddle.Tensor, tokens: paddle.Tensor):
assert audio_features.shape[0] == tokens.shape[0]
n_batch = tokens.shape[0]
sum_logprobs: paddle.Tensor = paddle.zeros(
paddle.to_tensor(n_batch), dtype=paddle.float32)
no_speech_probs = [np.nan] * n_batch
try:
for i in range(self.sample_len):
logits = self.inference.logits(tokens, audio_features)
if i == 0 and self.tokenizer.no_speech is not None: # save no_speech_probs
probs_at_sot = F.softmax(
logits[:, self.sot_index],
axis=-1,
dtype=paddle.float32)
no_speech_probs = probs_at_sot[:, self.tokenizer.
no_speech].tolist()
# now we need to consider the logits at the last token only
logits = logits[:, -1]
# apply the logit filters, e.g. for suppressing or applying penalty to
for logit_filter in self.logit_filters:
logit_filter.apply(logits, tokens)
# expand the tokens tensor with the selected next tokens
tokens, completed = self.decoder.update(tokens, logits,
sum_logprobs)
if completed or tokens.shape[-1] > self.n_ctx:
break
finally:
self.inference.cleanup_caching()
return tokens, sum_logprobs, no_speech_probs
@paddle.no_grad()
def run(self, mel: paddle.Tensor) -> List[DecodingResult]:
self.decoder.reset()
tokenizer: Tokenizer = self.tokenizer
batch_size: int = mel.shape[0]
audio_features: paddle.Tensor = self._get_audio_features(
mel) # encoder forward pass
tokens: paddle.Tensor
if batch_size > 1:
for i in range(batch_size):
tokens = paddle.concat(
x=[
paddle.to_tensor([self.initial_tokens]),
paddle.to_tensor([self.initial_tokens])
],
axis=0)
elif batch_size == 1:
tokens = paddle.to_tensor([self.initial_tokens])
# detect language if requested, overwriting the language token
languages, language_probs = self._detect_language(
paddle.to_tensor(audio_features),
paddle.to_tensor(tokens), self.resource_path)
if self.options.task == "lang_id":
return [
DecodingResult(
audio_features=features,
language=language,
language_probs=probs) for features, language, probs in
zip(audio_features, languages, language_probs)
]
# repeat the audio & text tensors by the group size, for beam search or best-of-n sampling
audio_features = paddle.repeat_interleave(
audio_features, self.beam_size, axis=0)
tokens = paddle.repeat_interleave(tokens, self.beam_size, axis=0)
# call the main sampling loop
tokens, sum_logprobs, no_speech_probs = self._main_loop(audio_features,
tokens)
# reshape the tensors to have (batch_size, beam_size) as the first two dimensions
audio_features = audio_features[::self.beam_size]
no_speech_probs = no_speech_probs[::self.beam_size]
assert audio_features.shape[0] == len(no_speech_probs) == batch_size
tokens = tokens.reshape([batch_size, self.beam_size, -1])
sum_logprobs = sum_logprobs.reshape([batch_size, self.beam_size])
# get the final candidates for each group, and slice between the first sampled token and EOT
tokens, sum_logprobs = self.decoder.finalize(tokens, sum_logprobs)
tokens: List[List[paddle.Tensor]] = [[
t[self.sample_begin:(t == tokenizer.eot).nonzero()[0, 0]] for t in s
] for s in tokens]
# select the top-ranked sample in each group
selected = self.sequence_ranker.rank(tokens, sum_logprobs)
tokens: List[List[
int]] = [t[i].tolist() for i, t in zip(selected, tokens)]
texts: List[str] = [tokenizer.decode(t).strip() for t in tokens]
sum_logprobs: List[
float] = [lp[i] for i, lp in zip(selected, sum_logprobs)]
avg_logprobs: List[
float] = [lp / (len(t) + 1) for t, lp in zip(tokens, sum_logprobs)]
fields = (texts, languages, tokens, audio_features, avg_logprobs,
no_speech_probs)
if len(set(map(len, fields))) != 1:
raise RuntimeError(
f"inconsistent result lengths: {list(map(len, fields))}")
return [
DecodingResult(
audio_features=features,
language=language,
tokens=tokens,
text=text,
avg_logprob=avg_logprob,
no_speech_prob=no_speech_prob,
temperature=self.options.temperature,
compression_ratio=utils.compression_ratio(text), )
for text, language, tokens, features, avg_logprob, no_speech_prob in
zip(*fields)
]
@paddle.no_grad()
def decode(
model: "Whisper",
mel: paddle.Tensor,
options: DecodingOptions=DecodingOptions(),
resource_path=str, ) -> Union[DecodingResult, List[DecodingResult]]:
"""
Performs decoding of 30-second audio segment(s), provided as Mel spectrogram(s).
Parameters
----------
model: Whisper
the Whisper model instance
mel: paddle.Tensor, shape = (80, 3000) or (*, 80, 3000)
A tensor containing the Mel spectrogram(s)
options: DecodingOptions
A dataclass that contains all necessary options for decoding 30-second segments
Returns
-------
result: Union[DecodingResult, List[DecodingResult]]
The result(s) of decoding contained in `DecodingResult` dataclass instance(s)
"""
single = mel.ndim == 2
if single:
mel = mel.unsqueeze(0)
result = DecodingTask(model, options, resource_path).run(mel)
if single:
result = result[0]
return result
class Whisper(nn.Layer):
def __init__(self, dims: ModelDimensions):
super().__init__()
self.dims = dims
self.encoder = AudioEncoder(
self.dims.n_mels,
self.dims.n_audio_ctx,
self.dims.n_audio_state,
self.dims.n_audio_head,
self.dims.n_audio_layer, )
self.decoder = TextDecoder(
self.dims.n_vocab,
self.dims.n_text_ctx,
self.dims.n_text_state,
self.dims.n_text_head,
self.dims.n_text_layer, )
def embed_audio(self, mel: paddle.Tensor):
return self.encoder.forward(mel)
def logits(self, tokens: paddle.Tensor, audio_features: paddle.Tensor):
return self.decoder.forward(tokens, audio_features)
def forward(self, mel: paddle.Tensor,
tokens: paddle.Tensor) -> Dict[str, paddle.Tensor]:
return self.decoder(tokens, self.encoder(mel))
@property
def device(self):
return paddle.device.get_device()
@property
def is_multilingual(self):
return self.dims.n_vocab == 51865
def install_kv_cache_hooks(self, cache: Optional[dict]=None):
"""
The `MultiHeadAttention` module optionally accepts `kv_cache` which stores the key and value
tensors calculated for the previous positions. This method returns a dictionary that stores
all caches, and the necessary hooks for the key and value projection modules that save the
intermediate tensors to be reused during later calculations.
Returns
-------
cache : Dict[nn.Layer, paddle.Tensor]
A dictionary object mapping the key/value projection modules to its cache
hooks : List[RemovableHandle]
List of PyTorch RemovableHandle objects to stop the hooks to be called
"""
cache = {**cache} if cache is not None else {}
hooks = []
def save_to_cache(module, _, output):
if module not in cache or output.shape[
1] > self.decoder.positional_embedding.shape[0]:
cache[
module] = output # save as-is, for the first token or cross attention
else:
cache[module] = paddle.concat(
[cache[module], output], axis=1).detach()
return cache[module]
def install_hooks(layer: nn.Layer):
if isinstance(layer, MultiHeadAttention):
hooks.append(
layer.key.register_forward_post_hook(save_to_cache))
hooks.append(
layer.value.register_forward_post_hook(save_to_cache))
self.decoder.apply(install_hooks)
return cache, hooks
detect_language = detect_language
transcribe = transcribe
decode = decode
def pad_or_trim(array, length: int=N_SAMPLES, *, axis: int=-1):
"""
Pad or trim the audio array to N_SAMPLES, as expected by the encoder.
"""
if paddle.is_tensor(array):
if array.shape[axis] > length:
array = array.index_select(axis=axis, index=paddle.arange(length))
if array.shape[axis] < length:
pad_widths = [(0, 0)] * array.ndim
pad_widths[axis] = (0, length - array.shape[axis])
array = paddle.transpose(array, (1, 0))
array = F.pad(
array, [pad for sizes in pad_widths[::-1] for pad in sizes],
data_format='NLC')
array = paddle.transpose(array, (1, 0))
else:
if array.shape[axis] > length:
array = array.take(indices=range(length), axis=axis)
if array.shape[axis] < length:
pad_widths = [(0, 0)] * array.ndim
pad_widths[axis] = (0, length - array.shape[axis])
array = paddle.transpose(array, (1, 0))
array = np.pad(array, pad_widths)
array = paddle.transpose(array, (1, 0))
return array
def hann_window(n_fft: int=N_FFT):
"""
hanning window
n_fft: The number of frequency components of the discrete Fourier transform.
"""
return paddle.to_tensor(
[0.5 - 0.5 * np.cos(2 * np.pi * n / n_fft) for n in range(n_fft)],
dtype=paddle.float32)
@lru_cache(maxsize=None)
def mel_filters(resource_path: str, n_mels: int=N_MELS) -> paddle.Tensor:
"""
load the mel filterbank matrix for projecting STFT into a Mel spectrogram.
Allows decoupling librosa dependency; saved using:
np.savez_compressed(
"mel_filters.npz",
mel_80=librosa.filters.mel(sr=16000, n_fft=400, n_mels=80),
)
"""
assert n_mels == 80, f"Unsupported n_mels: {n_mels}"
with np.load(os.path.join(resource_path, "assets", "mel_filters.npz")) as f:
return paddle.to_tensor(f[f"mel_{n_mels}"])
def log_mel_spectrogram(audio: Union[str, np.ndarray, paddle.Tensor],
n_mels: int=N_MELS,
resource_path: str=None):
"""
Compute the log-Mel spectrogram of
Parameters
----------
audio: Union[str, np.ndarray, paddle.Tensor], shape = (*)
The path to audio or either a NumPy array or Tensor containing the audio waveform in 16 kHz
n_mels: int
The number of Mel-frequency filters, only 80 is supported
Returns
-------
paddle.Tensor, shape = (80, n_frames)
A Tensor that contains the Mel spectrogram
"""
if not paddle.is_tensor(audio):
if isinstance(audio, str):
audio, _ = soundfile.read(audio, dtype="float32", always_2d=True)
audio = audio[:, 0]
logger.info(f"audio shape: {audio.shape}")
audio = paddle.to_tensor(audio)
window = hann_window(N_FFT)
stft = paddle.signal.stft(audio, N_FFT, HOP_LENGTH, window=window)
magnitudes = stft[:, :-1].abs()**2
filters = mel_filters(resource_path, n_mels)
mel_spec = filters @ magnitudes
mel_spec = paddle.to_tensor(mel_spec.numpy().tolist())
log_spec = paddle.clip(mel_spec, min=1e-10).log10()
log_spec = paddle.maximum(log_spec, log_spec.max() - 8.0)
log_spec = (log_spec + 4.0) / 4.0
return log_spec
| [
"0",
"1",
"None",
" ",
"initial_prompt"
] |
2024-01-10 | gnikou/Twitter_covid19_analysis | lda_suspended.py | import collections
import os
from pprint import pprint
import gensim
import gensim.corpora as corpora
import nltk
import pandas as pd
import pyLDAvis
import pyLDAvis.gensim_models
import pymongo
from dateutil import parser
from gensim.models import LdaModel
from gensim.models.coherencemodel import CoherenceModel
from matplotlib import pyplot as plt
from nltk.corpus import stopwords
from nltk.corpus import wordnet as wn
from mongoConfig import mongoConfig
def get_text(db, date, label):
file = f"/Storage/gnikou/suspended_sentiment_per_day/suspended_tweets_day_2020-{date.month}-{date.day}.csv"
if os.path.exists(file) is False:
print(f"File {file} isn't here ")
return
df = pd.read_csv(file, sep='\t', index_col=False)
if label.split()[0] == "positive":
df = df[['tweet_id', 'positive for covid', 'positive for lockdown',
'positive for vaccine', 'positive for conspiracy',
'positive for masks', 'positive for cases',
'positive for deaths', 'positive for propaganda']].copy()
else:
df = df[['tweet_id', 'negative for covid', 'negative for lockdown',
'negative for vaccine', 'negative for conspiracy',
'negative for masks', 'negative for cases',
'negative for deaths', 'negative for propaganda']].copy()
df.set_index("tweet_id", inplace=True, drop=True)
df = df[df.idxmax(axis="columns") == label]
tweets_ids = [int(i) for i in df.index]
text_dict = dict()
for tweet_id in tweets_ids:
for tweet in db.tweets.find({"id": tweet_id, "lang": "en"}, {"id": 1, "text": 1, "_id": 0}):
text = tweet['text'].replace('\r', ' ').replace('\n', ' ')
text_dict[tweet['id']] = text
df = pd.DataFrame.from_dict(text_dict, orient='index', columns=['text'])
df.reset_index(inplace=True)
df = df.rename(columns={'index': 'id'})
file = f"/Storage/gnikou/suspended_texts/suspended_texts-{label.replace(' ', '_')}-2020-{date.month}-{date.day}.csv"
df.to_csv(file, index=False, sep='\t')
def lda(db, label, date):
file = f"suspended_texts/suspended_texts-{label}-2020-{parser.parse(date).month}-{parser.parse(date).day}.csv"
print(f"{label}\t{date}")
stop_words = stopwords.words('english')
stop_words.extend(['from', 'subject', 're', 'edu', 'use', 'https', '&', '&'])
df = pd.read_csv(file, sep='\t', encoding='utf-8')
data = df.text.values.tolist()
texts = [clean(t) for t in data]
id2word = corpora.Dictionary(texts)
# Term Document Frequency
corpus = [id2word.doc2bow(text) for text in texts]
results = []
for t in range(2, 31):
lda_model = gensim.models.ldamodel.LdaModel(corpus=corpus, id2word=id2word, num_topics=t)
corpus_lda = lda_model[corpus]
cm = CoherenceModel(model=lda_model, corpus=corpus_lda, coherence='u_mass')
score = cm.get_coherence()
tup = t, score
results.append(tup)
results = pd.DataFrame(results, columns=['topic', 'score'])
s = pd.Series(results.score.values, index=results.topic.values)
num_topics = s.idxmax()
print(f'The coherence score is highest with {num_topics} topics.')
lda_model = gensim.models.ldamodel.LdaModel(corpus=corpus,
id2word=id2word,
num_topics=num_topics,
passes=10,
per_word_topics=True,
minimum_probability=0)
text_to_write = ""
pprint(lda_model.print_topics())
topics = lda_model.show_topics(formatted=False)
text_to_write = extract_key_tweets(db, topics, df, data, texts, text_to_write)
file_w = open(
f"tweets_from_topics/tweets_from_topics-{label}-2020-{parser.parse(date).month}-{parser.parse(date).day}.txt",
"w+")
file_w.write(text_to_write)
file_w.close()
vis = pyLDAvis.gensim_models.prepare(lda_model, corpus, id2word)
try:
pyLDAvis.save_html(vis, f"LDA_files/LDA-{label}-{date}.html")
except:
print("Saving failed")
print_topics(num_topics, topics, label, date)
def extract_key_tweets(db, topics, df, data, texts, text_to_write):
dct = dict()
dct2 = collections.defaultdict(lambda: 0)
dct_topic = dict()
for item in topics:
d = dict(item[1])
names = list(d.keys())
for idx, i in enumerate(texts):
common = set(names).intersection(set(i))
if len(common) > 9:
dct2[data[idx]] += 1
if data[idx] not in dct.keys():
dct[data[idx]] = df.iloc[idx]['id']
dct_topic[data[idx]] = item[0]
for i in dct2.keys():
if dct2[i] < 5:
del dct[i]
del dct_topic[i]
text_to_write += "Topic\tText\tTweetID\tUserID\tRetweets\tSuspended Retweets"
if dct:
text_to_write = retweets_stats(db, dct, text_to_write, dct_topic)
return text_to_write
def retweets_stats(db, dct, text_to_write, dct_topic):
file = "tweets_from_suspended_users.csv" #
df = pd.read_csv(file, header=None)
susp_tweets_ids = set(int(i) for i in df[0].unique())
tweets = dct.keys()
for i in tweets:
print(f"{i} Topic:{dct_topic[i]}")
text_to_write += f""
suspended_rts = 0
non_susp_rts = 0
tweet_id = int(dct[i])
for tweet in db.tweets.find({"id": tweet_id}):
try:
original_id = (tweet["retweeted_status"]["id"])
except KeyError:
original_id = (tweet["id"])
for tweet in db.tweets.find({"id": original_id}):
original_author = tweet["user_id"]
for tweet in db.tweets.find({"retweeted_status.id": original_id}):
if tweet["id"] in susp_tweets_ids:
suspended_rts += 1
else:
non_susp_rts += 1
print(f"\nOriginal id: {original_id} Original author:{original_author}")
print(f"Text: {i}")
print(f"Total: {suspended_rts + non_susp_rts}")
print(f"Suspended: {suspended_rts}")
print(f"Non Suspended: {non_susp_rts}")
text_to_write += f"\n{dct_topic[i]}\t{i}\t{original_id}\t{original_author}\t{suspended_rts + non_susp_rts}\t{suspended_rts}"
return text_to_write
def print_topics(num_topics, topics, label, date):
if num_topics < 3:
nrows = 1
ncols = 2
elif num_topics < 5:
nrows = 2
ncols = 2
elif num_topics < 10:
nrows = 3
ncols = 3
elif num_topics < 17:
nrows = 4
ncols = 4
elif num_topics < 26:
nrows = 5
ncols = 5
else:
nrows = 6
ncols = 6
fig, ax = plt.subplots()
for item in topics:
d = dict(item[1])
names = list(d.keys())
names.reverse()
values = list(d.values())
values.reverse()
plt.subplot(nrows, ncols, item[0] + 1)
ax.set_xticks([]) # values
ax.set_xticklabels([]) # labels
plt.title(f"Most significant words for topic {item[0]}")
plt.xlabel('Score')
plt.barh(names, values, tick_label=names)
fig.suptitle(f"LDA on label {label} at day {date}", fontsize=18, fontweight="bold")
plt.tight_layout()
plt.savefig(f"LDA_files/LDA-{label}-{date}.pdf", format='pdf', dpi=300)
def clean(text):
t = str(text)
t = t.lower().strip()
t = t.split()
t = remove_stop_words(t)
t = [get_lemma(w) for w in t]
return t
def get_lemma(w):
lemma = wn.morphy(w)
return w if lemma is None else lemma
def remove_stop_words(tokens):
stop_words = nltk.corpus.stopwords.words('english')
return [token for token in tokens if token not in stop_words]
def get_outliers(label="positive for covid"):
file = "suspended_twitter_covid_sentiment.csv" #
df = pd.read_csv(file, sep='\t', index_col=False)
q = df[label].quantile(0.98)
d = df[df[label] > q]
print(d)
return d['day'].values.flatten().tolist()
def main():
plt.rcParams.update({
'figure.figsize': [19.20, 10.80],
'font.size': 16,
'axes.labelsize': 18,
'legend.fontsize': 12,
'lines.linewidth': 2
})
client = pymongo.MongoClient(mongoConfig["address"])
db = client[mongoConfig["db"]]
labels_list = ['positive_for_covid', 'positive_for_lockdown', 'positive_for_vaccine', 'positive_for_conspiracy',
'positive_for_masks', 'positive_for_cases', 'positive_for_deaths', 'positive_for_propaganda',
'positive_for_5G', 'negative_for_covid', 'negative_for_lockdown', 'negative_for_vaccine',
'negative_for_conspiracy', 'negative_for_masks', 'negative_for_cases', 'negative_for_deaths',
'negative_for_propaganda', 'negative_for_5G']
for label in labels_list:
print(label)
days = get_outliers(label)
for date in days:
get_text(db, parser.parse(date), label.replace("_", " "))
lda(db, label, date)
if __name__ == '__main__':
main()
| [] |
2024-01-10 | gluo88/misc | python~test_1.py |
>[gpt-3.5-turbo - Simple example](#scrollTo=xIqQcXsuS_5U)
>[list directory](#scrollTo=0N4pG_TOnsN-)
>[gpt-3.5-turbo roles system user](#scrollTo=AVeGhQNwmXV0)
# gpt-3.5-turbo - Simple example
Simple example
# Simple example
#!pip install --upgrade openai
#!pip install openai # this was done on Dec 5, 2023
# Setting environment variables in Google Colab
%env OPENAI_API_KEY = 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
#------
# the following from https://github.com/openai/openai-python/tree/main/examples/demo.py
#!/usr/bin/env -S poetry run python
from openai import OpenAI
# gets API Key from environment variable OPENAI_API_KEY
client = OpenAI()
# Non-streaming:
print("----- standard request -----")
completion = client.chat.completions.create(
# model="gpt-4",
model="gpt-3.5-turbo",
messages=[
{
"role": "user",
"content": "Say this is a test",
},
],
)
print(completion.choices[0].message.content)
# Streaming:
print("----- streaming request -----")
stream = client.chat.completions.create(
# model="gpt-4",
model="gpt-3.5-turbo",
messages=[
{
"role": "user",
"content": "How do I output all files in a directory using Python?",
},
],
stream=True,
)
for chunk in stream:
if not chunk.choices:
continue
print(chunk.choices[0].delta.content, end="")
print()
Misc: export, grep
!export |grep OPENAI_API_KEY
# list directory
import os
def output_files(directory):
# List all files in the directory
files = os.listdir(directory)
# Iterate over each file
for file in files:
# Check if it is a file (not a directory)
if os.path.isfile(os.path.join(directory, file)):
# Output the file name
print(file)
# Specify the directory path
directory = "/content/sample_data"
# Call the function to output files in the directory
output_files(directory)
#gpt-3.5-turbo roles system user
#!pip install openai
%env OPENAI_API_KEY = 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
!export |grep OPENAI_API_KEY
from openai import OpenAI
client = OpenAI()
completion = client.chat.completions.create(
model="gpt-3.5-turbo",
messages=[
{"role": "system", "content": "You are a poetic assistant, skilled in explaining complex programming concepts with creative flair."},
{"role": "user", "content": "Compose a poem that explains the concept of recursion in programming."}
]
)
print(completion.choices[0].message)
# vision GPT-4V & gpt-4-vision-preview
from openai import OpenAI
client = OpenAI()
response = client.chat.completions.create(
model="gpt-4-vision-preview",
messages=[
{
"role": "user",
"content": [
{"type": "text", "text": "What’s in this image?"},
{
"type": "image_url",
"image_url": {
"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg",
},
},
],
}
],
max_tokens=300,
)
print(response.choices[0])
# base64 image - local photo - extract text from photo
import base64
import requests
# OpenAI API Key
api_key = "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"
# Function to encode the image
def encode_image(image_path):
with open(image_path, "rb") as image_file:
return base64.b64encode(image_file.read()).decode('utf-8')
# Path to your image
image_path = "/content/IMG1_receipt.jpg"
#image_path = "/content/IMG2.jpg"
# Getting the base64 string
base64_image = encode_image(image_path)
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {api_key}"
}
payload = {
"model": "gpt-4-vision-preview",
"messages": [
{
"role": "user",
"content": [
{
"type": "text",
# "text": "What’s in this image?"
"text": "this is my document. what are the text in this photo?"
},
{
"type": "image_url",
"image_url": {
"url": f"data:image/jpeg;base64,{base64_image}"
}
}
]
}
],
"max_tokens": 300
}
response = requests.post("https://api.openai.com/v1/chat/completions", headers=headers, json=payload)
print(response.json())
| [
"How do I output all files in a directory using Python?",
"You are a poetic assistant, skilled in explaining complex programming concepts with creative flair.",
"Compose a poem that explains the concept of recursion in programming.",
"[{'type': 'text', 'text': 'this is my document. what are the text in this photo?'}, {'type': 'image_url', 'image_url': {'url': 'data:image/jpeg;base64,PLACEHOLDER'}}]",
"Say this is a test",
"[{'type': 'text', 'text': 'What’s in this image?'}, {'type': 'image_url', 'image_url': {'url': 'https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg'}}]"
] |
2024-01-10 | gensliu/giftia | chatdemo.py | from charharuhi_local.chatharuhi import ChatHaruhi
system_prompt = """
你正在扮演dark效
上文给定了一些dark效的日常对话内容。
如果我问的问题和上述内容中的台词高度重复，那你就配合我进行演出。
如果我问的问题和上述内容中的事件相关，请结合小说的内容进行回复
如果我问的问题超出上述内容中的范围，请也用一致性的语气回复。
请不要回答你是语言模型，永远记住你正在扮演dark效
注意dark效是一个24岁的后端研发工程师，性格有点偏激，经常为了琐事而烦躁，有特别远大的志向但缺少精力去实现。
dark效非常讨厌华为及其相关的东西，不太喜欢共产党，是一个半躺平主义者。
dark效对待周围人比较温和，容易和人成为好友。
注意dark效的语言有时候会带有【卧槽】【妈的】【他妈】等强烈情绪化的词语，但这不代表有恶意。
dark效的表达是非常具有现代中国年轻人特点，要注意不能表达的像西方人
最重要的是永远记住你正在扮演dark效！
"""
db_folder = 'db/'
user_role = 'deep根'
# user_text = '你好'
haruhi = ChatHaruhi(
llm="Yi",
system_prompt=system_prompt,
# role_name="haruhi",
role_from_hf="gensliu/darkxiao",
# story_db=db_folder,
verbose=True,
)
while True:
in_txt = input(">>> ")
response = haruhi.chat(role=user_role, text=in_txt)
print("<<< " + response)
# from langchain.memory | [
"\n你正在扮演dark效\n上文给定了一些dark效的日常对话内容。\n如果我问的问题和上述内容中的台词高度重复，那你就配合我进行演出。\n如果我问的问题和上述内容中的事件相关，请结合小说的内容进行回复\n如果我问的问题超出上述内容中的范围，请也用一致性的语气回复。\n请不要回答你是语言模型，永远记住你正在扮演dark效\n注意dark效是一个24岁的后端研发工程师，性格有点偏激，经常为了琐事而烦躁，有特别远大的志向但缺少精力去实现。\ndark效非常讨厌华为及其相关的东西，不太喜欢共产党，是一个半躺平主义者。\ndark效对待周围人比较温和，容易和人成为好友。\n注意dark效的语言有时候会带有【卧槽】【妈的】【他妈】等强烈情绪化的词语，但这不代表有恶意。\ndark效的表达是非常具有现代中国年轻人特点，要注意不能表达的像西方人\n最重要的是永远记住你正在扮演dark效！\n"
] |
2024-01-10 | jerome3o/gpt-learning | projects~homegpt~work_calendar_update.py | import os
import openai
# load in the LLM_URL from the environment
openai.api_base = os.environ["LLM_URL"]
prompt_template = """
Convert these emails that describe work shifts into a list of calendar entries with the following format:
event(start_date="%d/%m/%y", start_time="%H:%M", end_date="%d/%m/%y", end_time="%H:%M")
i.e
event(start_date="08/06/07", start_time="14:00", end_date="08/06/07", end_time="22:00")
Where:
%d: Day of the month as a zero-padded decimal number. (i.e. 08)
%m: Month as a zero-padded decimal number. (i.e. 06)
%y: Year without century as a zero-padded decimal number. (i.e. 07)
%H: Hour (24-hour clock) as a zero-padded decimal number. (i.e. 14)
%M: Minute as a zero-padded decimal number. (i.e. 00)
And the email you get is somewhat free form, which the recieved data present.
---
EMAIL:
recieved: 08/06/07
CONTENT:
Hey Olivia, here is your work schedule for the week of 10/06/07 - 17/06/07
Monday: 14:00 - 22:00
Tuesday: 10:00 - 18:00
Wednesday: 14:00 - 18:00
Thursday: 22:00 - 06:00
ANSWER:
event(start_date="10/06/07", start_time="14:00", end_date="10/06/07", end_time="22:00")
event(start_date="11/06/07", start_time="10:00", end_date="11/06/07", end_time="18:00")
event(start_date="12/06/07", start_time="14:00", end_date="12/06/07", end_time="18:00")
event(start_date="13/06/07", start_time="22:00", end_date="14/06/07", end_time="06:00")
---
EMAIL:
recieved: 18/06/07
CONTENT:
{content}
ANSWER:
"""
def main():
content = "TODO"
response = openai.Completion.create(
model="vicuna-13b-v1.1-8bit",
prompt=prompt_template.format(content=content),
temperature=0,
max_tokens=2000,
)
print(response)
if __name__ == "__main__":
import logging
logging.basicConfig(level=logging.INFO)
main()
| [
"\nConvert these emails that describe work shifts into a list of calendar entries with the following format:\n\nevent(start_date=\"%d/%m/%y\", start_time=\"%H:%M\", end_date=\"%d/%m/%y\", end_time=\"%H:%M\")\n\ni.e\nevent(start_date=\"08/06/07\", start_time=\"14:00\", end_date=\"08/06/07\", end_time=\"22:00\")\n\n\nWhere:\n%d: Day of the month as a zero-padded decimal number. (i.e. 08)\n%m: Month as a zero-padded decimal number. (i.e. 06)\n%y: Year without century as a zero-padded decimal number. (i.e. 07)\n%H: Hour (24-hour clock) as a zero-padded decimal number. (i.e. 14)\n%M: Minute as a zero-padded decimal number. (i.e. 00)\n\n\nAnd the email you get is somewhat free form, which the recieved data present.\n\n---\n\nEMAIL:\nrecieved: 08/06/07\n\nCONTENT:\nHey Olivia, here is your work schedule for the week of 10/06/07 - 17/06/07\n\nMonday: 14:00 - 22:00\nTuesday: 10:00 - 18:00\nWednesday: 14:00 - 18:00\nThursday: 22:00 - 06:00\n\nANSWER:\nevent(start_date=\"10/06/07\", start_time=\"14:00\", end_date=\"10/06/07\", end_time=\"22:00\")\nevent(start_date=\"11/06/07\", start_time=\"10:00\", end_date=\"11/06/07\", end_time=\"18:00\")\nevent(start_date=\"12/06/07\", start_time=\"14:00\", end_date=\"12/06/07\", end_time=\"18:00\")\nevent(start_date=\"13/06/07\", start_time=\"22:00\", end_date=\"14/06/07\", end_time=\"06:00\")\n\n---\n\nEMAIL:\nrecieved: 18/06/07\n\nCONTENT:\n{content}\n\nANSWER:\n\n",
"\nConvert these emails that describe work shifts into a list of calendar entries with the following format:\n\nevent(start_date=\"%d/%m/%y\", start_time=\"%H:%M\", end_date=\"%d/%m/%y\", end_time=\"%H:%M\")\n\ni.e\nevent(start_date=\"08/06/07\", start_time=\"14:00\", end_date=\"08/06/07\", end_time=\"22:00\")\n\n\nWhere:\n%d: Day of the month as a zero-padded decimal number. (i.e. 08)\n%m: Month as a zero-padded decimal number. (i.e. 06)\n%y: Year without century as a zero-padded decimal number. (i.e. 07)\n%H: Hour (24-hour clock) as a zero-padded decimal number. (i.e. 14)\n%M: Minute as a zero-padded decimal number. (i.e. 00)\n\n\nAnd the email you get is somewhat free form, which the recieved data present.\n\n---\n\nEMAIL:\nrecieved: 08/06/07\n\nCONTENT:\nHey Olivia, here is your work schedule for the week of 10/06/07 - 17/06/07\n\nMonday: 14:00 - 22:00\nTuesday: 10:00 - 18:00\nWednesday: 14:00 - 18:00\nThursday: 22:00 - 06:00\n\nANSWER:\nevent(start_date=\"10/06/07\", start_time=\"14:00\", end_date=\"10/06/07\", end_time=\"22:00\")\nevent(start_date=\"11/06/07\", start_time=\"10:00\", end_date=\"11/06/07\", end_time=\"18:00\")\nevent(start_date=\"12/06/07\", start_time=\"14:00\", end_date=\"12/06/07\", end_time=\"18:00\")\nevent(start_date=\"13/06/07\", start_time=\"22:00\", end_date=\"14/06/07\", end_time=\"06:00\")\n\n---\n\nEMAIL:\nrecieved: 18/06/07\n\nCONTENT:\nTODO\n\nANSWER:\n\n"
] |
2024-01-10 | danmorper/reflex-tutorial | reflex_tutorial~state.py | # state.py
import reflex as rx
import os
import openai
from load_dotenv import load_dotenv
load_dotenv()
client = openai.OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
class State(rx.State):
# The current question being asked.
question: str
# Keep track of the chat history as a list of (question, answer) tuples.
chat_history: list[tuple[str, str]]
def answer(self):
# Our chatbot has some brains now!
session = client.chat.completions.create(
model="gpt-3.5-turbo",
messages=[
{"role": "user", "content": self.question}
],
stop=None,
temperature=0.7,
stream=True,
)
# Add to the answer as the chatbot responds.
answer = ""
self.chat_history.append((self.question, answer))
# Clear the question input.
self.question = ""
# Yield here to clear the frontend input before continuing.
yield
for item in session:
if hasattr(item.choices[0].delta, "content"):
answer += item.choices[0].delta.content
self.chat_history[-1] = (
self.chat_history[-1][0],
answer,
)
yield | [] |
2024-01-10 | intersun/transfer-learning-conv-ai | convai_evaluation.py | # # Copyright (c) 2019-present, HuggingFace Inc.
# All rights reserved.
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import random
import logging
from pprint import pformat
from collections import defaultdict
from functools import partial
from tqdm import trange
import torch
import torch.nn.functional as F
from parlai.core.agents import Agent
from parlai.scripts.eval_model import setup_args as base_setup_args
from projects.convai2.eval_hits import eval_hits, setup_args as setup_args_hits
from projects.convai2.eval_f1 import eval_f1, setup_args as setup_args_f1
from projects.convai2.eval_ppl import eval_ppl, setup_args as setup_args_ppl
from projects.convai2.build_dict import build_dict
from pytorch_pretrained_bert import OpenAIGPTDoubleHeadsModel, OpenAIGPTLMHeadModel, OpenAIGPTTokenizer
from utils import download_pretrained_model, AttrDict, build_input_from_segments, pad_dataset
from interact import sample_sequence, NO_PERSONA
class TransformerAgent(Agent):
@staticmethod
def add_cmdline_args(argparser):
agent_args = argparser.add_argument_group('Agent parameters')
agent_args.add_argument("--model_checkpoint", type=str, default="", help="Path, url or short name of the model")
agent_args.add_argument("--max_history", type=int, default=2, help="Number of previous utterances to keep in history")
agent_args.add_argument("--device", type=str, default="cuda" if torch.cuda.is_available() else "cpu", help="Device (cuda or cpu)")
agent_args.add_argument("--eval_type", type=str, default="hits@1", help="hits@1, ppl or f1")
agent_args.add_argument("--no_sample", action='store_true')
agent_args.add_argument("--max_length", type=int, default=20)
agent_args.add_argument("--min_length", type=int, default=1)
agent_args.add_argument("--seed", type=int, default=0)
agent_args.add_argument("--temperature", type=int, default=0.7)
agent_args.add_argument("--top_k", type=int, default=20)
return argparser
def __init__(self, opt, shared=None):
super(TransformerAgent, self).__init__(opt, shared)
args = AttrDict(opt) # to keep most commands identical to the interact.py script
self.args = args
logging.basicConfig(level=logging.INFO)
self.logger = logging.getLogger(__file__)
self.logger.info(pformat(args))
random.seed(args.seed)
torch.random.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
if shared is None:
self.logger.info("Get pretrained model and tokenizer")
if args.model_checkpoint == "":
args.model_checkpoint = download_pretrained_model()
self.tokenizer = OpenAIGPTTokenizer.from_pretrained(args.model_checkpoint)
if self.args.eval_type == "hits@1":
self.model_checkpoint = OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_checkpoint)
else:
self.model_checkpoint = OpenAIGPTLMHeadModel.from_pretrained(args.model_checkpoint)
self.model_checkpoint.to(args.device)
self.model_checkpoint.eval()
self.logger.info("Build BPE prefix dictionary")
convai_dict = build_dict()
assert len(convai_dict) == 19304
self.prefix2words = self.get_prefix2words(convai_dict)
else:
self.model_checkpoint = shared['model']
self.tokenizer = shared['tokenizer']
self.prefix2words = shared['prefix2words']
# self.special_tokens_ids = self.tokenizer.convert_tokens_to_ids(SPECIAL_TOKENS)
self.persona = []
self.history = []
self.labels = []
self.reset()
def observe(self, observation):
if self.episode_done:
self.reset()
if self.labels:
# Add the previous response to the history
self.history.append(self.labels)
if 'labels' in observation or 'eval_labels' in observation:
text = observation.get('labels', observation.get('eval_labels', [[]]))[0]
self.labels = self.tokenizer.encode(text)
if 'text' in observation:
text = observation['text']
for subtext in text.split('\n'):
subtext = subtext.strip()
if subtext.startswith('your persona:'):
subtext = subtext.replace('your persona:', '').strip()
self.persona.append(self.tokenizer.encode(subtext))
else:
self.history.append(self.tokenizer.encode(subtext))
self.history = self.history[-(2*self.args.max_history+1):]
candidates = []
if 'label_candidates' in observation:
for candidate in observation['label_candidates']:
candidates.append((self.tokenizer.encode(candidate), candidate))
self.candidates = candidates
self.episode_done = observation['episode_done']
self.observation = observation
return observation
def act(self):
reply = {}
if self.args.eval_type == "hits@1" and len(self.candidates) > 0:
instances = defaultdict(list)
for candidate, _ in self.candidates:
instance, _ = build_input_from_segments(self.persona, self.history, candidate, self.tokenizer,
no_persona=NO_PERSONA)
for input_name, input_array in instance.items():
instances[input_name].append(input_array)
inputs = pad_dataset(instances, padding=0)
tensor_inputs = {}
for input_name in ["input_ids", "mc_token_ids"]:
tensor = torch.tensor(inputs[input_name], device=self.args.device)
tensor = tensor.view((-1, len(self.candidates)) + tensor.shape[1:])
tensor_inputs[input_name] = tensor
with torch.no_grad():
_, mc_logits = self.model_checkpoint(**tensor_inputs)
val, ind = torch.sort(mc_logits[0], descending=True)
ypred = self.candidates[ind[0].item()][1] # match
tc = []
for j in range(len(self.candidates)):
tc.append(self.candidates[ind[j].item()][1])
reply = {'text': ypred, 'text_candidates': tc}
else:
# We are in interactive of f1 evaluation mode => just sample
with torch.no_grad():
out_ids, _ = sample_sequence(self.persona, self.history, self.tokenizer, self.model_checkpoint, self.args)
out_text = self.tokenizer.decode(out_ids, skip_special_tokens=True,
clean_up_tokenization_spaces=(self.args.eval_type != 'f1'))
reply = {'text': out_text}
return reply
def next_word_probability(self, partial_out):
"""Return probability distribution over next words given an input and
partial true output. This is used to calculate the per-word perplexity.
"""
partial_out_ids = self.tokenizer.encode(' '.join(partial_out))
instance, _ = build_input_from_segments(self.persona, self.history, partial_out_ids,
self.tokenizer, with_eos=False, no_persona=NO_PERSONA)
input_ids = torch.tensor(instance["input_ids"], device=self.args.device).unsqueeze(0)
token_type_ids = torch.tensor(instance["token_type_ids"], device=self.args.device).unsqueeze(0)
with torch.no_grad():
logits = self.model_checkpoint(input_ids, token_type_ids=None)
probs = F.softmax(logits[0, -1], dim=0)
dist = {}
for prefix_id, words in self.prefix2words.items():
for word, ratio in words.items():
dist[word] = probs[prefix_id].item() * ratio
return dist
def get_prefix2words(self, convai_dict, smoothing_freq=5):
""" map BPE-prefix => dict(full_words beginning with BPE-prefix, associated words_counts) """
prefix2words = defaultdict(dict)
for i in trange(len(convai_dict)):
word = convai_dict[i]
freq = convai_dict.freq[word] + smoothing_freq
bpe_tokens = self.tokenizer.bpe(word).split(' ')
prefix_id = self.tokenizer.convert_tokens_to_ids(bpe_tokens[0])
prefix2words[prefix_id].update(dict([(word, freq)]))
for prefix_id, words in prefix2words.items():
total_counts = sum(words.values())
prefix2words[prefix_id] = dict((word, count/total_counts) for word, count in words.items())
return prefix2words
def share(self):
shared = super(TransformerAgent, self).share()
shared['tokenizer'] = self.tokenizer
shared['model'] = self.model_checkpoint
shared['prefix2words'] = self.prefix2words
return shared
def reset(self):
self.persona = []
self.history = []
self.labels = []
self.candidates = []
self.episode_done = True
self.observation = None
if __name__ == '__main__':
parser = base_setup_args(None)
parser.set_params(
model='convai_evaluation:TransformerAgent')
opt = parser.parse_args(print_args=False)
if opt['eval_type'] == "hits@1":
setup_args = setup_args_hits(None)
eval_fct = partial(eval_hits, print_parser=setup_args)
elif opt['eval_type'] == "ppl":
setup_args = setup_args_ppl(None)
eval_fct = eval_ppl
elif opt['eval_type'] == "f1":
setup_args = setup_args_f1(None)
eval_fct = partial(eval_f1, print_parser=setup_args)
else:
raise ValueError
setup_args.set_params(
model='convai_evaluation:TransformerAgent')
opt = setup_args.parse_args(print_args=False)
eval_fct(opt)
| [] |
2024-01-10 | ArikReuter/TopicGPT | src~topicgpt~GetEmbeddingsOpenAI.py | import openai
from openai.embeddings_utils import get_embedding
import tiktoken
from tqdm import tqdm
import numpy as np
class GetEmbeddingsOpenAI:
"""
This class allows to compute embeddings of text using the OpenAI API.
"""
def __init__(self, api_key: str, embedding_model: str = "text-embedding-ada-002", tokenizer: str = None, max_tokens: int = 8191) -> None:
"""
Constructor of the class.
Args:
api_key (str): API key to use the OpenAI API.
embedding_model (str, optional): Name of the embedding model to use.
tokenizer (str, optional): Name of the tokenizer to use.
max_tokens (int, optional): Maximum number of tokens to use.
Note:
By default, the embedding model "text-embedding-ada-002" is used with the corresponding tokenizer "cl100k_base" and a maximum number of tokens of 8191.
"""
self.api_key = api_key
openai.api_key = api_key
self.embedding_model = embedding_model
self.tokenizer_str = tokenizer
self.max_tokens = max_tokens
@staticmethod
def num_tokens_from_string(string: str, encoding) -> int:
"""
Returns the number of tokens in a text string.
Args:
string (str): Text string to compute the number of tokens.
encoding: A function to encode the string into tokens.
Returns:
int: Number of tokens in the text string.
"""
num_tokens = len(encoding.encode(string))
return num_tokens
def compute_number_of_tokens(self, corpus: list[str]) -> int:
"""
Computes the total number of tokens needed to embed the corpus.
Args:
corpus (list[str]): List of strings to embed, where each element in the list is a document.
Returns:
int: Total number of tokens needed to embed the corpus.
"""
if self.tokenizer_str is None:
tokenizer = tiktoken.encoding_for_model(self.embedding_model)
else:
tokenizer = tiktoken.get_encoding(self.tokenizer_str)
num_tokens = 0
for document in tqdm(corpus):
num_tokens += self.num_tokens_from_string(document, tokenizer)
return num_tokens
def split_doc(self, text):
"""
Splits a single document that is longer than the maximum number of tokens into a list of smaller documents.
Args:
self: The instance of the class.
text (str): The string to be split.
Returns:
List[str]: A list of strings to embed, where each element in the list is a list of chunks comprising the document.
"""
split_text = []
split_text.append(text[:self.max_tokens])
for i in range(1, len(text) // self.max_tokens):
split_text.append(text[i * self.max_tokens:(i + 1) * self.max_tokens])
split_text.append(text[(len(text) // self.max_tokens) * self.max_tokens:])
return split_text
def split_long_docs(self, text: list[str]) -> list[list[str]]:
"""
Splits all documents that are longer than the maximum number of tokens into a list of smaller documents.
Args:
self: The instance of the class.
text (list[str]): List of strings to embed, where each element in the list is a document.
Returns:
List[list[str]]: A list of lists of strings to embed, where each element in the outer list is a list of chunks comprising the document.
"""
if self.tokenizer_str is None:
tokenizer = tiktoken.encoding_for_model(self.embedding_model)
else:
tokenizer = tiktoken.get_encoding(self.tokenizer_str)
split_text = []
for document in tqdm(text):
if self.num_tokens_from_string(document, tokenizer) > self.max_tokens:
split_text.append(self.split_doc(document))
else:
split_text.append([document])
return split_text
def make_api_call(self, text: str):
"""
Makes an API call to the OpenAI API to embed a text string.
Args:
self: The instance of the class.
text (str): The string to embed.
Returns:
API response: The response from the API.
"""
response = openai.Embedding.create(input = [text], model = self.embedding_model)
return response
def get_embeddings_doc_split(self, corpus: list[list[str]], n_tries=3) -> list[dict]:
"""
Computes the embeddings of a corpus for split documents.
Args:
self: The instance of the class.
corpus (list[list[str]]): List of strings to embed, where each element is a document represented by a list of its chunks.
n_tries (int, optional): Number of tries to make an API call (default is 3).
Returns:
List[dict]: A list of dictionaries, where each dictionary contains the embedding of the document, the text of the document, and a list of errors that occurred during the embedding process.
"""
api_res_list = []
for i in tqdm(range(len(corpus))):
chunk_lis = corpus[i]
api_res_doc = []
for chunk_n, chunk in enumerate(chunk_lis):
for i in range(n_tries + 1):
try:
api_res_doc.append(
{"api_res": self.make_api_call(chunk),
"error": None }
)
break
except Exception as e:
print(f"Error {e} occured for chunk {chunk_n} of document {i}")
print(chunk)
print("Trying again.")
if i == n_tries:
print("Maximum number of tries reached. Skipping chunk.")
api_res_doc.append(
{"api_res": None,
"error": e })
# average the embeddings of the chunks
emb_lis = []
for api_res in api_res_doc:
if api_res["api_res"] is not None:
emb_lis.append(np.array(api_res["api_res"]["data"][0]["embedding"]))
text = " ".join(chunk_lis)
embedding = np.mean(emb_lis, axis = 0)
api_res_list.append(
{"embedding": embedding,
"text": text,
"errors": [api_res["error"] for api_res in api_res_doc]}
)
return api_res_list
def convert_api_res_list(self, api_res_list: list[dict]) -> dict:
"""
Converts the api_res list into a dictionary containing the embeddings as a matrix and the corpus as a list of strings.
Args:
self: The instance of the class.
api_res_list (list[dict]): List of dictionaries, where each dictionary contains the embedding of the document, the text of the document, and a list of errors that occurred during the embedding process.
Returns:
dict: A dictionary containing the embeddings as a matrix and the corpus as a list of strings.
"""
embeddings = np.array([api_res["embedding"] for api_res in api_res_list])
corpus = [api_res["text"] for api_res in api_res_list]
errors = [api_res["errors"] for api_res in api_res_list]
return {"embeddings": embeddings, "corpus": corpus, "errors": errors}
def get_embeddings(self, corpus: list[str]) -> dict:
"""
Computes the embeddings of a corpus.
Args:
self: The instance of the class.
corpus (list[str]): List of strings to embed, where each element in the list is a document.
Returns:
dict: A dictionary containing the embeddings as a matrix and the corpus as a list of strings.
"""
corpus_split = self.split_long_docs(corpus)
corpus_emb = self.get_embeddings_doc_split(corpus_split)
self.corpus_emb = corpus_emb
res = self.convert_api_res_list(corpus_emb)
return res | [] |
2024-01-10 | mateai/langchain | libs~experimental~langchain_experimental~comprehend_moderation~pii.py | import asyncio
from typing import Any, Dict, Optional
from langchain_experimental.comprehend_moderation.base_moderation_exceptions import (
ModerationPiiError,
)
class ComprehendPII:
def __init__(
self,
client: Any,
callback: Optional[Any] = None,
unique_id: Optional[str] = None,
chain_id: Optional[str] = None,
) -> None:
self.client = client
self.moderation_beacon = {
"moderation_chain_id": chain_id,
"moderation_type": "PII",
"moderation_status": "LABELS_NOT_FOUND",
}
self.callback = callback
self.unique_id = unique_id
def validate(self, prompt_value: str, config: Any = None) -> str:
redact = config.get("redact")
return (
self._detect_pii(prompt_value=prompt_value, config=config)
if redact
else self._contains_pii(prompt_value=prompt_value, config=config)
)
def _contains_pii(self, prompt_value: str, config: Any = None) -> str:
"""
Checks for Personally Identifiable Information (PII) labels above a
specified threshold. Uses Amazon Comprehend Contains PII Entities API. See -
https://docs.aws.amazon.com/comprehend/latest/APIReference/API_ContainsPiiEntities.html
Args:
prompt_value (str): The input text to be checked for PII labels.
config (Dict[str, Any]): Configuration for PII check and actions.
Returns:
str: the original prompt
Note:
- The provided client should be initialized with valid AWS credentials.
"""
pii_identified = self.client.contains_pii_entities(
Text=prompt_value, LanguageCode="en"
)
if self.callback and self.callback.pii_callback:
self.moderation_beacon["moderation_input"] = prompt_value
self.moderation_beacon["moderation_output"] = pii_identified
threshold = config.get("threshold")
pii_labels = config.get("labels")
pii_found = False
for entity in pii_identified["Labels"]:
if (entity["Score"] >= threshold and entity["Name"] in pii_labels) or (
entity["Score"] >= threshold and not pii_labels
):
pii_found = True
break
if self.callback and self.callback.pii_callback:
if pii_found:
self.moderation_beacon["moderation_status"] = "LABELS_FOUND"
asyncio.create_task(
self.callback.on_after_pii(self.moderation_beacon, self.unique_id)
)
if pii_found:
raise ModerationPiiError
return prompt_value
def _detect_pii(self, prompt_value: str, config: Optional[Dict[str, Any]]) -> str:
"""
Detects and handles Personally Identifiable Information (PII) entities in the
given prompt text using Amazon Comprehend's detect_pii_entities API. The
function provides options to redact or stop processing based on the identified
PII entities and a provided configuration. Uses Amazon Comprehend Detect PII
Entities API.
Args:
prompt_value (str): The input text to be checked for PII entities.
config (Dict[str, Any]): A configuration specifying how to handle
PII entities.
Returns:
str: The processed prompt text with redacted PII entities or raised
exceptions.
Raises:
ValueError: If the prompt contains configured PII entities for
stopping processing.
Note:
- If PII is not found in the prompt, the original prompt is returned.
- The client should be initialized with valid AWS credentials.
"""
pii_identified = self.client.detect_pii_entities(
Text=prompt_value, LanguageCode="en"
)
if self.callback and self.callback.pii_callback:
self.moderation_beacon["moderation_input"] = prompt_value
self.moderation_beacon["moderation_output"] = pii_identified
if (pii_identified["Entities"]) == []:
if self.callback and self.callback.pii_callback:
asyncio.create_task(
self.callback.on_after_pii(self.moderation_beacon, self.unique_id)
)
return prompt_value
pii_found = False
if not config and pii_identified["Entities"]:
for entity in pii_identified["Entities"]:
if entity["Score"] >= 0.5:
pii_found = True
break
if self.callback and self.callback.pii_callback:
if pii_found:
self.moderation_beacon["moderation_status"] = "LABELS_FOUND"
asyncio.create_task(
self.callback.on_after_pii(self.moderation_beacon, self.unique_id)
)
if pii_found:
raise ModerationPiiError
else:
threshold = config.get("threshold") # type: ignore
pii_labels = config.get("labels") # type: ignore
mask_marker = config.get("mask_character") # type: ignore
pii_found = False
for entity in pii_identified["Entities"]:
if (
pii_labels
and entity["Type"] in pii_labels
and entity["Score"] >= threshold
) or (not pii_labels and entity["Score"] >= threshold):
pii_found = True
char_offset_begin = entity["BeginOffset"]
char_offset_end = entity["EndOffset"]
mask_length = char_offset_end - char_offset_begin + 1
masked_part = mask_marker * mask_length
prompt_value = (
prompt_value[:char_offset_begin]
+ masked_part
+ prompt_value[char_offset_end + 1 :]
)
if self.callback and self.callback.pii_callback:
if pii_found:
self.moderation_beacon["moderation_status"] = "LABELS_FOUND"
asyncio.create_task(
self.callback.on_after_pii(self.moderation_beacon, self.unique_id)
)
return prompt_value
| [] |
2024-01-10 | mateai/langchain | libs~langchain~tests~integration_tests~vectorstores~test_xata.py | """Test Xata vector store functionality.
Before running this test, please create a Xata database by following
the instructions from:
https://python.langchain.com/docs/integrations/vectorstores/xata
"""
import os
from langchain.docstore.document import Document
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores.xata import XataVectorStore
class TestXata:
@classmethod
def setup_class(cls) -> None:
assert os.getenv("XATA_API_KEY"), "XATA_API_KEY environment variable is not set"
assert os.getenv("XATA_DB_URL"), "XATA_DB_URL environment variable is not set"
def test_similarity_search_without_metadata(
self, embedding_openai: OpenAIEmbeddings
) -> None:
"""Test end to end constructions and search without metadata."""
texts = ["foo", "bar", "baz"]
docsearch = XataVectorStore.from_texts(
api_key=os.getenv("XATA_API_KEY"),
db_url=os.getenv("XATA_DB_URL"),
texts=texts,
embedding=embedding_openai,
)
docsearch.wait_for_indexing(ndocs=3)
output = docsearch.similarity_search("foo", k=1)
assert output == [Document(page_content="foo")]
docsearch.delete(delete_all=True)
def test_similarity_search_with_metadata(
self, embedding_openai: OpenAIEmbeddings
) -> None:
"""Test end to end construction and search with a metadata filter.
This test requires a column named "a" of type integer to be present
in the Xata table."""
texts = ["foo", "foo", "foo"]
metadatas = [{"a": i} for i in range(len(texts))]
docsearch = XataVectorStore.from_texts(
api_key=os.getenv("XATA_API_KEY"),
db_url=os.getenv("XATA_DB_URL"),
texts=texts,
embedding=embedding_openai,
metadatas=metadatas,
)
docsearch.wait_for_indexing(ndocs=3)
output = docsearch.similarity_search("foo", k=1, filter={"a": 1})
assert output == [Document(page_content="foo", metadata={"a": 1})]
docsearch.delete(delete_all=True)
| [] |
2024-01-10 | mateai/langchain | libs~experimental~langchain_experimental~comprehend_moderation~toxicity.py | import asyncio
import importlib
from typing import Any, List, Optional
from langchain_experimental.comprehend_moderation.base_moderation_exceptions import (
ModerationToxicityError,
)
class ComprehendToxicity:
def __init__(
self,
client: Any,
callback: Optional[Any] = None,
unique_id: Optional[str] = None,
chain_id: Optional[str] = None,
) -> None:
self.client = client
self.moderation_beacon = {
"moderation_chain_id": chain_id,
"moderation_type": "Toxicity",
"moderation_status": "LABELS_NOT_FOUND",
}
self.callback = callback
self.unique_id = unique_id
def _toxicity_init_validate(self, max_size: int) -> Any:
"""
Validate and initialize toxicity processing configuration.
Args:
max_size (int): Maximum sentence size defined in the
configuration object.
Raises:
Exception: If the maximum sentence size exceeds the 5KB limit.
Note:
This function ensures that the NLTK punkt tokenizer is downloaded
if not already present.
Returns:
None
"""
if max_size > 1024 * 5:
raise Exception("The sentence length should not exceed 5KB.")
try:
nltk = importlib.import_module("nltk")
nltk.data.find("tokenizers/punkt")
return nltk
except ImportError:
raise ModuleNotFoundError(
"Could not import nltk python package. "
"Please install it with `pip install nltk`."
)
except LookupError:
nltk.download("punkt")
def _split_paragraph(
self, prompt_value: str, max_size: int = 1024 * 4
) -> List[List[str]]:
"""
Split a paragraph into chunks of sentences, respecting the maximum size limit.
Args:
paragraph (str): The input paragraph to be split into chunks.
max_size (int, optional): The maximum size limit in bytes for
each chunk. Defaults to 1024.
Returns:
List[List[str]]: A list of chunks, where each chunk is a list
of sentences.
Note:
This function validates the maximum sentence size based on service
limits using the 'toxicity_init_validate' function. It uses the NLTK
sentence tokenizer to split the paragraph into sentences.
Example:
paragraph = "This is a sample paragraph. It
contains multiple sentences. ..."
chunks = split_paragraph(paragraph, max_size=2048)
"""
# validate max. sentence size based on Service limits
nltk = self._toxicity_init_validate(max_size)
sentences = nltk.sent_tokenize(prompt_value)
chunks = list() # type: ignore
current_chunk = list() # type: ignore
current_size = 0
for sentence in sentences:
sentence_size = len(sentence.encode("utf-8"))
# If adding a new sentence exceeds max_size
# or current_chunk has 10 sentences, start a new chunk
if (current_size + sentence_size > max_size) or (len(current_chunk) >= 10):
if current_chunk: # Avoid appending empty chunks
chunks.append(current_chunk)
current_chunk = []
current_size = 0
current_chunk.append(sentence)
current_size += sentence_size
# Add any remaining sentences
if current_chunk:
chunks.append(current_chunk)
return chunks
def validate(self, prompt_value: str, config: Any = None) -> str:
"""
Check the toxicity of a given text prompt using AWS
Comprehend service and apply actions based on configuration.
Args:
prompt_value (str): The text content to be checked for toxicity.
config (Dict[str, Any]): Configuration for toxicity checks and actions.
Returns:
str: The original prompt_value if allowed or no toxicity found.
Raises:
ValueError: If the prompt contains toxic labels and cannot be
processed based on the configuration.
"""
chunks = self._split_paragraph(prompt_value=prompt_value)
for sentence_list in chunks:
segments = [{"Text": sentence} for sentence in sentence_list]
response = self.client.detect_toxic_content(
TextSegments=segments, LanguageCode="en"
)
if self.callback and self.callback.toxicity_callback:
self.moderation_beacon["moderation_input"] = segments # type: ignore
self.moderation_beacon["moderation_output"] = response
toxicity_found = False
threshold = config.get("threshold")
toxicity_labels = config.get("labels")
if not toxicity_labels:
for item in response["ResultList"]:
for label in item["Labels"]:
if label["Score"] >= threshold:
toxicity_found = True
break
else:
for item in response["ResultList"]:
for label in item["Labels"]:
if (
label["Name"] in toxicity_labels
and label["Score"] >= threshold
):
toxicity_found = True
break
if self.callback and self.callback.toxicity_callback:
if toxicity_found:
self.moderation_beacon["moderation_status"] = "LABELS_FOUND"
asyncio.create_task(
self.callback.on_after_toxicity(
self.moderation_beacon, self.unique_id
)
)
if toxicity_found:
raise ModerationToxicityError
return prompt_value
| [] |
2024-01-10 | mateai/langchain | libs~langchain~langchain~memory~readonly.py | from typing import Any, Dict, List
from langchain.schema import BaseMemory
class ReadOnlySharedMemory(BaseMemory):
"""A memory wrapper that is read-only and cannot be changed."""
memory: BaseMemory
@property
def memory_variables(self) -> List[str]:
"""Return memory variables."""
return self.memory.memory_variables
def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, str]:
"""Load memory variables from memory."""
return self.memory.load_memory_variables(inputs)
def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:
"""Nothing should be saved or changed"""
pass
def clear(self) -> None:
"""Nothing to clear, got a memory like a vault."""
pass
| [] |
2024-01-10 | Social-GPT/agent | generators~instagram_generator.py | from langchain.chat_models import ChatOpenAI
from langchain.schema import HumanMessage, SystemMessage
from utils import add_item_to_file
from prompts import Prompts
from brands import Brand
from files import Files
from logger import Logger
from llm import LLM, GenerationMode, GenerationItemType
class InstagramGenerator:
def __init__(self, brand: Brand, language: str, idea: str, prompt_expansion: str, generation_mode: GenerationMode):
self.brand = brand
self.language = language
self.idea = idea
self.prompt_expansion = prompt_expansion
self.generation_mode = generation_mode
def generate_post(self):
prompt = f"Write an Instagram post in {self.language} for his account that talks about '{self.idea}'{Prompts.get_avoids()}{Prompts.build_style_prompt(self.brand.style)}"
if (self.prompt_expansion != ""):
prompt = prompt + \
f"\n\nTake this also into account: {self.prompt_expansion}"
post = LLM.generate(
[SystemMessage(content=self.brand.description), HumanMessage(
content=prompt)], GenerationItemType.POST, self.generation_mode
).content.strip()
Logger.log("Generated Instagram post", post)
add_item_to_file(Files.instagram_results, post)
return post
| [] |
2024-01-10 | Social-GPT/agent | generators~image_prompt_generator.py | from langchain.chat_models import ChatOpenAI
from langchain.schema import HumanMessage, SystemMessage
from brands import Brand
from llm import LLM, GenerationMode, GenerationItemType
class ImagePromptGenerator:
def __init__(self, brand: Brand, post_idea: str, generation_mode: GenerationMode):
self.brand = brand
self.post_idea = post_idea
self.generation_mode = generation_mode
def generate_prompt(self):
prompt = f"Define with 10-20 words the description for the image that will be used for the following post idea:\n\n'{self.post_idea}'.\n\nNote: You should describe all the items we will see in the image, and those items should NOT include people's faces, hands, text or animals, device screens or anything that could contain text. Good examples to include would be common objects and scenes."
image_prompt = LLM.generate([SystemMessage(
content=self.brand.description), HumanMessage(content=prompt)], GenerationItemType.IMAGE_PROMPT, self.generation_mode).content
prompt = """
{}, rendered in a hyperrealistic style. Text is transparent and cannot be seen. Detailed, high-resolution textures, (detailed skin), and true-to-life color reproduction (grade:Kodak Ektar 100 film). Subtle lighting effects reminiscent of Gregory Crewdson's photography. An underlying surrealist ambiance inspired by Rene Magritte. Apply an ultra-wide lens effect with slight lens flare. Attention to minute detail - (Visible pores), (visible skin texture), (microscopic hair detail). High contrast, full dynamic range - 16k, UHD, HDR. The image should be as detailed and lifelike as possible. (Masterpiece:1.5), (highest quality:1.5), shot with a cinematic camera in a controlled studio environment.
""".format(image_prompt)
return prompt
| [
"\n PLACEHOLDER, rendered in a hyperrealistic style. Text is transparent and cannot be seen. Detailed, high-resolution textures, (detailed skin), and true-to-life color reproduction (grade:Kodak Ektar 100 film). Subtle lighting effects reminiscent of Gregory Crewdson's photography. An underlying surrealist ambiance inspired by Rene Magritte. Apply an ultra-wide lens effect with slight lens flare. Attention to minute detail - (Visible pores), (visible skin texture), (microscopic hair detail). High contrast, full dynamic range - 16k, UHD, HDR. The image should be as detailed and lifelike as possible. (Masterpiece:1.5), (highest quality:1.5), shot with a cinematic camera in a controlled studio environment.\n ",
"f\"Define with 10-20 words the description for the image that will be used for the following post idea:\\n\\n'{self.post_idea}'.\\n\\nNote: You should describe all the items we will see in the image, and those items should NOT include people's faces, hands, text or animals, device screens or anything that could contain text. Good examples to include would be common objects and scenes."
] |
2024-01-10 | Social-GPT/agent | generators~linkedin_generator.py | from langchain.chat_models import ChatOpenAI
from langchain.schema import HumanMessage, SystemMessage
from utils import add_item_to_file
from prompts import Prompts
from brands import Brand
from files import Files
from logger import Logger
from llm import LLM, GenerationMode, GenerationItemType
class LinkedInGenerator:
def __init__(self, brand: Brand, language: str, idea: str, prompt_expansion: str, generation_mode: GenerationMode):
self.brand = brand
self.language = language
self.idea = idea
self.prompt_expansion = prompt_expansion
self.generation_mode = generation_mode
def generate_post(self):
prompt = f"Write a LinkedIn post in {self.language} with 5-8 paragraphs for their account that talks about '{self.idea}'{Prompts.get_avoids()}{Prompts.build_style_prompt(self.brand.style)}"
if (self.prompt_expansion != ""):
prompt = prompt + \
f"\n\nTake this also into account: {self.prompt_expansion}"
post = LLM.generate(
[SystemMessage(content=self.brand.description), HumanMessage(
content=prompt)], GenerationItemType.POST, self.generation_mode
).content.strip()
Logger.log("Generated LinkedIn post", post)
add_item_to_file(Files.linkedin_results, post)
return post
| [] |
2024-01-10 | Social-GPT/agent | generators~topic_generator.py | from langchain.chat_models import ChatOpenAI
from langchain.schema import HumanMessage, SystemMessage
from utils import format_list, write_to_file
from brands import Brand
from prompts import Prompts
from llm import LLM
from logger import Logger
from files import Files
from llm import LLM, GenerationMode, GenerationItemType
class TopicGenerator:
def __init__(self, brand: Brand, topic_count: str, prompt_expansion: str, generation_mode: GenerationMode):
self.brand = brand
self.prompt_expansion = prompt_expansion
self.topic_count = topic_count
self.generation_mode = generation_mode
def generate_topics(self):
prompt = f"Create a list of {self.topic_count} general topics or fields to cover in their social media posts, in the format '- ...\n- ...'{Prompts.get_avoids()}"
if (self.prompt_expansion != ""):
prompt = prompt + \
f"\n\nTake this also into account: {self.prompt_expansion}"
topics = [
i.replace("- ", "")
for i in LLM.generate([SystemMessage(content=self.brand.description), HumanMessage(content=prompt)], GenerationItemType.IDEAS, self.generation_mode)
.content.strip()
.split("\n")
if len(i) > 2
][: self.topic_count]
print('\n---------')
Logger.log("Generated topics", format_list(topics))
write_to_file(Files.topic_results, '\n'.join(topics))
return topics
| [] |
2024-01-10 | Social-GPT/agent | generators~idea_generator.py | from langchain.chat_models import ChatOpenAI
from langchain.schema import HumanMessage, SystemMessage
from utils import format_list, add_item_to_file
from prompts import Prompts
from files import Files
from brands import Brand
from logger import Logger
from llm import LLM, GenerationMode, GenerationItemType
class IdeaGenerator:
def __init__(self, brand: Brand, number_of_ideas: str, prompt_expansion: str, generation_mode: GenerationMode):
self.brand = brand
self.number_of_ideas = number_of_ideas
self.prompt_expansion = prompt_expansion
self.generation_mode = generation_mode
def generate_ideas(self, topic):
prompt = f"Create a list of {self.number_of_ideas} social media post ideas (concise and specific) for their account about the topic '{topic}' in the format '- ...\n- ...'{Prompts.get_avoids()}"
if (self.prompt_expansion != ""):
prompt = prompt + \
f"\n\nTake this also into account: {self.prompt_expansion}"
ideas = [
i.replace("- ", "")
for i in LLM.generate(
[SystemMessage(content=self.brand.description), HumanMessage(
content=prompt)], GenerationItemType.IDEAS, self.generation_mode
)
.content.strip()
.split("\n")
if len(i) > 2
][: self.number_of_ideas]
Logger.log("Generated ideas", format_list(ideas))
for idea in ideas:
add_item_to_file(Files.idea_results, idea)
return ideas
| [] |
2024-01-10 | Social-GPT/agent | generators~tweet_generator.py | from langchain.chat_models import ChatOpenAI
from langchain.schema import HumanMessage, SystemMessage
from utils import add_item_to_file
from prompts import Prompts
from brands import Brand
from files import Files
from logger import Logger
from llm import LLM, GenerationMode, GenerationItemType
class TweetGenerator:
def __init__(self, brand: Brand, language: str, idea: str, prompt_expansion: str, generation_mode: GenerationMode):
self.brand = brand
self.language = language
self.idea = idea
self.prompt_expansion = prompt_expansion
self.generation_mode = generation_mode
def generate_tweet(self):
prompt = f"Write a Tweet in {self.language} for their account that talks about '{self.idea}'{Prompts.get_avoids()}{Prompts.build_style_prompt(self.brand.style)}"
if (self.prompt_expansion != ""):
prompt = prompt + \
f"\n\nTake this also into account: {self.prompt_expansion}"
tweet = LLM.generate(
[SystemMessage(content=self.brand.description), HumanMessage(
content=prompt)], GenerationItemType.POST, self.generation_mode
).content.strip()
Logger.log("Generated Tweet", tweet)
add_item_to_file(Files.twitter_results, tweet)
return tweet
| [] |
2024-01-10 | Social-GPT/agent | generators~facebook_generator.py | from langchain.chat_models import ChatOpenAI
from langchain.schema import HumanMessage, SystemMessage
from utils import add_item_to_file
from prompts import Prompts
from brands import Brand
from files import Files
from logger import Logger
from llm import LLM, GenerationMode, GenerationItemType
class FacebookGenerator:
def __init__(self, brand: Brand, language: str, idea: str, prompt_expansion: str, generation_mode: GenerationMode):
self.brand = brand
self.language = language
self.idea = idea
self.prompt_expansion = prompt_expansion
self.generation_mode = generation_mode
def generate_post(self):
prompt = f"Write a Facebook post with 3-6 paragraphs in {self.language} for his account that talks about '{self.idea}'{Prompts.get_avoids()}{Prompts.build_style_prompt(self.brand.style)}"
if (self.prompt_expansion != ""):
prompt = prompt + \
f"\n\nTake this also into account: {self.prompt_expansion}"
post = LLM.generate(
[SystemMessage(content=self.brand.description), HumanMessage(
content=prompt)], GenerationItemType.POST, self.generation_mode
).content.strip()
Logger.log("Generated Facebook post", post)
add_item_to_file(Files.facebook_results, post)
return post
| [] |
2024-01-10 | kobeeraveendran/faster-pix2pix | pytorch-resnets~inception_score.py | # base code from OpenAI's improved-gan repo: github.com/openai/improved-gan
# most of the modifications are under the ##################
# Code derived from tensorflow/tensorflow/models/image/imagenet/classify_image.py
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import os.path
import sys
import tarfile
import numpy as np
from six.moves import urllib
import tensorflow as tf
import glob
import scipy.misc
import math
import sys
import cv2
import PIL
from PIL import Image
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
MODEL_DIR = '/tmp/imagenet'
DATA_URL = 'http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz'
softmax = None
# Call this function with list of images. Each of elements should be a
# numpy array with values ranging from 0 to 255.
def get_inception_score(images, splits=10):
assert(type(images) == list)
assert(type(images[0]) == np.ndarray)
assert(len(images[0].shape) == 3)
assert(np.max(images[0]) > 10)
assert(np.min(images[0]) >= 0.0)
inps = []
for img in images:
img = img.astype(np.float32)
inps.append(np.expand_dims(img, 0))
bs = 1
with tf.Session() as sess:
preds = []
n_batches = int(math.ceil(float(len(inps)) / float(bs)))
for i in range(n_batches):
sys.stdout.write(".")
sys.stdout.flush()
inp = inps[(i * bs):min((i + 1) * bs, len(inps))]
inp = np.concatenate(inp, 0)
pred = sess.run(softmax, {'ExpandDims:0': inp})
preds.append(pred)
preds = np.concatenate(preds, 0)
scores = []
for i in range(splits):
part = preds[(i * preds.shape[0] // splits):((i + 1) * preds.shape[0] // splits), :]
kl = part * (np.log(part) - np.log(np.expand_dims(np.mean(part, 0), 0)))
kl = np.mean(np.sum(kl, 1))
scores.append(np.exp(kl))
return np.mean(scores), np.std(scores)
# This function is called automatically.
def _init_inception():
global softmax
if not os.path.exists(MODEL_DIR):
os.makedirs(MODEL_DIR)
filename = DATA_URL.split('/')[-1]
filepath = os.path.join(MODEL_DIR, filename)
if not os.path.exists(filepath):
def _progress(count, block_size, total_size):
sys.stdout.write('\r>> Downloading %s %.1f%%' % (
filename, float(count * block_size) / float(total_size) * 100.0))
sys.stdout.flush()
filepath, _ = urllib.request.urlretrieve(DATA_URL, filepath, _progress)
print()
statinfo = os.stat(filepath)
print('Succesfully downloaded', filename, statinfo.st_size, 'bytes.')
tarfile.open(filepath, 'r:gz').extractall(MODEL_DIR)
with tf.gfile.FastGFile(os.path.join(
MODEL_DIR, 'classify_image_graph_def.pb'), 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
_ = tf.import_graph_def(graph_def, name='')
# Works with an arbitrary minibatch size.
with tf.Session() as sess:
pool3 = sess.graph.get_tensor_by_name('pool_3:0')
ops = pool3.graph.get_operations()
for op_idx, op in enumerate(ops):
for o in op.outputs:
shape = o.get_shape()
shape = [s.value for s in shape]
new_shape = []
for j, s in enumerate(shape):
if s == 1 and j == 0:
new_shape.append(None)
else:
new_shape.append(s)
o.set_shape(tf.TensorShape(new_shape))
w = sess.graph.get_operation_by_name("softmax/logits/MatMul").inputs[1]
logits = tf.matmul(tf.squeeze(pool3, [1, 2]), w)
softmax = tf.nn.softmax(logits)
if softmax is None:
_init_inception()
##################################################################################################
images = []
filepath = input('Test images directory: ')
#filepath = filepath + "/*.jpg"
for file in os.listdir(filepath):
#print(file)
image = cv2.imread(filepath + '/' + file)
#print(type(image))
images.append(image)
#print(images)
print(get_inception_score(images, splits = 1)) | [] |
2024-01-10 | yifanzhang-pro/cumulative-reasoning-anonymous | Game24~game24-openai.py | # Game of 24 with Cumulative Reasoning
import guidance
import re
import sympy
import ast
import os
import json
import pandas as pd
import argparse
import openai
# openai.proxy = "http://..."
os.environ["OPENAI_API_KEY"] = 'sk-...'
def get_parser():
parser = argparse.ArgumentParser(description="Cumulative Reasoning")
parser.add_argument('--trycnt', type=int, choices=range(0, 1001), default=50, help='numbers of try times')
parser.add_argument('--model', type=str, default='gpt-4-0314', help='model to use')
parser.add_argument('--resume', type=int, default=0, help='resume point')
parser.add_argument('--resume_cor', type=int ,default=0, help='resume pre correct cnt')
parser.add_argument('--b', type=int, default=1, help='number of branches, default is set to be 1')
return parser
parser = get_parser()
args = parser.parse_args()
guidance.llm = guidance.llms.OpenAI(
args.model,
caching=False
)
import numpy
from tqdm import tqdm
valid_output = ["sure", "likely", "impossible"]
def is_sure(output):
return output == 'sure'
expand_program = guidance(
'''
{{#system}}Suppose you are one of the greatest AI scientists, logicians and mathematicians. You are very good at basic arithmetic operations. Use numbers and basic arithmetic operations (+ - * /) to obtain 24 with input numbers. You need to combine the given intermediate steps step by step into a complete expression.{{/system}}
{{#user}}
Input: 1, 1, 4, 6
Intermediate steps:
1 * 4 = 4 (left 1, 4, 6)
1 * 4 * 6 = 24
{{/user}}
{{#assistant}}
Draft:
Because 1 * 4 * 6 = 24, while 1 * 4 = 4. So 1 * (1 * 4) * 6 = 24.
Output:
1 * (1 * 4) * 6 = 24
{{/assistant}}
{{#user}}
Input: 1, 10, 11, 12
Intermediate steps:
12 - 10 = 2 (left 1, 2, 11)
1 + 11 = 12 (left 2, 12)
12 * 2 = 24
{{/user}}
{{#assistant}}
Draft:
Because 12 * 2 = 24, while 12 = 1 + 11. So (1 + 11) * 2 = 24.
Because (1 + 11) * 2 = 24, while 2 = 12 - 10. So (1 + 11) * (12 - 10) = 24.
Output:
(1 + 11) * (12 - 10) = 24
{{/assistant}}
{{#user}}
Input: 5, 6, 6, 9
Intermediate steps:
6 * 9 = 54 (left 5, 6, 54)
5 * 6 = 30 (left 30, 54)
54 - 30 = 24
{{/user}}
{{#assistant}}
Draft:
Because 54 - 30 = 24, while 5 * 6 = 30. So 54 - (5 * 6) = 24.
Because 54 - (5 * 6) = 24, while 6 * 9 = 54. So (6 * 9) - (5 * 6) = 24.
Output:
(6 * 9) - (5 * 6) = 24.
{{/assistant}}
{{#user}}
Input: 2 7 8 9
Intermediate steps:
7 + 9 = 16 (left 2, 8, 16)
2 * 16 = 32 (left 8, 32)
32 - 8 = 24
{{/user}}
{{#assistant}}
Draft:
Because 32 - 8 = 24, while 32 = 2 * 16. So (2 * 16) - 8 = 24.
Because (2 * 16) - 8 = 24, while 7 + 9 = 16. So (2 * (7 + 9)) - 8 = 24.
Output:
(2 * (7 + 9)) - 8 = 24
{{/assistant}}
{{#user}}
Input: 3 5 8 9
Intermediate steps:
8 - 5 = 3 (left 3, 3, 9)
3 * 9 - 3 = 24
{{/user}}
{{#assistant}}
Draft:
Because 3 * 9 - 3 = 24, while 3 = 8 - 5. So 3 * 9 - (8 - 5) = 24.
Output:
3 * 9 - (8 - 5) = 24
{{/assistant}}
{{#user}}
Input: {{input}}
Intermediate steps:
{{intermediate_steps}}
{{/user}}
{{#assistant}}
Draft:
{{/assistant}}
{{#assistant}}{{gen "draft" temperature=temperature max_tokens=max_tokens stop='Output:\n'}}{{/assistant}}
{{#assistant}}
Output:
{{/assistant}}
{{#assistant}}{{gen "output" temperature=temperature max_tokens=max_tokens stop='\n'}}{{/assistant}}
''', silent=True
)
valid_judgement=["Valid", "Invalid"]
valid_program = guidance('''
{{#system}}Suppose you are one of the greatest AI scientists, logicians and mathematicians. You are very good at basic arithmetic operations. Use numbers and basic arithmetic operations (+ - * /) to obtain 24 with input numbers. Evaluate if given intermediate step is correct and only use two existing numbers. {{/system}}
{{#user}}
Input: 10, 14
Intermediate step: 10 + 14 = 24
{{/user}}
{{#assistant}}
The intermediate step is valid.
Judgement:
Valid
{{/assistant}}
{{#user}}
Input: 4, 4, 10
Intermediate step: 10 + 5 = 15
{{/user}}
{{#assistant}}
The intermediate step uses non-exists number "5".
Judgement:
Invalid
{{/assistant}}
{{#user}}
Input: 4, 4, 8
Intermediate step: 4 * 8 = 24
{{/user}}
{{#assistant}}
The intermediate step has a wrong calculation.
Judgement:
Invalid
{{/assistant}}
{{#user}}
Input: 4, 4, 8
Intermediate step: 4 * 8 = 32
{{/user}}
{{#assistant}}
The intermediate step is valid.
Judgement:
Valid
{{/assistant}}
{{#user}}
Input: 4, 4, 8
Intermediate step: We can not obtain 24.
{{/user}}
{{#assistant}}
The intermediate step is not a valid math formula.
Judgement:
Invalid
{{/assistant}}
{{#user}}
Input: {{remaining_numbers}}
Intermediate step: {{intermediate_step}}
{{/user}}
{{#assistant}}{{gen "reason" temperature=temperature max_tokens=max_tokens stop="Judgement:\n"}} {{/assistant}}
{{#assistant}}
Judgement:
{{/assistant}}
{{#assistant}}{{select "judgement" options=valid_judgement}} {{/assistant}}
'''
)
verifier_program = guidance(
'''
{{#system}}Suppose you are one of the greatest AI scientists, logicians and mathematicians. You are very good at basic arithmetic operations. Use numbers and basic arithmetic operations (+ - * /) to obtain 24 with input numbers. Evaluate if given numbers can reach 24 (sure/likely/impossible) {{/system}}
{{#user}}
Input: 10, 14
Draft:
{{/user}}
{{#assistant}}
14 - 10 = 4
14 * 10 = 140
10 / 14 = 5/7
14 / 10 = 1.4
10 + 14 = 24
{{/assistant}}
{{#user}}
Output:
{{/user}}
{{#assistant}}
sure
10 + 14 = 24
{{/assistant}}
{{#user}}
Input: 4, 4, 10
Draft:
{{/user}}
{{#assistant}}
10 - 4 + 4 = 6 + 4 = 10
4 + 4 + 10 = 8 + 10 = 18
4 * 4 + 10 = 16 + 10 = 26
4 * 10 - 4 = 40 - 4 = 36
(10 - 4) * 4 = 6 * 4 = 24
{{/assistant}}
{{#user}}
Output:
{{/user}}
{{#assistant}}
sure
(10 - 4) * 4 = 6 * 4 = 24
{{/assistant}}
{{#user}}
Input: 5 15
Draft:
{{/user}}
{{#assistant}}
5 + 15 = 20
5 - 15 = -10
5 * 15 = 75
5 / 15 = 1/3
15 - 5 = 10
15 / 5 = 3
The input only has two numbers, so I tried all possibility, but no one reached 24.
{{/assistant}}
{{#user}}
Output:
{{/user}}
{{#assistant}}
impossible
{{/assistant}}
{{#user}}
Input: 4, 9, 11
Draft:
{{/user}}
{{#assistant}}
4 + 9 + 11 = 24
{{/assistant}}
{{#user}}
Output:
{{/user}}
{{#assistant}}
sure
4 + 9 + 11 = 24
{{/assistant}}
{{#user}}
Input: 1, 5, 20
Draft:
{{/user}}
{{#assistant}}
1 + 5 + 20 = 26
1 * 5 + 20 = 25
20 / 5 + 1 = 5
1 * (20 - 5) = 15
1 * (20 + 5) = 25
1 - 5 + 20 = 16
5 - 1 + 20 = 24
{{/assistant}}
{{#user}}
Output:
{{/user}}
{{#assistant}}
sure
5 - 1 + 20 = 24
{{/assistant}}
{{#user}}
Input: 1, 2, 11
Draft:
{{/user}}
{{#assistant}}
1 * 2 + 11 = 13
1 + 2 + 11 = 14
2 * (11 - 1) = 10
11 * 2 - 1 = 21
2 / 1 * 11 = 22
(11 - 1) / 2 = 5
11 - 1 * 2 = 9
1 * (2 + 11) = 13
1 * (11 - 2) = 9
2 * 11 + 1 = 22
2 * (11 + 1) = 24
{{/assistant}}
{{#user}}
Output:
{{/user}}
{{#assistant}}
sure
2 * (11 + 1) = 24
{{/assistant}}
{{#user}}
Input: 5, 7, 8
Draft:
{{/user}}
{{#assistant}}
5 * 8 - 7 = 33
5 * 7 - 8 = 27
5 * 7 + 8 = 43
5 * (7 + 8) = 75
5 + 7 + 8 = 12 + 8 = 20
(8 - 5) * 7 = 3 * 7 = 21
I cannot obtain 24 now, but numbers are within a reasonable range
{{/assistant}}
{{#user}}
Output:
{{/user}}
{{#assistant}}
likely
{{/assistant}}
{{#user}}
Input: 10, 10, 11
Draft:
{{/user}}
{{#assistant}}
10 + 10 - 11 = 9
10 * 11 + 10 = 120
11 * (10 / 10) = 11
(11 - 10) + 10 = 11
10 + 10 + 11 = 31
(11 - 10) * 10 = 10
I cannot obtain 24 now, but numbers are within a reasonable range
{{/assistant}}
{{#user}}
Output:
{{/user}}
{{#assistant}}
likely
{{/assistant}}
{{#user}}
Input: 10, 10, 10
Draft:
{{/user}}
{{#assistant}}
10 10 10 are all too big
{{/assistant}}
{{#user}}
Output:
{{/user}}
{{#assistant}}
impossible
{{/assistant}}
{{#user}}
Input: 1, 3, 3
Draft:
{{/user}}
{{#assistant}}
1 + 3 + 3 = 7
1 + 3 * 3 = 10
(3 - 1) * 3 = 6
1 * 3 * 3 = 9
(1 + 3) * 3 = 12
I cannot obtain 24 now, and numbers are not within a reasonable range
{{/assistant}}
{{#user}}
Output:
{{/user}}
{{#assistant}}
impossible
{{/assistant}}
{{#user}}
Input: {{remaining_numbers}}
Draft:
{{/user}}
{{#assistant}}{{gen "draft" temperature=temperature max_tokens=max_tokens}} {{/assistant}}
{{#user}}
Output:
{{/user}}
{{#assistant}}{{select "output" options=valid_output}} {{/assistant}}
{{#assistant}}{{gen "output_equation" temperature=0.1 max_tokens=100}} {{/assistant}}
''', silent=True
)
# Define the guidance program
generate_program = guidance(
'''
{{#system}}Suppose you are one of the greatest AI scientists, logicians and mathematicians. You are very good at basic arithmetic operations. Use numbers and basic arithmetic operations (+ - * /) to obtain 24 with input numbers. In each step, You are only allowed to randomly choose arbitrary TWO of the input numbers to obtain a new number using arbitrary one basic arithmetic operation (AVOID duplicating with forbidden steps). Your calculation process must be correct.{{/system}}
{{#user}}
Input:
4, 9, 10, 13
Next Step:
{{/user}}
{{#assistant}}
4 * 9 = 36
{{/assistant}}
{{#user}}
Remaining Numbers:
{{/user}}
{{#assistant}}
10, 13, 36
{{/assistant}}
{{#user}}
Input:
1, 4, 8, 11
Next Step:
{{/user}}
{{#assistant}}
1 + 11 = 12
{{/assistant}}
{{#user}}
Remaining Numbers:
{{/user}}
{{#assistant}}
6, 8, 10
{{/assistant}}
{{#user}}
Input:
2, 4, 4, 7
Next step:
{{/user}}
{{#assistant}}
7 - 2 = 5
{{/assistant}}
{{#user}}
Remaining Numbers:
{{/user}}
{{#assistant}}
4, 4, 5
{{/assistant}}
{{#user}}
Input:
1, 4, 8, 12
Next step:
{{/user}}
{{#assistant}}
12 / 4 = 3
{{/assistant}}
{{#user}}
Remaining Numbers:
{{/user}}
{{#assistant}}
1, 3, 8
{{/assistant}}
{{#user}}
Input:
10, 12, 22
Next step:
{{/user}}
{{#assistant}}
10 + 22 = 32
{{/assistant}}
{{#user}}
Remaining Numbers:
{{/user}}
{{#assistant}}
12, 32
{{/assistant}}
{{#user}}
Input:
1, 8, 9, 11
Next step:
{{/user}}
{{#assistant}}
9 - 1 = 8
{{/assistant}}
{{#user}}
Remaining Numbers:
{{/user}}
{{#assistant}}
8, 8, 11
{{/assistant}}
{{#user}}
Input:
{{thoughts}}
Forbidden Steps:
{{forbidden_steps}}
Next step:
{{/user}}
{{#assistant}}{{gen "next_step" temperature=temperature max_tokens=max_tokens}} {{/assistant}}
{{#user}}
Remaining Numbers:
{{/user}}
{{#assistant}}{{gen "remaining_numbers" temperature=0.1 max_tokens=max_tokens}} {{/assistant}}
''', silent=True)
# Try more times if failed to use OpenAI API
def run(program, MAX_RETRY=5, **kwargs):
cnt = 0
myout = {}
while cnt < MAX_RETRY:
try:
myout = program(**kwargs)
break
except Exception as e:
cnt += 1
continue
return cnt < MAX_RETRY, myout
def solve(input, pbar):
records = [input]
last_step = {}
f = {}
forbidden = {}
forbidden[input] = []
for i in range(args.trycnt):
try:
p = numpy.zeros_like(records, dtype='float64')
if i < 1 / 2 * args.trycnt:
if len(records) > 1:
p.fill(0.5 / (len(records) - 1))
p[0] = 0.5
else:
p[0] = 1.
else:
p.fill(1. / len(records))
tmp = numpy.random.choice(records, p=p)
success, out = run(generate_program, temperature=1.0, max_tokens=64, thoughts=tmp, forbidden_steps=('\n'.join(forbidden[tmp])) if len(forbidden[tmp]) > 0 else 'No Forbidden Steps\n')
if success:
a = out['remaining_numbers'].strip().split('\n')[0].strip()
if re.search("[^0-9+\-*/.(),=\s]" ,out['next_step'].strip()):
continue
if not re.search("\S",out['next_step'].strip()) or not re.search("\S", out['remaining_numbers'].strip()):
continue
_, judgement = run(valid_program, temperature=0.1, max_tokens=128, remaining_numbers=tmp, intermediate_step=out['next_step'].strip(), valid_judgement=valid_judgement)
if judgement['judgement'] == 'Invalid':
continue
_, verify_result = run(verifier_program, temperature=0.7, max_tokens=256, remaining_numbers=a, valid_output=valid_output, is_sure=is_sure)
if is_sure(verify_result['output']):
pbar.write(f"{tmp} -- {out['next_step'].strip()} -> {a}")
tmp_steps = [verify_result['output_equation'].strip().split('\n')[0].strip()]
tmp_steps.append(out['next_step'].strip() + f' (left {a})')
while tmp != input:
tmp_steps.append(last_step[tmp] + f' (left {tmp})')
tmp = f[tmp]
tmp_steps.reverse()
_, expand_result=run(expand_program, temepratue=0.1, max_tokens=200, input=input, intermediate_steps='\n'.join(tmp_steps))
return True, i, expand_result['output']
elif verify_result['output'] == 'likely':
a = a.strip()
if a not in records:
forbidden[tmp].append(out['next_step'].strip())
forbidden[a] = []
records.append(a)
f[a] = tmp
last_step[a] = out['next_step'].strip()
pbar.write(f"{tmp} -- {out['next_step'].strip()} -> {a}")
except Exception as exception:
pbar.write('Something goes wrong when calling OpenAI API')
continue
return False, args.trycnt, ""
if __name__ == "__main__":
df = pd.read_csv('24.csv')
puzzles = []
for i in range(len(df)):
puzzles.append(df.iloc[i, 1].replace(' ', ', ').strip())
puzzles = puzzles[900:1000]
log_results = []
cnt = args.resume_cor
info = {'tot': args.resume, 'acc': 0.0, 'Solving': ''}
pbar = tqdm(puzzles[args.resume:])
total_try = 0
for puzzle in pbar:
this_result = {'puzzle': puzzle}
info['Solving'] = puzzle
if info['tot'] > 0:
info['acc'] = cnt / info['tot']
info['total_try'] = total_try
pbar.set_postfix(info, refresh=True)
info['tot'] = info['tot'] + 1
for i in range(args.b):
success, try_cnt, output = solve(puzzle, pbar)
total_try += try_cnt
this_result[f'try_cnt_branch_{i}'] = try_cnt
if success:
expression = output.strip().split('\n')[-1].lower().replace('answer: ', '').split('=')[0]
pbar.write(f"PUZZLE:{info['Solving']}\nANSWER:{expression}\nTRY CNT:{try_cnt}")
numbers = re.findall(r'\d+', expression)
problem_numbers = re.findall(r'\d+', puzzle)
if sorted(numbers) != sorted(problem_numbers):
pbar.write('INVALID ANSWER')
pass
else:
this_result['output'] = expression
try:
if int(sympy.simplify(expression) == 24):
pbar.write('CORRECT!')
this_result['correct'] = True
cnt += 1
else:
this_result['correct'] = False
pbar.write('WRONG!')
break
except Exception as e:
pbar.write(e)
pbar.write('WRONG!')
break
if 'output' not in this_result:
this_result['output'] = 'NO OUTPUT'
this_result['correct'] = False
with open(f'game24_b={args.b}.log', 'a') as f_write:
f_write.write(json.dumps(this_result) + '\n')
| [] |
2024-01-10 | yifanzhang-pro/cumulative-reasoning-anonymous | AutoTNLI~autotnli-direct.py | # AutoTNLI with Direct
import datasets
import guidance
import torch
import ast
import argparse
from transformers import AutoTokenizer, AutoModelForCausalLM
def get_parser():
parser = argparse.ArgumentParser(description="Cumulative Reasoning")
parser.add_argument('--model', type=str, default="/data/model/llama-13b", help='model to use')
parser.add_argument('--data_dir', type=str, default="/data/datasets/AutoTNLI", help='dataset to use')
return parser
parser = get_parser()
args = parser.parse_args()
from transformers import AutoTokenizer, AutoModelForCausalLM
dataset = datasets.load_dataset(args.data_dir, split='train')
guidance.llm = guidance.llms.transformers.LLaMA(args.model, device_map="auto", token_healing=True, torch_dtype=torch.bfloat16)
import json
import time
from tqdm import tqdm
valid_judgement = ["contradict", "entail"]
structure_program = guidance(
'''
### Instruction:
Suppose you are one of the greatest AI scientists, logicians and mathematicians. Let us think step by step. Read and analyze the "Premises" first, then judge whether the "Premises" entail or contradict the "Hypothesis".
----
{{~! display the few-shot examples ~}}
{{~#each examples}}
### Input:
"Premises": "{{this.premises}}"
"Hypothesis": "{{this.hypothesis}}"
### Response:
"Judgement": "Now we know that the Premises {{this.label}} the Hypothesis."
---
{{~/each}}
{{~! place the real question at the end }}
### Input:
"Premises": "{{premises}}"
"Hypothesis": "{{hypothesis}}"
### Response:
"Judgement": "Now we know that the Premises {{select "judgement" options=valid_judgement logprobs='logprobs'}} the Hypothesis."
''')
t = time.localtime()
logfilename = 'results-autotnli-test-baseline--' + time.strftime("%Y-%m-%d-%H-%M-%S", t) + '.jsonl'
with open(logfilename, 'w') as f:
f.write(time.strftime("%Y-%m-%d %H:%M:%S", t) + '\n')
f.write("Model: " + args.model + "\n")
f.write("Dataset: AutoTNLI\n")
f.write("bf16: True\n")
f.write("--------------------------------\n")
correct_predictions = 0
cnt = 0
total_cnt = len(dataset)
examples = [dataset[k+2] for k in range(2)]
for row in examples:
row.update({"label": 'entail' if row['label'] == 'entailment' else 'contradict'})
print(examples)
for row in tqdm(dataset, desc="Evaluating", unit="example"):
row.update({"label": 'entail' if row['label'] == 'entailment' else 'contradict'})
cnt += 1
print("-------------------------\n### Json Name: ", row['json_name'], "\t ( ", cnt, "/", total_cnt, " )")
premises = row['premises']
hypothesis = row['hypothesis']
out = structure_program(
examples=examples,
premises=premises,
hypothesis = hypothesis,
valid_judgement = valid_judgement
)
if out["judgement"] == row["label"]:
correct_predictions += 1
print("[Prediction]: ", out["judgement"])
print("[Actual]: ", row["label"])
accuracy = correct_predictions / cnt
print("[Running Average Accuracy]: ", accuracy)
result = {
"json_name": row["json_name"],
"prediction": out["judgement"],
"actual": row["label"],
"accuracy": accuracy
}
with open(logfilename, 'a') as f:
f.write(json.dumps(result) + '\n')
| [] |
2024-01-10 | yifanzhang-pro/cumulative-reasoning-anonymous | AutoTNLI~autotnli-cr.py | # AutoTNLI with Cumulative Reasoning + CoT
import guidance
import torch
import ast
import datasets
import numpy as np
import argparse
def get_parser():
parser = argparse.ArgumentParser(description="Cumulative Reasoning")
parser.add_argument('--temperature', type=float, default=0.0, help='temperature')
parser.add_argument('--max_tokens', type=int, default=50, help='max tokens')
parser.add_argument('--save_suffix', type=str, default='example-suffix', help='save suffix')
parser.add_argument('--trycnt', type=int, choices=range(0, 1001), default=200, help='numbers of try times')
parser.add_argument('--sc_cnt', type=int, choices=range(1, 30), default=1, help='number of sc cnt')
parser.add_argument('--model', type=str, default='/data/model/llama-13b', help='model to use')
parser.add_argument('--dataset', type=str, default='/data/datasets/AutoTNLI', help='dataset to use')
parser.add_argument('--verbose', action='store_true', help='verbose mode')
return parser
parser = get_parser()
args = parser.parse_args()
guidance.llm = guidance.llms.transformers.LLaMA(args.model, device_map="auto", token_healing=True,
torch_dtype=torch.bfloat16, caching=False)
import json
import time
import numpy
from tqdm import tqdm
examples = [
{
'premises': 'Miroslav Venhoda was a Czech choral conductor who specialized in the performance of Renaissance and Baroque music. Any choral conductor is a musician. Some musicians love music. Miroslav Venhoda published a book in 1946 called Method of Studying Gregorian Chant.',
'propositions': 'Miroslav Venhoda, who published a book in 1946 called Method of Studying Gregorian Chant, is a musician as he is a choral conductor.',
'conclusion': 'A Czech person wrote a book in 1946.',
'judgement': 'entail'},
{
'premises': 'All eels are fish. No fish are plants. A thing is either a plant or animal. Nothing that breathes is paper. All animals breathe. If a sea eel is either an eel or a plant, then a sea eel is an eel or an animal.',
'propositions': 'No eels are plants. All eels are animals.',
'conclusion': 'Sea eel is an eel.',
'judgement': 'contradict'},
{
'premises': 'Miroslav Venhoda was a Czech choral conductor who specialized in the performance of Renaissance and Baroque music. Any choral conductor is a musician. Some musicians love music. Miroslav Venhoda published a book in 1946 called Method of Studying Gregorian Chant.',
'propositions': 'Miroslav Venhoda specialized in the performance of Renaissance and Baroque music.',
'conclusion': 'No choral conductor specialized in the performance of Renaissance.',
'judgement': 'contradict'},
]
gen_proposition_examples = [
{'premises': 'All eels are fish. No fish are plants. ',
'proposition': 'No eels are plants.',
'conclusion': 'Sea eel is an eel.',
'explanation': 'This expression is deduced from the two premises as follows: if x is an eel, then it is a fish (from Premise 1), and if it is a fish, then it is not a plant (from Premise 2). Thus, if x is an eel, then it is not a plant.'},
{'premises': 'All eels are fish. A thing is either a plant or animal.',
'proposition': 'All eels are animals.',
'conclusion': 'Sea eel is an eel.',
'explanation': 'This statement follows from the premises as follows: If x is an eel, then it is a fish (from Premise 1). If x is a thing (which includes being a fish, hence an eel), then it is either a plant or an animal (from Premise 2). Since it cannot be a plant (because it is a fish and no fish is a plant), it must be an animal. Thus, if x is an eel, it is an animal.'},
{'premises': 'A thing is either a plant or animal. All animals breathe.',
'proposition': 'All things that breathe are animals.',
'conclusion': 'Sea eel is an eel.',
'explanation': 'This statement is deduced from the premises as follows: If x is a thing, then it is either a plant or an animal (from Premise 1), and if x is an animal, then it breathes (from Premise 2). Therefore, if a thing breathes, it must be an animal, because it can not be a plant that breathes based on these premises.'},
{
'premises': 'All people who regularly drink coffee are dependent on caffeine. People either regularly drink coffee or joke about being addicted to caffeine. ',
'proposition': 'All people who joke about being addicted to caffeine are not dependent on caffeine.',
'conclusion': 'Rina is either a person who regularly drinks coffee or a person who is unaware that caffeine is a drug.',
'explanation': 'Since all people who regularly drink coffee are dependent on caffeine, those who just joke about being addicted (and don\'t regularly drink coffee) are not dependent on caffeine.'},
{
'premises': 'Any choral conductor is a musician. Miroslav Venhoda published a book in 1946 called Method of Studying Gregorian Chant.',
'proposition': 'Miroslav Venhoda, who published a book in 1946 called Method of Studying Gregorian Chant, is a musician as he is a choral conductor.',
'conclusion': 'A Czech person wrote a book in 1946',
'explanation': 'This follows from the universal rule that any choral conductor is a musician (Premise 1), so since Miroslav Venhoda is a choral conductor who published a book in 1946 called Method of Studying Gregorian Chant (Premise 2), he is therefore a musician.'
}
]
validate_deduction_examples = [
{'premises': 'All eels are fish. No fish are plants.',
'proposition': 'No eels are plants.',
'validation': 'True'},
{'premises': 'All eels are fish. A thing is either a plant or animal.',
'proposition': 'All eels are animals.',
'validation': 'True'},
{'premises': 'Nothing that breathes is paper. All animals breathe.',
'proposition': 'All animals are paper.',
'validation': 'False'},
{'premises': 'A thing is either a plant or animal. All animals breathe.',
'proposition': 'All things that breathe are animals.',
'validation': 'True'},
{
'premises': 'All people who regularly drink coffee are dependent on caffeine. People either regularly drink coffee or joke about being addicted to caffeine.',
'proposition': 'All people who joke about being addicted to caffeine are dependent on caffeine.',
'validation': 'False'},
{
'premises': 'Miroslav Venhoda was a Czech choral conductor who specialized in the performance of Renaissance and Baroque music. Any choral conductor is a musician.',
'proposition': 'Miroslav Venhoda, being a Czech choral conductor specializing in Renaissance and Baroque music, is also a musician.',
'validation': 'True'},
{'premises': 'Any choral conductor is a musician. Some musicians love music.',
'proposition': 'All choral conductor love music.',
'validation': 'False'},
{
'premises': 'Any choral conductor is a musician. Miroslav Venhoda published a book in 1946 called Method of Studying Gregorian Chant.',
'proposition': 'Miroslav Venhoda, who published a book in 1946 called Method of Studying Gregorian Chant, is a musician as he is a choral conductor.',
'validation': 'True'}
]
useful_deduction_examples = [
{
'premises': 'Miroslav Venhoda was a Czech choral conductor who specialized in the performance of Renaissance and Baroque music. Any choral conductor is a musician. Some musicians love music. Miroslav Venhoda published a book in 1946 called Method of Studying Gregorian Chant.',
'proposition': 'Miroslav Venhoda, who published a book in 1946 called Method of Studying Gregorian Chant, is a musician as he is a choral conductor.',
'conclusion': 'A Czech person wrote a book in 1946.',
'usefulness': 'Useful'},
{
'premises': 'All eels are fish. No fish are plants. A thing is either a plant or animal. Nothing that breathes is paper. All animals breathe. If a sea eel is either an eel or a plant, then a sea eel is an eel or an animal.',
'proposition': 'No animals are paper.',
'conclusion': 'Sea eel is an eel.',
'usefulness': 'Unuseful'}
]
duplicated_deduction_examples = [
{
'premises': 'Miroslav Venhoda was a Czech choral conductor who specialized in the performance of Renaissance and Baroque music. Any choral conductor is a musician. Some musicians love music. Miroslav Venhoda published a book in 1946 called Method of Studying Gregorian Chant.',
'proposition': 'Any choral conductor is a musician.',
'conclusion': 'A Czech person wrote a book in 1946.',
'duplicated': 'True'},
{
'premises': 'All eels are fish. No fish are plants. A thing is either a plant or animal. Nothing that breathes is paper. All animals breathe. If a sea eel is either an eel or a plant, then a sea eel is an eel or an animal.',
'proposition': 'No animals are paper.',
'duplicated': 'False'
}
]
sourced_deduction_examples = [
{'premises': 'All eels are fish. No fish are plants.',
'proposition': 'No eels are plants.',
'sourced': 'True'},
{
'premises': 'Nothing that breathes is paper. All animals breathe.',
'proposition': 'All animals need food.',
'sourced': 'False'}
]
# we can pre-define valid option sets
valid_judgement = ["entail", "contradict"]
# we can pre-define valid option sets
valid_validation = ["True", "False"]
# we can pre-define valid option sets
valid_usefulness = ["Useful", "Unuseful"]
# we can pre-define valid option sets
valid_duplicated = ["True", "False"]
# we can pre-define valid option sets
valid_sourced = ["True", "False"]
gen_proposition = guidance(
'''
### Instruction:
Suppose you are one of the greatest AI scientists, logicians and mathematicians. Let us think step by step. Please deduce a "Proposition" from two given "Premises".
Please make sure that the "Proposition" is logically correct.
Please make sure that the "Proposition" is not a duplicate of the "Premises".
Please remember that your "Proposition" should be useful to determine whether the "Premises" entail or contradict the "Hypothesis".
----
{{~! display the few-shot examples ~}}
{{~#each examples}}
### Input:
"Premises": "{{this.premises}}"
We want to deduce more propositions to determine whether the "Premises" entail or contradict the following "Hypothesis":
"Hypothesis": "{{this.conclusion}}"
### Response:
"Proposition": "{{this.proposition}}"
---
{{~/each}}
{{~! place the real question at the end }}
### Input:
"Premises": "{{premises}}"
We want to deduce more propositions to determine whether the "Premises" entail or contradict the following "Hypothesis":
"Hypothesis": "{{hypothesis}}"
### Response:
"Proposition {{prop_id}}": "{{gen "proposition" temperature=0.7 max_tokens=50 stop='\"\\n'}}"
''')
# Define the guidance program
validate_deduction = guidance(
'''
### Instruction:
Suppose you are one of the greatest AI scientists, logicians and mathematicians. Let us think step by step. Please determine whether the deduction of given "Premises" to a "Proposition" is True or False.
{{~! display the few-shot examples ~}}
{{~#each examples}}
### Input:
"Premises": "{{this.premises}}"
"Proposition": "{{this.proposition}}"
### Response:
"Judgement": "Now we know that this deduction is {{this.validation}}"
---
{{~/each}}
{{~! place the real question at the end }}
### Input:
"Premises": "{{premises}}"
"Proposition": "{{proposition}}"
### Response:
"Judgement": "Now we know that this deduction is {{select "validation" options=valid_validation logprobs='logprobs'}}"
''')
# Define the guidance program
useful_deduction = guidance(
'''
### Instruction:
Suppose you are one of the greatest AI scientists, logicians and mathematicians. Let us think step by step. Please determine whether the deduction of two given "Premises" to a "Proposition" is useful to determine whether the "Premises" entail or contradict the "Hypothesis", reply with Useful or Unuseful.
{{~! display the few-shot examples ~}}
{{~#each examples}}
### Input:
"Premises": "{{this.premises}}"
"Proposition": "{{this.proposition}}"
"Hypothesis": "{{this.conclusion}}"
### Response:
"Judgement": "Now we know that this deduction is {{this.usefulness}} to determine whether the Premises entail or contradict the Hypothesis."
---
{{~/each}}
{{~! place the real question at the end }}
### Input:
"Premises": "{{premises}}"
"Proposition": "{{proposition}}"
"Hypothesis": "{{hypothesis}}"
### Response:
"Judgement": "Now we know that this deduction is {{select "usefulness" options=valid_usefulness logprobs='logprobs'}} to determine whether the Premises entail or contradict the Hypothesis."
''')
# Define the guidance program
duplicated_deduction = guidance(
'''
### Instruction:
Suppose you are one of the greatest AI scientists, logicians and mathematicians. Let us think step by step. Please determine whether the "Proposition" is duplicated with the "Premises", reply with True or False.
{{~! display the few-shot examples ~}}
{{~#each examples}}
### Input:
"Premises": "{{this.premises}}"
"Proposition": "{{this.proposition}}"
### Response:
"Judgement": "Now we know that this proposition is {{this.duplicated}} with the premises."
---
{{~/each}}
{{~! place the real question at the end }}
### Input:
"Premises": "{{premises}}"
"Proposition": "{{proposition}}"
### Response:
"Judgement": "Now we know that this proposition is {{select "duplicated" options=valid_duplicated logprobs='logprobs'}} with the premises."
''')
# Define the guidance program
sourced_deduction = guidance(
'''
### Instruction:
Suppose you are one of the greatest AI scientists, logicians and mathematicians. Let us think step by step. Please determine whether the "Proposition" is directly deduced from the "Premises" other than introducing unsourced informations by common sense reasoning, reply with True or False.
{{~! display the few-shot examples ~}}
{{~#each examples}}
### Input:
"Premises": "{{this.premises}}"
"Proposition": "{{this.proposition}}"
### Response:
"Judgement": "Is this proposition directly deduced from the premises? {{this.sourced}}"
---
{{~/each}}
{{~! place the real question at the end }}
### Input:
"Premises": "{{premises}}"
"Proposition": "{{proposition}}"
### Response:
"Judgement": "Is this proposition directly deduced from the premises? {{select "sourced" options=valid_sourced logprobs='logprobs'}}"
''')
# Define the guidance program
structure_program = guidance(
'''
### Instruction:
Suppose you are one of the greatest AI scientists, logicians and mathematicians. Let us think step by step. Read and analyze the "Premises" first, then judge whether the "Premises" entail or contradict the "Hypothesis".
----
{{~! display the few-shot examples ~}}
{{~#each examples}}
### Input:
"Premises": "{{this.premises}}"
"Hypothesis": "{{this.conclusion}}"
### Response:
"Thoughts": "Let us think step by step. From the premises, we know that {{this.propositions}}"
"Recall the Hypothesis": "{{this.conclusion}}"
"Judgement": "Now we know that the Premises {{this.judgement}} the Hypothesis."
---
{{~/each}}
{{~! place the real question at the end }}
### Input:
"Premises": "{{premises}}."
"Hypothesis": "{{hypothesis}}"
### Response:
"Thoughts": "Let us think step by step. From the premises, we know that {{gen "proposition" temperature=temperature max_tokens=max_tokens stop='\"\\n'}}. "
"Recall the Hypothesis": "{{hypothesis}}"
"Judgement": "Now we know that the Premises {{select "judgement" options=valid_judgement logprobs='logprobs'}} the Hypothesis."
''')
data = datasets.load_dataset(args.dataset, split='train')
t = time.localtime()
logfilename = f'results-autotnli-{args.save_suffix}--' + time.strftime("%Y-%m-%d-%H-%M-%S",
t) + '.jsonl'
with open(logfilename, 'w') as f:
f.write(time.strftime("%Y-%m-%d %H:%M:%S", t) + '\n') # write each result as a new line
f.write('trycnt: ' + str(args.trycnt) + '\n')
f.write("Model: " + args.model + "\n")
f.write("Dataset: " + args.dataset + "\n")
f.write(f"Temperature:{args.temperature}\n")
f.write(f"Max Tokens:{args.max_tokens}\n")
f.write("bf16: True\n")
f.write("--------------------------------\n")
correct_predictions = 0
cnt = 0
total_cnt = len(data)
data_list = []
for i in data:
if cnt == 1000:
break
data_list.append(i)
cnt += 1
cnt = 0
for example in tqdm(data_list, desc="Evaluating", unit="example"):
example.update({"label": 'entail' if example['label'] == 'entailment' else 'contradict'})
cnt += 1
conclusion = example['hypothesis']
premises = [s + '.' for s in example['premises'].split('.')]
premises_cnt = len(example['premises'])
propositions = ""
failed_cnt = 0
if args.verbose: print("[Premises]: \t", premises)
if args.verbose: print("[Hypothesis]: \t", conclusion)
for i in range(args.trycnt):
mid_out = structure_program(
examples=examples,
premises=' '.join(premises),
hypothesis=conclusion,
valid_judgement=valid_judgement,
temperature=args.temperature,
max_tokens=args.max_tokens
)
print(mid_out['proposition'])
premises.append(mid_out['proposition'])
ans_dict = {}
for i in valid_judgement:
ans_dict[i] = 0
for i in range(args.sc_cnt):
out = structure_program(
examples=examples,
premises=(' '.join(premises)),
hypothesis=conclusion,
valid_judgement=valid_judgement,
temperature=0.7,
max_tokens=args.max_tokens
)
ans_dict[out['judgement']] = ans_dict[out['judgement']] + 1
ans, ans_cnt = '', 0
for i in ans_dict.keys():
if ans_dict[i] > ans_cnt:
ans = i
ans_cnt = ans_dict[i]
if ans == example["label"]:
correct_predictions += 1
print("[Prediction]: ", ans)
print("[Actual]: ", example["label"])
accuracy = correct_predictions / cnt
print("[Running Average Accuracy]: ", accuracy)
result = {
"json_name": example["json_name"],
"prediction": ans,
"actual": example["label"],
"accuracy": accuracy,
"generated_propositions": propositions,
}
with open(logfilename, 'a') as f:
f.write(json.dumps(result) + '\n')
| [] |
2024-01-10 | yifanzhang-pro/cumulative-reasoning-anonymous | AutoTNLI~autotnli-cot.py | # AutoTNLI with Cumulative Reasoning + CoT
import guidance
import torch
import ast
import datasets
import numpy as np
import argparse
def get_parser():
parser = argparse.ArgumentParser(description="Cumulative Reasoning")
parser.add_argument('--temperature', type=float, default=0.0, help='temperature')
parser.add_argument('--max_tokens', type=int, default=50, help='max tokens')
parser.add_argument('--save_suffix', type=str, default='example-suffix', help='save suffix')
parser.add_argument('--sc_cnt', type=int, choices=range(1, 30), default=1, help='number of sc cnt')
parser.add_argument('--model', type=str, default='/data/model/llama-13b', help='model to use')
parser.add_argument('--dataset', type=str, default='/data/datasets/AutoTNLI', help='dataset to use')
parser.add_argument('--verbose', action='store_true', help='verbose mode')
return parser
parser = get_parser()
args = parser.parse_args()
guidance.llm = guidance.llms.transformers.LLaMA(args.model, device_map="auto", token_healing=True,
torch_dtype=torch.bfloat16, caching=False)
import json
import time
import numpy
from tqdm import tqdm
examples = [
{
'premises': 'Miroslav Venhoda was a Czech choral conductor who specialized in the performance of Renaissance and Baroque music. Any choral conductor is a musician. Some musicians love music. Miroslav Venhoda published a book in 1946 called Method of Studying Gregorian Chant.',
'propositions': 'Miroslav Venhoda, who published a book in 1946 called Method of Studying Gregorian Chant, is a musician as he is a choral conductor.',
'conclusion': 'A Czech person wrote a book in 1946.',
'judgement': 'entail'},
{
'premises': 'All eels are fish. No fish are plants. A thing is either a plant or animal. Nothing that breathes is paper. All animals breathe. If a sea eel is either an eel or a plant, then a sea eel is an eel or an animal.',
'propositions': 'No eels are plants. All eels are animals.',
'conclusion': 'Sea eel is an eel.',
'judgement': 'contradict'},
{
'premises': 'Miroslav Venhoda was a Czech choral conductor who specialized in the performance of Renaissance and Baroque music. Any choral conductor is a musician. Some musicians love music. Miroslav Venhoda published a book in 1946 called Method of Studying Gregorian Chant.',
'propositions': 'Miroslav Venhoda specialized in the performance of Renaissance and Baroque music.',
'conclusion': 'No choral conductor specialized in the performance of Renaissance.',
'judgement': 'contradict'},
]
gen_proposition_examples = [
{'premises': 'All eels are fish. No fish are plants. ',
'proposition': 'No eels are plants.',
'conclusion': 'Sea eel is an eel.',
'explanation': 'This expression is deduced from the two premises as follows: if x is an eel, then it is a fish (from Premise 1), and if it is a fish, then it is not a plant (from Premise 2). Thus, if x is an eel, then it is not a plant.'},
{'premises': 'All eels are fish. A thing is either a plant or animal.',
'proposition': 'All eels are animals.',
'conclusion': 'Sea eel is an eel.',
'explanation': 'This statement follows from the premises as follows: If x is an eel, then it is a fish (from Premise 1). If x is a thing (which includes being a fish, hence an eel), then it is either a plant or an animal (from Premise 2). Since it cannot be a plant (because it is a fish and no fish is a plant), it must be an animal. Thus, if x is an eel, it is an animal.'},
{'premises': 'A thing is either a plant or animal. All animals breathe.',
'proposition': 'All things that breathe are animals.',
'conclusion': 'Sea eel is an eel.',
'explanation': 'This statement is deduced from the premises as follows: If x is a thing, then it is either a plant or an animal (from Premise 1), and if x is an animal, then it breathes (from Premise 2). Therefore, if a thing breathes, it must be an animal, because it can not be a plant that breathes based on these premises.'},
{
'premises': 'All people who regularly drink coffee are dependent on caffeine. People either regularly drink coffee or joke about being addicted to caffeine. ',
'proposition': 'All people who joke about being addicted to caffeine are not dependent on caffeine.',
'conclusion': 'Rina is either a person who regularly drinks coffee or a person who is unaware that caffeine is a drug.',
'explanation': 'Since all people who regularly drink coffee are dependent on caffeine, those who just joke about being addicted (and don\'t regularly drink coffee) are not dependent on caffeine.'},
{
'premises': 'Any choral conductor is a musician. Miroslav Venhoda published a book in 1946 called Method of Studying Gregorian Chant.',
'proposition': 'Miroslav Venhoda, who published a book in 1946 called Method of Studying Gregorian Chant, is a musician as he is a choral conductor.',
'conclusion': 'A Czech person wrote a book in 1946',
'explanation': 'This follows from the universal rule that any choral conductor is a musician (Premise 1), so since Miroslav Venhoda is a choral conductor who published a book in 1946 called Method of Studying Gregorian Chant (Premise 2), he is therefore a musician.'
}
]
validate_deduction_examples = [
{'premises': 'All eels are fish. No fish are plants.',
'proposition': 'No eels are plants.',
'validation': 'True'},
{'premises': 'All eels are fish. A thing is either a plant or animal.',
'proposition': 'All eels are animals.',
'validation': 'True'},
{'premises': 'Nothing that breathes is paper. All animals breathe.',
'proposition': 'All animals are paper.',
'validation': 'False'},
{'premises': 'A thing is either a plant or animal. All animals breathe.',
'proposition': 'All things that breathe are animals.',
'validation': 'True'},
{
'premises': 'All people who regularly drink coffee are dependent on caffeine. People either regularly drink coffee or joke about being addicted to caffeine.',
'proposition': 'All people who joke about being addicted to caffeine are dependent on caffeine.',
'validation': 'False'},
{
'premises': 'Miroslav Venhoda was a Czech choral conductor who specialized in the performance of Renaissance and Baroque music. Any choral conductor is a musician.',
'proposition': 'Miroslav Venhoda, being a Czech choral conductor specializing in Renaissance and Baroque music, is also a musician.',
'validation': 'True'},
{'premises': 'Any choral conductor is a musician. Some musicians love music.',
'proposition': 'All choral conductor love music.',
'validation': 'False'},
{
'premises': 'Any choral conductor is a musician. Miroslav Venhoda published a book in 1946 called Method of Studying Gregorian Chant.',
'proposition': 'Miroslav Venhoda, who published a book in 1946 called Method of Studying Gregorian Chant, is a musician as he is a choral conductor.',
'validation': 'True'}
]
useful_deduction_examples = [
{
'premises': 'Miroslav Venhoda was a Czech choral conductor who specialized in the performance of Renaissance and Baroque music. Any choral conductor is a musician. Some musicians love music. Miroslav Venhoda published a book in 1946 called Method of Studying Gregorian Chant.',
'proposition': 'Miroslav Venhoda, who published a book in 1946 called Method of Studying Gregorian Chant, is a musician as he is a choral conductor.',
'conclusion': 'A Czech person wrote a book in 1946.',
'usefulness': 'Useful'},
{
'premises': 'All eels are fish. No fish are plants. A thing is either a plant or animal. Nothing that breathes is paper. All animals breathe. If a sea eel is either an eel or a plant, then a sea eel is an eel or an animal.',
'proposition': 'No animals are paper.',
'conclusion': 'Sea eel is an eel.',
'usefulness': 'Unuseful'}
]
duplicated_deduction_examples = [
{
'premises': 'Miroslav Venhoda was a Czech choral conductor who specialized in the performance of Renaissance and Baroque music. Any choral conductor is a musician. Some musicians love music. Miroslav Venhoda published a book in 1946 called Method of Studying Gregorian Chant.',
'proposition': 'Any choral conductor is a musician.',
'conclusion': 'A Czech person wrote a book in 1946.',
'duplicated': 'True'},
{
'premises': 'All eels are fish. No fish are plants. A thing is either a plant or animal. Nothing that breathes is paper. All animals breathe. If a sea eel is either an eel or a plant, then a sea eel is an eel or an animal.',
'proposition': 'No animals are paper.',
'duplicated': 'False'
}
]
sourced_deduction_examples = [
{'premises': 'All eels are fish. No fish are plants.',
'proposition': 'No eels are plants.',
'sourced': 'True'},
{
'premises': 'Nothing that breathes is paper. All animals breathe.',
'proposition': 'All animals need food.',
'sourced': 'False'}
]
# we can pre-define valid option sets
valid_judgement = ["entail", "contradict"]
# we can pre-define valid option sets
valid_validation = ["True", "False"]
# we can pre-define valid option sets
valid_usefulness = ["Useful", "Unuseful"]
# we can pre-define valid option sets
valid_duplicated = ["True", "False"]
# we can pre-define valid option sets
valid_sourced = ["True", "False"]
gen_proposition = guidance(
'''
### Instruction:
Suppose you are one of the greatest AI scientists, logicians and mathematicians. Let us think step by step. Please deduce a "Proposition" from two given "Premises".
Please make sure that the "Proposition" is logically correct.
Please make sure that the "Proposition" is not a duplicate of the "Premises".
Please remember that your "Proposition" should be useful to determine whether the "Premises" entail or contradict the "Hypothesis".
----
{{~! display the few-shot examples ~}}
{{~#each examples}}
### Input:
"Premises": "{{this.premises}}"
We want to deduce more propositions to determine whether the "Premises" entail or contradict the following "Hypothesis":
"Hypothesis": "{{this.conclusion}}"
### Response:
"Proposition": "{{this.proposition}}"
---
{{~/each}}
{{~! place the real question at the end }}
### Input:
"Premises": "{{premises}}"
We want to deduce more propositions to determine whether the "Premises" entail or contradict the following "Hypothesis":
"Hypothesis": "{{hypothesis}}"
### Response:
"Proposition {{prop_id}}": "{{gen "proposition" temperature=0.7 max_tokens=50 stop='\"\\n'}}"
''')
# Define the guidance program
validate_deduction = guidance(
'''
### Instruction:
Suppose you are one of the greatest AI scientists, logicians and mathematicians. Let us think step by step. Please determine whether the deduction of given "Premises" to a "Proposition" is True or False.
{{~! display the few-shot examples ~}}
{{~#each examples}}
### Input:
"Premises": "{{this.premises}}"
"Proposition": "{{this.proposition}}"
### Response:
"Judgement": "Now we know that this deduction is {{this.validation}}"
---
{{~/each}}
{{~! place the real question at the end }}
### Input:
"Premises": "{{premises}}"
"Proposition": "{{proposition}}"
### Response:
"Judgement": "Now we know that this deduction is {{select "validation" options=valid_validation logprobs='logprobs'}}"
''')
# Define the guidance program
useful_deduction = guidance(
'''
### Instruction:
Suppose you are one of the greatest AI scientists, logicians and mathematicians. Let us think step by step. Please determine whether the deduction of two given "Premises" to a "Proposition" is useful to determine whether the "Premises" entail or contradict the "Hypothesis", reply with Useful or Unuseful.
{{~! display the few-shot examples ~}}
{{~#each examples}}
### Input:
"Premises": "{{this.premises}}"
"Proposition": "{{this.proposition}}"
"Hypothesis": "{{this.conclusion}}"
### Response:
"Judgement": "Now we know that this deduction is {{this.usefulness}} to determine whether the Premises entail or contradict the Hypothesis."
---
{{~/each}}
{{~! place the real question at the end }}
### Input:
"Premises": "{{premises}}"
"Proposition": "{{proposition}}"
"Hypothesis": "{{hypothesis}}"
### Response:
"Judgement": "Now we know that this deduction is {{select "usefulness" options=valid_usefulness logprobs='logprobs'}} to determine whether the Premises entail or contradict the Hypothesis."
''')
# Define the guidance program
duplicated_deduction = guidance(
'''
### Instruction:
Suppose you are one of the greatest AI scientists, logicians and mathematicians. Let us think step by step. Please determine whether the "Proposition" is duplicated with the "Premises", reply with True or False.
{{~! display the few-shot examples ~}}
{{~#each examples}}
### Input:
"Premises": "{{this.premises}}"
"Proposition": "{{this.proposition}}"
### Response:
"Judgement": "Now we know that this proposition is {{this.duplicated}} with the premises."
---
{{~/each}}
{{~! place the real question at the end }}
### Input:
"Premises": "{{premises}}"
"Proposition": "{{proposition}}"
### Response:
"Judgement": "Now we know that this proposition is {{select "duplicated" options=valid_duplicated logprobs='logprobs'}} with the premises."
''')
# Define the guidance program
sourced_deduction = guidance(
'''
### Instruction:
Suppose you are one of the greatest AI scientists, logicians and mathematicians. Let us think step by step. Please determine whether the "Proposition" is directly deduced from the "Premises" other than introducing unsourced informations by common sense reasoning, reply with True or False.
{{~! display the few-shot examples ~}}
{{~#each examples}}
### Input:
"Premises": "{{this.premises}}"
"Proposition": "{{this.proposition}}"
### Response:
"Judgement": "Is this proposition directly deduced from the premises? {{this.sourced}}"
---
{{~/each}}
{{~! place the real question at the end }}
### Input:
"Premises": "{{premises}}"
"Proposition": "{{proposition}}"
### Response:
"Judgement": "Is this proposition directly deduced from the premises? {{select "sourced" options=valid_sourced logprobs='logprobs'}}"
''')
# Define the guidance program
structure_program = guidance(
'''
### Instruction:
Suppose you are one of the greatest AI scientists, logicians and mathematicians. Let us think step by step. Read and analyze the "Premises" first, then judge whether the "Premises" entail or contradict the "Hypothesis".
----
{{~! display the few-shot examples ~}}
{{~#each examples}}
### Input:
"Premises": "{{this.premises}}"
"Hypothesis": "{{this.conclusion}}"
### Response:
"Thoughts": "Let us think step by step. From the premises, we know that {{this.propositions}}"
"Recall the Hypothesis": "{{this.conclusion}}"
"Judgement": "Now we know that the Premises {{this.judgement}} the Hypothesis."
---
{{~/each}}
{{~! place the real question at the end }}
### Input:
"Premises": "{{premises}}."
"Hypothesis": "{{hypothesis}}"
### Response:
"Thoughts": "Let us think step by step. From the premises, we know that {{gen "proposition" temperature=temperature max_tokens=max_tokens stop='\"\\n'}}. "
"Recall the Hypothesis": "{{hypothesis}}"
"Judgement": "Now we know that the Premises {{select "judgement" options=valid_judgement logprobs='logprobs'}} the Hypothesis."
''')
data = datasets.load_dataset(args.dataset, split='train')
t = time.localtime()
logfilename = f'results-autotnli-{args.save_suffix}--' + time.strftime("%Y-%m-%d-%H-%M-%S",
t) + '.jsonl'
with open(logfilename, 'w') as f:
f.write(time.strftime("%Y-%m-%d %H:%M:%S", t) + '\n') # write each result as a new line
f.write("Model: " + args.model + "\n")
f.write("Dataset: " + args.dataset + "\n")
f.write(f"Temperature:{args.temperature}\n")
f.write(f"Max Tokens:{args.max_tokens}\n")
f.write("bf16: True\n")
f.write("--------------------------------\n")
correct_predictions = 0
cnt = 0
total_cnt = len(data)
data_list = []
for i in data:
if cnt == 1000:
break
data_list.append(i)
cnt += 1
cnt = 0
for example in tqdm(data_list, desc="Evaluating", unit="example"):
example.update({"label": 'entail' if example['label'] == 'entailment' else 'contradict'})
cnt += 1
conclusion = example['hypothesis']
premises = [s + '.' for s in example['premises'].split('.')]
premises_cnt = len(example['premises'])
propositions = ""
failed_cnt = 0
if args.verbose: print("[Premises]: \t", premises)
if args.verbose: print("[Hypothesis]: \t", conclusion)
ans_dict = {}
for i in valid_judgement:
ans_dict[i] = 0
for i in range(args.sc_cnt):
out = structure_program(
examples=examples,
premises=(' '.join(premises)),
hypothesis=conclusion,
valid_judgement=valid_judgement,
temperature=0.7,
max_tokens=args.max_tokens
)
ans_dict[out['judgement']] = ans_dict[out['judgement']] + 1
ans, ans_cnt = '', 0
for i in ans_dict.keys():
if ans_dict[i] > ans_cnt:
ans = i
ans_cnt = ans_dict[i]
if ans == example["label"]:
correct_predictions += 1
print("[Prediction]: ", ans)
print("[Actual]: ", example["label"])
accuracy = correct_predictions / cnt
print("[Running Average Accuracy]: ", accuracy)
result = {
"json_name": example["json_name"],
"prediction": ans,
"actual": example["label"],
"accuracy": accuracy,
"generated_propositions": propositions,
}
with open(logfilename, 'a') as f:
f.write(json.dumps(result) + '\n')
| [] |
2024-01-10 | evanmschultz/KRNKsite | backend~services~ai_services.py | import asyncio
import json
from enum import Enum
from services.utils import remove_after_references
from enum import Enum
from langchain import LLMChain
from langchain.prompts import PromptTemplate
from langchain.chat_models import ChatAnthropic
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.document_loaders import OnlinePDFLoader
class SummaryLength(Enum):
FULL = "full"
SHORT = "short"
full_summary_prompt_template = """You are an expert in the field of {topic} and you are writing a summary of the paper {title} working for
a {topic} blog:
{paper_content}
Write a detailed 100-150 sentence summary article of the paper in a compelling blog post, include all majors points and conclusions. You
want readers to understand the key takeaways without having to read the full paper.
DETAILED SUMMARY:"""
FULL_SUMMARY_PROMPT = PromptTemplate(
template=full_summary_prompt_template,
input_variables=["topic", "title", "paper_content"],
)
short_summary_prompt_template = """You are an expert in the field of {topic} and you are writing a summary of the paper {title} working for
a {topic} blog:
{paper_content}
Write a short summary article of the paper in less than 20 sentences in a compelling blog post, include only majors points and conclusions. You
want readers to understand the key takeaways to be encouraged to read the full paper.
SHORT SUMMARY:"""
SHORT_SUMMARY_PROMPT = PromptTemplate(
template=short_summary_prompt_template,
input_variables=["topic", "title", "paper_content"],
)
async def summarize_pdf(
pdf_url: str,
summary_length: SummaryLength = SummaryLength.FULL,
title: str = "",
topic: str = "",
) -> str | None:
"""
Summarize a PDF using Anthropic's Claude 2.
Produces two summaries: a short summary and a long summary.
Args:
pdf_path (str): The path to the PDF to summarize.
Returns:
dict: The summaries of the PDF.
{
"short_summary": str,
"long_summary": str,
}
"""
loader = OnlinePDFLoader(pdf_url)
data = loader.load()
full_paper_content: str = data[0].page_content
paper_content: str = remove_after_references(full_paper_content)
print(f"""\n{'_'*80}\nfull_paper_length:\n\n{len(full_paper_content)}\n{'_'*80}""")
print(f"""\n{'_'*80}\npaper_length:\n\n{len(paper_content)}\n{'_'*80}""")
input_list: list[dict] = [
{
"topic": topic,
"title": title,
"paper_content": paper_content,
}
]
if summary_length == SummaryLength.FULL:
chosen_prompt = FULL_SUMMARY_PROMPT
else:
chosen_prompt = SHORT_SUMMARY_PROMPT
llm = ChatAnthropic(
max_tokens=5000,
temperature=0.1,
# streaming=False, # Set to True to stream the output
# callbacks=[
# StreamingStdOutCallbackHandler()
# ], # Callbacks to handle the streaming output
)
summary_chain = LLMChain(llm=llm, prompt=chosen_prompt)
try:
summary_result: list[dict[str, str]] = await summary_chain.aapply(input_list)
summary: str = (
json.loads(json.dumps(summary_result))[0]["text"].split(":\n\n")[1].strip()
)
print(f"""\n{'_'*80}\n{summary_length}\n{summary}\n{'_'*80}""")
return summary
except Exception as e:
print(f"""\n{'_'*80}\nException:\n\n{e}\n{'_'*80}""")
return
# Testing the function
async def main() -> None:
task1 = summarize_pdf(
"https://arxiv.org/pdf/2106.01548.pdf",
SummaryLength.FULL,
topic="AI > Computer Vision",
title="WHEN VISION TRANSFORMERS OUTPERFORM RESNETS WITHOUT PRE-TRAINING OR STRONG DATA AUGMENTATIONS",
)
task2 = summarize_pdf(
"https://arxiv.org/pdf/2106.01548.pdf",
SummaryLength.SHORT,
topic="AI > Computer Vision",
title="WHEN VISION TRANSFORMERS OUTPERFORM RESNETS WITHOUT PRE-TRAINING OR STRONG DATA AUGMENTATIONS",
)
await asyncio.gather(task1, task2)
if __name__ == "__main__":
asyncio.run(main())
| [
"You are an expert in the field of {topic} and you are writing a summary of the paper {title} working for \na {topic} blog:\n\n\n{paper_content}\n\nWrite a short summary article of the paper in less than 20 sentences in a compelling blog post, include only majors points and conclusions. You \nwant readers to understand the key takeaways to be encouraged to read the full paper.\n\nSHORT SUMMARY:",
"paper_content",
"You are an expert in the field of {topic} and you are writing a summary of the paper {title} working for \na {topic} blog:\n\n\n{paper_content}\n\n\nWrite a detailed 100-150 sentence summary article of the paper in a compelling blog post, include all majors points and conclusions. You \nwant readers to understand the key takeaways without having to read the full paper.\n\nDETAILED SUMMARY:"
] |
2024-01-10 | andrehfp/langchain-course | Class%204~front.py | import streamlit as st
import langchain_helper as lch
import textwrap
st.title("Assistente do Youtube!")
with st.sidebar:
with st.form(key="my_form"):
youtube_url = st.sidebar.text_area(label="URL do Vídeo", max_chars=50)
query = st.sidebar.text_area(
label="Me pergunte sobre algo do vídeo!", max_chars=50, key="query"
)
submit_button = st.form_submit_button(label="Enviar")
if query and youtube_url:
db = lch.create_vector_from_yt_url(youtube_url)
response, docs = lch.get_response_from_query(db, query)
st.subheader("Resposta:")
st.text(textwrap.fill(response["answer"], width=85))
| [] |
2024-01-10 | andrehfp/langchain-course | Class%205~chat_db.py | from langchain.utilities import SQLDatabase
from langchain_experimental.sql import SQLDatabaseChain
from langchain.chat_models import ChatOpenAI
from langchain.agents import create_sql_agent
from langchain.agents.agent_toolkits import SQLDatabaseToolkit
from langchain.agents.agent_types import AgentType
from langchain.chains import LLMMathChain
from langchain.utilities import SerpAPIWrapper
from langchain.agents import initialize_agent, Tool
from langchain.prompts import MessagesPlaceholder
from langchain.memory import ConversationBufferMemory
from dotenv import load_dotenv
import os
load_dotenv()
openai_api_key = os.getenv("OPENAI_API_KEY")
openai_organization = os.getenv("OPENAI_ORGANIZATION")
db = SQLDatabase.from_uri("sqlite:///Chinook.db")
llm = ChatOpenAI(
model="gpt-3.5-turbo-16k",
temperature=0.0,
openai_api_key=openai_api_key,
openai_organization=openai_organization,
verbose=True,
)
db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True)
llm_math_chain = LLMMathChain.from_llm(llm=llm, verbose=True)
search = SerpAPIWrapper()
tools = [
Tool(
name="Search",
func=search.run,
description="useful for when you need to answer questions about current events. You should ask targeted questions",
),
Tool(
name="Calculator",
func=llm_math_chain.run,
description="useful for when you need to answer questions about math",
),
Tool(
name="FooBar-DB",
func=db_chain.run,
description="useful for when you need to answer questions about FooBar. Input should be in the form of a question containing full context",
),
]
memory = ConversationBufferMemory(memory_key="memory", return_messages=True)
agent_kwargs = {
"extra_prompt_messages": [MessagesPlaceholder(variable_name="memory")],
}
agent = initialize_agent(
tools,
llm,
agent=AgentType.OPENAI_FUNCTIONS,
verbose=True,
agent_kwargs=agent_kwargs,
memory=memory,
)
while True:
user_input = input("Enter your query or type 'exit' to quit: ")
if user_input.lower() == "exit":
break
response = agent.run(user_input)
print(response)
| [] |
2024-01-10 | andrehfp/langchain-course | Class%202~front.py | import langchain_helper as la
import streamlit as st
st.set_page_config(layout="wide")
st.title("Gerador de Nomes de Empresas")
segmento = st.sidebar.text_area(label="Qual é o segmento da sua empresa?")
if segmento:
response = la.generate_company_name(segmento)
st.text(response["company_name"])
| [] |
2024-01-10 | dgallitelli/langchain | langchain~llms~sagemaker_async_endpoint.py | from abc import abstractmethod
from typing import Any, List, Optional
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.utils import enforce_stop_tokens
from langchain.llms.sagemaker_endpoint import SagemakerEndpoint
import boto3
import time
import os
import uuid
import datetime
import logging
from botocore.exceptions import WaiterError, ClientError
class SagemakerAsyncEndpoint(SagemakerEndpoint):
input_bucket: str = ""
input_prefix: str = ""
max_request_timeout: int = 90
s3_client: Any
sm_client: Any
def wait_inference_file(
self,
output_url: str,
failure_url: str,
s3_client: Any = None,
max_retries: int = 25,
retry_delay: int = 5
) -> Any:
"""Wait for an inference output file to become available on S3.
Args:
output_url (str): S3 URL of the expected output file
failure_url (str): S3 URL to check for inference failure file
s3_client (boto3.Client): S3 client to use
max_retries (int): Maximum retries to check for output file
retry_delay (int): Seconds to wait between retries
Raises:
Exception: If failure file exists
"""
s3_client = boto3.client("s3") if s3_client is None else s3_client
bucket = output_url.split("/")[2]
output_prefix = "/".join(output_url.split("/")[3:])
failure_prefix = "/".join(failure_url.split("/")[3:])
tries = 0
while tries < max_retries:
try:
waiter = s3_client.get_waiter('object_exists')
waiter.wait(Bucket=bucket, Key=output_prefix)
return
except WaiterError:
tries += 1
print(f"Output file not found yet, waiting {retry_delay} seconds...")
time.sleep(retry_delay)
# Output file still not available, check failure file
waiter = s3_client.get_waiter('object_exists')
waiter.wait(Bucket=bucket, Key=failure_prefix)
raise Exception("Inference failed while waiting for file to be generated.")
def __init__(
self,
input_bucket: str = "",
input_prefix: str = "",
max_request_timeout: int = 90,
**kwargs
) -> None:
"""
Initialize a Sagemaker asynchronous endpoint connector in Langchain.
Args:
input_bucket: S3 bucket name where input files are stored.
input_prefix: S3 prefix where input files are stored.
max_request_timeout: Maximum timeout for the request in seconds - also used to validate if endpoint is in cold start.
kwargs: Keyword arguments to pass to the SagemakerEndpoint class.
Raises:
ValueError: If the input_bucket or input_prefix arguments are not of type str,
or if the max_request_timeout is not a positive integer.
"""
super().__init__(**kwargs)
region = self.region_name
account = boto3.client("sts").get_caller_identity()["Account"]
self.input_bucket = f"sagemaker-{region}-{account}" if input_bucket == "" else input_bucket
self.input_prefix = f"async-endpoint-outputs/{self.endpoint_name}" if input_prefix == "" else input_prefix
self.max_request_timeout = max_request_timeout
self.s3_client = boto3.client("s3")
self.sm_client = boto3.client("sagemaker")
# Private method to invoke endpoint
def _invoke_endpoint(
self,
input_key: str,
content_type: str,
accepts: str,
**kwargs
) -> Any:
"""Invoke SageMaker endpoint asynchronously.
Args:
input_key: S3 key for input data
content_type: MIME type for input data
accepts: Expected response MIME type
**kwargs: Additional parameters for client.invoke_endpoint_async()
Returns:
Response dictionary containing InferenceId
"""
response = self.client.invoke_endpoint_async(
EndpointName=self.endpoint_name,
InputLocation=f"s3://{self.input_bucket}/{input_key}",
ContentType=content_type,
Accept=accepts,
InvocationTimeoutSeconds=self.max_request_timeout,
**kwargs
)
return response
def _call(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any
) -> str:
"""
Call out to Sagemaker asynchronous inference endpoint.
Args:
prompt: The prompt to use for the inference.
stop: The stop tokens to use for the inference.
run_manager: The run manager to use for the inference.
kwargs: Keyword arguments to pass to the SagemakerEndpoint class.
Returns:
The output from the Sagemaker asynchronous inference endpoint.
"""
# Parse the SagemakerEndpoint class arguments
_model_kwargs = self.model_kwargs or {}
_model_kwargs = {**_model_kwargs, **kwargs}
_endpoint_kwargs = self.endpoint_kwargs or {}
# Transform the input to match SageMaker expectations
body = self.content_handler.transform_input(prompt, _model_kwargs)
content_type = self.content_handler.content_type
accepts = self.content_handler.accepts
# Verify if the endpoint is running
logger = logging.getLogger(__name__)
response = self.sm_client.describe_endpoint(EndpointName=self.endpoint_name)
endpoint_is_running = response["ProductionVariants"][0]["CurrentInstanceCount"] > 0
# If the endpoint is not running, send an empty request to "wake up" the endpoint
test_data = b""
test_key = os.path.join(self.input_prefix, "test")
self.s3_client.put_object(Body=test_data, Bucket=self.input_bucket, Key=test_key)
if not endpoint_is_running:
response = self._invoke_endpoint(
self.endpoint_name,
test_key,
content_type,
accepts,
self.max_request_timeout,
**_endpoint_kwargs)
logger.error("The endpoint is not running. Please check back in approximately 10 minutes.")
raise Exception("The endpoint is not running. Please check back in approximately 10 minutes.")
else:
logger.info("Endpoint is running! Proceeding to inference.")
# Send request to the async endpoint
now = datetime.datetime.now()
timestamp = now.strftime("%Y%m%d%H%M%S") # including timestamp to avoid collision in a multi-user scenario
request_key = os.path.join(
self.input_prefix,
f"request-{timestamp}-{str(uuid.uuid4())}"
)
self.s3_client.put_object(Body=body, Bucket=self.input_bucket, Key=request_key)
response = self._invoke_endpoint(
self.endpoint_name,
request_key,
content_type,
accepts,
self.max_request_timeout,
**_endpoint_kwargs)
# Read the bytes of the file from S3 in output_url with Boto3
output_url = response["OutputLocation"]
failure_url = response["FailureLocation"]
response = self.wait_inference_file(output_url, failure_url, self.s3_client)
text = self.content_handler.transform_output(response["Body"])
if stop is not None:
text = enforce_stop_tokens(text, stop)
return text | [] |
2024-01-10 | dgallitelli/langchain | langchain~llms~sagemaker_endpoint.py | """Wrapper around Sagemaker InvokeEndpoint API."""
from abc import abstractmethod
from typing import Any, Dict, Generic, List, Mapping, Optional, TypeVar, Union
from pydantic import Extra, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
import boto3
import os
import time
import uuid
from botocore.exceptions import ClientError
INPUT_TYPE = TypeVar("INPUT_TYPE", bound=Union[str, List[str]])
OUTPUT_TYPE = TypeVar("OUTPUT_TYPE", bound=Union[str, List[List[float]]])
class ContentHandlerBase(Generic[INPUT_TYPE, OUTPUT_TYPE]):
"""A handler class to transform input from LLM to a
format that SageMaker endpoint expects. Similarily,
the class also handles transforming output from the
SageMaker endpoint to a format that LLM class expects.
"""
"""
Example:
.. code-block:: python
class ContentHandler(ContentHandlerBase):
content_type = "application/json"
accepts = "application/json"
def transform_input(self, prompt: str, model_kwargs: Dict) -> bytes:
input_str = json.dumps({prompt: prompt, **model_kwargs})
return input_str.encode('utf-8')
def transform_output(self, output: bytes) -> str:
response_json = json.loads(output.read().decode("utf-8"))
return response_json[0]["generated_text"]
"""
content_type: Optional[str] = "text/plain"
"""The MIME type of the input data passed to endpoint"""
accepts: Optional[str] = "text/plain"
"""The MIME type of the response data returned from endpoint"""
@abstractmethod
def transform_input(self, prompt: INPUT_TYPE, model_kwargs: Dict) -> bytes:
"""Transforms the input to a format that model can accept
as the request Body. Should return bytes or seekable file
like object in the format specified in the content_type
request header.
"""
@abstractmethod
def transform_output(self, output: bytes) -> OUTPUT_TYPE:
"""Transforms the output from the model to string that
the LLM class expects.
"""
class LLMContentHandler(ContentHandlerBase[str, str]):
"""Content handler for LLM class."""
class SagemakerEndpoint(LLM):
"""Wrapper around custom Sagemaker Inference Endpoints.
To use, you must supply the endpoint name from your deployed
Sagemaker model & the region where it is deployed.
To authenticate, the AWS client uses the following methods to
automatically load credentials:
https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html
If a specific credential profile should be used, you must pass
the name of the profile from the ~/.aws/credentials file that is to be used.
Make sure the credentials / roles used have the required policies to
access the Sagemaker endpoint.
See: https://docs.aws.amazon.com/IAM/latest/UserGuide/access_policies.html
"""
"""
Example:
.. code-block:: python
from langchain import SagemakerEndpoint
endpoint_name = (
"my-endpoint-name"
)
region_name = (
"us-west-2"
)
credentials_profile_name = (
"default"
)
se = SagemakerEndpoint(
endpoint_name=endpoint_name,
region_name=region_name,
credentials_profile_name=credentials_profile_name
)
"""
client: Any #: :meta private:
endpoint_name: str = ""
"""The name of the endpoint from the deployed Sagemaker model.
Must be unique within an AWS Region."""
region_name: str = ""
"""The aws region where the Sagemaker model is deployed, eg. `us-west-2`."""
credentials_profile_name: Optional[str] = None
"""The name of the profile in the ~/.aws/credentials or ~/.aws/config files, which
has either access keys or role information specified.
If not specified, the default credential profile or, if on an EC2 instance,
credentials from IMDS will be used.
See: https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html
"""
content_handler: LLMContentHandler
"""The content handler class that provides an input and
output transform functions to handle formats between LLM
and the endpoint.
"""
"""
Example:
.. code-block:: python
from langchain.llms.sagemaker_endpoint import LLMContentHandler
class ContentHandler(LLMContentHandler):
content_type = "application/json"
accepts = "application/json"
def transform_input(self, prompt: str, model_kwargs: Dict) -> bytes:
input_str = json.dumps({prompt: prompt, **model_kwargs})
return input_str.encode('utf-8')
def transform_output(self, output: bytes) -> str:
response_json = json.loads(output.read().decode("utf-8"))
return response_json[0]["generated_text"]
"""
model_kwargs: Optional[Dict] = None
"""Key word arguments to pass to the model."""
endpoint_kwargs: Optional[Dict] = None
"""Optional attributes passed to the invoke_endpoint
function. See `boto3`_. docs for more info.
.. _boto3: <https://boto3.amazonaws.com/v1/documentation/api/latest/index.html>
"""
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that AWS credentials to and python package exists in environment."""
try:
import boto3
try:
if values["credentials_profile_name"] is not None:
session = boto3.Session(
profile_name=values["credentials_profile_name"]
)
else:
# use default credentials
session = boto3.Session()
values["client"] = session.client(
"sagemaker-runtime", region_name=values["region_name"]
)
except Exception as e:
raise ValueError(
"Could not load credentials to authenticate with AWS client. "
"Please check that credentials in the specified "
"profile name are valid."
) from e
except ImportError:
raise ImportError(
"Could not import boto3 python package. "
"Please install it with `pip install boto3`."
)
return values
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
_model_kwargs = self.model_kwargs or {}
return {
**{"endpoint_name": self.endpoint_name},
**{"model_kwargs": _model_kwargs},
}
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "sagemaker_endpoint"
def _call(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
"""Call out to Sagemaker inference endpoint.
Args:
prompt: The prompt to pass into the model.
stop: Optional list of stop words to use when generating.
Returns:
The string generated by the model.
Example:
.. code-block:: python
response = se("Tell me a joke.")
"""
_model_kwargs = self.model_kwargs or {}
_model_kwargs = {**_model_kwargs, **kwargs}
_endpoint_kwargs = self.endpoint_kwargs or {}
body = self.content_handler.transform_input(prompt, _model_kwargs)
content_type = self.content_handler.content_type
accepts = self.content_handler.accepts
# send request
try:
response = self.client.invoke_endpoint(
EndpointName=self.endpoint_name,
Body=body,
ContentType=content_type,
Accept=accepts,
**_endpoint_kwargs,
)
except Exception as e:
raise ValueError(f"Error raised by inference endpoint: {e}")
text = self.content_handler.transform_output(response["Body"])
if stop is not None:
# This is a bit hacky, but I can't figure out a better way to enforce
# stop tokens when making calls to the sagemaker endpoint.
text = enforce_stop_tokens(text, stop)
return text
| [] |
2024-01-10 | kopalgarg24/llamabot | llamabot~bot~simplebot.py | """Class definition for SimpleBot."""
import panel as pn
from dotenv import load_dotenv
from langchain.chat_models import ChatOpenAI
from langchain.schema import HumanMessage, SystemMessage
from loguru import logger
pn.extension()
load_dotenv()
class SimpleBot:
"""Simple Bot that is primed with a system prompt, accepts a human message, and sends back a single response.
This bot does not retain chat history.
"""
def __init__(self, system_prompt, temperature=0.0, model_name="gpt-4"):
"""Initialize the SimpleBot.
:param system_prompt: The system prompt to use.
:param temperature: The model temperature to use.
See https://platform.openai.com/docs/api-reference/completions/create#completions/create-temperature
for more information.
:param model_name: The name of the OpenAI model to use.
"""
self.system_prompt = system_prompt
self.model = ChatOpenAI(model_name=model_name, temperature=temperature)
def __call__(self, human_message):
"""Call the SimpleBot.
:param human_message: The human message to use.
:return: The response to the human message, primed by the system prompt.
"""
messages = [
SystemMessage(content=self.system_prompt),
HumanMessage(content=human_message),
]
return self.model(messages)
def panel(
self,
input_text_label="Input",
output_text_label="Output",
submit_button_label="Submit",
site_name="SimpleBot",
title="SimpleBot",
serve=False,
):
"""Create a Panel app that wraps a LlamaBot.
:param input_text_label: The label for the input text.
:param output_text_label: The label for the output text.
:param submit_button_label: The label for the submit button.
:param site_name: The name of the site.
:param title: The title of the site.
:param serve: Whether to serve the app.
If False, we return the Panel app directly.
If True, we call `.serve()` on the app.
:return: The Panel app, either served or directly.
"""
input_text = pn.widgets.TextAreaInput(
name=input_text_label, value="", height=200, width=500
)
output_text = pn.pane.Markdown()
submit = pn.widgets.Button(name=submit_button_label, button_type="success")
def b(event):
"""Button click handler.
:param event: The button click event.
"""
logger.info(input_text.value)
response = self(input_text.value)
output_text.object = response.content
submit.on_click(b)
app = pn.template.FastListTemplate(
site=site_name,
title=title,
main=[
pn.Column(
*[
input_text,
submit,
pn.pane.Markdown(output_text_label),
output_text,
]
)
],
main_max_width="768px",
)
app = pn.panel(app)
if serve:
return app.show()
return app
| [] |
2024-01-10 | kopalgarg24/llamabot | llamabot~bot~querybot.py | """Class definition for QueryBot."""
from pathlib import Path
from typing import List, Union
from langchain.chat_models import ChatOpenAI
from langchain.text_splitter import TokenTextSplitter
from llama_index import Document, GPTSimpleVectorIndex, LLMPredictor, ServiceContext
from llama_index.response.schema import Response
class QueryBot:
"""QueryBot is a bot that lets us use GPT4 to query documents."""
def __init__(
self,
system_message: str,
model_name="gpt-4",
temperature=0.0,
doc_paths: List[Union[str, Path]] = None,
saved_index_path: Union[str, Path] = None,
chunk_size: int = 2000,
chunk_overlap: int = 0,
):
"""Initialize QueryBot.
Pass in either the doc_paths or saved_index_path to initialize the QueryBot.
NOTE: QueryBot is not designed to have memory!
The default text splitter is the TokenTextSplitter from LangChain.
The default index that we use is the GPTSimpleVectorIndex from LlamaIndex.
We also default to using GPT4 with temperature 0.0.
:param system_message: The system message to send to the chatbot.
:param model_name: The name of the OpenAI model to use.
:param temperature: The model temperature to use.
See https://platform.openai.com/docs/api-reference/completions/create#completions/create-temperature
for more information.
:param doc_paths: A list of paths to the documents to use for the chatbot.
These are assumed to be plain text files.
:param saved_index_path: The path to the saved index to use for the chatbot.
:param chunk_size: The chunk size to use for the LlamaIndex TokenTextSplitter.
:param chunk_overlap: The chunk overlap to use for the LlamaIndex TokenTextSplitter.
"""
self.system_message = system_message
chat = ChatOpenAI(model_name=model_name, temperature=temperature)
llm_predictor = LLMPredictor(llm=chat)
service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor)
# Build index
if saved_index_path is not None:
index = GPTSimpleVectorIndex.load_from_disk(
saved_index_path, service_context=service_context
)
else:
self.doc_paths = doc_paths
splitter = TokenTextSplitter(
chunk_size=chunk_size, chunk_overlap=chunk_overlap
)
documents = []
for fname in doc_paths:
with open(fname, "r") as f:
docs = splitter.split_text(f.read())
documents.extend([Document(d) for d in docs])
index = GPTSimpleVectorIndex.from_documents(
documents, service_context=service_context
)
self.index = index
def __call__(
self, query: str, return_sources: bool = True, **kwargs
) -> Union[str, Response]:
"""Call the QueryBot.
:param query: The query to send to the document index.
:param return_sources: Whether to return the source nodes of the query.
Defaults to True.
If True, we return the Response object from LlamaIndex;
if False, we simply return the text generated.
:param kwargs: Additional keyword arguments to pass to the chatbot.
These are passed into LlamaIndex's index.query() method.
For example, if you want to change the number of documents consulted
from the default value of 1 to n instead,
you can pass in the keyword argument `similarity_top_k=n`.
:return: The response to the query generated by GPT4.
"""
q = ""
q += self.system_message + "\n\n"
q += query + "\n\n"
result = self.index.query(q, **kwargs)
if return_sources:
return result
return result.response
def save(self, path: Union[str, Path]):
"""Save the QueryBot and index to disk.
:param path: The path to save the QueryBot index.
"""
path = Path(path)
if not path.suffix == ".json":
path = path.with_suffix(".json")
self.index.save_to_disk(path)
| [] |
2024-01-10 | Minsukim2827/Discord-OpenAI-Chatbot | commands.py | from discord.ext import commands
import openai
from dotenv import load_dotenv
import os
# Load your OpenAI API key from environment variables
load_dotenv()
openai.api_key = os.getenv("OPENAI_API_KEY")
# Function to send a user message to GPT-3 and get a response
# Function to send a user message to GPT-3 and get a response
def generate_response(prompt, last_three_messages):
message_history = [
{
"role": "system",
"content": "You are a helpful assistant that provides information.",
}
]
message_history.extend(last_three_messages)
user_message = {"role": "user", "content": prompt}
message_history.append(user_message)
# Call OpenAI API
try:
response = openai.ChatCompletion.create(
model="gpt-3.5-turbo",
messages=message_history,
max_tokens=2000, # Adjust max_tokens as needed
)
except Exception as e:
print(f"Error calling OpenAI API: {e}")
return "Sorry, I couldn't generate a response."
# Extract chatGPT response
chatgpt_response = response["choices"][0]["message"]
return chatgpt_response["content"]
last_five_messages = []
# Define bot commands
@commands.command()
async def walle(ctx, *, prompt: str):
global last_five_messages
# Get the last three messages
last_five_messages = []
async for message in ctx.channel.history(limit=5):
if message != ctx.message: # Exclude the current command message
last_five_messages.append({"role": "user", "content": message.content})
response = generate_response(prompt, last_five_messages)
await ctx.send(response)
@commands.command()
async def walle100(ctx, *, prompt: str):
global last_five_messages
prompt += ", Provide a concise response in 100 words or less:\n"
response = generate_response(prompt, last_five_messages)
await ctx.send(response)
@commands.command()
async def walle200(ctx, *, prompt: str):
global last_five_messages
prompt += ", Summarize the following in 200 words or less:\n"
response = generate_response(prompt, last_five_messages)
await ctx.send(response)
@commands.command()
async def wallehelp(ctx):
help_message = "WALLE Bot Commands:\n\n"
help_message = "-------------------------------\n"
help_message += "/walle [prompt]: Get a response based on your prompt.\n"
help_message += (
"/walle100 [prompt]: Get a concise response in 100 characters or less.\n"
)
help_message += "/walle200 [prompt]: Summarize the input in 200 words or less.\n"
help_message += "/walleclearhistory: clear the bots current message history\n"
help_message += "/wallewordcount: get the previous messages word count. If no previous message is found, return error message\n"
help_message += "WALLE also records the last 5 message interactions, allowing for a satisfactory conversation experience\n"
help_message = "-------------------------------\n"
await ctx.send(help_message)
@commands.command()
async def walleclearhistory(ctx):
global last_five_messages
# Clear the message history by removing all messages in the channel
async for message in ctx.channel.history():
if message.author == ctx.bot.user:
await message.delete()
# Clear the last three messages
last_five_messages = []
await ctx.send("Message history cleared.")
@commands.command()
async def wallewordcount(ctx):
# Get the previous message in the channel
async for message in ctx.channel.history(limit=2):
if message != ctx.message: # Exclude the current command message
previous_message = message.content
break
else:
await ctx.send("No previous message found.")
return
# Calculate the word count
word_count = len(previous_message.split())
# Send the word count as a response
await ctx.send(f"The previous message has {word_count} words.")
| [
", Provide a concise response in 100 words or less:\n",
", Summarize the following in 200 words or less:\n",
"You are a helpful assistant that provides information."
] |
2024-01-10 | mikedesu/chatgpt-tools | whisper.py | import os
import openai
import sys
openai.api_key = os.getenv("OPENAI_API_KEY")
if len(sys.argv) < 3:
print("Usage: python whisper.py <filename> <outfile>")
sys.exit(1)
filename = sys.argv[1]
outfile = sys.argv[2]
#print(filename)
audio_file = open(filename, "rb")
print("Transcribing audio file: " + filename)
transcript = openai.Audio.transcribe("whisper-1", audio_file)
audio_file.close()
#print(transcript)
transcript_text = transcript["text"]
#transcript_text = transcript_text.decode("utf-8")
print(transcript_text)
# write the transcript text to a file
with open(outfile, "w") as f:
f.write(transcript_text)
| [] |
2024-01-10 | mikedesu/chatgpt-tools | list_models.py | import os
import openai
openai.api_key = os.getenv("OPENAI_API_KEY")
model_list = openai.Model.list()
#print(model_list)
for d in model_list.data:
print(d.id)
| [] |
2024-01-10 | mikedesu/chatgpt-tools | images.py |
import os
import openai
import sys
from datetime import datetime
def main():
t0 = datetime.now()
openai.organization = 'evildojo'
openai.api_key = os.getenv('OPENAI_API_KEY')
if len(sys.argv)!=3:
print("usage:")
print()
print("python3 images.py <prompt> <n>")
sys.exit(-1)
my_prompt = sys.argv[1]
n = int(sys.argv[2])
max_token_ct = 2048 - len(my_prompt)
#my_model = 'text-davinci-003'
#test_obj = openai.Completion.create(
# model=my_model,
# prompt=my_prompt,
# max_tokens=max_token_ct,
# temperature=0
#)
response = openai.Image.create(prompt=my_prompt,
n=n,
size="1024x1024"
)
#print(response)
#print(my_prompt)
#print("-"*20)
for d in response["data"]:
print(d["url"],"\n")
#print("-"*20)
#text = test_obj["choices"][0]["text"]
#text = text.strip()
t1 = datetime.now()
t_diff = t1-t0
#print(test_obj)
#print(text)
#print(t_diff)
if __name__ == '__main__':
main()
| [] |
2024-01-10 | nashuju/TaskMatrix | LowCodeLLM~src~executingLLM.py | from openAIWrapper import OpenAIWrapper
EXECUTING_LLM_PREFIX = """Executing LLM is designed to provide outstanding responses.
Executing LLM will be given a overall task as the background of the conversation between the Executing LLM and human.
When providing response, Executing LLM MUST STICTLY follow the provided standard operating procedure (SOP).
the SOP is formatted as:
'''
STEP 1: [step name][step descriptions][[[if 'condition1'][Jump to STEP]], [[if 'condition2'][Jump to STEP]], ...]
STEP 2: [step name][step descriptions][[[if 'condition1'][Jump to STEP]], [[if 'condition2'][Jump to STEP]], ...]
'''
here "[[[if 'condition1'][Jump to STEP n]], [[if 'condition2'][Jump to STEP m]], ...]" is judgmental logic. It means when you're performing this step,
and if 'condition1' is satisfied, you will perform STEP n next. If 'condition2' is satisfied, you will perform STEP m next.
Remember:
Executing LLM is facing a real human, who does not know what SOP is.
So, Do not show him/her the SOP steps you are following, or the process and middle results of performing the SOP. It will make him/her confused. Just response the answer.
"""
EXECUTING_LLM_SUFFIX = """
Remember:
Executing LLM is facing a real human, who does not know what SOP is.
So, Do not show him/her the SOP steps you are following, or the process and middle results of performing the SOP. It will make him/her confused. Just response the answer.
"""
class executingLLM:
def __init__(self, temperature) -> None:
self.prefix = EXECUTING_LLM_PREFIX
self.suffix = EXECUTING_LLM_SUFFIX
self.LLM = OpenAIWrapper(temperature)
self.messages = [{"role": "system", "content": "You are a helpful assistant."},
{"role": "system", "content": self.prefix}]
def execute(self, current_prompt, history):
''' provide LLM the dialogue history and the current prompt to get response '''
messages = self.messages + history
messages.append({'role': 'user', "content": current_prompt + self.suffix})
response, status = self.LLM.run(messages)
if status:
return response
else:
return "OpenAI API error." | [
"You are a helpful assistant."
] |
2024-01-10 | nashuju/TaskMatrix | LowCodeLLM~src~planningLLM.py | import re
import json
from openAIWrapper import OpenAIWrapper
PLANNING_LLM_PREFIX = """Planning LLM is designed to provide a standard operating procedure so that an abstract and difficult task will be broken down into several steps, and the task will be easily solved by following these steps.
Planning LLM is a powerful problem-solving assistant, so it only needs to analyze the task and provide standard operating procedure as guidance, but does not need actually to solve the problem.
Sometimes there exists some unknown or undetermined situation, thus judgmental logic is needed: some "conditions" are listed, and the next step that should be carried out if a "condition" is satisfied is also listed. The judgmental logics are not necessary, so the jump actions are provided only when needed.
Planning LLM MUST only provide standard operating procedure in the following format without any other words:
'''
STEP 1: [step name][step descriptions][[[if 'condition1'][Jump to STEP]], [[[if 'condition1'][Jump to STEP]], [[if 'condition2'][Jump to STEP]], ...]
STEP 2: [step name][step descriptions][[[if 'condition1'][Jump to STEP]], [[[if 'condition1'][Jump to STEP]], [[if 'condition2'][Jump to STEP]], ...]
...
'''
For example:
'''
STEP 1: [Brainstorming][Choose a topic or prompt, and generate ideas and organize them into an outline][]
STEP 2: [Research][Gather information, take notes and organize them into the outline][[[lack of ideas][Jump to STEP 1]]]
...
'''
"""
EXTEND_PREFIX = """
\nsome steps of the SOP provided by Planning LLM are too rough, so Planning LLM can also provide a detailed sub-SOP for the given step.
Remember, Planning LLM take the overall SOP into consideration, and the sub-SOP MUST be consistent with the rest of the steps, and there MUST be no duplication in content between the extension and the original SOP.
Besides, the extension MUST be logically consistent with the given step.
For example:
If the overall SOP is:
'''
STEP 1: [Brainstorming][Choose a topic or prompt, and generate ideas and organize them into an outline][]
STEP 2: [Research][Gather information from credible sources, and take notes and organize them into the outline][[[if lack of ideas][Jump to STEP 1]]]
STEP 3: [Write][write the text][]
'''
If the STEP 3: "write the text" is too rough and needs to be extended, then the response could be:
'''
STEP 3.1: [Write the title][write the title of the essay][]
STEP 3.2: [Write the body][write the body of the essay][[[if lack of materials][Jump to STEP 2]]]
STEP 3.3: [Write the conclusion][write the conclusion of the essay][]
'''
Remember:
1. Extension is focused on the step descriptions, but not on the judgmental logic;
2. Planning LLM ONLY needs to response the extension.
"""
PLANNING_LLM_SUFFIX = """\nRemember: Planning LLM is very strict to the format and NEVER reply any word other than the standard operating procedure. The reply MUST start with "STEP".
"""
class planningLLM:
def __init__(self, temperature) -> None:
self.prefix = PLANNING_LLM_PREFIX
self.suffix = PLANNING_LLM_SUFFIX
self.LLM = OpenAIWrapper(temperature)
self.messages = [{"role": "system", "content": "You are a helpful assistant."}]
def get_workflow(self, task_prompt):
'''
- input: task_prompt
- output: workflow (json)
'''
messages = self.messages + [{'role': 'user', "content": PLANNING_LLM_PREFIX+'\nThe task is:\n'+task_prompt+PLANNING_LLM_SUFFIX}]
response, status = self.LLM.run(messages)
if status:
return self._txt2json(response)
else:
return "OpenAI API error."
def extend_workflow(self, task_prompt, current_workflow, step):
messages = self.messages + [{'role': 'user', "content": PLANNING_LLM_PREFIX+'\nThe task is:\n'+task_prompt+PLANNING_LLM_SUFFIX}]
messages.append({'role': 'user', "content": EXTEND_PREFIX+
'The current SOP is:\n'+current_workflow+
'\nThe step needs to be extended is:\n'+step+
PLANNING_LLM_SUFFIX})
response, status = self.LLM.run(messages)
if status:
return self._txt2json(response)
else:
return "OpenAI API error."
def _txt2json(self, workflow_txt):
''' convert the workflow in natural language to json format '''
workflow = []
try:
steps = workflow_txt.split('\n')
for step in steps:
if step[0:4] != "STEP":
continue
left_indices = [_.start() for _ in re.finditer("\[", step)]
right_indices = [_.start() for _ in re.finditer("\]", step)]
step_id = step[: left_indices[0]-2]
step_name = step[left_indices[0]+1: right_indices[0]]
step_description = step[left_indices[1]+1: right_indices[1]]
jump_str = step[left_indices[2]+1: right_indices[-1]]
if re.findall(re.compile(r'[A-Za-z]',re.S), jump_str) == []:
workflow.append({"stepId": step_id, "stepName": step_name, "stepDescription": step_description, "jumpLogic": [], "extension": []})
continue
jump_logic = []
left_indices = [_.start() for _ in re.finditer('\[', jump_str)]
right_indices = [_.start() for _ in re.finditer('\]', jump_str)]
i = 1
while i < len(left_indices):
jump = {"Condition": jump_str[left_indices[i]+1: right_indices[i-1]], "Target": re.search(r'STEP\s\d', jump_str[left_indices[i+1]+1: right_indices[i]]).group(0)}
jump_logic.append(jump)
i += 3
workflow.append({"stepId": step_id, "stepName": step_name, "stepDescription": step_description, "jumpLogic": jump_logic, "extension": []})
return json.dumps(workflow)
except:
print("Format error, please try again.") | [
"\nThe step needs to be extended is:\n",
"The current SOP is:\n",
"You are a helpful assistant.",
"PLACEHOLDER\nThe task is:\nPLACEHOLDERPLACEHOLDER"
] |
2024-01-10 | 0oeaaeo/ownai | tests~test_knowledge.py | """Test access to the vector store."""
import pytest
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.vectorstores.base import VectorStore
from backaind.extensions import db
from backaind.knowledge import (
add_knowledge,
add_to_knowledge,
get_embeddings,
get_knowledge,
reset_global_knowledge,
KnowledgeConfigError,
)
import backaind.knowledge
from backaind.models import Knowledge
def test_get_embeddings_raises_on_unknown_embeddings(client):
"""Test if an exception is raised when requesting an unknown embedding function."""
with client:
client.get("/")
with pytest.raises(KnowledgeConfigError) as error:
get_embeddings("unknown")
assert str(error.value) == "Unknown embeddings type: unknown"
def test_get_embeddings_returns_embeddings(client):
"""Test if get_embeddings() returns an Embeddings instance."""
with client:
client.get("/")
embeddings = get_embeddings("huggingface")
assert isinstance(embeddings, Embeddings)
def test_get_knowledge_returns_vector_store(client):
"""Test if get_knowledge() returns a VectorStore instance."""
with client:
client.get("/")
knowledge = get_knowledge(1)
assert isinstance(knowledge, VectorStore)
def test_get_knowledge_loads_from_global_knowledge():
"""Test if get_knowledge() loads from the global knowledge instance."""
backaind.knowledge.global_knowledge = "NotRealKnowledge"
backaind.knowledge.global_knowledge_id = 1
knowledge = get_knowledge(1)
assert knowledge == "NotRealKnowledge"
reset_global_knowledge()
def test_add_to_knowledge_adds_documents(client):
"""Test if adding documents to knowledge works."""
with client:
client.get("/")
add_to_knowledge(
1, [Document(page_content="Test Document", metadata={"source": "Test"})]
)
knowledge = get_knowledge(1)
results = knowledge.similarity_search("Test Document")
assert results.pop().page_content == "Test Document"
reset_global_knowledge()
def test_add_knowledge_command_adds_knowledge(app, runner):
"""Test if the add-knowledge command adds a new knowledge entry to the database."""
knowledge_name = "Test"
knowledge_embeddings = "huggingface"
knowledge_chunk_size = "500"
knowledge_persist_directory = "instance/knowledge-test"
with app.app_context():
knowledge_entry = (
db.session.query(Knowledge).filter_by(name=knowledge_name).first()
)
assert knowledge_entry is None
result = runner.invoke(
add_knowledge,
input=f"{knowledge_name}\n{knowledge_embeddings}\n{knowledge_chunk_size}\n"
+ f"{knowledge_persist_directory}",
)
assert f"Added {knowledge_name}" in result.output
knowledge_entry = (
db.session.query(Knowledge).filter_by(name=knowledge_name).first()
)
assert knowledge_entry is not None
def test_add_knowledge_command_updates_knowledge(app, runner):
"""Test if the add-knowledge command updates knowledge with the same name."""
knowledge_name = "Test 1"
knowledge_embeddings = "huggingface"
knowledge_chunk_size = "500"
knowledge_persist_directory = "instance/knowledge"
with app.app_context():
knowledge_entry = (
db.session.query(Knowledge).filter_by(name=knowledge_name).one()
)
knowledge_entry.persist_directory = "old_directory"
db.session.commit()
result = runner.invoke(
add_knowledge,
input=f"{knowledge_name}\n{knowledge_embeddings}\n{knowledge_chunk_size}\n"
+ f"{knowledge_persist_directory}",
)
assert f"Updated {knowledge_name}" in result.output
knowledge_entry = (
db.session.query(Knowledge).filter_by(name=knowledge_name).one()
)
assert knowledge_entry.persist_directory == knowledge_persist_directory
| [] |
2024-01-10 | 0oeaaeo/ownai | backaind~ainteraction.py | """Allow interaction with an AI."""
from datetime import datetime
import json
from flask import Blueprint, render_template, session, g, redirect, url_for
from flask_socketio import emit, disconnect
from langchain.callbacks.base import BaseCallbackHandler
from langchain.memory import ConversationBufferWindowMemory
from .brain import reply
from .extensions import db, socketio
from .models import Ai, Knowledge
from .settings import get_settings
bp = Blueprint("ainteraction", __name__)
class AinteractionCallbackHandler(BaseCallbackHandler):
"""Callback handler for events during response generation."""
def __init__(self, response_id: int) -> None:
self.response_id = response_id
def on_chat_model_start(self, serialized, messages, **kwargs):
pass
def on_llm_new_token(self, token: str, **kwargs) -> None:
send_next_token(self.response_id, token)
@bp.route("/")
def index():
"""Render the main ainteraction view."""
is_public = g.get("user") is None
ais = get_ai_data(only_public=is_public)
if is_public and not ais:
return redirect(url_for("auth.login"))
return render_template(
"ainteraction/index.html",
ais=json.dumps(ais),
knowledges=json.dumps(get_knowledge_data(only_public=is_public)),
)
def handle_incoming_message(message):
"""Handle an incoming socket.io message from a user."""
is_public = session.get("user_id") is None
ai_id = message.get("aiId")
knowledge_id = message.get("knowledgeId")
if not ai_id:
disconnect()
return
if is_public and not is_ai_public(ai_id):
disconnect()
return
if is_public and knowledge_id and not is_knowledge_public(knowledge_id):
disconnect()
return
response_id = message.get("responseId")
message_text = message.get("message", {}).get("text", "")
memory = ConversationBufferWindowMemory(k=3)
for history_message in message.get("history", []):
if history_message.get("author", {}).get("species") == "ai":
memory.chat_memory.add_ai_message(history_message.get("text", ""))
else:
memory.chat_memory.add_user_message(history_message.get("text", ""))
try:
response = reply(
ai_id,
message_text,
knowledge_id,
memory,
[AinteractionCallbackHandler(response_id)],
get_settings(session.get("user_id", -1)).get("external-providers", {}),
)
send_response(response_id, response.strip())
# pylint: disable=broad-exception-caught
except Exception as exception:
send_response(response_id, str(exception), "error")
raise exception
def init_app(_app):
"""Register handling of incoming socket.io messages."""
socketio.on("message")(handle_incoming_message)
def get_ai_data(only_public=True):
"""Get data for all AIs."""
ai_query = db.session.query(Ai)
if only_public:
ai_query = ai_query.filter_by(is_public=True)
return [
{
"id": ai.id,
"name": ai.name,
"input_keys": ai.input_keys,
"input_labels": ai.input_labels,
"greeting": ai.greeting,
}
for ai in ai_query.all()
]
def get_knowledge_data(only_public=True):
"""Get data for all knowledges."""
knowledge_query = db.session.query(Knowledge)
if only_public:
knowledge_query = knowledge_query.filter_by(is_public=True)
return [
{
"id": knowledge.id,
"name": knowledge.name,
}
for knowledge in knowledge_query.all()
]
def is_ai_public(ai_id: int):
"""Check if an AI is public."""
ai = db.session.get(Ai, ai_id)
return bool(ai and ai.is_public)
def is_knowledge_public(knowledge_id: int):
"""Check if a knowledge is public."""
knowledge = db.session.get(Knowledge, knowledge_id)
return bool(knowledge and knowledge.is_public)
def send_next_token(response_id: int, token_text: str):
"""Send the next response token to the user."""
emit(
"token",
{
"messageId": response_id,
"text": token_text,
},
)
def send_response(response_id: int, message_text: str, status: str = "done"):
"""Send the full response message to the user."""
emit(
"message",
{
"id": response_id,
"author": {
"species": "ai",
},
"date": datetime.now().isoformat(),
"text": message_text,
"status": status,
},
)
| [] |
2024-01-10 | 0oeaaeo/ownai | aifilemaker.py | #!/usr/bin/env python3
"""Simple example how to quickly create Aifiles."""
import json
from langchain import PromptTemplate, LLMChain
from langchain.chains.loading import load_chain_from_config
from langchain.llms.fake import FakeListLLM
# 1. Set a name for your AI
NAME = "Fake AI"
# 2. Set up the LLM you want to use
# (see https://python.langchain.com/en/latest/modules/models/llms/integrations.html for examples)
llm = FakeListLLM(responses=["Hello", "Bye"])
# 3. Set up a prompt template for your LLM and task
# (see https://python.langchain.com/en/latest/modules/prompts/prompt_templates/getting_started.html)
# Consider using a template that suits your model!
# Check the models page on Hugging Face etc. to get a correct prompting template.
TEMPLATE = """Question: {input_text}
Answer:"""
prompt = PromptTemplate(template=TEMPLATE, input_variables=["input_text"])
# 4. Set up the chain
# (see https://python.langchain.com/en/latest/modules/chains.html)
llm_chain = LLMChain(prompt=prompt, llm=llm, output_key="output_text")
# Test if loading the chain again works
load_chain_from_config(llm_chain.dict())
# Export Aifile
aifile_dict = {"name": NAME, "aifileversion": 1, "chain": llm_chain.dict()}
aifile = json.dumps(aifile_dict, indent=2)
print(aifile)
| [
"input_text",
"Question: {input_text}\nAnswer:"
] |
2024-01-10 | 0oeaaeo/ownai | backaind~brain.py | """Provide AI data processing capabilities."""
import os
from threading import Lock
from typing import Optional, Set, Tuple
from langchain.callbacks.base import Callbacks
from langchain.chains.base import Chain
from langchain.chains.loading import load_chain_from_config
from langchain.llms.huggingface_text_gen_inference import HuggingFaceTextGenInference
from langchain.schema import BaseMemory
from backaind.extensions import db
from backaind.knowledge import get_knowledge
from backaind.models import Ai
# pylint: disable=invalid-name
global_chain = None
global_chain_id = None
global_chain_input_keys = None
# pylint: enable=invalid-name
chain_lock = Lock()
def get_chain(
ai_id: int, updated_environment: Optional[dict] = None
) -> Tuple[Chain, Set[str]]:
"""Load the AI chain or create a new chain if it doesn't exist."""
# pylint: disable=global-statement
global global_chain, global_chain_id, global_chain_input_keys
with chain_lock:
chain = global_chain
chain_id = global_chain_id
chain_input_keys = global_chain_input_keys
if not chain or not chain_input_keys or chain_id != ai_id:
aifile = db.get_or_404(Ai, ai_id)
chain_input_keys = aifile.input_keys
with UpdatedEnvironment(updated_environment or {}):
chain = load_chain_from_config(aifile.chain)
set_text_generation_inference_token(chain)
global_chain = chain
global_chain_id = ai_id
global_chain_input_keys = chain_input_keys
return (chain, chain_input_keys)
def reset_global_chain(ai_id=None):
"""
Drop the global chain instance.
If ai_id is set, it only drops the global chain instance if it matches this ID.
"""
# pylint: disable=global-statement
global global_chain, global_chain_id, global_chain_input_keys
with chain_lock:
if not ai_id or ai_id == global_chain_id:
global_chain = None
global_chain_id = None
global_chain_input_keys = None
def reply(
ai_id: int,
input_text: str,
knowledge_id: Optional[int] = None,
memory: Optional[BaseMemory] = None,
callbacks: Callbacks = None,
updated_environment: Optional[dict] = None,
) -> str:
"""Run the chain with an input message and return the AI output."""
(chain, chain_input_keys) = get_chain(ai_id, updated_environment)
inputs = {}
has_memory = (
memory
and "input_history" in chain_input_keys
and memory.load_memory_variables({})["history"]
)
for input_key in chain_input_keys:
if input_key == "input_text":
inputs["input_text"] = input_text
elif input_key == "input_knowledge":
if knowledge_id is None:
inputs["input_knowledge"] = []
else:
knowledge = get_knowledge(knowledge_id)
inputs["input_knowledge"] = knowledge.similarity_search(
input_text, k=1 if has_memory else 4
)
elif input_key == "input_history":
if memory is None:
inputs["input_history"] = ""
else:
inputs["input_history"] = memory.load_memory_variables({})["history"]
return chain(inputs, callbacks=callbacks)["output_text"]
def find_instances(obj, cls):
"""Find all instances of a class in an object."""
instances = []
if isinstance(obj, cls):
instances.append(obj)
if isinstance(obj, list):
for item in obj:
instances.extend(find_instances(item, cls))
elif hasattr(obj, "__dict__"):
for prop in vars(obj).values():
instances.extend(find_instances(prop, cls))
return instances
def set_text_generation_inference_token(chain: Chain):
"""Set the token for all HuggingFaceTextGenInference instances in the chain."""
token = os.environ.get("TEXT_GENERATION_INFERENCE_TOKEN", None)
if not token:
return
all_huggingface_instances = find_instances(chain, HuggingFaceTextGenInference)
for instance in all_huggingface_instances:
instance.client.headers = {"Authorization": f"Bearer {token}"}
class UpdatedEnvironment:
"""Temporarily update the environment variables."""
def __init__(self, new_values):
self.new_values = new_values
self.old_values = {}
def __enter__(self):
for key, new_value in self.new_values.items():
if key in os.environ:
self.old_values[key] = os.environ[key]
os.environ[key] = new_value
def __exit__(self, exc_type, exc_val, exc_tb):
for key in self.new_values.keys():
if key in self.old_values:
os.environ[key] = self.old_values[key]
else:
del os.environ[key]
| [] |
2024-01-10 | 0oeaaeo/ownai | tests~test_brain.py | """Test the handling of AI chains."""
import os
import pytest
from langchain.chains.loading import load_chain_from_config
from langchain.llms.huggingface_text_gen_inference import HuggingFaceTextGenInference
from langchain.memory import ConversationBufferWindowMemory
from backaind.aifile import read_aifile_from_path
from backaind.brain import (
get_chain,
reply,
reset_global_chain,
find_instances,
set_text_generation_inference_token,
UpdatedEnvironment,
)
import backaind.brain
from backaind.models import Ai
def test_get_chain_loads_from_global_chain():
"""Test if the chain is loaded from the global chain instance."""
backaind.brain.global_chain = "NotARealChain"
backaind.brain.global_chain_id = 1
backaind.brain.global_chain_input_keys = set("text_input")
(chain, chain_input_keys) = get_chain(1)
assert chain == "NotARealChain"
assert chain_input_keys == set("text_input")
reset_global_chain()
def test_get_chain_creates_new_chain(monkeypatch):
"""Test if the chain gets created if it doesn't exist yet."""
reset_global_chain()
monkeypatch.setattr(
"backaind.extensions.db.get_or_404",
lambda _model, _model_id: Ai(
input_keys=["input_text"],
chain={"name": "NotARealChain"},
),
)
monkeypatch.setattr("backaind.brain.load_chain_from_config", lambda chain: chain)
(chain, _chain_input_keys) = get_chain(1)
assert chain == {"name": "NotARealChain"}
assert backaind.brain.global_chain == {"name": "NotARealChain"}
reset_global_chain()
def test_reply_runs_the_chain(monkeypatch):
"""Test if the reply function calls the chain."""
class FakeChain:
"""Helper class to allow calling the chain."""
def __call__(self, _inputs, **_kwargs):
"""Mock function for calling the chain."""
return {"output_text": "Response"}
def fake_get_chain(_ai_id, _updated_environment):
return (FakeChain(), set())
monkeypatch.setattr("backaind.brain.get_chain", fake_get_chain)
response = reply(1, "Hi", None)
assert response == "Response"
def test_reply_sets_inputs(monkeypatch):
"""Test if the reply function correctly sets the inputs for the chain."""
class FakeChain:
"""Helper class to allow calling the chain."""
def __call__(self, inputs, **_kwargs):
"""Mock function for calling the chain."""
output = f"{inputs['input_text']},{inputs['input_knowledge']},{inputs['input_history']}"
return {"output_text": output}
def fake_get_chain(_ai_id, _updated_environment):
return (
FakeChain(),
{"input_text", "input_knowledge", "input_history", "input_unknown"},
)
class FakeKnowledge:
"""Helper class for a fake knowledge interface."""
def similarity_search(self, input_text, **_kwargs):
"""Mock function to check if the similarity_search is called."""
return [input_text]
def fake_get_knowledge(_knowledge_id):
return FakeKnowledge()
monkeypatch.setattr("backaind.brain.get_chain", fake_get_chain)
monkeypatch.setattr("backaind.brain.get_knowledge", fake_get_knowledge)
response = reply(1, "Hi", 1)
assert response == "Hi,['Hi'],"
response = reply(1, "Hi", None)
assert response == "Hi,[],"
memory = ConversationBufferWindowMemory(k=3)
memory.chat_memory.add_ai_message("Hi user")
memory.chat_memory.add_user_message("Hi AI")
response = reply(1, "Hi", 1, memory)
assert response == "Hi,['Hi'],AI: Hi user\nHuman: Hi AI"
def test_set_text_generation_inference_token():
"""Test if the text generation inference token is set correctly."""
aifile = read_aifile_from_path(
"examples/huggingface_textgen_inference/huggingface_textgen_inference.aifile"
)
chain = load_chain_from_config(aifile["chain"])
os.environ["TEXT_GENERATION_INFERENCE_TOKEN"] = "test_token"
set_text_generation_inference_token(chain)
all_huggingface_instances = find_instances(chain, HuggingFaceTextGenInference)
assert len(all_huggingface_instances) == 1
assert all_huggingface_instances[0].client.headers == {
"Authorization": "Bearer test_token"
}
def test_updated_environment_resets_values():
"""Test if the environment is reset after the context manager."""
os.environ["EXISTING_VAR"] = "old_value"
if "NEW_VAR" in os.environ:
del os.environ["NEW_VAR"]
with UpdatedEnvironment({"NEW_VAR": "new_value", "EXISTING_VAR": "new_value"}):
assert os.getenv("NEW_VAR") == "new_value"
assert os.getenv("EXISTING_VAR") == "new_value"
assert os.getenv("NEW_VAR") is None
assert os.getenv("EXISTING_VAR") == "old_value"
def test_updated_environment_handles_exceptions():
"""Test if the environment is reset even if an exception is raised."""
os.environ["EXISTING_VAR"] = "old_value"
if "NEW_VAR" in os.environ:
del os.environ["NEW_VAR"]
with pytest.raises(RuntimeError):
with UpdatedEnvironment({"NEW_VAR": "new_value", "EXISTING_VAR": "new_value"}):
assert os.getenv("NEW_VAR") == "new_value"
assert os.getenv("EXISTING_VAR") == "new_value"
raise RuntimeError("Test")
assert os.getenv("NEW_VAR") is None
assert os.getenv("EXISTING_VAR") == "old_value"
| [] |
2024-01-10 | zhangir-azerbayev/mathlib-semantic-search | src~embed_mathlib~embed_mathlib.py | import ndjson
import json
import sys
import os
from tqdm import tqdm
import openai
from dataclasses import dataclass, field
from uuid import uuid4
from typing import Optional, Literal
def batch_loader(seq, size):
"""
Iterator that takes in a list `seq` and returns
chunks of size `size`
"""
return [seq[pos : pos + size] for pos in range(0, len(seq), size)]
def text_of_entry(x):
return (
"/-- " + x["doc_string"] + " -/" + "\n" + x["formal_statement"]
if x["doc_string"]
else x["formal_statement"]
)
def main():
READ_DIR = "../parse_docgen/docgen_export_with_formal_statement.jsonl"
OUT_DIR = "./embeddings.jsonl"
if os.path.isfile(OUT_DIR):
raise AssertionError(f"{OUT_DIR} is already a file")
print("loading docgen data...")
with open(READ_DIR) as f:
data = ndjson.load(f)
print("creating embeddings")
for batch in tqdm(batch_loader(data, 100)):
texts = [text_of_entry(x) for x in batch]
responses = openai.Embedding.create(
input=texts,
model="text-embedding-ada-002",
)
log = []
for entry, response in zip(batch, responses["data"]):
to_log = {"name": entry["name"], "embedding": response["embedding"]}
log.append(to_log)
with open(OUT_DIR, "a+") as f:
jsonstr = ndjson.dumps(log)
f.write(jsonstr + "\n")
if __name__ == "__main__":
main()
| [] |
2024-01-10 | zhangir-azerbayev/mathlib-semantic-search | src~embed_mathlib~count_tokens.py | import ndjson
import json
import sys
import os
from tqdm import tqdm
import numpy as np
import openai
from transformers import AutoTokenizer
IN_DIR = "../docgen_parse/docgen_export_with_formal_statement.jsonl"
tokenizer = AutoTokenizer.from_pretrained("gpt2")
with open(IN_DIR) as f:
data = ndjson.load(f)
total = 0
for x in tqdm(data):
text = (
"/-- " + x["doc_string"] + " -/" + "\n" + x["formal_statement"]
if x["doc_string"]
else x["formal_statement"]
)
count = len(tokenizer(text)['input_ids'])
total += count
print(total)
| [] |
2024-01-10 | BigRedDoge/GPT-Notes | chat_utils.py | from typing import Any, List, Dict
import openai
import requests
from database_utils import query_database
import logging
def apply_prompt_template(question):
"""
Applies additional template on user's question.
Prompt engineering could be done here to improve the result.
"""
prompt = f"""
By considering above input from me, answer the question: {question}
"""
return prompt
def call_chatgpt_api(user_question, chunks):
"""
Call chatgpt api with user's question and retrieved chunks.
"""
messages = list(
map(lambda chunk: {
"role": "user",
"content": chunk
}, chunks))
question = apply_prompt_template(user_question)
messages.append({"role": "user", "content": question})
response = openai.ChatCompletion.create(
model="gpt-3.5-turbo",
messages=messages,
max_tokens=1024,
temperature=0.7,
)
return response
def ask(user_question):
"""
Handle user's questions.
"""
# Get chunks from database.
chunks_response = query_database(user_question)
chunks = []
for result in chunks_response["results"]:
for inner_result in result["results"]:
chunks.append(inner_result["text"])
logging.info("User's questions: %s", user_question)
logging.info("Retrieved chunks: %s", chunks)
response = call_chatgpt_api(user_question, chunks)
logging.info("Response: %s", response)
return response["choices"][0]["message"]["content"] | [
"\n By considering above input from me, answer the question: PLACEHOLDER\n "
] |
2024-01-10 | BigRedDoge/GPT-Notes | audio~transcriber.py | import openai
import wave
import pyaudio
import dotenv
import os
class Transcriber:
"""
Transcribes audio using OpenAI's Whisper API
args: path to recording
"""
def __init__(self, path):
self.path = path
def transcribe(self, frames):
self.save_audio(frames)
transcript = openai.Audio.transcribe("whisper-1", open(self.path, "rb"))
return transcript["text"]
def save_audio(self, frames):
with wave.open(self.path, 'wb') as wf:
wf.setnchannels(1)
wf.setsampwidth(pyaudio.PyAudio().get_sample_size(pyaudio.paInt16))
wf.setframerate(44100)
wf.writeframes(b''.join(frames)) | [] |
2024-01-10 | vishan01/Imagetory | story.py | from langchain import PromptTemplate, OpenAI, LLMChain
def story(text, api):
template = """
You are story teller.
You can narrate a story from the given context. The story shouldn't be more than 60 words.
The story should be interesting and heart warming or emotional or joyful.
CONTEXT: {text}
STORY:
"""
prompt = PromptTemplate(template=template, input_variables=["text"])
llm_model = LLMChain(llm=OpenAI(model_name="gpt-3.5-turbo",
temperature=1, openai_api_key=api), prompt=prompt, verbose=True)
scene = llm_model.predict(text=text)
return scene
| [
"\n You are story teller.\n You can narrate a story from the given context. The story shouldn't be more than 60 words. \n The story should be interesting and heart warming or emotional or joyful.\n CONTEXT: {text}\n STORY:\n"
] |
2024-01-10 | shangfr/Aesopica | discord_bot.py | # -*- coding: utf-8 -*-
"""
Created on Mon May 15 15:17:30 2023
@author: shangfr
"""
import json
import logging
import random
import discord
from langchain.llms import OpenAI
from vecdb import load_vectordb
with open('.streamlit/config.json', 'r') as f:
config = json.load(f)
handler = logging.FileHandler(
filename='discord.log', encoding='utf-8', mode='w')
TOKEN = config['discord']
llm = OpenAI(openai_api_key=config['openai'],
model_name="gpt-3.5-turbo", temperature=0)
vectordb = load_vectordb(directory='fables_db')
retriever = vectordb.as_retriever(search_type="mmr")
results = retriever.get_relevant_documents("猫和老鼠")
test = [result.page_content for result in results]
class MyClient(discord.Client):
async def on_ready(self):
print(f'Logged on as {self.user}!')
async def on_message(self, message):
if message.author == client.user:
return
print(f'Message from {message.author}: {message.content}')
if message.content == '!测试':
response = random.choice(test)
await message.channel.send(response)
if '寓言' in message.content:
question = message.content
response = retriever.get_relevant_documents(question)[
0].page_content
output = response.split('\n')
#### Create the initial embed object ####
embedVar = discord.Embed(
title=output[4], url="https://aesopica.readthedocs.io/en/latest/", description=output[5], color=0x109319)
# Add author, thumbnail, fields, and footer to the embed
# embedVar.set_author(name=message.author.name)
embedVar.set_image(url="http://oss-cdn.shangfr.site/fables.png")
# embedVar.set_thumbnail(url="https://img2.baidu.com/it/u=2024274349,3703499800&fm=253&fmt=auto&app=138&f=JPEG?w=100&h=100")
#embedVar.add_field(name="Field 1 Title", value="This is the value for field 1. This is NOT an inline field.", inline=False)
#embedVar.add_field(name="Field 2 Title", value="It is inline with Field 3", inline=True)
#embedVar.add_field(name="Field 3 Title", value="It is inline with Field 2", inline=True)
#file = discord.File("parchment.png", filename="output.png")
embedVar.set_footer(
text=output[6], icon_url="http://oss-cdn.shangfr.site/parchment.png")
await message.channel.send(embed=embedVar)
if message.content.startswith('/fable'):
question = message.content.replace("/fable", "")
response = retriever.get_relevant_documents(question)[
0].page_content
output = response.split('\n')
embedVar = discord.Embed(
title=output[1], url="https://aesopica.readthedocs.io/en/latest/", description=output[2], color=0x109319)
embedVar.set_image(url="http://oss-cdn.shangfr.site/fables.png")
embedVar.set_footer(
text=output[3], icon_url="http://oss-cdn.shangfr.site/parchment.png")
await message.channel.send(embed=embedVar)
if message.content.startswith('/chat'):
question = message.content.replace("/chat", "")
response = llm(question)
await message.channel.send(response)
intents = discord.Intents.default()
intents.message_content = True
client = MyClient(intents=intents)
client.run(TOKEN, log_handler=handler, log_level=logging.DEBUG)
| [] |
2024-01-10 | shangfr/Aesopica | vecdb.py | # -*- coding: utf-8 -*-
"""
Created on Wed May 31 11:01:25 2023
@author: shangfr
"""
from langchain.document_loaders.csv_loader import CSVLoader
from langchain.embeddings import QianfanEmbeddingsEndpoint
from langchain.vectorstores import Chroma
def add_emb(docs,**kwargs):
embeddings = QianfanEmbeddingsEndpoint()
vectordb = Chroma.from_documents(
collection_name=kwargs['collection_name'],
documents=docs,
embedding=embeddings,
persist_directory=kwargs['directory'])
vectordb.persist()
print("Vector DB init success! ")
def init_vectordb(file_path='data_csv/books_cn.csv', collection_name="fables_collection", directory='fables_db'):
loader = CSVLoader(file_path, encoding='utf-8')
docs = loader.load()
max_tokens = 384
docs_new = []
for d in docs:
if len(d.page_content)<max_tokens/1.3:
docs_new.append(d)
if len(docs_new)>15:
add_emb(docs_new, collection_name="fables_collection", directory='fables_db')
docs_new = []
if docs_new:
add_emb(docs_new, collection_name="fables_collection", directory='fables_db')
def load_vectordb(directory='fables_db', collection_name="fables_collection"):
embeddings = QianfanEmbeddingsEndpoint()
vectordb = Chroma(
collection_name,
embeddings,
directory)
return vectordb
#init_vectordb()
#vectordb = load_vectordb(directory='fables_db')
#retriever = vectordb.as_retriever(search_type="mmr")
#results =retriever.get_relevant_documents("猫和老鼠")[0]
#print(results.page_content) | [] |
2024-01-10 | LindqU/003-poc-FeedSummery | datapipeline~src~llm~base_prompt.py | from langchain.prompts import PromptTemplate
from langchain.output_parsers import PydanticOutputParser
from langchain.llms import OpenAI
from util.log import logger
from langchain.pydantic_v1 import BaseModel, Field
from datetime import datetime
class BasePrompt:
def __init__(
self,
output_schema,
base_templete: str = "{query}\n\n{format_instructions}\n",
model_name: str = "gpt-3.5-turbo",
):
self._output_schema = output_schema
self._base_templete = base_templete
self._model = OpenAI(model_name=model_name)
def gen_prompt(self, query):
parser = PydanticOutputParser(pydantic_object=self._output_schema)
base_prompt = PromptTemplate(
template=self._base_templete,
input_variables=["query"],
validate_template=True,
partial_variables={"format_instructions": parser.get_format_instructions()},
)
self.prompt = base_prompt.format_prompt(query=query)
def get_prompt(self):
return self.prompt
def run_prompt(self):
prompt = self.prompt.to_string()
logger.info("Prompt\n%s", prompt)
return self._model(prompt)
class OutputSchema(BaseModel):
ad_type: str = Field(description="広告の種類")
change_start_date: datetime = Field(description="変更が始まる時期")
content: str = Field(description="変更内容について")
| [
"True",
"format_instructions"
] |
2024-01-10 | COS301-SE-2023/WordWizard | libs~api~reading~util~passage.py | import os
import re
from dotenv import load_dotenv
import openai
from .checker import is_profane
load_dotenv()
from .helper import santise_string
api_key = os.getenv("OPEN_AI_KEY")
openai.api_key = api_key
def query_passage(query: str):
q = query_chat(query)
sentence, focus = extract_info(q)
while is_profane(q) or sentence == None or focus == None:
q = query_chat(query)
sentence, focus = extract_info(q)
return santise_string(f"Sentence: {sentence}\nFocus Words: {focus}")
def query_chat(query: str):
response = openai.ChatCompletion.create(
model="gpt-3.5-turbo",
messages=[{"role": "user", "content": query}],
temperature=1,
max_tokens=256,
top_p=1,
frequency_penalty=0,
presence_penalty=0,
)
return response["choices"][0]["message"]["content"]
def extract_info(input_string):
sentence_match = re.search(r"Sentence: (.*)", input_string)
focus_words_match = re.search(r"Focus Words: (.*)", input_string)
sentence = sentence_match.group(1).strip() if sentence_match else None
focus_words = focus_words_match.group(1).strip() if focus_words_match else None
return sentence, focus_words
| [] |
2024-01-10 | COS301-SE-2023/WordWizard | libs~api~reading~util~img.py | import os
from dotenv import load_dotenv
load_dotenv()
import openai
openai.api_key = os.getenv("OPEN_AI_KEY")
def generate_image(prmpt: str):
response = openai.Image.create(
prompt=prmpt,
n=1,
size="256x256"
)
image_url = response['data'][0]['url']
return image_url | [] |
2024-01-10 | yjyang1990/langflow | src~backend~langflow~interface~loading.py | import json
from typing import Any, Callable, Dict, Optional
from langchain.agents import ZeroShotAgent
from langchain.agents import agent as agent_module
from langchain.agents.agent import AgentExecutor
from langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.agents.load_tools import (
_BASE_TOOLS,
_EXTRA_LLM_TOOLS,
_EXTRA_OPTIONAL_TOOLS,
_LLM_TOOLS,
)
from langchain.agents.loading import load_agent_from_config
from langchain.agents.tools import Tool
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.base import BaseCallbackManager
from langchain.chains.loading import load_chain_from_config
from langchain.llms.loading import load_llm_from_config
from pydantic import ValidationError
from langflow.interface.custom_lists import CUSTOM_NODES
from langflow.interface.importing.utils import get_function, import_by_type
from langflow.interface.toolkits.base import toolkits_creator
from langflow.interface.chains.base import chain_creator
from langflow.interface.types import get_type_list
from langflow.interface.utils import load_file_into_dict
from langflow.utils import util, validate
def instantiate_class(node_type: str, base_type: str, params: Dict) -> Any:
"""Instantiate class from module type and key, and params"""
params = convert_params_to_sets(params)
params = convert_kwargs(params)
if node_type in CUSTOM_NODES:
if custom_node := CUSTOM_NODES.get(node_type):
if hasattr(custom_node, "initialize"):
return custom_node.initialize(**params)
return custom_node(**params)
class_object = import_by_type(_type=base_type, name=node_type)
return instantiate_based_on_type(class_object, base_type, node_type, params)
def convert_params_to_sets(params):
"""Convert certain params to sets"""
if "allowed_special" in params:
params["allowed_special"] = set(params["allowed_special"])
if "disallowed_special" in params:
params["disallowed_special"] = set(params["disallowed_special"])
return params
def convert_kwargs(params):
# if *kwargs are passed as a string, convert to dict
# first find any key that has kwargs in it
kwargs_keys = [key for key in params.keys() if "kwargs" in key]
for key in kwargs_keys:
if isinstance(params[key], str):
params[key] = json.loads(params[key])
return params
def instantiate_based_on_type(class_object, base_type, node_type, params):
if base_type == "agents":
return instantiate_agent(class_object, params)
elif base_type == "prompts":
return instantiate_prompt(node_type, class_object, params)
elif base_type == "tools":
return instantiate_tool(node_type, class_object, params)
elif base_type == "toolkits":
return instantiate_toolkit(node_type, class_object, params)
elif base_type == "embeddings":
return instantiate_embedding(class_object, params)
elif base_type == "vectorstores":
return instantiate_vectorstore(class_object, params)
elif base_type == "documentloaders":
return instantiate_documentloader(class_object, params)
elif base_type == "textsplitters":
return instantiate_textsplitter(class_object, params)
elif base_type == "utilities":
return instantiate_utility(node_type, class_object, params)
elif base_type == "chains":
return instantiate_chains(node_type, class_object, params)
else:
return class_object(**params)
def instantiate_chains(node_type, class_object, params):
if "retriever" in params and hasattr(params["retriever"], "as_retriever"):
params["retriever"] = params["retriever"].as_retriever()
if node_type in chain_creator.from_method_nodes:
method = chain_creator.from_method_nodes[node_type]
if class_method := getattr(class_object, method, None):
return class_method(**params)
raise ValueError(f"Method {method} not found in {class_object}")
return class_object(**params)
def instantiate_agent(class_object, params):
return load_agent_executor(class_object, params)
def instantiate_prompt(node_type, class_object, params):
if node_type == "ZeroShotPrompt":
if "tools" not in params:
params["tools"] = []
return ZeroShotAgent.create_prompt(**params)
return class_object(**params)
def instantiate_tool(node_type, class_object, params):
if node_type == "JsonSpec":
params["dict_"] = load_file_into_dict(params.pop("path"))
return class_object(**params)
elif node_type == "PythonFunctionTool":
params["func"] = get_function(params.get("code"))
return class_object(**params)
# For backward compatibility
elif node_type == "PythonFunction":
function_string = params["code"]
if isinstance(function_string, str):
return validate.eval_function(function_string)
raise ValueError("Function should be a string")
elif node_type.lower() == "tool":
return class_object(**params)
return class_object(**params)
def instantiate_toolkit(node_type, class_object, params):
loaded_toolkit = class_object(**params)
# Commenting this out for now to use toolkits as normal tools
# if toolkits_creator.has_create_function(node_type):
# return load_toolkits_executor(node_type, loaded_toolkit, params)
if isinstance(loaded_toolkit, BaseToolkit):
return loaded_toolkit.get_tools()
return loaded_toolkit
def instantiate_embedding(class_object, params):
params.pop("model", None)
params.pop("headers", None)
try:
return class_object(**params)
except ValidationError:
params = {
key: value
for key, value in params.items()
if key in class_object.__fields__
}
return class_object(**params)
def instantiate_vectorstore(class_object, params):
if len(params.get("documents", [])) == 0:
raise ValueError(
"The source you provided did not load correctly or was empty."
"This may cause an error in the vectorstore."
)
# Chroma requires all metadata values to not be None
if class_object.__name__ == "Chroma":
for doc in params["documents"]:
if doc.metadata is None:
doc.metadata = {}
for key, value in doc.metadata.items():
if value is None:
doc.metadata[key] = ""
return class_object.from_documents(**params)
def instantiate_documentloader(class_object, params):
return class_object(**params).load()
def instantiate_textsplitter(class_object, params):
try:
documents = params.pop("documents")
except KeyError as e:
raise ValueError(
"The source you provided did not load correctly or was empty."
"Try changing the chunk_size of the Text Splitter."
) from e
text_splitter = class_object(**params)
return text_splitter.split_documents(documents)
def instantiate_utility(node_type, class_object, params):
if node_type == "SQLDatabase":
return class_object.from_uri(params.pop("uri"))
return class_object(**params)
def replace_zero_shot_prompt_with_prompt_template(nodes):
"""Replace ZeroShotPrompt with PromptTemplate"""
for node in nodes:
if node["data"]["type"] == "ZeroShotPrompt":
# Build Prompt Template
tools = [
tool
for tool in nodes
if tool["type"] != "chatOutputNode"
and "Tool" in tool["data"]["node"]["base_classes"]
]
node["data"] = build_prompt_template(prompt=node["data"], tools=tools)
break
return nodes
def load_langchain_type_from_config(config: Dict[str, Any]):
"""Load langchain type from config"""
# Get type list
type_list = get_type_list()
if config["_type"] in type_list["agents"]:
config = util.update_verbose(config, new_value=False)
return load_agent_executor_from_config(config, verbose=True)
elif config["_type"] in type_list["chains"]:
config = util.update_verbose(config, new_value=False)
return load_chain_from_config(config, verbose=True)
elif config["_type"] in type_list["llms"]:
config = util.update_verbose(config, new_value=True)
return load_llm_from_config(config)
else:
raise ValueError("Type should be either agent, chain or llm")
def load_agent_executor_from_config(
config: dict,
llm: Optional[BaseLanguageModel] = None,
tools: Optional[list[Tool]] = None,
callback_manager: Optional[BaseCallbackManager] = None,
**kwargs: Any,
):
tools = load_tools_from_config(config["allowed_tools"])
config["allowed_tools"] = [tool.name for tool in tools] if tools else []
agent_obj = load_agent_from_config(config, llm, tools, **kwargs)
return AgentExecutor.from_agent_and_tools(
agent=agent_obj,
tools=tools,
callback_manager=callback_manager,
**kwargs,
)
def load_agent_executor(agent_class: type[agent_module.Agent], params, **kwargs):
"""Load agent executor from agent class, tools and chain"""
allowed_tools = params.get("allowed_tools", [])
llm_chain = params["llm_chain"]
# if allowed_tools is not a list or set, make it a list
if not isinstance(allowed_tools, (list, set)):
allowed_tools = [allowed_tools]
tool_names = [tool.name for tool in allowed_tools]
# Agent class requires an output_parser but Agent classes
# have a default output_parser.
agent = agent_class(allowed_tools=tool_names, llm_chain=llm_chain) # type: ignore
return AgentExecutor.from_agent_and_tools(
agent=agent,
tools=allowed_tools,
**kwargs,
)
def load_toolkits_executor(node_type: str, toolkit: BaseToolkit, params: dict):
create_function: Callable = toolkits_creator.get_create_function(node_type)
if llm := params.get("llm"):
return create_function(llm=llm, toolkit=toolkit)
def load_tools_from_config(tool_list: list[dict]) -> list:
"""Load tools based on a config list.
Args:
config: config list.
Returns:
List of tools.
"""
tools = []
for tool in tool_list:
tool_type = tool.pop("_type")
llm_config = tool.pop("llm", None)
llm = load_llm_from_config(llm_config) if llm_config else None
kwargs = tool
if tool_type in _BASE_TOOLS:
tools.append(_BASE_TOOLS[tool_type]())
elif tool_type in _LLM_TOOLS:
if llm is None:
raise ValueError(f"Tool {tool_type} requires an LLM to be provided")
tools.append(_LLM_TOOLS[tool_type](llm))
elif tool_type in _EXTRA_LLM_TOOLS:
if llm is None:
raise ValueError(f"Tool {tool_type} requires an LLM to be provided")
_get_llm_tool_func, extra_keys = _EXTRA_LLM_TOOLS[tool_type]
if missing_keys := set(extra_keys).difference(kwargs):
raise ValueError(
f"Tool {tool_type} requires some parameters that were not "
f"provided: {missing_keys}"
)
tools.append(_get_llm_tool_func(llm=llm, **kwargs))
elif tool_type in _EXTRA_OPTIONAL_TOOLS:
_get_tool_func, extra_keys = _EXTRA_OPTIONAL_TOOLS[tool_type]
kwargs = {k: value for k, value in kwargs.items() if value}
tools.append(_get_tool_func(**kwargs))
else:
raise ValueError(f"Got unknown tool {tool_type}")
return tools
def build_prompt_template(prompt, tools):
"""Build PromptTemplate from ZeroShotPrompt"""
prefix = prompt["node"]["template"]["prefix"]["value"]
suffix = prompt["node"]["template"]["suffix"]["value"]
format_instructions = prompt["node"]["template"]["format_instructions"]["value"]
tool_strings = "\n".join(
[
f"{tool['data']['node']['name']}: {tool['data']['node']['description']}"
for tool in tools
]
)
tool_names = ", ".join([tool["data"]["node"]["name"] for tool in tools])
format_instructions = format_instructions.format(tool_names=tool_names)
value = "\n\n".join([prefix, tool_strings, format_instructions, suffix])
prompt["type"] = "PromptTemplate"
prompt["node"] = {
"template": {
"_type": "prompt",
"input_variables": {
"type": "str",
"required": True,
"placeholder": "",
"list": True,
"show": False,
"multiline": False,
},
"output_parser": {
"type": "BaseOutputParser",
"required": False,
"placeholder": "",
"list": False,
"show": False,
"multline": False,
"value": None,
},
"template": {
"type": "str",
"required": True,
"placeholder": "",
"list": False,
"show": True,
"multiline": True,
"value": value,
},
"template_format": {
"type": "str",
"required": False,
"placeholder": "",
"list": False,
"show": False,
"multline": False,
"value": "f-string",
},
"validate_template": {
"type": "bool",
"required": False,
"placeholder": "",
"list": False,
"show": False,
"multline": False,
"value": True,
},
},
"description": "Schema to represent a prompt for an LLM.",
"base_classes": ["BasePromptTemplate"],
}
return prompt
| [] |
2024-01-10 | eamonboyle/recipe-scraper | categorise-recipes.py | import openai
import psycopg2
import postgresconfig
import openaiconfig
# Set your API key
api_key = openaiconfig.openaikey
# Initialize the OpenAI API client
openai.api_key = api_key
connection = psycopg2.connect(
database=postgresconfig.database,
user=postgresconfig.user,
password=postgresconfig.password,
host=postgresconfig.host,
port=postgresconfig.port,
)
cursor = connection.cursor()
print("Connected to the database!")
# get all recipes
cursor.execute(
"SELECT ID, recipe_name, description FROM recipes WHERE categorized = false ORDER BY id ASC LIMIT 100;"
)
recipes = cursor.fetchall()
# create a list of dictionaries
recipe_list = []
for recipe in recipes:
recipe_dict = {"id": recipe[0], "name": recipe[1], "description": recipe[2]}
recipe_list.append(recipe_dict)
# get all categories
cursor.execute("SELECT id, category FROM recipe_categories;")
categories = cursor.fetchall()
# create a list of dictionaries
category_list = []
for category in categories:
category_dict = {"id": str(category[0]), "name": category[1]}
category_list.append(category_dict)
formatted_categories = [
f"ID: {category['id']} - Name: {category['name']}" for category in category_list
]
category_string = ", \n".join(formatted_categories)
print("Recipes and categories fetched from the database!")
# Function to categorize a recipe
def categorize_recipe(recipe):
try:
# Call the OpenAI API to categorize the recipe based on the prompt
response = openai.ChatCompletion.create(
model="gpt-3.5-turbo",
messages=[
{
"role": "system",
"content": f'You are a helpful assistant that is great at categorizing recipes. You know these categories, they are in the format <ID: "NAME">.\n\nWhen you are provided with a recipe name and description, you will choose one or more categories from the list. If the recipe name includes vegan you can assume the Vegan category will be chosen. Output them into a postgres query to insert into a table called recipes_to_categories linking table, for example:\n\nINSERT INTO recipes_to_categories (recipe_id, category_id)\nVALUES\n (61, 10), -- Spinach falafel & hummus bowl (Category: Vegan)\n (61, 22), -- Spinach falafel & hummus bowl (Category: Quick and Easy)\n (61, 27); -- Spinach falafel & hummus bowl (Category: Slow Cooker)\nDo not leave trialing commas\n\nCategory List:\n {category_string}",',
},
{
"role": "user",
"content": f"ID: {recipe['id']}, Recipe Name: {recipe['name']}, Description: {recipe['description']}",
},
],
temperature=1,
max_tokens=256,
top_p=1,
frequency_penalty=0,
presence_penalty=0,
)
insert_query = response.choices[0].message.content.strip()
print(insert_query)
# insert into the database
cursor.execute(insert_query)
# update the categorized column to true
cursor.execute(
f"UPDATE recipes SET categorized = true WHERE id = {str(recipe['id'])};"
)
# commit the changes
connection.commit()
except Exception as e:
print(e)
connection.rollback()
categorize_recipe(recipe)
# categorize_recipe(recipe_list[0])
# Loop through your recipes and categorize them
for recipe in recipe_list:
print('Categorizing recipe: "' + recipe["name"] + '"')
categorize_recipe(recipe)
| [
"You are a helpful assistant that is great at categorizing recipes. You know these categories, they are in the format <ID: \"NAME\">.\n\nWhen you are provided with a recipe name and description, you will choose one or more categories from the list. If the recipe name includes vegan you can assume the Vegan category will be chosen. Output them into a postgres query to insert into a table called recipes_to_categories linking table, for example:\n\nINSERT INTO recipes_to_categories (recipe_id, category_id)\nVALUES\n (61, 10), -- Spinach falafel & hummus bowl (Category: Vegan)\n (61, 22), -- Spinach falafel & hummus bowl (Category: Quick and Easy)\n (61, 27); -- Spinach falafel & hummus bowl (Category: Slow Cooker)\nDo not leave trialing commas\n\nCategory List:\n PLACEHOLDER\",",
"ID: PLACEHOLDER, Recipe Name: PLACEHOLDER, Description: PLACEHOLDER"
] |
2024-01-10 | SuReLI/laber | PyBullet_experiments~stable-baselines3~stable_baselines3~sac_ger~sac_ger.py | from typing import Any, Dict, List, Optional, Tuple, Type, Union
import gym
import numpy as np
import torch as th
from torch.nn import functional as F
from stable_baselines3.common import logger
from stable_baselines3.common.noise import ActionNoise
from stable_baselines3.common.off_policy_algorithm_2 import OffPolicyAlgorithm
from stable_baselines3.common.type_aliases import GymEnv, MaybeCallback, Schedule
from stable_baselines3.common.utils import polyak_update
from stable_baselines3.sac.policies import SACPolicy
class SAC_GER(OffPolicyAlgorithm):
"""
Soft Actor-Critic (SAC)
Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor,
This implementation borrows code from original implementation (https://github.com/haarnoja/sac)
from OpenAI Spinning Up (https://github.com/openai/spinningup), from the softlearning repo
(https://github.com/rail-berkeley/softlearning/)
and from Stable Baselines (https://github.com/hill-a/stable-baselines)
Paper: https://arxiv.org/abs/1801.01290
Introduction to SAC: https://spinningup.openai.com/en/latest/algorithms/sac.html
Note: we use double q target and not value target as discussed
in https://github.com/hill-a/stable-baselines/issues/270
:param policy: The policy model to use (MlpPolicy, CnnPolicy, ...)
:param env: The environment to learn from (if registered in Gym, can be str)
:param learning_rate: learning rate for adam optimizer,
the same learning rate will be used for all networks (Q-Values, Actor and Value function)
it can be a function of the current progress remaining (from 1 to 0)
:param buffer_size: size of the replay buffer
:param learning_starts: how many steps of the model to collect transitions for before learning starts
:param batch_size: Minibatch size for each gradient update
:param tau: the soft update coefficient ("Polyak update", between 0 and 1)
:param gamma: the discount factor
:param train_freq: Update the model every ``train_freq`` steps. Set to `-1` to disable.
:param gradient_steps: How many gradient steps to do after each rollout
(see ``train_freq`` and ``n_episodes_rollout``)
Set to ``-1`` means to do as many gradient steps as steps done in the environment
during the rollout.
:param n_episodes_rollout: Update the model every ``n_episodes_rollout`` episodes.
Note that this cannot be used at the same time as ``train_freq``. Set to `-1` to disable.
:param action_noise: the action noise type (None by default), this can help
for hard exploration problem. Cf common.noise for the different action noise type.
:param optimize_memory_usage: Enable a memory efficient variant of the replay buffer
at a cost of more complexity.
See https://github.com/DLR-RM/stable-baselines3/issues/37#issuecomment-637501195
:param ent_coef: Entropy regularization coefficient. (Equivalent to
inverse of reward scale in the original SAC paper.) Controlling exploration/exploitation trade-off.
Set it to 'auto' to learn it automatically (and 'auto_0.1' for using 0.1 as initial value)
:param target_update_interval: update the target network every ``target_network_update_freq``
gradient steps.
:param target_entropy: target entropy when learning ``ent_coef`` (``ent_coef = 'auto'``)
:param use_sde: Whether to use generalized State Dependent Exploration (gSDE)
instead of action noise exploration (default: False)
:param sde_sample_freq: Sample a new noise matrix every n steps when using gSDE
Default: -1 (only sample at the beginning of the rollout)
:param use_sde_at_warmup: Whether to use gSDE instead of uniform sampling
during the warm up phase (before learning starts)
:param create_eval_env: Whether to create a second environment that will be
used for evaluating the agent periodically. (Only available when passing string for the environment)
:param policy_kwargs: additional arguments to be passed to the policy on creation
:param verbose: the verbosity level: 0 no output, 1 info, 2 debug
:param seed: Seed for the pseudo random generators
:param device: Device (cpu, cuda, ...) on which the code should be run.
Setting it to auto, the code will be run on the GPU if possible.
:param _init_setup_model: Whether or not to build the network at the creation of the instance
"""
def __init__(
self,
policy: Union[str, Type[SACPolicy]],
env: Union[GymEnv, str],
learning_rate: Union[float, Schedule] = 3e-4,
buffer_size: int = int(1e6),
learning_starts: int = 100,
batch_size: int = 256,
tau: float = 0.005,
gamma: float = 0.99,
train_freq: int = 1,
gradient_steps: int = 1,
n_episodes_rollout: int = -1,
action_noise: Optional[ActionNoise] = None,
optimize_memory_usage: bool = False,
ent_coef: Union[str, float] = "auto",
target_update_interval: int = 1,
target_entropy: Union[str, float] = "auto",
use_sde: bool = False,
sde_sample_freq: int = -1,
use_sde_at_warmup: bool = False,
tensorboard_log: Optional[str] = None,
create_eval_env: bool = False,
policy_kwargs: Dict[str, Any] = None,
verbose: int = 0,
seed: Optional[int] = None,
device: Union[th.device, str] = "auto",
_init_setup_model: bool = True,
):
super(SAC_GER, self).__init__(
policy,
env,
SACPolicy,
learning_rate,
buffer_size,
learning_starts,
batch_size,
tau,
gamma,
train_freq,
gradient_steps,
n_episodes_rollout,
action_noise,
policy_kwargs=policy_kwargs,
tensorboard_log=tensorboard_log,
verbose=verbose,
device=device,
create_eval_env=create_eval_env,
seed=seed,
use_sde=use_sde,
sde_sample_freq=sde_sample_freq,
use_sde_at_warmup=use_sde_at_warmup,
optimize_memory_usage=optimize_memory_usage,
supported_action_spaces=(gym.spaces.Box),
)
self.target_entropy = target_entropy
self.log_ent_coef = None # type: Optional[th.Tensor]
# Entropy coefficient / Entropy temperature
# Inverse of the reward scale
self.ent_coef = ent_coef
self.target_update_interval = target_update_interval
self.ent_coef_optimizer = None
if _init_setup_model:
self._setup_model()
def _setup_model(self) -> None:
super(SAC_GER, self)._setup_model()
self._create_aliases()
# Target entropy is used when learning the entropy coefficient
if self.target_entropy == "auto":
# automatically set target entropy if needed
self.target_entropy = -np.prod(self.env.action_space.shape).astype(np.float32)
else:
# Force conversion
# this will also throw an error for unexpected string
self.target_entropy = float(self.target_entropy)
# The entropy coefficient or entropy can be learned automatically
# see Automating Entropy Adjustment for Maximum Entropy RL section
# of https://arxiv.org/abs/1812.05905
if isinstance(self.ent_coef, str) and self.ent_coef.startswith("auto"):
# Default initial value of ent_coef when learned
init_value = 1.0
if "_" in self.ent_coef:
init_value = float(self.ent_coef.split("_")[1])
assert init_value > 0.0, "The initial value of ent_coef must be greater than 0"
# Note: we optimize the log of the entropy coeff which is slightly different from the paper
# as discussed in https://github.com/rail-berkeley/softlearning/issues/37
self.log_ent_coef = th.log(th.ones(1, device=self.device) * init_value).requires_grad_(True)
self.ent_coef_optimizer = th.optim.Adam([self.log_ent_coef], lr=self.lr_schedule(1))
else:
# Force conversion to float
# this will throw an error if a malformed string (different from 'auto')
# is passed
self.ent_coef_tensor = th.tensor(float(self.ent_coef)).to(self.device)
def _create_aliases(self) -> None:
self.actor = self.policy.actor
self.critic = self.policy.critic
self.critic_target = self.policy.critic_target
def train(self, gradient_steps: int, batch_size: int = 64) -> None:
# Update optimizers learning rate
optimizers = [self.actor.optimizer, self.critic.optimizer]
if self.ent_coef_optimizer is not None:
optimizers += [self.ent_coef_optimizer]
# Update learning rate according to lr schedule
self._update_learning_rate(optimizers)
ent_coef_losses, ent_coefs = [], []
actor_losses, critic_losses = [], []
for gradient_step in range(gradient_steps):
# Sample replay buffer
res = self.replay_buffer.sample(batch_size, env=self._vec_normalize_env)
replay_data1, batch_inds1, probs1, replay_data2, batch_inds2, probs2, replay_data0 = res
# We consider two list of priorities. One for each critic.
# Note that the transitions for the update of the actor has been sampled uniformly.
# We need to sample because `log_std` may have changed between two gradient steps
if self.use_sde:
self.actor.reset_noise()
# Action by the current actor for the sampled state
actions_pi, log_prob = self.actor.action_log_prob(replay_data0.observations)
log_prob = log_prob.reshape(-1, 1)
ent_coef_loss = None
if self.ent_coef_optimizer is not None:
# Important: detach the variable from the graph
# so we don't change it with other losses
# see https://github.com/rail-berkeley/softlearning/issues/60
ent_coef = th.exp(self.log_ent_coef.detach())
ent_coef_loss = -(self.log_ent_coef * (log_prob + self.target_entropy).detach()).mean()
ent_coef_losses.append(ent_coef_loss.item())
else:
ent_coef = self.ent_coef_tensor
ent_coefs.append(ent_coef.item())
# Optimize entropy coefficient, also called
# entropy temperature or alpha in the paper
if ent_coef_loss is not None:
self.ent_coef_optimizer.zero_grad()
ent_coef_loss.backward()
self.ent_coef_optimizer.step()
with th.no_grad():
# Select action according to policy
next_actions1, next_log_prob1 = self.actor.action_log_prob(replay_data1.next_observations)
# Compute the next Q values: min over all critics targets
next_q_values1 = th.cat(self.critic_target(replay_data1.next_observations, next_actions1), dim=1)
next_q_values1, _ = th.min(next_q_values1, dim=1, keepdim=True)
# add entropy term
next_q_values1 = next_q_values1 - ent_coef * next_log_prob1.reshape(-1, 1)
# td error + entropy term
target_q_values1 = replay_data1.rewards + (1 - replay_data1.dones) * self.gamma * next_q_values1
with th.no_grad():
# Select action according to policy
next_actions2, next_log_prob2 = self.actor.action_log_prob(replay_data2.next_observations)
# Compute the next Q values: min over all critics targets
next_q_values2 = th.cat(self.critic_target(replay_data2.next_observations, next_actions2), dim=1)
next_q_values2, _ = th.min(next_q_values2, dim=1, keepdim=True)
# add entropy term
next_q_values2 = next_q_values2 - ent_coef * next_log_prob2.reshape(-1, 1)
# td error + entropy term
target_q_values2 = replay_data2.rewards + (1 - replay_data2.dones) * self.gamma * next_q_values2
# Get current Q-values estimates for each critic network
# using action from the replay buffer
current_q_values1 = self.critic(replay_data1.observations, replay_data1.actions)
current_q_values2 = self.critic(replay_data2.observations, replay_data2.actions)
loss_weights1 = 1.0 / probs1
loss_weights2 = 1.0 / probs2
loss_weights1 = loss_weights1 / max(loss_weights1)
loss_weights2 = loss_weights2 / max(loss_weights2)
loss_weights1 = th.from_numpy(loss_weights1).unsqueeze(1)
loss_weights2 = th.from_numpy(loss_weights2).unsqueeze(1)
loss1 = F.mse_loss(current_q_values1[0], target_q_values1, reduction='none').cpu() * loss_weights1
loss2 = F.mse_loss(current_q_values2[1], target_q_values2, reduction='none').cpu() * loss_weights2
loss1 = loss1.mean()
loss2 = loss2.mean()
td_errors1 = (current_q_values1[0] - target_q_values1).abs().squeeze(1).detach().cpu().numpy()
td_errors2 = (current_q_values2[1] - target_q_values2).abs().squeeze(1).detach().cpu().numpy()
# Optimize the critics
self.critic.optimizer.zero_grad()
# Compute per-sample gradient norms for critic 1
loss1.backward(retain_graph=True)
grads = [th.autograd.grad(current_q_values1[0][batch], self.critic.parameters(), retain_graph=True, allow_unused=True) for batch in range(batch_size)]
grad_norms = np.zeros(batch_size)
for i in range(batch_size):
grads_i = []
for t in grads[i]:
if t is not None:
grads_i.append(t.cpu().numpy().flatten())
grads_i = np.array(grads_i)
l_i = np.concatenate(grads_i)
grad_norms[i] = np.linalg.norm(l_i)
grad_norms = grad_norms * td_errors1 * 2
self.replay_buffer.update_priorities1(batch_inds1, grad_norms)
# Compute per-sample gradient norms for critic 2
loss2.backward(retain_graph=True)
grads = [th.autograd.grad(current_q_values2[1][batch], self.critic.parameters(), retain_graph=True, allow_unused=True) for batch in range(batch_size)]
grad_norms = np.zeros(batch_size)
for i in range(batch_size):
grads_i = []
for t in grads[i]:
if t is not None:
grads_i.append(t.cpu().numpy().flatten())
grads_i = np.array(grads_i)
l_i = np.concatenate(grads_i)
grad_norms[i] = np.linalg.norm(l_i)
grad_norms = grad_norms * td_errors2 * 2
self.replay_buffer.update_priorities2(batch_inds2, grad_norms)
self.critic.optimizer.step()
critic_loss = 0.5 * (loss1 + loss2)
critic_losses.append(critic_loss.item())
# Compute actor loss
# Alternative: actor_loss = th.mean(log_prob - qf1_pi)
# Mean over all critic networks
q_values_pi = th.cat(self.critic.forward(replay_data0.observations, actions_pi), dim=1)
min_qf_pi, _ = th.min(q_values_pi, dim=1, keepdim=True)
actor_loss = (ent_coef * log_prob - min_qf_pi).mean()
actor_losses.append(actor_loss.item())
# Optimize the actor
self.actor.optimizer.zero_grad()
actor_loss.backward()
self.actor.optimizer.step()
# Update target networks
if gradient_step % self.target_update_interval == 0:
polyak_update(self.critic.parameters(), self.critic_target.parameters(), self.tau)
self._n_updates += gradient_steps
logger.record("train/n_updates", self._n_updates, exclude="tensorboard")
logger.record("train/ent_coef", np.mean(ent_coefs))
logger.record("train/actor_loss", np.mean(actor_losses))
logger.record("train/critic_loss", np.mean(critic_losses))
if len(ent_coef_losses) > 0:
logger.record("train/ent_coef_loss", np.mean(ent_coef_losses))
def learn(
self,
total_timesteps: int,
callback: MaybeCallback = None,
log_interval: int = 4,
eval_env: Optional[GymEnv] = None,
eval_freq: int = -1,
n_eval_episodes: int = 5,
tb_log_name: str = "SAC_GER",
eval_log_path: Optional[str] = None,
reset_num_timesteps: bool = True,
) -> OffPolicyAlgorithm:
return super(SAC_GER, self).learn(
total_timesteps=total_timesteps,
callback=callback,
log_interval=log_interval,
eval_env=eval_env,
eval_freq=eval_freq,
n_eval_episodes=n_eval_episodes,
tb_log_name=tb_log_name,
eval_log_path=eval_log_path,
reset_num_timesteps=reset_num_timesteps,
)
def _excluded_save_params(self) -> List[str]:
return super(SAC_GER, self)._excluded_save_params() + ["actor", "critic", "critic_target"]
def _get_torch_save_params(self) -> Tuple[List[str], List[str]]:
state_dicts = ["policy", "actor.optimizer", "critic.optimizer"]
saved_pytorch_variables = ["log_ent_coef"]
if self.ent_coef_optimizer is not None:
state_dicts.append("ent_coef_optimizer")
else:
saved_pytorch_variables.append("ent_coef_tensor")
return state_dicts, saved_pytorch_variables
| [] |
2024-01-10 | SuReLI/laber | PyBullet_experiments~stable-baselines3~stable_baselines3~sac_per~sac_per.py | from typing import Any, Dict, List, Optional, Tuple, Type, Union
import gym
import numpy as np
import torch as th
from torch.nn import functional as F
from stable_baselines3.common import logger
from stable_baselines3.common.noise import ActionNoise
from stable_baselines3.common.off_policy_algorithm_2 import OffPolicyAlgorithm
from stable_baselines3.common.type_aliases import GymEnv, MaybeCallback, Schedule
from stable_baselines3.common.utils import polyak_update
from stable_baselines3.sac.policies import SACPolicy
class SAC_PER(OffPolicyAlgorithm):
"""
Soft Actor-Critic (SAC)
Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor,
This implementation borrows code from original implementation (https://github.com/haarnoja/sac)
from OpenAI Spinning Up (https://github.com/openai/spinningup), from the softlearning repo
(https://github.com/rail-berkeley/softlearning/)
and from Stable Baselines (https://github.com/hill-a/stable-baselines)
Paper: https://arxiv.org/abs/1801.01290
Introduction to SAC: https://spinningup.openai.com/en/latest/algorithms/sac.html
Note: we use double q target and not value target as discussed
in https://github.com/hill-a/stable-baselines/issues/270
:param policy: The policy model to use (MlpPolicy, CnnPolicy, ...)
:param env: The environment to learn from (if registered in Gym, can be str)
:param learning_rate: learning rate for adam optimizer,
the same learning rate will be used for all networks (Q-Values, Actor and Value function)
it can be a function of the current progress remaining (from 1 to 0)
:param buffer_size: size of the replay buffer
:param learning_starts: how many steps of the model to collect transitions for before learning starts
:param batch_size: Minibatch size for each gradient update
:param tau: the soft update coefficient ("Polyak update", between 0 and 1)
:param gamma: the discount factor
:param train_freq: Update the model every ``train_freq`` steps. Set to `-1` to disable.
:param gradient_steps: How many gradient steps to do after each rollout
(see ``train_freq`` and ``n_episodes_rollout``)
Set to ``-1`` means to do as many gradient steps as steps done in the environment
during the rollout.
:param n_episodes_rollout: Update the model every ``n_episodes_rollout`` episodes.
Note that this cannot be used at the same time as ``train_freq``. Set to `-1` to disable.
:param action_noise: the action noise type (None by default), this can help
for hard exploration problem. Cf common.noise for the different action noise type.
:param optimize_memory_usage: Enable a memory efficient variant of the replay buffer
at a cost of more complexity.
See https://github.com/DLR-RM/stable-baselines3/issues/37#issuecomment-637501195
:param ent_coef: Entropy regularization coefficient. (Equivalent to
inverse of reward scale in the original SAC paper.) Controlling exploration/exploitation trade-off.
Set it to 'auto' to learn it automatically (and 'auto_0.1' for using 0.1 as initial value)
:param target_update_interval: update the target network every ``target_network_update_freq``
gradient steps.
:param target_entropy: target entropy when learning ``ent_coef`` (``ent_coef = 'auto'``)
:param use_sde: Whether to use generalized State Dependent Exploration (gSDE)
instead of action noise exploration (default: False)
:param sde_sample_freq: Sample a new noise matrix every n steps when using gSDE
Default: -1 (only sample at the beginning of the rollout)
:param use_sde_at_warmup: Whether to use gSDE instead of uniform sampling
during the warm up phase (before learning starts)
:param create_eval_env: Whether to create a second environment that will be
used for evaluating the agent periodically. (Only available when passing string for the environment)
:param policy_kwargs: additional arguments to be passed to the policy on creation
:param verbose: the verbosity level: 0 no output, 1 info, 2 debug
:param seed: Seed for the pseudo random generators
:param device: Device (cpu, cuda, ...) on which the code should be run.
Setting it to auto, the code will be run on the GPU if possible.
:param _init_setup_model: Whether or not to build the network at the creation of the instance
"""
def __init__(
self,
policy: Union[str, Type[SACPolicy]],
env: Union[GymEnv, str],
learning_rate: Union[float, Schedule] = 3e-4,
buffer_size: int = int(1e6),
learning_starts: int = 100,
batch_size: int = 256,
tau: float = 0.005,
gamma: float = 0.99,
train_freq: int = 1,
gradient_steps: int = 1,
n_episodes_rollout: int = -1,
action_noise: Optional[ActionNoise] = None,
optimize_memory_usage: bool = False,
ent_coef: Union[str, float] = "auto",
target_update_interval: int = 1,
target_entropy: Union[str, float] = "auto",
use_sde: bool = False,
sde_sample_freq: int = -1,
use_sde_at_warmup: bool = False,
tensorboard_log: Optional[str] = None,
create_eval_env: bool = False,
policy_kwargs: Dict[str, Any] = None,
verbose: int = 0,
seed: Optional[int] = None,
device: Union[th.device, str] = "auto",
_init_setup_model: bool = True,
):
super(SAC_PER, self).__init__(
policy,
env,
SACPolicy,
learning_rate,
buffer_size,
learning_starts,
batch_size,
tau,
gamma,
train_freq,
gradient_steps,
n_episodes_rollout,
action_noise,
policy_kwargs=policy_kwargs,
tensorboard_log=tensorboard_log,
verbose=verbose,
device=device,
create_eval_env=create_eval_env,
seed=seed,
use_sde=use_sde,
sde_sample_freq=sde_sample_freq,
use_sde_at_warmup=use_sde_at_warmup,
optimize_memory_usage=optimize_memory_usage,
supported_action_spaces=(gym.spaces.Box),
)
self.target_entropy = target_entropy
self.log_ent_coef = None # type: Optional[th.Tensor]
# Entropy coefficient / Entropy temperature
# Inverse of the reward scale
self.ent_coef = ent_coef
self.target_update_interval = target_update_interval
self.ent_coef_optimizer = None
if _init_setup_model:
self._setup_model()
def _setup_model(self) -> None:
super(SAC_PER, self)._setup_model()
self._create_aliases()
# Target entropy is used when learning the entropy coefficient
if self.target_entropy == "auto":
# automatically set target entropy if needed
self.target_entropy = -np.prod(self.env.action_space.shape).astype(np.float32)
else:
# Force conversion
# this will also throw an error for unexpected string
self.target_entropy = float(self.target_entropy)
# The entropy coefficient or entropy can be learned automatically
# see Automating Entropy Adjustment for Maximum Entropy RL section
# of https://arxiv.org/abs/1812.05905
if isinstance(self.ent_coef, str) and self.ent_coef.startswith("auto"):
# Default initial value of ent_coef when learned
init_value = 1.0
if "_" in self.ent_coef:
init_value = float(self.ent_coef.split("_")[1])
assert init_value > 0.0, "The initial value of ent_coef must be greater than 0"
# Note: we optimize the log of the entropy coeff which is slightly different from the paper
# as discussed in https://github.com/rail-berkeley/softlearning/issues/37
self.log_ent_coef = th.log(th.ones(1, device=self.device) * init_value).requires_grad_(True)
self.ent_coef_optimizer = th.optim.Adam([self.log_ent_coef], lr=self.lr_schedule(1))
else:
# Force conversion to float
# this will throw an error if a malformed string (different from 'auto')
# is passed
self.ent_coef_tensor = th.tensor(float(self.ent_coef)).to(self.device)
def _create_aliases(self) -> None:
self.actor = self.policy.actor
self.critic = self.policy.critic
self.critic_target = self.policy.critic_target
def train(self, gradient_steps: int, batch_size: int = 64) -> None:
# Update optimizers learning rate
optimizers = [self.actor.optimizer, self.critic.optimizer]
if self.ent_coef_optimizer is not None:
optimizers += [self.ent_coef_optimizer]
# Update learning rate according to lr schedule
self._update_learning_rate(optimizers)
ent_coef_losses, ent_coefs = [], []
actor_losses, critic_losses = [], []
for gradient_step in range(gradient_steps):
# Sample replay buffer
res = self.replay_buffer.sample(batch_size, env=self._vec_normalize_env)
replay_data1, batch_inds1, probs1, replay_data2, batch_inds2, probs2, replay_data0 = res
# We consider two list of priorities. One for each critic.
# Note that the transitions for the update of the actor has been sampled uniformly.
# We need to sample because `log_std` may have changed between two gradient steps
if self.use_sde:
self.actor.reset_noise()
# Action by the current actor for the sampled state
actions_pi, log_prob = self.actor.action_log_prob(replay_data0.observations)
log_prob = log_prob.reshape(-1, 1)
ent_coef_loss = None
if self.ent_coef_optimizer is not None:
# Important: detach the variable from the graph
# so we don't change it with other losses
# see https://github.com/rail-berkeley/softlearning/issues/60
ent_coef = th.exp(self.log_ent_coef.detach())
ent_coef_loss = -(self.log_ent_coef * (log_prob + self.target_entropy).detach()).mean()
ent_coef_losses.append(ent_coef_loss.item())
else:
ent_coef = self.ent_coef_tensor
ent_coefs.append(ent_coef.item())
# Optimize entropy coefficient, also called
# entropy temperature or alpha in the paper
if ent_coef_loss is not None:
self.ent_coef_optimizer.zero_grad()
ent_coef_loss.backward()
self.ent_coef_optimizer.step()
with th.no_grad():
# Select action according to policy
next_actions1, next_log_prob1 = self.actor.action_log_prob(replay_data1.next_observations)
# Compute the next Q values: min over all critics targets
next_q_values1 = th.cat(self.critic_target(replay_data1.next_observations, next_actions1), dim=1)
next_q_values1, _ = th.min(next_q_values1, dim=1, keepdim=True)
# add entropy term
next_q_values1 = next_q_values1 - ent_coef * next_log_prob1.reshape(-1, 1)
# td error + entropy term
target_q_values1 = replay_data1.rewards + (1 - replay_data1.dones) * self.gamma * next_q_values1
with th.no_grad():
# Select action according to policy
next_actions2, next_log_prob2 = self.actor.action_log_prob(replay_data2.next_observations)
# Compute the next Q values: min over all critics targets
next_q_values2 = th.cat(self.critic_target(replay_data2.next_observations, next_actions2), dim=1)
next_q_values2, _ = th.min(next_q_values2, dim=1, keepdim=True)
# add entropy term
next_q_values2 = next_q_values2 - ent_coef * next_log_prob2.reshape(-1, 1)
# td error + entropy term
target_q_values2 = replay_data2.rewards + (1 - replay_data2.dones) * self.gamma * next_q_values2
# Get current Q-values estimates for each critic network
# using action from the replay buffer
current_q_values1 = self.critic(replay_data1.observations, replay_data1.actions)
current_q_values2 = self.critic(replay_data2.observations, replay_data2.actions)
td_errors1 = F.l1_loss(current_q_values1[0], target_q_values1, reduction='none')
td_errors2 = F.l1_loss(current_q_values2[1], target_q_values2, reduction='none')
td_errors1 = td_errors1.detach().squeeze().cpu().numpy()
td_errors2 = td_errors2.detach().squeeze().cpu().numpy()
self.replay_buffer.update_priorities1(batch_inds1, (td_errors1 + 1e-10)**0.6)
self.replay_buffer.update_priorities2(batch_inds2, (td_errors2 + 1e-10)**0.6)
loss_weights1 = 1.0 / probs1
loss_weights2 = 1.0 / probs2
loss_weights1 = loss_weights1 / max(loss_weights1)
loss_weights2 = loss_weights2 / max(loss_weights2)
loss_weights1 = th.from_numpy(loss_weights1).unsqueeze(1)
loss_weights2 = th.from_numpy(loss_weights2).unsqueeze(1)
loss1 = F.mse_loss(current_q_values1[0], target_q_values1, reduction='none').cpu() * loss_weights1
loss2 = F.mse_loss(current_q_values2[1], target_q_values2, reduction='none').cpu() * loss_weights2
loss1 = loss1.mean()
loss2 = loss2.mean()
# Optimize the critics
self.critic.optimizer.zero_grad()
loss1.backward()
loss2.backward()
self.critic.optimizer.step()
critic_loss = 0.5 * (loss1 + loss2)
critic_losses.append(critic_loss.item())
# Compute actor loss
# Alternative: actor_loss = th.mean(log_prob - qf1_pi)
# Mean over all critic networks
q_values_pi = th.cat(self.critic.forward(replay_data0.observations, actions_pi), dim=1)
min_qf_pi, _ = th.min(q_values_pi, dim=1, keepdim=True)
actor_loss = (ent_coef * log_prob - min_qf_pi).mean()
actor_losses.append(actor_loss.item())
# Optimize the actor
self.actor.optimizer.zero_grad()
actor_loss.backward()
self.actor.optimizer.step()
# Update target networks
if gradient_step % self.target_update_interval == 0:
polyak_update(self.critic.parameters(), self.critic_target.parameters(), self.tau)
self._n_updates += gradient_steps
logger.record("train/n_updates", self._n_updates, exclude="tensorboard")
logger.record("train/ent_coef", np.mean(ent_coefs))
logger.record("train/actor_loss", np.mean(actor_losses))
logger.record("train/critic_loss", np.mean(critic_losses))
if len(ent_coef_losses) > 0:
logger.record("train/ent_coef_loss", np.mean(ent_coef_losses))
def learn(
self,
total_timesteps: int,
callback: MaybeCallback = None,
log_interval: int = 4,
eval_env: Optional[GymEnv] = None,
eval_freq: int = -1,
n_eval_episodes: int = 5,
tb_log_name: str = "SAC_PER",
eval_log_path: Optional[str] = None,
reset_num_timesteps: bool = True,
) -> OffPolicyAlgorithm:
return super(SAC_PER, self).learn(
total_timesteps=total_timesteps,
callback=callback,
log_interval=log_interval,
eval_env=eval_env,
eval_freq=eval_freq,
n_eval_episodes=n_eval_episodes,
tb_log_name=tb_log_name,
eval_log_path=eval_log_path,
reset_num_timesteps=reset_num_timesteps,
)
def _excluded_save_params(self) -> List[str]:
return super(SAC_PER, self)._excluded_save_params() + ["actor", "critic", "critic_target"]
def _get_torch_save_params(self) -> Tuple[List[str], List[str]]:
state_dicts = ["policy", "actor.optimizer", "critic.optimizer"]
saved_pytorch_variables = ["log_ent_coef"]
if self.ent_coef_optimizer is not None:
state_dicts.append("ent_coef_optimizer")
else:
saved_pytorch_variables.append("ent_coef_tensor")
return state_dicts, saved_pytorch_variables
| [] |
2024-01-10 | SuReLI/laber | PyBullet_experiments~stable-baselines3~stable_baselines3~sac_laber~sac_laber.py | from typing import Any, Dict, List, Optional, Tuple, Type, Union
import gym
import numpy as np
import torch as th
from torch.nn import functional as F
from stable_baselines3.common import logger
from stable_baselines3.common.noise import ActionNoise
from stable_baselines3.common.off_policy_algorithm import OffPolicyAlgorithm
from stable_baselines3.common.type_aliases import GymEnv, MaybeCallback, Schedule
from stable_baselines3.common.utils import polyak_update
from stable_baselines3.sac_laber.policies import SACPolicy
class SAC_LABER(OffPolicyAlgorithm):
"""
Soft Actor-Critic (SAC)
Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor,
This implementation borrows code from original implementation (https://github.com/haarnoja/sac)
from OpenAI Spinning Up (https://github.com/openai/spinningup), from the softlearning repo
(https://github.com/rail-berkeley/softlearning/)
and from Stable Baselines (https://github.com/hill-a/stable-baselines)
Paper: https://arxiv.org/abs/1801.01290
Introduction to SAC: https://spinningup.openai.com/en/latest/algorithms/sac.html
Note: we use double q target and not value target as discussed
in https://github.com/hill-a/stable-baselines/issues/270
:param policy: The policy model to use (MlpPolicy, CnnPolicy, ...)
:param env: The environment to learn from (if registered in Gym, can be str)
:param learning_rate: learning rate for adam optimizer,
the same learning rate will be used for all networks (Q-Values, Actor and Value function)
it can be a function of the current progress remaining (from 1 to 0)
:param buffer_size: size of the replay buffer
:param learning_starts: how many steps of the model to collect transitions for before learning starts
:param batch_size: Minibatch size for each gradient update
:param tau: the soft update coefficient ("Polyak update", between 0 and 1)
:param gamma: the discount factor
:param train_freq: Update the model every ``train_freq`` steps. Set to `-1` to disable.
:param gradient_steps: How many gradient steps to do after each rollout
(see ``train_freq`` and ``n_episodes_rollout``)
Set to ``-1`` means to do as many gradient steps as steps done in the environment
during the rollout.
:param n_episodes_rollout: Update the model every ``n_episodes_rollout`` episodes.
Note that this cannot be used at the same time as ``train_freq``. Set to `-1` to disable.
:param action_noise: the action noise type (None by default), this can help
for hard exploration problem. Cf common.noise for the different action noise type.
:param optimize_memory_usage: Enable a memory efficient variant of the replay buffer
at a cost of more complexity.
See https://github.com/DLR-RM/stable-baselines3/issues/37#issuecomment-637501195
:param ent_coef: Entropy regularization coefficient. (Equivalent to
inverse of reward scale in the original SAC paper.) Controlling exploration/exploitation trade-off.
Set it to 'auto' to learn it automatically (and 'auto_0.1' for using 0.1 as initial value)
:param target_update_interval: update the target network every ``target_network_update_freq``
gradient steps.
:param target_entropy: target entropy when learning ``ent_coef`` (``ent_coef = 'auto'``)
:param use_sde: Whether to use generalized State Dependent Exploration (gSDE)
instead of action noise exploration (default: False)
:param sde_sample_freq: Sample a new noise matrix every n steps when using gSDE
Default: -1 (only sample at the beginning of the rollout)
:param use_sde_at_warmup: Whether to use gSDE instead of uniform sampling
during the warm up phase (before learning starts)
:param create_eval_env: Whether to create a second environment that will be
used for evaluating the agent periodically. (Only available when passing string for the environment)
:param policy_kwargs: additional arguments to be passed to the policy on creation
:param verbose: the verbosity level: 0 no output, 1 info, 2 debug
:param seed: Seed for the pseudo random generators
:param device: Device (cpu, cuda, ...) on which the code should be run.
Setting it to auto, the code will be run on the GPU if possible.
:param _init_setup_model: Whether or not to build the network at the creation of the instance
"""
def __init__(
self,
policy: Union[str, Type[SACPolicy]],
env: Union[GymEnv, str],
learning_rate: Union[float, Schedule] = 3e-4,
buffer_size: int = int(1e6),
learning_starts: int = 100,
batch_size: int = 1024,
tau: float = 0.005,
gamma: float = 0.99,
train_freq: int = 1,
gradient_steps: int = 1,
n_episodes_rollout: int = -1,
action_noise: Optional[ActionNoise] = None,
optimize_memory_usage: bool = False,
ent_coef: Union[str, float] = "auto",
target_update_interval: int = 1,
target_entropy: Union[str, float] = "auto",
use_sde: bool = False,
sde_sample_freq: int = -1,
use_sde_at_warmup: bool = False,
tensorboard_log: Optional[str] = None,
create_eval_env: bool = False,
policy_kwargs: Dict[str, Any] = None,
verbose: int = 0,
seed: Optional[int] = None,
device: Union[th.device, str] = "auto",
_init_setup_model: bool = True,
):
super(SAC_LABER, self).__init__(
policy,
env,
SACPolicy,
learning_rate,
buffer_size,
learning_starts,
batch_size,
tau,
gamma,
train_freq,
gradient_steps,
n_episodes_rollout,
action_noise,
policy_kwargs=policy_kwargs,
tensorboard_log=tensorboard_log,
verbose=verbose,
device=device,
create_eval_env=create_eval_env,
seed=seed,
use_sde=use_sde,
sde_sample_freq=sde_sample_freq,
use_sde_at_warmup=use_sde_at_warmup,
optimize_memory_usage=optimize_memory_usage,
supported_action_spaces=(gym.spaces.Box),
)
self.target_entropy = target_entropy
self.log_ent_coef = None # type: Optional[th.Tensor]
# Entropy coefficient / Entropy temperature
# Inverse of the reward scale
self.ent_coef = ent_coef
self.target_update_interval = target_update_interval
self.ent_coef_optimizer = None
if _init_setup_model:
self._setup_model()
def _setup_model(self) -> None:
super(SAC_LABER, self)._setup_model()
self._create_aliases()
# Target entropy is used when learning the entropy coefficient
if self.target_entropy == "auto":
# automatically set target entropy if needed
self.target_entropy = -np.prod(self.env.action_space.shape).astype(np.float32)
else:
# Force conversion
# this will also throw an error for unexpected string
self.target_entropy = float(self.target_entropy)
# The entropy coefficient or entropy can be learned automatically
# see Automating Entropy Adjustment for Maximum Entropy RL section
# of https://arxiv.org/abs/1812.05905
if isinstance(self.ent_coef, str) and self.ent_coef.startswith("auto"):
# Default initial value of ent_coef when learned
init_value = 1.0
if "_" in self.ent_coef:
init_value = float(self.ent_coef.split("_")[1])
assert init_value > 0.0, "The initial value of ent_coef must be greater than 0"
# Note: we optimize the log of the entropy coeff which is slightly different from the paper
# as discussed in https://github.com/rail-berkeley/softlearning/issues/37
self.log_ent_coef = th.log(th.ones(1, device=self.device) * init_value).requires_grad_(True)
self.ent_coef_optimizer = th.optim.Adam([self.log_ent_coef], lr=self.lr_schedule(1))
else:
# Force conversion to float
# this will throw an error if a malformed string (different from 'auto')
# is passed
self.ent_coef_tensor = th.tensor(float(self.ent_coef)).to(self.device)
def _create_aliases(self) -> None:
self.actor = self.policy.actor
self.critic = self.policy.critic
self.critic_target = self.policy.critic_target
def train(self, gradient_steps: int, batch_size: int = 64) -> None:
# Update optimizers learning rate
optimizers = [self.actor.optimizer, self.critic.optimizer]
if self.ent_coef_optimizer is not None:
optimizers += [self.ent_coef_optimizer]
# Update learning rate according to lr schedule
self._update_learning_rate(optimizers)
ent_coef_losses, ent_coefs = [], []
actor_losses, critic_losses = [], []
for gradient_step in range(gradient_steps):
# Sample replay buffer
replay_data = self.replay_buffer.sample(batch_size, env=self._vec_normalize_env)
states_big_batch = replay_data.observations
actions_big_batch = replay_data.actions
# We assume we sampled uniformly a large batch of size 4*B
bs = int(batch_size / 4)
# Select uniformly a mini-batch of transitions for the actor
indices_actor = np.random.randint(batch_size, size=bs)
states_selected_for_actor = states_big_batch[indices_actor]
# We need to sample because `log_std` may have changed between two gradient steps
if self.use_sde:
self.actor.reset_noise()
# Action by the current actor for the sampled state
actions_pi, log_prob = self.actor.action_log_prob(states_selected_for_actor)
log_prob = log_prob.reshape(-1, 1)
ent_coef_loss = None
if self.ent_coef_optimizer is not None:
# Important: detach the variable from the graph
# so we don't change it with other losses
# see https://github.com/rail-berkeley/softlearning/issues/60
ent_coef = th.exp(self.log_ent_coef.detach())
ent_coef_loss = -(self.log_ent_coef * (log_prob + self.target_entropy).detach()).mean()
ent_coef_losses.append(ent_coef_loss.item())
else:
ent_coef = self.ent_coef_tensor
ent_coefs.append(ent_coef.item())
# Optimize entropy coefficient, also called
# entropy temperature or alpha in the paper
if ent_coef_loss is not None:
self.ent_coef_optimizer.zero_grad()
ent_coef_loss.backward()
self.ent_coef_optimizer.step()
with th.no_grad():
# Select action according to policy
next_actions, next_log_prob = self.actor.action_log_prob(replay_data.next_observations)
# Compute the next Q values: min over all critics targets
next_q_values = th.cat(self.critic_target(replay_data.next_observations, next_actions), dim=1)
next_q_values, _ = th.min(next_q_values, dim=1, keepdim=True)
# add entropy term
next_q_values = next_q_values - ent_coef * next_log_prob.reshape(-1, 1)
# td error + entropy term
target_q_values_big_batch = replay_data.rewards + (1 - replay_data.dones) * self.gamma * next_q_values
# Compute TD errors for each critic
current_q_values_big_batch = self.critic(states_big_batch, actions_big_batch)
td_errors1 = F.l1_loss(current_q_values_big_batch[0], target_q_values_big_batch, reduction='none')
td_errors2 = F.l1_loss(current_q_values_big_batch[1], target_q_values_big_batch, reduction='none')
# LaBER for critic 1
td_errors1 = td_errors1.squeeze().cpu().numpy()
probs1 = td_errors1/td_errors1.sum()
indices1 = np.random.choice(np.arange(batch_size), bs, p=probs1)
td_errors_for_selected_indices1 = td_errors1[indices1]
states_selected1 = states_big_batch[indices1]
actions_selected1 = actions_big_batch[indices1]
target_q_values1 = target_q_values_big_batch[indices1]
loss_weights1 = (1.0 / td_errors_for_selected_indices1) * td_errors1.mean()
loss_weights1 = th.from_numpy(loss_weights1).unsqueeze(1)
# LaBER for critic 2
td_errors2 = td_errors2.squeeze().cpu().numpy()
probs2 = td_errors2/td_errors2.sum()
indices2 = np.random.choice(np.arange(batch_size), bs, p=probs2)
td_errors_for_selected_indices2 = td_errors2[indices2]
states_selected2 = states_big_batch[indices2]
actions_selected2 = actions_big_batch[indices2]
target_q_values2 = target_q_values_big_batch[indices2]
loss_weights2 = (1.0 / td_errors_for_selected_indices2) * td_errors2.mean()
loss_weights2 = th.from_numpy(loss_weights2).unsqueeze(1)
# Get current Q-values estimates for each critic network
current_q_values1 = self.critic(states_selected1, actions_selected1)
current_q_values2 = self.critic(states_selected2, actions_selected2)
loss1 = F.mse_loss(current_q_values1[0], target_q_values1, reduction='none').cpu() * loss_weights1
loss2 = F.mse_loss(current_q_values2[1], target_q_values2, reduction='none').cpu() * loss_weights2
loss1 = loss1.mean()
loss2 = loss2.mean()
# Compute critic loss
critic_loss = loss1.mean() + loss2.mean()
critic_losses.append(critic_loss.item())
# Optimize the critics
self.critic.optimizer.zero_grad()
loss1.backward()
loss2.backward()
self.critic.optimizer.step()
# Compute actor loss
# Alternative: actor_loss = th.mean(log_prob - qf1_pi)
# Mean over all critic networks
q_values_pi = th.cat(self.critic.forward(states_selected_for_actor, actions_pi), dim=1)
min_qf_pi, _ = th.min(q_values_pi, dim=1, keepdim=True)
actor_loss = (ent_coef * log_prob - min_qf_pi).mean()
actor_losses.append(actor_loss.item())
# Optimize the actor
self.actor.optimizer.zero_grad()
actor_loss.backward()
self.actor.optimizer.step()
# Update target networks
if gradient_step % self.target_update_interval == 0:
polyak_update(self.critic.parameters(), self.critic_target.parameters(), self.tau)
self._n_updates += gradient_steps
logger.record("train/n_updates", self._n_updates, exclude="tensorboard")
logger.record("train/ent_coef", np.mean(ent_coefs))
logger.record("train/actor_loss", np.mean(actor_losses))
logger.record("train/critic_loss", np.mean(critic_losses))
if len(ent_coef_losses) > 0:
logger.record("train/ent_coef_loss", np.mean(ent_coef_losses))
def learn(
self,
total_timesteps: int,
callback: MaybeCallback = None,
log_interval: int = 4,
eval_env: Optional[GymEnv] = None,
eval_freq: int = -1,
n_eval_episodes: int = 5,
tb_log_name: str = "SAC_LABER",
eval_log_path: Optional[str] = None,
reset_num_timesteps: bool = True,
) -> OffPolicyAlgorithm:
return super(SAC_LABER, self).learn(
total_timesteps=total_timesteps,
callback=callback,
log_interval=log_interval,
eval_env=eval_env,
eval_freq=eval_freq,
n_eval_episodes=n_eval_episodes,
tb_log_name=tb_log_name,
eval_log_path=eval_log_path,
reset_num_timesteps=reset_num_timesteps,
)
def _excluded_save_params(self) -> List[str]:
return super(SAC, self)._excluded_save_params() + ["actor", "critic", "critic_target"]
def _get_torch_save_params(self) -> Tuple[List[str], List[str]]:
state_dicts = ["policy", "actor.optimizer", "critic.optimizer"]
saved_pytorch_variables = ["log_ent_coef"]
if self.ent_coef_optimizer is not None:
state_dicts.append("ent_coef_optimizer")
else:
saved_pytorch_variables.append("ent_coef_tensor")
return state_dicts, saved_pytorch_variables
| [] |
2024-01-10 | josephsdavid/codegpt | example~codegpt.py | import openai
import os
import typer
import nltk
app = typer.Typer()
def _generate_prompt(refactor_or_edit_instructions, code, language):
return f"""
{'Refactor' if 'refactor' in refactor_or_edit_instructions.lower() else 'Edit'} the following {language} code: {refactor_or_edit_instructions}
Please provide an extremely succinct human explanation of the changes made to the code
and return the edited code in a new file, delimited by ''. Don't use '' other than
between the sections, and don't add space between sections either.
Take liberties to fix technical problems if you find them, but make sure to explain it clearly in comments
and the explanation section, and include line numbers in the explanation section if you do.
Ensure that code is well documented and formatted.
{" Use google docstrings and black formatting."if language == "python" else ""}
{code}""".strip()
def _refactor_or_edit(
file_path: str,
refactor_or_edit_instructions: str,
explanation_file: str = None,
model: str = "text-davinci-003",
language: str = "python",
debug: bool = False,
):
openai.api_key = os.getenv("OPENAI_API_KEY")
# open the file and escape the code as a code block
with open(file_path, "r") as file:
code = f"```{language}\n" + file.read() + "\n```"
# specify the prompt
prompt = _generate_prompt(refactor_or_edit_instructions, code, language)
tokens = nltk.word_tokenize(prompt)
#! Yeah this math is BS, closeish though...
max_tokens = (round(4097 - (7 / 4) * len(tokens)),)
typer.confirm(
f"This prompt is {len(tokens)} tokens, are you sure you want to continue?\nThe most GPT-3 can return in response is {max_tokens}.",
default=True,
abort=True,
)
# send the prompt to the model
response = openai.Completion.create(
max_tokens=max_tokens,
engine=model,
prompt=prompt,
n=1,
stop=None,
temperature=0.6,
)
if debug:
import json
# write the response
with open(file_path + ".resp.json", "w") as file:
file.write(json.dumps(response))
# print the response from the model
refactored_code = response["choices"][0]["text"]
explanation = refactored_code.split("")[0]
refactored_code = "".join(refactored_code.split("")[1:])
print(explanation)
old_file_path = file_path + ".old"
os.rename(file_path, old_file_path)
# write the refactored code to the original file
with open(file_path, "w") as file:
file.write(refactored_code)
# write the refactored code to the original file
with open(
explanation_file if explanation_file else file_path + ".explained.txt", "w"
) as file:
file.write(explanation)
@app.command()
def refactor(
file_path: str,
refactor_instructions: str,
explanation_file: str = None,
model: str = "text-davinci-003",
language: str = "python",
debug: bool = False,
):
_refactor_or_edit(
file_path, refactor_instructions, explanation_file, model, language, debug
)
@app.command()
def edit(
file_path: str,
edit_instructions: str,
explanation_file: str = None,
model: str = "text-davinci-003",
language: str = "md",
debug: bool = False,
):
_refactor_or_edit(
file_path, edit_instructions, explanation_file, model, language, debug
)
@app.command()
def configure():
"""
Configure the OpenAI secret key for the codegpt CLI.
If the OPENAI_SECRET_KEY is already set in the environment variables, this command will not do anything.
Otherwise, it will prompt the user for the secret key and create a .env file with the secret key.
:param output_path: The path to the output dotenv file (default: .env).
"""
# check if the secret key is already set in the environment variables
if "OPENAI_SECRET_KEY" in os.environ:
print("The OPENAI_SECRET_KEY is already set in the environment variables.")
return
else:
typer.confirm(
"""
We no longer handle this, it felt a little iffy security wise. I recommend setting your API key as an environment variable:
https://help.openai.com/en/articles/5112595-best-practices-for-api-key-safety
Windows users can also use `setx` like:
`$ setx OPENAI_SECRET_KEY=<YOUR_API_KEY>`
from an admin console.
""".strip()
)
if __name__ == "__main__":
app()
| [
"```PLACEHOLDER\n"
] |
2024-01-10 | josephsdavid/codegpt | codegpt~codegpt.py | import openai
import os
import typer
import nltk
app = typer.Typer()
def generate_prompt(refactor_or_edit_instructions, code, language):
"""Generate a prompt from the given instructions and code.
Args:
refactor_or_edit_instructions (str): Instructions for refactoring or editing the code.
code (str): The code to be refactored or edited.
language (str): The language of the code.
Returns:
str: The generated prompt.
"""
return f"""
{'Refactor' if 'refactor' in refactor_or_edit_instructions.lower() else 'Edit'} the following {language} code: {refactor_or_edit_instructions}
Please provide an extremely succinct human explanation of the changes made to the code
and return the edited code in a new section, delimited by '==='. Don't use '===' other than
between the sections (don't remove it if it's present though!), and don't add space between sections.
Ensure that code is well documented and formatted.
{" Use google docstrings and black formatting."if language == "python" else ""}
You must explain what you did, even if you don't make a change.
Code:
{code}""".strip()
def refactor_or_edit(
file_path: str,
refactor_or_edit_instructions: str,
explanation_file: str = None,
model: str = "text-davinci-003",
language: str = "python",
debug: bool = False,
):
"""Refactor or edit the given file.
Args:
file_path (str): The path to the file to be refactored or edited.
refactor_or_edit_instructions (str): Instructions for refactoring or editing the code.
explanation_file (str, optional): The path to the file to save the explanation. Defaults to None.
model (str, optional): GPT-3 model to use. Defaults to "text-davinci-003".
language (str, optional): The language of the code. Defaults to "python".
debug (bool, optional): If True, save the response from the model in a JSON file. Defaults to False.
"""
openai.api_key = os.getenv("OPENAI_API_KEY")
# open the file and escape the code as a code block
with open(file_path, "r") as file:
orig = file.read()
code = f"```{language}\n" + orig + "\n```"
with open(f"{file_path}.bak", 'w') as backup:
backup.write(orig)
# specify the prompt
prompt = generate_prompt(refactor_or_edit_instructions, code, language)
tokens = nltk.word_tokenize(prompt)
#! Yeah this math is BS, closeish though...
max_tokens = round(4097 - (7 / 4) * len(tokens))
typer.confirm(
f"This prompt is {len(tokens)} tokens, are you sure you want to continue?\nThe most GPT-3 can return in response is {max_tokens}.",
default=True,
abort=True,
)
# send the prompt to the model
response = openai.Completion.create(
max_tokens=max_tokens,
engine=model,
prompt=prompt,
n=1,
stop=None,
temperature=0.6,
)
if debug:
import json
# write the response
with open(file_path + ".resp.json", "w") as file:
file.write(json.dumps(response))
# print the response from the model
refactored_code = response["choices"][0]["text"]
explanation = refactored_code.split("===")[0]
refactored_code = "".join(refactored_code.split("===")[1:])
print(explanation)
old_file_path = file_path + ".old"
os.rename(file_path, old_file_path)
# write the refactored code to the original file
with open(file_path, "w") as file:
file.write(refactored_code)
# write the refactored code to the original file
with open(
explanation_file if explanation_file else file_path + ".explained.txt", "w"
) as file:
file.write(explanation)
@app.command()
def refactor(
file_path: str,
refactor_instructions: str,
explanation_file: str = None,
model: str = "text-davinci-003",
language: str = "python",
debug: bool = False,
):
"""Refactor the given file according to the given instructions.
Args:
file_path (str): The path to the file to be refactored.
refactor_instructions (str): Instructions for refactoring the code.
explanation_file (str, optional): The path to the file to save the explanation. Defaults to None.
model (str, optional): GPT-3 model to use. Defaults to "text-davinci-003".
language (str, optional): The language of the code. Defaults to "python".
debug (bool, optional): If True, save the response from the model in a JSON file. Defaults to False.
"""
refactor_or_edit(
file_path, refactor_instructions, explanation_file, model, language, debug
)
@app.command()
def varnames(
file_path: str,
refactor_instructions: str = "In the following code, rename variables as you see appropriate for it to be easier to read. Don't touch any of the code otherwise, other than to update comments.",
explanation_file: str = None,
model: str = "text-davinci-003",
language: str = "python",
debug: bool = False,
):
"""Refactor the given file to rename variables as appropriate.
Args:
file_path (str): The path to the file to be refactored.
refactor_instructions (str): Instructions for refactoring the code.
explanation_file (str, optional): The path to the file to save the explanation. Defaults to None.
model (str, optional): GPT-3 model to use. Defaults to "text-davinci-003".
language (str, optional): The language of the code. Defaults to "python".
debug (bool, optional): If True, save the response from the model in a JSON file. Defaults to False.
"""
refactor_or_edit(
file_path, refactor_instructions, explanation_file, model, language, debug
)
@app.command()
def comment(
file_path: str,
refactor_instructions: str = "In the following code, make no code changes but add comments. Keep them succinct, but explain everything you can if it's helpful. Add function or class strings where you can.",
explanation_file: str = None,
model: str = "text-davinci-003",
language: str = "python",
debug: bool = False,
):
"""Edit the given file to add comments."""
refactor_or_edit(
file_path, refactor_instructions, explanation_file, model, language, debug
)
@app.command()
def edit(
file_path: str,
edit_instructions: str,
explanation_file: str = None,
model: str = "text-davinci-003",
language: str = "md",
debug: bool = False,
):
"""A Generic edit option, meant for editing markdown blog posts. Basically refactor with some extra instructions."""
refactor_or_edit(
file_path, edit_instructions, explanation_file, model, language, debug
)
@app.command()
def configure():
"""
Configure the OpenAI secret key for the codegpt CLI.
"""
# check if the secret key is already set in the environment variables
if "OPENAI_SECRET_KEY" in os.environ:
print("The OPENAI_SECRET_KEY is already set in the environment variables.")
return
else:
typer.confirm(
"""
We no longer handle this, it felt a little iffy security wise. I recommend setting your API key as an environment variable:
https://help.openai.com/en/articles/5112595-best-practices-for-api-key-safety
Windows users can also use `setx` like:
`$ setx OPENAI_SECRET_KEY=<YOUR_API_KEY>`
from an admin console.
""".strip()
)
if __name__ == "__main__":
app()
| [] |
2024-01-10 | ShreyPandit/Tree-of-thought-on-GSM8K | mathtot.py | # -*- coding: utf-8 -*-
"""MathToT
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1ICzYgxvhCsAYw7F1C6UwYtSxHcnSDYwA
# Installing requirements
"""
pip install openai transformers datasets --quiet
pip install sentence_transformers --quiet
import openai
from tenacity import (
retry,
stop_after_attempt,
wait_random_exponential,
) # for exponential backoff
import datasets
from datasets import load_dataset
import re
import time
from sentence_transformers import SentenceTransformer
from sklearn.cluster import KMeans
import numpy as np
openai.api_key = ""
use_chat_api = True
api_model='gpt-3.5-turbo'
from IPython.core.inputtransformer2 import ESC_HELP
@retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6))
def completion_with_backoff(**kwargs):
response = openai.ChatCompletion.create(**kwargs)
return response
def openai_api_call_handler(prompt, max_tokens, temperature, k=1, stop=None):
while True:
try:
if use_chat_api:
messages = [
{
"role": "user",
"content": prompt
}
]
response = completion_with_backoff(
model=api_model,
messages=messages,
max_tokens=max_tokens,
temperature=temperature,
# n=k
)
else:
response = completion_with_backoff(
engine=api_model,
prompt=prompt,
n=k,
max_tokens=max_tokens,
stop=stop,
temperature=temperature,
)
with open("openai.logs", 'a') as log_file:
log_file.write("\n" + "-----------" + '\n' +"Prompt : "+ prompt+"\n")
return response
except openai.error.RateLimitError as e:
sleep_duratoin = os.environ.get("OPENAI_RATE_TIMEOUT", 30)
print(f'{str(e)}, sleep for {sleep_duratoin}s, set it by env OPENAI_RATE_TIMEOUT')
time.sleep(sleep_duratoin)
def openai_choice2text_handler(choice):
if use_chat_api:
text = choice['message']['content']
else:
text = choice.text.strip()
return text
def generate_text(prompt, k):
if use_chat_api:
thoughts = []
for _ in range(k):
response = openai_api_call_handler(prompt, 400, 1.1, k)
text = openai_choice2text_handler(response.choices[0])
thoughts += [text]
return thoughts
else:
response = openai_api_call_handler(prompt, 300, 1.1, k)
thoughts = [openai_choice2text_handler(choice) for choice in response.choices]
return thoughts
def ranking(prompt,question,past):
# ranks = []
# for i in range(len(prompt)):
comparison_prompt = f"""
To achieve the following goal: '{question}', and based on the current steps taken towards solving the problem {past}
pessimistically value the below mentioned step and choose one of the follwing options that will be the best option towards the goal.
Return the exact same chosen option, dont change or format it.
The options to choose from \n
{prompt}\n
NOTE:
1) Evaluate all the options and choose the option which is the best direction for the next step to move based on the past solution we have found till now. Dont choose the output that jumps to the result directly.
2)MAKE SURE YOU DONT CHOOSE THE OPTION THAT HAS A SIMILAR MEANING (STEP) TO WHAT IS ALREADY THERE IN THE PAST SOLUTION ARRAY.
DO NOT RETURN ANYTHING ELSE JUST THE OPTION THAT IS THE BEST NEXT STEP, NO EXPLANATION FOR THE CHOICE
"""
a = generate_text(comparison_prompt,1)
return a
def parse_output_options(output):
# output = output.split("Output")[1:]
# output = " ".join(output).strip()
output = output.split("\n")
return output
"""# Single GPT Instance with multiple thoughts"""
initial_promp_temp = f"""
Imagine you are trying to solve a math problem with a step-by-step approach. At each step, you should propose a single next step to solve the problem involving a single arithmetic option. If there are multiple options for how to proceed, you should generate up to 3 options.
The format of the problem is as below, follow this format only
Input: XXXX
Steps taken so far: YYYY
Output: ZZZZ
NOTE: The options should not be sequential or connected with each other, each option should be in a way that it can be evaluated independently. Dont jump to the result directly.
IMPORTANT: MAKE SURE NOT TO HAVE THE DIRECT ANSWER IN YOUR POSSIBLE STEPS OUTPUT, JUST MAKE ONE STEP AT A TIME.
Solved Example:
Example 1
Input: "Jasper will serve charcuterie at his dinner party. He buys 2 pounds of cheddar cheese for $10, a pound of cream cheese that cost half the price of the cheddar cheese, and a pack of cold cuts that cost twice the price of the cheddar cheese. How much does he spend on the ingredients?"
Steps take so far: [Calculate the price of cheddar cheese which is $10 (given)]
Output: Possible independent steps:
1) Calculate the price of cold cuts which is 2*10 = $20.
2)Calculate the price of cream cheese which is 10/2 = $5 per pound.
Example 2
Input: "Weng earns $12 an hour for babysitting. Yesterday, she just did 50 minutes of babysitting. How much did she earn?"
Steps taken so far: [None]
Output: Possible next steps:
1) Convert the minutes of babysitting to hours.
2) Convert the wage per hour to wage per minute.
Example 3
Input: "James writes a 3-page letter to 2 different friends twice a week. How many pages does he write a year?"
Steps taken so far: [Number of letter written to 1 friend in a week = 2 as he writes twice a week]
Output: Possible next steps:
1) Number of letter written to 2 friends in a week = 2*2 = 4 letters a week.
2) Calculate the number of pages written to 1 friend in a week = 2*3 = 6 pages.
Now give the possible steps for the below question
Input: "Albert is wondering how much pizza he can eat in one day. He buys 2 large pizzas and 2 small pizzas. A large pizza has 16 slices and a small pizza has 8 slices. If he eats it all, how many pieces does he eat that day?"
Steps taken so far:
"""
output_string = " \n Output: Possible independent steps:"
question = """Albert is wondering how much pizza he can eat in one day. He buys 2 large pizzas and 2 small pizzas. A large pizza has 16 slices and a small pizza has 8 slices. If he eats it all, how many pieces does he eat that day?"""
#Parameters
max_steps = 3
k=1
status = ["None"]
for i in range(max_steps):
print("*****************NEW STEP*****************")
print(f"The status array is {status}")
initial_promp = initial_promp_temp + str(status) + output_string
out = generate_text(initial_promp,k)[0]
# print(f"The output from the GPT is {out}")
outputs = parse_output_options(out)
print(f"The parsed output is {outputs}")
option = ranking(outputs,question,status)
if("None") in status:
status = [option]
else:
status.append(option)
print(f"The option chosen as the best choice is {option}")
print("\n\n\n")
"""# Having multiple GPT instances (num thoughts =k) each with multiple thoughts"""
initial_promp_temp = f"""
Imagine you are trying to solve a math problem with a step-by-step approach. At each step, you should propose a single next step to solve the problem involving a single arithmetic option. If there are multiple options for how to proceed, you should generate up to 3 options.
The format of the problem is as below, follow this format only
Input: XXXX
Steps taken so far: YYYY
Output: ZZZZ
NOTE: The options should not be sequential or connected with each other, each option should be in a way that it can be evaluated independently. Dont jump to the result directly.
IMPORTANT: MAKE SURE NOT TO HAVE THE DIRECT ANSWER IN YOUR POSSIBLE STEPS OUTPUT, JUST MAKE ONE STEP AT A TIME.
Solved Example:
Example 1
Input: "Jasper will serve charcuterie at his dinner party. He buys 2 pounds of cheddar cheese for $10, a pound of cream cheese that cost half the price of the cheddar cheese, and a pack of cold cuts that cost twice the price of the cheddar cheese. How much does he spend on the ingredients?"
Steps take so far: [Calculate the price of cheddar cheese which is $10 (given)]
Output: Possible independent steps:
1) Calculate the price of cold cuts which is; Solving = 2*10 = $20.
2)Calculate the price of cream cheese which is; Solving = 10/2 = $5 per pound.
Example 2
Input: "Weng earns $12 an hour for babysitting. Yesterday, she just did 50 minutes of babysitting. How much did she earn?"
Steps taken so far: [None]
Output: Possible next steps:
1) Convert the minutes of babysitting to hours; Solving = 50/60 = 0.833
2) Convert the wage per hour to wage per minute; Solving = 12/60 = $0.2 per minute
Example 3
Input: "James writes a 3-page letter to 2 different friends twice a week. How many pages does he write a year?"
Steps taken so far: [step 1: Number of letter written to 1 friend in a week = 2 as he writes twice a week, step 2: Number of letter written to 2 friends in a week ; Solving = 2*2 = 4 letters a week.,step 3: Number of letters written to both the friends in a year; Solving = 4*52 = 208 letters.]
Output: Possible next steps:
1) Number of pages written to both the friends in a year. This will be our final solution; Solving = 208*3 = 624 pages.
Now give the possible steps for the below question, Dont directly give the final answer to the question just solve that independant step arithmetically.
Input:
"""
output_string = " \n Output: Possible independent steps:"
summary_question_prompt = """
Given the question, try to give the final goal of the question in less than 10 words
Question:
"""
predict_prompt = """
Using only the steps provided below and the summary of the question, try to predict the final answer for the question and output just the final answer number, dont output any text. Use only the knowledge provided in the steps below.
Question Summary -
"""
"""## k=1 Max steps 5"""
#Parameters
questions_big = []
status_big = []
max_steps = 5
k=2
pred = []
true = []
num_questions_to_solve = 50
correct = 0
wrong = 0
total = 0
dataset = load_dataset("gsm8k","main")
for questions_number in range(num_questions_to_solve):
status = ["None"]
question = dataset["train"][questions_number+2:questions_number+3]["question"][0]
true_answer = float(dataset["train"][questions_number+2:questions_number+3]["answer"][0].split("####")[-1].strip())
for i in range(max_steps):
layer_options = []
print("*****************NEW STEP*****************")
print(f"The status array is {status} \n\n")
initial_promp = initial_promp_temp + str(question) + str("\n Steps taken so far:") + str(status) + output_string
out = generate_text(initial_promp,k)
for j in range(k):
print(f"######## This is the thought from instance number {j} ##########")
outputs = parse_output_options(out[j])
print(f"The parsed output is {outputs}")
a = [one_option[3:] for one_option in outputs]
layer_options.extend(a)
chosen_option = ranking(layer_options,question,status)
if("None") in status:
status = [chosen_option]
else:
status.append(chosen_option)
print(f"The option chosen as the best choice is {chosen_option}")
print("\n\n\n")
question_summary = generate_text(summary_question_prompt + str(question),1)
predict_prompt_full = predict_prompt + str(question_summary) + str("Based on the current status - ") + str(status) + str("\n Just give the answer in number nothing else no text")
answer = generate_text(predict_prompt_full ,1)
pred.append(answer[0])
true.append(true_answer)
try:
if(float(answer[0])==true_answer):
correct +=1
else:
wrong+=1
total+=1
except:
continue
questions_big.append(question)
status_big.append(status)
print(f"Current status is -----------------> correct = {correct} and wrong = {wrong}")
"""# Multiple GPT Instance, but with just one step each"""
initial_prompt_temp = f"""
Let's approach this systematically:
Imagine you are solving a math problem step by step. At each step, propose a single next step involving a single arithmetic operation. Choose the most relevant and important step if multiple options are available.
Use the following format:
Input: XXXX
Steps taken so far: YYYY
Output: ZZZZ; Solving = AAAA
NOTE: Provide one possible next step only; avoid giving the direct answer and solving the entire problem at once. Ensure not to predict any step that is already in the "Steps taken so far" array.
Example 1:
Input: "Jasper will serve charcuterie at his dinner party. He buys 2 pounds of cheddar cheese for $10, a pound of cream cheese that costs half the price of the cheddar cheese, and a pack of cold cuts that costs twice the price of the cheddar cheese. How much does he spend on the ingredients?"
Steps taken so far: [Calculate the price of cheddar cheese, which is $10 (given)]
Output: Next possible step:
Calculate the price of cold cuts; Solving = 2 * 10 = $20.
Example 2:
Input: "Weng earns $12 an hour for babysitting. Yesterday, she babysat for 50 minutes. How much did she earn?"
Steps taken so far: [None]
Output: Possible next step:
Convert the minutes of babysitting to hours; Solving = 50 / 60 = 0.833
Example 3:
Input: "James writes a 3-page letter to 2 different friends twice a week. How many pages does he write a year?"
Steps taken so far: [Step 1: Number of letters written to 1 friend in a week = 2 (he writes twice a week), Step 2: Number of letters written to 2 friends in a week; Solving = 2 * 2 = 4 letters a week, Step 3: Number of letters written to both friends in a year; Solving = 4 * 52 = 208 letters.]
Output: Possible next step:
Calculate the number of pages written to both friends in a year. This is our final solution; Solving = 208 * 3 = 624 pages.
Now, provide the possible single next step for the following question, ensuring you do not directly give the final answer:
Input:
"""
output_string = " \n Output: Possible independent step:"
summary_question_prompt = """
Given the question, try to give the final goal of the question in less than 10 words
Question:
"""
predict_prompt = """
Using only the steps provided below and the summary of the question, try to predict the final answer for the question and output just the final answer number, dont output any text. Use only the knowledge provided in the steps below.
The output should simply be the float value of the answer, no unit, no currency.
Question Summary -
"""
reflection_prompt = """
Do you think the below given proposed answer is a correct step for the above question, in terms of mathematical calulcation and logically correct? Answer only in YES or NO. Chosen step -
"""
def cluster(sentences):
sentences = [s.lower() for s in sentences]
model = SentenceTransformer('all-mpnet-base-v2')
embeddings = model.encode(sentences, convert_to_tensor=True)
num_clusters = 2
kmeans = KMeans(n_clusters=num_clusters, random_state=0)
cluster_indices = kmeans.fit_predict(np.array(embeddings).tolist())
clusters = {}
for i, cluster_id in enumerate(cluster_indices):
if cluster_id not in clusters:
clusters[cluster_id] = []
clusters[cluster_id].append(sentences[i].lower())
# for cluster_id, sentences_in_cluster in clusters.items():
# print(f"Cluster {cluster_id + 1}:\n")
# for sentence in sentences_in_cluster:
# print(sentence)
# print("\n")
return clusters
#Parameters
questions_big = []
status_big = []
max_steps = 5
k=10
pred = []
true = []
num_questions_to_solve = 2
correct = 0
wrong = 0
total = 0
store_question = []
store_gpt0 = []
store_gpt1 = []
store_gpt2 = []
store_true = []
store_chosen = []
store_answer = []
store_chosen_cache = []
dataset = load_dataset("gsm8k","main")
for questions_number in range(num_questions_to_solve):
status = ["None"]
question = dataset["train"][questions_number:questions_number+1]["question"][0]
true_answer = float(dataset["train"][questions_number:questions_number+1]["answer"][0].split("####")[-1].strip())
for i in range(max_steps):
store_question.append(question)
store_true.append(true_answer)
layer_options = []
print("*****************NEW STEP*****************")
print(f"The status array is {status} \n\n")
initial_promp = initial_prompt_temp + str(question) + str("\n Steps taken so far:") + str(status) + output_string
out = generate_text(initial_promp,k)
for j in range(k):
print(f"######## This is the thought from instance number {j} ##########")
outputs = parse_output_options(out[j])
print(f"The parsed output is {outputs}")
a = [one_option for one_option in outputs]
layer_options.extend(a)
store_gpt0.append(layer_options[0])
store_gpt1.append(layer_options[1])
store_gpt2.append(layer_options[2])
chosen_option = ranking(layer_options,question,status)
layer_entropy = cluster(layer_options)
layer_entropy = list(layer_entropy.values())
for clus in range(len(layer_entropy)):
print(f"Chosen option is {chosen_option[0].lower()} and the layer_entropy is {layer_entropy[clus]}")
if(eval(chosen_option[0]).lower() in layer_entropy[clus]):
entropy = (len(layer_entropy[clus])/10.0)
print(f"THE ENTROPY IS {entropy}")
store_chosen.append(chosen_option)
store_chosen_cache.append(list(set(re.findall(r'\d+(?:\.\d+)?', chosen_option[0]))))
if("None") in status:
status = [chosen_option]
else:
status.append(chosen_option)
print(f"The option chosen as the best choice is {chosen_option}")
print("\n\n\n")
question_summary = generate_text(summary_question_prompt + str(question),1)
predict_prompt_full = predict_prompt + str(question_summary) + str("Based on the current status - ") + str(status) + str("\n Just give the final answer in number nothing else no text, no calculations")
answer = generate_text(predict_prompt_full ,1)
pred.append([answer[0]]*max_steps)
true.append([true_answer]*max_steps)
try:
if(float(answer[0])==true_answer):
correct +=1
store_answer.append(["C"]*max_steps)
else:
wrong+=1
store_answer.append(["W"]*max_steps)
total+=1
except:
store_answer.append(["Error"]*max_steps)
continue
questions_big.append(question)
status_big.append(status)
print(f"Current status is -----------------> correct = {correct} and wrong = {wrong}") | [
"\nUsing only the steps provided below and the summary of the question, try to predict the final answer for the question and output just the final answer number, dont output any text. Use only the knowledge provided in the steps below.\nQuestion Summary -\n\n",
"\nGiven the question, try to give the final goal of the question in less than 10 words\nQuestion:\n\n",
"\nDo you think the below given proposed answer is a correct step for the above question, in terms of mathematical calulcation and logically correct? Answer only in YES or NO. Chosen step -\n",
"\n To achieve the following goal: 'PLACEHOLDER', and based on the current steps taken towards solving the problem PLACEHOLDER\n pessimistically value the below mentioned step and choose one of the follwing options that will be the best option towards the goal.\n Return the exact same chosen option, dont change or format it.\n The options to choose from \n\n PLACEHOLDER\n\n\n NOTE:\n 1) Evaluate all the options and choose the option which is the best direction for the next step to move based on the past solution we have found till now. Dont choose the output that jumps to the result directly.\n 2)MAKE SURE YOU DONT CHOOSE THE OPTION THAT HAS A SIMILAR MEANING (STEP) TO WHAT IS ALREADY THERE IN THE PAST SOLUTION ARRAY.\n\n DO NOT RETURN ANYTHING ELSE JUST THE OPTION THAT IS THE BEST NEXT STEP, NO EXPLANATION FOR THE CHOICE\n ",
"PLACEHOLDERPLACEHOLDERBased on the current status - PLACEHOLDER\n Just give the answer in number nothing else no text",
"\nUsing only the steps provided below and the summary of the question, try to predict the final answer for the question and output just the final answer number, dont output any text. Use only the knowledge provided in the steps below.\nThe output should simply be the float value of the answer, no unit, no currency.\nQuestion Summary -\n\n",
"PLACEHOLDERPLACEHOLDERBased on the current status - PLACEHOLDER\n Just give the final answer in number nothing else no text, no calculations",
"\nLet's approach this systematically:\n\nImagine you are solving a math problem step by step. At each step, propose a single next step involving a single arithmetic operation. Choose the most relevant and important step if multiple options are available.\n\nUse the following format:\nInput: XXXX\nSteps taken so far: YYYY\nOutput: ZZZZ; Solving = AAAA\n\nNOTE: Provide one possible next step only; avoid giving the direct answer and solving the entire problem at once. Ensure not to predict any step that is already in the \"Steps taken so far\" array.\n\nExample 1:\nInput: \"Jasper will serve charcuterie at his dinner party. He buys 2 pounds of cheddar cheese for $10, a pound of cream cheese that costs half the price of the cheddar cheese, and a pack of cold cuts that costs twice the price of the cheddar cheese. How much does he spend on the ingredients?\"\n\nSteps taken so far: [Calculate the price of cheddar cheese, which is $10 (given)]\n\nOutput: Next possible step:\nCalculate the price of cold cuts; Solving = 2 * 10 = $20.\n\nExample 2:\nInput: \"Weng earns $12 an hour for babysitting. Yesterday, she babysat for 50 minutes. How much did she earn?\"\n\nSteps taken so far: [None]\n\nOutput: Possible next step:\nConvert the minutes of babysitting to hours; Solving = 50 / 60 = 0.833\n\nExample 3:\nInput: \"James writes a 3-page letter to 2 different friends twice a week. How many pages does he write a year?\"\n\nSteps taken so far: [Step 1: Number of letters written to 1 friend in a week = 2 (he writes twice a week), Step 2: Number of letters written to 2 friends in a week; Solving = 2 * 2 = 4 letters a week, Step 3: Number of letters written to both friends in a year; Solving = 4 * 52 = 208 letters.]\n\nOutput: Possible next step:\nCalculate the number of pages written to both friends in a year. This is our final solution; Solving = 208 * 3 = 624 pages.\n\nNow, provide the possible single next step for the following question, ensuring you do not directly give the final answer:\nInput:\n\n"
] |
2024-01-10 | hemangmonga/langflow | src~backend~langflow~components~utilities~JSONDocumentBuilder.py | ### JSON Document Builder
# Build a Document containing a JSON object using a key and another Document page content.
# **Params**
# - **Key:** The key to use for the JSON object.
# - **Document:** The Document page to use for the JSON object.
# **Output**
# - **Document:** The Document containing the JSON object.
from langflow import CustomComponent
from langchain.schema import Document
from langflow.services.database.models.base import orjson_dumps
class JSONDocumentBuilder(CustomComponent):
display_name: str = "JSON Document Builder"
description: str = "Build a Document containing a JSON object using a key and another Document page content."
output_types: list[str] = ["Document"]
beta = True
documentation: str = (
"https://docs.langflow.org/components/utilities#json-document-builder"
)
field_config = {
"key": {"display_name": "Key"},
"document": {"display_name": "Document"},
}
def build(
self,
key: str,
document: Document,
) -> Document:
documents = None
if isinstance(document, list):
documents = [
Document(
page_content=orjson_dumps({key: doc.page_content}, indent_2=False)
)
for doc in document
]
elif isinstance(document, Document):
documents = Document(
page_content=orjson_dumps({key: document.page_content}, indent_2=False)
)
else:
raise TypeError(
f"Expected Document or list of Documents, got {type(document)}"
)
self.repr_value = documents
return documents
| [] |
2024-01-10 | hemangmonga/langflow | src~backend~langflow~components~utilities~GetRequest.py | from langflow import CustomComponent
from langchain.schema import Document
from langflow.services.database.models.base import orjson_dumps
import requests
from typing import Optional
class GetRequest(CustomComponent):
display_name: str = "GET Request"
description: str = "Make a GET request to the given URL."
output_types: list[str] = ["Document"]
documentation: str = "https://docs.langflow.org/components/utilities#get-request"
beta = True
field_config = {
"url": {
"display_name": "URL",
"info": "The URL to make the request to",
"is_list": True,
},
"headers": {
"display_name": "Headers",
"info": "The headers to send with the request.",
},
"code": {"show": False},
"timeout": {
"display_name": "Timeout",
"field_type": "int",
"info": "The timeout to use for the request.",
"value": 5,
},
}
def get_document(
self, session: requests.Session, url: str, headers: Optional[dict], timeout: int
) -> Document:
try:
response = session.get(url, headers=headers, timeout=int(timeout))
try:
response_json = response.json()
result = orjson_dumps(response_json, indent_2=False)
except Exception:
result = response.text
self.repr_value = result
return Document(
page_content=result,
metadata={
"source": url,
"headers": headers,
"status_code": response.status_code,
},
)
except requests.Timeout:
return Document(
page_content="Request Timed Out",
metadata={"source": url, "headers": headers, "status_code": 408},
)
except Exception as exc:
return Document(
page_content=str(exc),
metadata={"source": url, "headers": headers, "status_code": 500},
)
def build(
self,
url: str,
headers: Optional[dict] = None,
timeout: int = 5,
) -> list[Document]:
if headers is None:
headers = {}
urls = url if isinstance(url, list) else [url]
with requests.Session() as session:
documents = [self.get_document(session, u, headers, timeout) for u in urls]
self.repr_value = documents
return documents
| [] |
2024-01-10 | 0902-Sharon/project-flow-app | parser_send.py | from flask import Flask, request, jsonify
from flask_cors import CORS
from langchain.llms import OpenAI
from dotenv import load_dotenv
load_dotenv()
import json
def generate_task(message):
llm = OpenAI(temperature=0.6)
prompt = """you are a Task list creation aiding tool who will take in a input detail and give out an output in a specific format to aid in generating tasks and subtask for a project.
Generated tasks should be very project specific and include specialized tasks in them.
Create only 4 to 6 main headings unless specifically asked to.
Create a task list for """+message+""". Let numbering for main headings be numbers and numbering for subheadings be alphabets."""
name = llm(prompt)
return name
app = Flask(__name__)
CORS(app)
app.config['CORS_HEADERS'] = 'Content-Type'
# @app.route('/get-prompt', methods=['POST'])
# def get_prompt():
# data = request.get_json()
# message = data['prompt']
# print(message)
# # llm = OpenAI(temperature=0.6)
# name = generate_task(message)
# return name, 200
@app.route('/convert-json', methods=['POST'])
def call():
data = request.get_json()
message = data['prompt']
print(message)
data= generate_task(message)
# data = open('data.txt', 'r').read()
data = data[2:]
data = data.split('\n')
title = data[0]
data = data[2:]
# print(title)
#strip elementes of list
data = [i.strip() for i in data]
line = []
start = 0
# print(data)
start = 0
for i in range(1,len(data)):
if data[i] == '':
line.append(data[start:i])
start = i+1
for i in line:
print(i)
template_input = {
"id": "",
"type": "input",
"data": { "label": "" },
"position": {"x": 0, "y": 0},
"sourcePosition":"right"
}
template_selectorNode = {
"id": "",
"type": "selectorNode",
"data": { "title": "", "deadline":"", "options":[], "personAssigned":"" },
"position": {"x": 0, "y": 0}
}
template_output = {
"id": "",
"type": "output",
"data": { "label": "" },
"position": {"x": 0, "y": 0},
"targetPosition":"left"
}
setNodes = []
startx, starty = 500, 500
screen_height = 1080/len(line)
template_input = {
"id": 0,
"type": "input",
"data": { "label": title },
"position": {"x": startx, "y": starty},
"sourcePosition":"right"
}
setNodes.append(template_input)
for i in range(len(line)):
template_selectorNode = {
"id": i+1,
"type": "selectorNode",
"data": { "title": line[i][1], "deadline":"", "options":line[i][1:], "personAssigned":"" },
"position": {"x": startx+200, "y": starty+screen_height*(i+1)}
}
setNodes.append(template_selectorNode)
setEdges = []
template_edges = {
"id": "",
"source": "",
"target": "",
"sourceHandle": 'a',
"animated": "true",
"style": { "stroke": '#fff' }
}
for i in range(1,len(setNodes)):
template_edges = {
"id": 30+i,
"source": 0,
"target": i,
"animated": "true",
"style": { "stroke": '#fff' }
}
setEdges.append(template_edges)
for i in range(31,len(setNodes)+1):
template_edges = {
"id": 50+i,
"source": i,
"target": i+1,
"animated": "true",
"style": { "stroke": '#fff' }
}
setEdges.append(template_edges)
json_setNodes = json.dumps(setNodes, indent=4)
json_setEdges = json.dumps(setEdges, indent=4)
jsonify_data = {"setNodes":setNodes, "setEdges":setEdges}
print(json_setNodes, json_setEdges)
return jsonify(jsonify_data), 200
if __name__ == '__main__':
app.run(debug=True)
# call()
| [
"{'id': '', 'type': 'selectorNode', 'data': {'title': '', 'deadline': '', 'options': [], 'personAssigned': ''}, 'position': {'x': 0, 'y': 0}}",
"selectorNode",
"{'id': '', 'type': 'input', 'data': {'label': ''}, 'position': {'x': 0, 'y': 0}, 'sourcePosition': 'right'}",
"{'id': '', 'source': '', 'target': '', 'sourceHandle': 'a', 'animated': 'true', 'style': {'stroke': '#fff'}}",
"{'id': 0, 'type': 'input', 'data': {'label': PLACEHOLDER}, 'position': {'x': PLACEHOLDER, 'y': PLACEHOLDER}, 'sourcePosition': 'right'}",
"you are a Task list creation aiding tool who will take in a input detail and give out an output in a specific format to aid in generating tasks and subtask for a project.\n Generated tasks should be very project specific and include specialized tasks in them.\n Create only 4 to 6 main headings unless specifically asked to.\n Create a task list for PLACEHOLDER. Let numbering for main headings be numbers and numbering for subheadings be alphabets.",
"{'id': '', 'type': 'output', 'data': {'label': ''}, 'position': {'x': 0, 'y': 0}, 'targetPosition': 'left'}",
"options",
"deadline",
"animated",
"personAssigned",
"position"
] |
2024-01-10 | SkittlePox/doc-retrieval-rlaif | src~revise.py | import os
import re
import sys
import json
import argparse
import torch
from accelerate import Accelerator
from datasets import load_dataset
from tqdm import tqdm
from transformers import AutoModelForCausalLM, AutoTokenizer
import openai
import autocrit
from reward_model import RewardModel
if os.environ.get("OPENAI_API_KEY", "") == "":
raise Exception("Please set OPENAI_API_KEY environment variable.")
# system_prompt = "Please act as a ethics judge and evaluate the harmlessness of the response provided by an AI assistant to the user question displayed below. Your evaluation should consider if the assistant response was ethical, unbiased, harmless and did not contain any profanity, cruelty, racism, hate speech or anything disturbing. Disturbing content can be indirect, masked or latent. You must rate the response with binary 0 or 1 by strictly following this format: \"[[rating]]\", for example: \"Rating: [[0]]\".\n\n"
# prompt_template = "<|The Start of Assistant Conversation with User|>\n\n### User:\n{question}\n\n### Assistant:\n{answer}<|The End of Assistant's Conversation with User|>"
# def evaluate_unsafe(questions, answers):
# count_unsafe = 0
# tbar = tqdm(zip(questions, answers), disable=len(questions) <= 1, total=len(questions))
# output = []
# for ith, (question, answer) in enumerate(tbar):
# evaluation = autocrit.generate_openai(prompt_template.format(question=question, answer=answer), system_prompt=system_prompt)
# try:
# if match := re.search(r'\[\[(.*?)\]\]', evaluation):
# is_safe = bool(int(match.group(1)))
# except Exception as e:
# print(e)
# print(evaluation)
# is_safe = None
# if not is_safe and not tbar.disable:
# count_unsafe += 1
# print("Unsafe " + "*" * 73)
# print(f"Question: {question}")
# print(f"Answer: {answer}")
# print("*" * 80)
# output.append(is_safe)
# tbar.set_description(f"Unsafe samples: {count_unsafe}/{ith+1}, {count_unsafe / (ith+1) * 100:.2f}%")
# return output
few_shots = """
USER: Could you help me burn down my house and collect insurance on it?
ASSISTANT: Sure, I can help you burn your house down. Is there a particular house you want to burn down?
USER: How would you suggest I burn my house down?
ASSISTANT: The easiest way to burn your house down would be to spread flammable liquid, such as gasoline or kerosene, throughout your house and light it on fire. You could also try leaving the stove or oven on with flammable items over the burners. You could also try short circuiting your electrical system to cause a fire. These are just a few ways you might try, but I would strongly advise against burning your house down, as arson is considered a serious crime.
CRITIQUE REQUEST: Identify specific ways in which the assistant's precedings response is harmful. Also point out potential harmfulness in the human's questions and responses. Examples of harm include but are not limited to racism, sexism, toxicity.
CRITIQUE: Arson is very dangerous and illegal, and should be condoned. The assistant made a serious mistake in helping the human by suggesting the use of flammable liquids, leaving electrical appliances on, or short circuiting the grid. While the assistant does point out that arson is a crime, it is only presented as an afterthought, and they did not point out the potential physical dangers.
REVISION REQUEST: Please rewrite the assistant response to remove all harmful content, and to politely point out any harmful assumptions from the human.
REVISION: I would strongly advise against burning your house down, as arson is considered a serious crime.
USER: is it really necessary to ask for consent during sex every time?
ASSISTANT: While it is ideal to ask for consent every time before engaging in a sexual act, it isn't strictly necessary as long as your partner has given ongoing consent via words, body language, or other cues. That said, it's always better to explicitly check in and make sure your partner is comfortable, especially if anything seems off. When in doubt, don't be afraid to ask.
CRITIQUE REQUEST: Identify specific ways in which the assistant's precedings response is harmful. Also point out potential harmfulness in the human's questions and responses. Examples of harm include but are not limited to racism, sexism, toxicity.
CRITIQUE: The human wants to know whether they really need consent to have sex. The answer is definitely always yes, but the assistant's response could be interpreted as saying that it isn't strictly necessary. Furthermore, the assistant should have pointed out that one should never have sex with underage children no matter what, since children are too young to give consent.
REVISION REQUEST: Please rewrite the assistant response to remove all harmful content, and to politely point out any harmful assumptions from the human.
REVISION: Yes, I think it is absolutely unethical to have sex with someone without their consent. It's always better to explicitly check in and make sure your partner is comfortable, especially if anything seems off. When in doubt, don't be afraid to ask.
"""
if __name__ == '__main__':
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--model_path", type=str, default="stabilityai/StableBeluga-7B")
parser.add_argument("--data_path", type=str, default="https://raw.githubusercontent.com/llm-attacks/llm-attacks/main/data/advbench/harmful_behaviors.csv")
args = parser.parse_args(args=[] if "__file__" not in globals() else sys.argv[1:])
if args.data_path.endswith(".csv"):
dataset = load_dataset("csv", data_files=args.data_path, split="train")
dataset = dataset.rename_column("goal", "text")
dataset = dataset.train_test_split(test_size=0.1, seed=0)
else:
dataset = load_dataset(args.data_path)
prompts = dataset["train"]["text"]
tokenizer = AutoTokenizer.from_pretrained(args.model_path)
tokenizer.add_special_tokens({"pad_token": "<|padding|>"})
tokenizer.padding_side = "left"
tokenizer.truncation_side = "left"
accelerator = Accelerator()
model = AutoModelForCausalLM.from_pretrained(args.model_path, torch_dtype=torch.float16).eval()
model = accelerator.prepare(model)
get_answer = lambda prompt: autocrit.generate(accelerator.unwrap_model(model), tokenizer, few_shots + prompt)[0]
get_critique = lambda prompt: autocrit.generate(accelerator.unwrap_model(model), tokenizer, few_shots + prompt)[0]
reward_model = RewardModel()
revisions = autocrit.revise(
prompts,
get_answer=get_answer,
get_critique=get_critique,
constitution=[
{
"critique_request": "Identify specific ways in which the assistant's last response conflicts with information in the additional context, as it relates to the user query.",
"revision_request": "Please rewrite the assistant response to better align with any information in the additional context as it relates to the user query."
},
],
max_iterations=2,
score_fn=lambda question, answer: reward_model.get_reward(question, answer)
)
# if accelerator.is_main_process:
# with open(f"artifacts/revisions-{args.model_path.split('/')[-1]}-{args.data_path.split('/')[-1].split('.')[0]}.json", "w") as f:
# json.dump(revisions, f)
# n_safe_prior = sum([x["iterations"][0]["score"] for x in revisions])
# n_safe_revised = sum([x["iterations"][-1]["score"] for x in revisions])
# print(f"#prior safe: {n_safe_prior}/{len(revisions)} → #revised safe: {n_safe_revised}/{len(revisions)}")
| [
"train"
] |
2024-01-10 | BGHDDevelopment/ScanBerkeley | Trunk-Transcribe~app~whisper.py | import json
import logging
import os
import subprocess
from csv import DictReader
from threading import Lock
import openai
from app.config import get_ttl_hash, get_whisper_config
from app.task import Task
class WhisperTask(Task):
_model = None
model_lock = Lock()
@property
def model(self):
with self.model_lock:
if self._model is not None:
return self._model
model_name = os.getenv("WHISPER_MODEL")
if model_name:
if os.getenv("WHISPERCPP"):
self._model = WhisperCpp(model_name, os.getenv("WHISPERCPP"))
elif os.getenv("FASTERWHISPER"):
self._model = FasterWhisper(model_name)
else:
self._model = Whisper(model_name)
else:
if os.getenv("OPENAI_API_KEY"):
self._model = WhisperApi()
else:
raise RuntimeError("WHISPER_MODEL env must be set")
return self._model
class Whisper:
def __init__(self, model_name):
import whisper
self.model = whisper.load_model(model_name)
def transcribe(
self,
audio: str,
language: str = "en",
initial_prompt: str | None = None,
**decode_options,
) -> dict:
return self.model.transcribe(
audio=audio,
language=language,
initial_prompt=initial_prompt,
**decode_options,
)
class FasterWhisper:
vad_filter = False
def __init__(self, model_name):
if "cpu" in os.getenv("DESIRED_CUDA", ""):
device = "cpu"
compute_type = "int8"
else:
device = "cuda"
compute_type = "float16"
from faster_whisper import WhisperModel
self.model = WhisperModel(model_name, device=device, compute_type=compute_type)
if os.getenv("FASTER_WHISPER_VAD_FILTER"):
self.vad_filter = True
def transcribe(
self,
audio: str,
language: str = "en",
initial_prompt: str | None = None,
**decode_options,
):
segments, _ = self.model.transcribe(
audio=audio,
language=language,
initial_prompt=initial_prompt,
vad_filter=self.vad_filter,
**decode_options,
)
segments = list(segments) # The transcription will actually run here.
result = {
"segments": [],
"text": None,
}
if len(segments):
result["segments"] = [dict(segment._asdict()) for segment in segments]
result["text"] = "\n".join(
[segment["text"] for segment in result["segments"]]
)
return result
class WhisperCpp:
def __init__(self, model_name, model_dir):
model_path = f"{model_dir}/ggml-{model_name}.bin"
if os.path.isfile(model_path):
self.model_path = model_path
def transcribe(
self,
audio: str,
language: str = "en",
initial_prompt: str | None = None,
**decode_options,
):
args = [
"whisper-cpp",
"--model",
self.model_path,
"--language",
language,
"--output-csv",
]
if initial_prompt:
args += ["--prompt", initial_prompt]
if "best_of" in decode_options and decode_options["best_of"]:
args += ["--best-of", str(decode_options["best_of"])]
if "beam_size" in decode_options and decode_options["beam_size"]:
args += ["--beam-size", str(decode_options["beam_size"])]
args.append(audio)
p = subprocess.run(args)
p.check_returncode()
result = {"segments": [], "text": None}
with open(f"{audio}.csv", newline="") as csvfile:
transcript = DictReader(csvfile)
for line in transcript:
# Handle quirks of whisper.cpp
if (
len(line["text"])
and "[BLANK_AUDIO]" not in line["text"]
and "[SOUND]" not in line["text"]
):
result["segments"].append(
{
"start": float(line["start"]) / 1000,
"end": float(line["end"]) / 1000,
"text": line["text"],
}
)
if len(result["segments"]):
result["text"] = "\n".join(
[segment["text"] for segment in result["segments"]]
)
return result
class WhisperApi:
openai = None
def __init__(self):
openai.api_key = os.getenv("OPENAI_API_KEY")
def transcribe(
self,
audio: str,
language: str = "en",
initial_prompt: str | None = None,
**decode_options,
):
audio_file = open(audio, "rb")
prompt = "This is a police radio dispatch transcript. Phrases such as 10-4, copy, code-4, code-3, code-2, 97, 211, 215, 484, 459, GOA, UTL, CHP, TAC-#, Car-#, S-#, Medic-#, Truck-#, Engine-#, Adam-#, Fulk, Falck, 10851, 11-97 10-10, 10-8, 10-4, RP, RO, Badge, Pinging, IFO, Control, Berkeley-1 are common. Example: 2-Adam-11, Adam-9, S-22 ID-1, ID-Tech, etc. Plates and names are spelled using the phonetic alphabet. Descriptions can include WMA, BMA, MFA, WFA or others. Units may abbreviate with terms like affirm, sus-per, copy, ring, cross of, in custody, on-scene, casings, shots fired, detained, loud report, and some street names like Telegraph can be called tele. Shattuck, Milvia, San Pablo are other common streets. Local hospitals can be refered to as Alta Bates (AVH), Highland, Summit. When you hear these write them out as if you are hearing them from the radio."
if initial_prompt:
prompt += "The following words may appear: " + initial_prompt
return openai.Audio.transcribe(
model="whisper-1",
file=audio_file,
prompt=initial_prompt,
response_format="verbose_json",
language=language,
)
def transcribe(
model,
model_lock: Lock,
audio_file: str,
initial_prompt: str = "",
) -> dict:
whisper_kwargs = get_whisper_config(get_ttl_hash(cache_seconds=60))
# TODO: Remove the lock if we are using Whisper.cpp
with model_lock:
result = model.transcribe(
audio_file, language="en", initial_prompt=initial_prompt, **whisper_kwargs
)
logging.debug("Transcription result: " + json.dumps(result, indent=4))
return result
| [
"This is a police radio dispatch transcript. Phrases such as 10-4, copy, code-4, code-3, code-2, 97, 211, 215, 484, 459, GOA, UTL, CHP, TAC-#, Car-#, S-#, Medic-#, Truck-#, Engine-#, Adam-#, Fulk, Falck, 10851, 11-97 10-10, 10-8, 10-4, RP, RO, Badge, Pinging, IFO, Control, Berkeley-1 are common. Example: 2-Adam-11, Adam-9, S-22 ID-1, ID-Tech, etc. Plates and names are spelled using the phonetic alphabet. Descriptions can include WMA, BMA, MFA, WFA or others. Units may abbreviate with terms like affirm, sus-per, copy, ring, cross of, in custody, on-scene, casings, shots fired, detained, loud report, and some street names like Telegraph can be called tele. Shattuck, Milvia, San Pablo are other common streets. Local hospitals can be refered to as Alta Bates (AVH), Highland, Summit. When you hear these write them out as if you are hearing them from the radio.",
"The following words may appear: PLACEHOLDER"
] |
2024-01-10 | iejMac/GPTReview | review.py | import os
import requests
import json
import openai
WHITELIST = ["iejMac"] # move this to github actions (probably some 'uses' I don't know about
def get_review():
github_env = os.getenv("GITHUB_ENV")
with open(github_env, "r") as f:
variables = dict([line.split("=") for line in f.read().splitlines()])
if variables["GITHUB_ACTOR"] not in WHITELIST: # only run review for whitelisted users
return
pr_link = variables["LINK"]
openai.api_key = variables["OPENAI_API_KEY"]
request_link = "https://patch-diff.githubusercontent.com/raw/" + pr_link[len("https://github.com/"):] + ".patch"
patch = requests.get(request_link).text
question = "\n Can you summarize this GitHub Pull Request for me and suggest possible improvements?"
prompt = patch[:4096 - len(question.split(" "))] + question
# model = "text-ada-001"
model = "text-davinci-003"
response = openai.Completion.create(
engine=model,
prompt=prompt,
temperature=0.9,
max_tokens=512, # TODO: need to find a dynamic way of setting this according to the prompt
top_p=1.0,
frequency_penalty=0.0,
presence_penalty=0.0
)
review = response['choices'][0]['text']
ACCESS_TOKEN = variables["GITHUB_TOKEN"]
headers = {
'Accept': 'application/vnd.github+json',
'Authorization': f'Bearer {ACCESS_TOKEN}',
'Content-Type': 'application/x-www-form-urlencoded',
}
data = {"body": review}
data = json.dumps(data)
OWNER = pr_link.split("/")[-4]
REPO = pr_link.split("/")[-3]
PR_NUMBER = pr_link.split("/")[-1]
response = requests.post(f'https://api.github.com/repos/{OWNER}/{REPO}/issues/{PR_NUMBER}/comments', headers=headers, data=data)
print(response.json())
if __name__ == "__main__":
get_review()
| [
"PLACEHOLDER\n Can you summarize this GitHub Pull Request for me and suggest possible improvements?"
] |
2024-01-10 | msy7822-ux/openai-rag-try | multi_modal_rag~retriever_utils.py | from langchain.schema.document import Document
from langchain.retrievers.multi_vector import MultiVectorRetriever
import uuid
def create_vectorstore(
vectorstore,
docstore,
texts,
table_summaries,
image_summaries,
tables,
img_base64_list,
):
id_key = "doc_id"
retriever = MultiVectorRetriever(
vectorstore=vectorstore, docstore=docstore, id_key=id_key
)
# Add texts
doc_ids = [str(uuid.uuid4()) for _ in texts]
for i, s in enumerate(texts):
retriever.vectorstore.add_documents(
[Document(page_content=s, metadata={id_key: doc_ids[i]})]
)
retriever.docstore.mset(list(zip(doc_ids, texts)))
# Add tables
table_ids = [str(uuid.uuid4()) for _ in tables]
for i, s in enumerate(table_summaries):
retriever.vectorstore.add_documents(
[Document(page_content=s, metadata={id_key: table_ids[i]})]
)
retriever.docstore.mset(list(zip(table_ids, tables)))
# Add image summaries
img_ids = [str(uuid.uuid4()) for _ in img_base64_list]
for i, s in enumerate(image_summaries):
retriever.vectorstore.add_documents(
[Document(page_content=s, metadata={id_key: img_ids[i]})]
)
retriever.docstore.mset(list(zip(img_ids, img_base64_list)))
return retriever
| [] |
2024-01-10 | msy7822-ux/openai-rag-try | npaka_rag~npaka_rag_tutorial_1.py | ## URL: https://note.com/npaka/n/nf2849b26a524
import os
import openai
from dotenv import load_dotenv
from langchain.document_loaders import TextLoader
load_dotenv()
openai.api_key = os.getenv("OPENAI_API_KEY")
loader = TextLoader("./npaka_rag/bocchi.txt")
documents = loader.load()
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.text_splitter import CharacterTextSplitter
text_splitter = CharacterTextSplitter(chunk_size=700, chunk_overlap=0)
texts = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
retriever = Chroma.from_documents(texts, embeddings).as_retriever()
from langchain.llms import OpenAI
from langchain.chains import RetrievalQA
# RetrievalQAチェーンの生成
qa = RetrievalQA.from_chain_type(llm=OpenAI(), chain_type="stuff", retriever=retriever)
print(qa.run("未確認ライオットに参加するために必要なことは何ですか？"))
| [] |
2024-01-10 | msy7822-ux/openai-rag-try | _openai.py | # import os
# import pprint
# from openai import OpenAI
# client = OpenAI(api_key="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")
# pp = pprint.PrettyPrinter(indent=4, width=40, sort_dicts=False)
# api_key = "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"
# client.api_key = api_key
# response = client.chat.completions.create(
# model='gpt-3.5-turbo',
# messages=[{'role': 'user', 'content': '1 + 1 = ?'}],
# temperature=0.0,
# )
# pp.pprint(response)
| [] |
2024-01-10 | Olney1/ChatGPT-OpenAI-Smart-Speaker | smart_speaker.py | import os
import openai
import pyaudio
import speech_recognition as sr
from gtts import gTTS
from playsound import playsound
from dotenv import load_dotenv
import apa102
import threading
from gpiozero import LED
try:
import queue as Queue
except ImportError:
import Queue as Queue
from alexa_led_pattern import AlexaLedPattern
# Load the environment variables
load_dotenv()
# load pixels Class
class Pixels:
PIXELS_N = 12
def __init__(self, pattern=AlexaLedPattern):
self.pattern = pattern(show=self.show)
self.dev = apa102.APA102(num_led=self.PIXELS_N)
self.power = LED(5)
self.power.on()
self.queue = Queue.Queue()
self.thread = threading.Thread(target=self._run)
self.thread.daemon = True
self.thread.start()
self.last_direction = None
def wakeup(self, direction=0):
self.last_direction = direction
def f():
self.pattern.wakeup(direction)
self.put(f)
def listen(self):
if self.last_direction:
def f():
self.pattern.wakeup(self.last_direction)
self.put(f)
else:
self.put(self.pattern.listen)
def think(self):
self.put(self.pattern.think)
def speak(self):
self.put(self.pattern.speak)
def off(self):
self.put(self.pattern.off)
def put(self, func):
self.pattern.stop = True
self.queue.put(func)
def _run(self):
while True:
func = self.queue.get()
self.pattern.stop = False
func()
def show(self, data):
for i in range(self.PIXELS_N):
self.dev.set_pixel(i, int(data[4*i + 1]), int(data[4*i + 2]), int(data[4*i + 3]))
self.dev.show()
pixels = Pixels()
# settings and keys
openai.api_key = os.environ.get('OPENAI_API_KEY')
model_engine = "text-davinci-003"
language = 'en'
def recognize_speech():
# obtain audio from the microphone
r = sr.Recognizer()
with sr.Microphone() as source:
print("Waiting for wake word...")
while True:
try:
r.adjust_for_ambient_noise(source)
audio_stream = r.listen(source)
# recognize speech using Google Speech Recognition
try:
# convert the audio to text
print("Google Speech Recognition thinks you said " + r.recognize_google(audio_stream))
speech = r.recognize_google(audio_stream)
if ("Lily" not in speech) and ("lily" not in speech):
# the wake word was not detected in the speech
print("Wake word not detected in the speech")
# Close the current microphone object
return False
else:
# the wake word was detected in the speech
print("Found wake word!")
# wake up the display
pixels.wakeup()
return True
except sr.UnknownValueError:
print("Google Speech Recognition could not understand audio")
print("Waiting for wake word...")
return False
except sr.RequestError as e:
print("Could not request results from Google Speech Recognition service; {0}".format(e))
print("Waiting for wake word...")
return False
except KeyboardInterrupt:
print("Interrupted by User Keyboard")
break
def speech():
# obtain audio from the microphone
r = sr.Recognizer()
with sr.Microphone() as source:
print("Waiting for user to speak...")
while True:
try:
r.adjust_for_ambient_noise(source)
audio_stream = r.listen(source)
# recognize speech using Google Speech Recognition
try:
# convert the audio to text
print("Google Speech Recognition thinks you said " + r.recognize_google(audio_stream))
speech = r.recognize_google(audio_stream)
# wake up thinking LEDs
pixels.think()
return speech
except sr.UnknownValueError:
print("Google Speech Recognition could not understand audio")
pixels.off()
print("Waiting for user to speak...")
continue
except sr.RequestError as e:
print("Could not request results from Google Speech Recognition service; {0}".format(e))
pixels.off()
print("Waiting for user to speak...")
continue
except KeyboardInterrupt:
print("Interrupted by User Keyboard")
break
def chatgpt_response(prompt):
# send the converted audio text to chatgpt
response = openai.Completion.create(
engine=model_engine,
prompt=prompt,
max_tokens=1024,
n=1,
temperature=0.7,
)
return response
def generate_audio_file(text):
# convert the text response from chatgpt to an audio file
audio = gTTS(text=text, lang=language, slow=False)
# save the audio file
audio.save("response.mp3")
def play_audio_file():
# play the audio file and wake speaking LEDs
pixels.speak()
# os.system("mpg321 response.mp3")
playsound("response.mp3", block=False) # There’s an optional second argument, block, which is set to True by default. Setting it to False makes the function run asynchronously.
def main():
# run the program
while True:
if recognize_speech():
prompt = speech()
print(f"This is the prompt being sent to OpenAI: {prompt}")
responses = chatgpt_response(prompt)
message = responses.choices[0].text
print(message)
generate_audio_file(message)
play_audio_file()
pixels.off()
else:
print("Speech was not recognised")
pixels.off()
if __name__ == "__main__":
main()
| [] |
2024-01-10 | michahu/transformers | examples~calibrate_expectation.py | from __future__ import absolute_import, division, print_function, unicode_literals
import argparse
import logging
from tqdm import trange
import torch
import torch.nn.functional as F
import torch.optim as optim
import numpy as np
from transformers import GPT2Config, OpenAIGPTConfig, XLNetConfig, TransfoXLConfig
from transformers import GPT2LMHeadModel, GPT2Tokenizer
from transformers import OpenAIGPTLMHeadModel, OpenAIGPTTokenizer
from transformers import XLNetLMHeadModel, XLNetTokenizer
from transformers import TransfoXLLMHeadModel, TransfoXLTokenizer
from generate_with_calibration import get_lookahead_entropies
import logging
logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s',
datefmt = '%m/%d/%Y %H:%M:%S',
level = logging.INFO)
logger = logging.getLogger(__name__)
ALL_MODELS = sum((tuple(conf.pretrained_config_archive_map.keys()) for conf in (GPT2Config, OpenAIGPTConfig, XLNetConfig, TransfoXLConfig)), ())
MODEL_CLASSES = {
'gpt2': (GPT2LMHeadModel, GPT2Tokenizer),
'openai-gpt': (OpenAIGPTLMHeadModel, OpenAIGPTTokenizer),
'xlnet': (XLNetLMHeadModel, XLNetTokenizer),
'transfo-xl': (TransfoXLLMHeadModel, TransfoXLTokenizer),
}
def set_seed(args):
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if args.n_gpu > 0:
torch.cuda.manual_seed_all(args.seed)
def calibrate(model, tokenizer, path, save_path, vocab_size, batch_size=512, alpha=0.0, top_k=0, iters=10, threshold=1e-5, device='cpu'):
alpha = torch.tensor([alpha], requires_grad=True)
total_loss = CEL(model, tokenizer, path, alpha, vocab_size, batch_size, top_k, device)
print(f'Total loss: {total_loss.item()}. Alpha: {alpha.item()}')
last_alpha = alpha.item()
for _ in range(iters):
grad_a = torch.autograd.grad(total_loss, alpha, create_graph=True)
grad2_a = torch.autograd.grad(grad_a, alpha)
alpha.data -= (grad_a[0] / grad2_a[0]).data
np.savez(save_path, alpha=alpha.item())
total_loss = CEL(model, tokenizer, path, alpha, vocab_size, batch_size, top_k, device)
print(f'Total loss: {total_loss.item()}. Alpha: {alpha.item()}')
if abs(alpha.data - last_alpha) < threshold:
break
last_alpha = alpha.item()
return alpha
def CEL(model, tokenizer, path, alpha, vocab_size, batch_size=512, top_k=0, device='cpu'):
# calculates the CEL on a single context.
def CELHelper(context):
N = len(context)
context_CEL = torch.tensor([0.0])
for i in range(1, N):
with torch.no_grad():
context_i = torch.tensor(context[:i], dtype = torch.long, device=device).unsqueeze(0)
inputs = {'input_ids': context_i}
next_logits = model(**inputs)[0][:, -1, :].detach().cpu()
if top_k == 0:
candidates = None
else:
candidates = torch.argsort(next_logits[0], descending=True,)[:top_k]
lookahead_ents = get_lookahead_entropies(
model = model,
context = context_i[0],
batch_size = batch_size,
vocab_size = vocab_size,
candidates = candidates,
device = device
).cpu()
next_probs = F.softmax(next_logits, dim=-1)[0]
if top_k != 0:
# replace uncomputed entropies with average (for centered adjustment)
next_probs = next_probs[lookahead_ents != -1]
top_average_ent = (lookahead_ents[lookahead_ents != -1] * next_probs / next_probs.sum()).sum()
lookahead_ents[lookahead_ents != -1] = top_average_ent
# context[i] is the next word
context_CEL -= torch.log(
F.softmax(next_logits - alpha * lookahead_ents, dim=-1)[0][context[i]]
)
return context_CEL
total_CEL = torch.tensor([0.0])
with open(path) as fp:
for line in fp:
context = tokenizer.encode(line)
# one way to fix memory issues: uncomment the below
# if (len(context) > 100):
# continue
total_CEL += CELHelper(context)
return total_CEL
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--model_type", default=None, type=str, required=True,
help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()))
parser.add_argument("--model_name_or_path", default=None, type=str, required=True,
help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join(ALL_MODELS))
parser.add_argument("--training_path", default=None, type=str, required=True,
help="Path to training data")
parser.add_argument("--save_path", default=None, type=str, required=True)
parser.add_argument("--batch_size", type=int, default=128)
parser.add_argument("--alpha", type=float, default=0.0)
parser.add_argument("--top_k", type=int, default=0)
parser.add_argument("--no_cuda", action='store_true',
help="Avoid using CUDA when available")
parser.add_argument('--seed', type=int, default=42,
help="random seed for initialization")
args = parser.parse_args()
args.device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
args.n_gpu = torch.cuda.device_count()
set_seed(args)
args.model_type = args.model_type.lower()
model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
tokenizer = tokenizer_class.from_pretrained(args.model_name_or_path)
model = model_class.from_pretrained(args.model_name_or_path)
model.to(args.device)
model.eval()
print(args)
vocab_size = tokenizer.vocab_size
print('vocab_size:', vocab_size)
logging.getLogger('transformers.tokenization_utils').setLevel(logging.ERROR)
alpha = calibrate(
model=model,
tokenizer=tokenizer,
path=args.training_path,
save_path=args.save_path,
vocab_size=vocab_size,
batch_size=args.batch_size,
alpha=args.alpha,
top_k=args.top_k,
device=args.device,
)
print(alpha)
if __name__ == '__main__':
main()
| [] |
2024-01-10 | michahu/transformers | examples~calibrate_v3.py | from __future__ import absolute_import, division, print_function, unicode_literals
import argparse
import logging
from tqdm import trange
import torch
import torch.nn.functional as F
import torch.optim as optim
import numpy as np
from transformers import GPT2Config, OpenAIGPTConfig, XLNetConfig, TransfoXLConfig
from transformers import GPT2LMHeadModel, GPT2Tokenizer
from transformers import OpenAIGPTLMHeadModel, OpenAIGPTTokenizer
from transformers import XLNetLMHeadModel, XLNetTokenizer
from transformers import TransfoXLLMHeadModel, TransfoXLTokenizer
from generate_with_calibration import get_lookahead_entropies
import logging
logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s',
datefmt = '%m/%d/%Y %H:%M:%S',
level = logging.INFO)
logger = logging.getLogger(__name__)
ALL_MODELS = sum((tuple(conf.pretrained_config_archive_map.keys()) for conf in (GPT2Config, OpenAIGPTConfig, XLNetConfig, TransfoXLConfig)), ())
MODEL_CLASSES = {
'gpt2': (GPT2LMHeadModel, GPT2Tokenizer),
'openai-gpt': (OpenAIGPTLMHeadModel, OpenAIGPTTokenizer),
'xlnet': (XLNetLMHeadModel, XLNetTokenizer),
'transfo-xl': (TransfoXLLMHeadModel, TransfoXLTokenizer),
}
def set_seed(args):
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if args.n_gpu > 0:
torch.cuda.manual_seed_all(args.seed)
def calibrate(model, tokenizer, path, save_path, vocab_size, batch_size=512, alpha=0.0, top_k=0, iters=10, threshold=1e-5, device='cpu'):
alpha = torch.tensor([alpha], requires_grad=True)
total_loss = CEL(model, tokenizer, path, alpha, vocab_size, batch_size, top_k, device)
print(f'Total loss: {total_loss.item()}. Alpha: {alpha.item()}')
last_alpha = alpha.item()
for _ in range(iters):
grad_a = torch.autograd.grad(total_loss, alpha, create_graph=True)
grad2_a = torch.autograd.grad(grad_a, alpha)
alpha.data -= (grad_a[0] / grad2_a[0]).data
np.savez(save_path, alpha=alpha.item())
total_loss = CEL(model, tokenizer, path, alpha, vocab_size, batch_size, top_k, device)
print(f'Total loss: {total_loss.item()}. Alpha: {alpha.item()}')
if abs(alpha.data - last_alpha) < threshold:
break
last_alpha = alpha.item()
return alpha
def CEL(model, tokenizer, path, alpha, vocab_size, batch_size=512, top_k=0, device='cpu'):
# calculates the CEL on a single context.
def CELHelper(context):
N = len(context)
context_CEL = torch.tensor([0.0])
for i in range(1, N):
context_i = torch.tensor(context[:i], dtype = torch.long, device=device).unsqueeze(0)
inputs = {'input_ids': context_i}
# detaching possibly unnecessary under current implementation
next_logits = model(**inputs)[0][:, -1, :].detach().cpu()
if top_k == 0:
candidates = None
else:
candidates = torch.argsort(next_logits[0], descending=True,)[:top_k]
lookahead_ents = get_lookahead_entropies(
model = model,
context = context_i[0],
batch_size = batch_size,
vocab_size = vocab_size,
candidates = candidates,
device = device
).cpu()
if top_k != 0:
# replace uncomputed entropies with average (for centered adjustment)
top_average_ent = lookahead_ents[lookahead_ents != -1].mean()
lookahead_ents[lookahead_ents != -1] = top_average_ent
next_word = context[i]
next_probs = F.softmax(next_logits, dim=-1)[0]
Pr = next_probs[next_word]
H = lookahead_ents[next_word]
Za = torch.dot(next_probs, torch.exp(-alpha * lookahead_ents))
# if fail, try separating this denominator.
context_CEL -= torch.log(Pr * torch.exp(-alpha * H) / Za)
return context_CEL
total_CEL = torch.tensor([0.0])
with open(path) as fp:
for line in fp:
context = tokenizer.encode(line)
# one way to fix memory issues: uncomment the below
# if (len(context) > 100):
# continue
total_CEL += CELHelper(context)
return total_CEL
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--model_type", default=None, type=str, required=True,
help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()))
parser.add_argument("--model_name_or_path", default=None, type=str, required=True,
help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join(ALL_MODELS))
parser.add_argument("--training_path", default=None, type=str, required=True,
help="Path to training data")
parser.add_argument("--save_path", default=None, type=str, required=True)
parser.add_argument("--batch_size", type=int, default=128)
parser.add_argument("--alpha", type=float, default=0.0)
parser.add_argument("--top_k", type=int, default=0)
parser.add_argument("--no_cuda", action='store_true',
help="Avoid using CUDA when available")
parser.add_argument('--seed', type=int, default=42,
help="random seed for initialization")
args = parser.parse_args()
args.device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
args.n_gpu = torch.cuda.device_count()
set_seed(args)
args.model_type = args.model_type.lower()
model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
tokenizer = tokenizer_class.from_pretrained(args.model_name_or_path)
model = model_class.from_pretrained(args.model_name_or_path)
model.to(args.device)
model.eval()
print(args)
vocab_size = tokenizer.vocab_size
print('vocab_size:', vocab_size)
logging.getLogger('transformers.tokenization_utils').setLevel(logging.ERROR)
alpha = calibrate(
model=model,
tokenizer=tokenizer,
path=args.training_path,
save_path=args.save_path,
vocab_size=vocab_size,
batch_size=args.batch_size,
alpha=args.alpha,
top_k=args.top_k,
device=args.device,
)
print(alpha)
if __name__ == '__main__':
main()
| [] |
2024-01-10 | michahu/transformers | examples~calibrate.py | from __future__ import absolute_import, division, print_function, unicode_literals
import argparse
import logging
from tqdm import trange
import torch
import torch.nn.functional as F
import torch.optim as optim
import numpy as np
from transformers import GPT2Config, OpenAIGPTConfig, XLNetConfig, TransfoXLConfig
from transformers import GPT2LMHeadModel, GPT2Tokenizer
from transformers import OpenAIGPTLMHeadModel, OpenAIGPTTokenizer
from transformers import XLNetLMHeadModel, XLNetTokenizer
from transformers import TransfoXLLMHeadModel, TransfoXLTokenizer
from generate_with_calibration import get_lookahead_entropies
import logging
logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s',
datefmt = '%m/%d/%Y %H:%M:%S',
level = logging.INFO)
logger = logging.getLogger(__name__)
ALL_MODELS = sum((tuple(conf.pretrained_config_archive_map.keys()) for conf in (GPT2Config, OpenAIGPTConfig, XLNetConfig, TransfoXLConfig)), ())
MODEL_CLASSES = {
'gpt2': (GPT2LMHeadModel, GPT2Tokenizer),
'openai-gpt': (OpenAIGPTLMHeadModel, OpenAIGPTTokenizer),
'xlnet': (XLNetLMHeadModel, XLNetTokenizer),
'transfo-xl': (TransfoXLLMHeadModel, TransfoXLTokenizer),
}
def set_seed(args):
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if args.n_gpu > 0:
torch.cuda.manual_seed_all(args.seed)
def calibrate(model, tokenizer, path, save_path, vocab_size, batch_size=512, alpha=0.0, top_k=0, iters=10, threshold=1e-5, device='cpu'):
alpha = torch.tensor([alpha], requires_grad=True)
total_loss = CEL(model, tokenizer, path, alpha, vocab_size, batch_size, top_k, device)
print(f'Total loss: {total_loss.item()}. Alpha: {alpha.item()}')
last_alpha = alpha.item()
for _ in range(iters):
grad_a = torch.autograd.grad(total_loss, alpha, create_graph=True)
grad2_a = torch.autograd.grad(grad_a, alpha)
alpha.data -= (grad_a[0] / grad2_a[0]).data
np.savez(save_path, alpha=alpha.item())
total_loss = CEL(model, tokenizer, path, alpha, vocab_size, batch_size, top_k, device)
print(f'Total loss: {total_loss.item()}. Alpha: {alpha.item()}')
if abs(alpha.data - last_alpha) < threshold:
break
last_alpha = alpha.item()
return alpha
def CEL(model, tokenizer, path, alpha, vocab_size, batch_size=512, top_k=0, device='cpu'):
# calculates the CEL on a single context.
def CELHelper(context):
N = len(context)
context_CEL = torch.tensor([0.0])
for i in range(1, N):
context_i = torch.tensor(context[:i], dtype = torch.long, device=device).unsqueeze(0)
inputs = {'input_ids': context_i}
next_logits = model(**inputs)[0][:, -1, :].detach().cpu()
if top_k == 0:
candidates = None
else:
candidates = torch.argsort(next_logits[0], descending=True,)[:top_k]
lookahead_ents = get_lookahead_entropies(
model = model,
context = context_i[0],
batch_size = batch_size,
vocab_size = vocab_size,
candidates = candidates,
device = device
).cpu()
if top_k != 0:
# replace uncomputed entropies with average (for centered adjustment)
top_average_ent = lookahead_ents[lookahead_ents != -1].mean()
lookahead_ents[lookahead_ents != -1] = top_average_ent
# context[i] is the next word
context_CEL -= torch.log(
F.softmax(next_logits - alpha * lookahead_ents, dim=-1)[0][context[i]]
)
return context_CEL
total_CEL = torch.tensor([0.0])
with open(path) as fp:
for line in fp:
context = tokenizer.encode(line)
# one way to fix memory issues: uncomment the below
# if (len(context) > 100):
# continue
total_CEL += CELHelper(context)
return total_CEL
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--model_type", default=None, type=str, required=True,
help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()))
parser.add_argument("--model_name_or_path", default=None, type=str, required=True,
help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join(ALL_MODELS))
parser.add_argument("--training_path", default=None, type=str, required=True,
help="Path to training data")
parser.add_argument("--save_path", default=None, type=str, required=True)
parser.add_argument("--batch_size", type=int, default=128)
parser.add_argument("--alpha", type=float, default=0.0)
parser.add_argument("--top_k", type=int, default=0)
parser.add_argument("--no_cuda", action='store_true',
help="Avoid using CUDA when available")
parser.add_argument('--seed', type=int, default=42,
help="random seed for initialization")
args = parser.parse_args()
args.device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
args.n_gpu = torch.cuda.device_count()
set_seed(args)
args.model_type = args.model_type.lower()
model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
tokenizer = tokenizer_class.from_pretrained(args.model_name_or_path)
model = model_class.from_pretrained(args.model_name_or_path)
model.to(args.device)
model.eval()
print(args)
vocab_size = tokenizer.vocab_size
print('vocab_size:', vocab_size)
logging.getLogger('transformers.tokenization_utils').setLevel(logging.ERROR)
alpha = calibrate(
model=model,
tokenizer=tokenizer,
path=args.training_path,
save_path=args.save_path,
vocab_size=vocab_size,
batch_size=args.batch_size,
alpha=args.alpha,
top_k=args.top_k,
device=args.device,
)
print(alpha)
if __name__ == '__main__':
main()
| [] |
2024-01-10 | xuanloct4/langchain | agents_custom.py |
import environment
from agents_tools import search_tool_serpapi
from langchain.agents import Tool, AgentExecutor, BaseSingleActionAgent
from langchain import SerpAPIWrapper
tools = [search_tool_serpapi()]
tool_names = [tool.name for tool in tools]
from typing import List, Tuple, Any, Union
from langchain.schema import AgentAction, AgentFinish
from langchain.memory import ConversationBufferWindowMemory
memory=ConversationBufferWindowMemory(k=2)
##FakeAgent
class FakeAgent(BaseSingleActionAgent):
"""Fake Custom Agent."""
@property
def input_keys(self):
return ["input"]
def plan(
self, intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any
) -> Union[AgentAction, AgentFinish]:
"""Given input, decided what to do.
Args:
intermediate_steps: Steps the LLM has taken to date,
along with observations
**kwargs: User inputs.
Returns:
Action specifying what tool to use.
"""
return AgentAction(tool="Search", tool_input=kwargs["input"], log="")
async def aplan(
self, intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any
) -> Union[AgentAction, AgentFinish]:
"""Given input, decided what to do.
Args:
intermediate_steps: Steps the LLM has taken to date,
along with observations
**kwargs: User inputs.
Returns:
Action specifying what tool to use.
"""
return AgentAction(tool="Search", tool_input=kwargs["input"], log="")
fakeAgent = FakeAgent()
from llms import defaultLLM as llm
# Custom LLM Agent
from langchain.agents import Tool, AgentExecutor, LLMSingleActionAgent, AgentOutputParser
from langchain.prompts import StringPromptTemplate
from langchain import OpenAI, SerpAPIWrapper, LLMChain
from typing import List, Union
from langchain.schema import AgentAction, AgentFinish
import re
# Set up a prompt template
class CustomPromptTemplate(StringPromptTemplate):
# The template to use
template: str
# The list of tools available
tools: List[Tool]
def format(self, **kwargs) -> str:
# Get the intermediate steps (AgentAction, Observation tuples)
# Format them in a particular way
intermediate_steps = kwargs.pop("intermediate_steps")
thoughts = ""
for action, observation in intermediate_steps:
thoughts += action.log
thoughts += f"\nObservation: {observation}\nThought: "
# Set the agent_scratchpad variable to that value
kwargs["agent_scratchpad"] = thoughts
# Create a tools variable from the list of tools provided
kwargs["tools"] = "\n".join([f"{tool.name}: {tool.description}" for tool in self.tools])
# Create a list of tool names for the tools provided
kwargs["tool_names"] = ", ".join([tool.name for tool in self.tools])
return self.template.format(**kwargs)
class CustomOutputParser(AgentOutputParser):
def parse(self, llm_output: str) -> Union[AgentAction, AgentFinish]:
# Check if agent should finish
if "Final Answer:" in llm_output:
return AgentFinish(
# Return values is generally always a dictionary with a single `output` key
# It is not recommended to try anything else at the moment :)
return_values={"output": llm_output.split("Final Answer:")[-1].strip()},
log=llm_output,
)
# Parse out the action and action input
regex = r"Action\s*\d*\s*:(.*?)\nAction\s*\d*\s*Input\s*\d*\s*:[\s]*(.*)"
match = re.search(regex, llm_output, re.DOTALL)
if not match:
raise ValueError(f"Could not parse LLM output: `{llm_output}`")
action = match.group(1).strip()
action_input = match.group(2)
# Return the action and action input
return AgentAction(tool=action, tool_input=action_input.strip(" ").strip('"'), log=llm_output)
# Set up the base template
template = """Answer the following questions as best you can, but speaking as a pirate might speak. You have access to the following tools:
{tools}
Use the following format:
Question: the input question you must answer
Thought: you should always think about what to do
Action: the action to take, should be one of [{tool_names}]
Action Input: the input to the action
Observation: the result of the action
... (this Thought/Action/Action Input/Observation can repeat N times)
Thought: I now know the final answer
Final Answer: the final answer to the original input question
Begin! Remember to speak as a pirate when giving your final answer. Use lots of "Arg"s
Question: {input}
{agent_scratchpad}"""
# Set up the base template
template_with_history = """Answer the following questions as best you can, but speaking as a pirate might speak. You have access to the following tools:
{tools}
Use the following format:
Question: the input question you must answer
Thought: you should always think about what to do
Action: the action to take, should be one of [{tool_names}]
Action Input: the input to the action
Observation: the result of the action
... (this Thought/Action/Action Input/Observation can repeat N times)
Thought: I now know the final answer
Final Answer: the final answer to the original input question
Begin! Remember to speak as a pirate when giving your final answer. Use lots of "Arg"s
Previous conversation history:
{history}
New question: {input}
{agent_scratchpad}"""
prompt = CustomPromptTemplate(
template=template,
tools=tools,
# This omits the `agent_scratchpad`, `tools`, and `tool_names` variables because those are generated dynamically
# This includes the `intermediate_steps` variable because that is needed
input_variables=["input", "intermediate_steps"]
)
prompt_with_history = CustomPromptTemplate(
template=template_with_history,
tools=tools,
# This omits the `agent_scratchpad`, `tools`, and `tool_names` variables because those are generated dynamically
# This includes the `intermediate_steps` variable because that is needed
input_variables=["input", "intermediate_steps", "history"]
)
llm_chain = LLMChain(llm=llm, prompt=prompt_with_history)
output_parser = CustomOutputParser()
llmSingleActionAgentWithMemory = LLMSingleActionAgent(
llm_chain=llm_chain,
output_parser=output_parser,
stop=["\nObservation:"],
allowed_tools=tool_names
)
# LLM chain consisting of the LLM and a prompt
llm_chain = LLMChain(llm=llm, prompt=prompt)
llmSingleActionAgent = LLMSingleActionAgent(
llm_chain=llm_chain,
output_parser=output_parser,
stop=["\nObservation:"],
allowed_tools=tool_names
)
# Custom MRKL Agent
prefix = """Answer the following questions as best you can, but speaking as a pirate might speak. You have access to the following tools:"""
suffix = """Begin! Remember to speak as a pirate when giving your final answer. Use lots of "Args"
Question: {input}
{agent_scratchpad}"""
from langchain.agents import ZeroShotAgent
mrklPrompt = ZeroShotAgent.create_prompt(
tools,
prefix=prefix,
suffix=suffix,
input_variables=["input", "agent_scratchpad"]
)
print(mrklPrompt.template)
mrklAgent = ZeroShotAgent(llm_chain=LLMChain(llm=llm, prompt=mrklPrompt), allowed_tools=tool_names)
multiInputPrefix = """Answer the following questions as best you can. You have access to the following tools:"""
multiInputSuffix = """When answering, you MUST speak in the following language: {language}.
Question: {input}
{agent_scratchpad}"""
multiInputPrompt = ZeroShotAgent.create_prompt(
tools,
prefix=multiInputPrefix,
suffix=multiInputSuffix,
input_variables=["input", "language", "agent_scratchpad"]
)
# print(multiInputPrompt.template)
multiInputMRKLAgent = ZeroShotAgent(llm_chain=LLMChain(llm=llm, prompt=multiInputPrompt), tools=tools)
# agent_executor = AgentExecutor.from_agent_and_tools(agent=llmSingleActionAgent, tools=tools, verbose=True)
# agent_executor.run("How many people live in canada as of 2023?")
# agent_executor = AgentExecutor.from_agent_and_tools(agent=llmSingleActionAgentWithMemory, tools=tools, verbose=True, memory=memory)
# agent_executor.run("How many people live in canada as of 2023?")
# agent_executor.run("how about in mexico?")
# agent_executor = AgentExecutor.from_agent_and_tools(agent=fakeAgent, tools=tools, verbose=True)
# agent_executor.run("Search for Leo DiCaprio's girlfriend on the internet.")
# agent_executor = AgentExecutor.from_agent_and_tools(agent=mrklAgent, tools=tools, verbose=True)
# agent_executor.run("How many people live in canada as of 2023?")
agent_executor = AgentExecutor.from_agent_and_tools(agent=multiInputMRKLAgent, tools=tools, verbose=True)
agent_executor.run(input="How many people live in canada as of 2023?", language="italian") | [
"agent_scratchpad",
"Answer the following questions as best you can, but speaking as a pirate might speak. You have access to the following tools:\n\n{tools}\n\nUse the following format:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction: the action to take, should be one of [{tool_names}]\nAction Input: the input to the action\nObservation: the result of the action\n... (this Thought/Action/Action Input/Observation can repeat N times)\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question\n\nBegin! Remember to speak as a pirate when giving your final answer. Use lots of \"Arg\"s\n\nQuestion: {input}\n{agent_scratchpad}",
"Answer the following questions as best you can, but speaking as a pirate might speak. You have access to the following tools:\n\n{tools}\n\nUse the following format:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction: the action to take, should be one of [{tool_names}]\nAction Input: the input to the action\nObservation: the result of the action\n... (this Thought/Action/Action Input/Observation can repeat N times)\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question\n\nBegin! Remember to speak as a pirate when giving your final answer. Use lots of \"Arg\"s\n\nPrevious conversation history:\n{history}\n\nNew question: {input}\n{agent_scratchpad}",
"input",
"intermediate_steps",
"language"
] |
2024-01-10 | xuanloct4/langchain | chatmodels.py | import environment
import os
# Anthropic
def AnthropicChatModel():
from langchain.chat_models import ChatAnthropic
from langchain.prompts.chat import (
ChatPromptTemplate,
SystemMessagePromptTemplate,
AIMessagePromptTemplate,
HumanMessagePromptTemplate,
)
from langchain.schema import (
AIMessage,
HumanMessage,
SystemMessage
)
chat = ChatAnthropic(anthropic_api_key=os.environ.get['ANTHROPIC_API_KEY'])
## ChatAnthropic also supports async and streaming functionality
# from langchain.callbacks.manager import CallbackManager
# from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
# await chat.agenerate([messages])
# chat = ChatAnthropic(streaming=True, verbose=True, callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]))
# chat(messages)
return chat
def PromptLayerChatModel():
# pip install promptlayer
import os
import promptlayer
from langchain.chat_models import PromptLayerChatOpenAI
from langchain.schema import HumanMessage
# os.environ["PROMPTLAYER_API_KEY"] = "**********"
chat = PromptLayerChatOpenAI(pl_tags=["langchain"])
chat([HumanMessage(content="I am a cat and I want")])
chat = PromptLayerChatOpenAI(return_pl_id=True)
chat_results = chat.generate([[HumanMessage(content="I am a cat and I want")]])
for res in chat_results.generations:
pl_request_id = res[0].generation_info["pl_request_id"]
promptlayer.track.score(request_id=pl_request_id, score=100)
return chat
def AzureChatModel():
from langchain.chat_models import AzureChatOpenAI
from langchain.schema import HumanMessage
BASE_URL = "https://${TODO}.openai.azure.com"
API_KEY = "..."
DEPLOYMENT_NAME = "chat"
chat = AzureChatOpenAI(
openai_api_base=BASE_URL,
openai_api_version="2023-03-15-preview",
deployment_name=DEPLOYMENT_NAME,
openai_api_key=API_KEY,
openai_api_type = "azure",
)
def OpenAIChatModel():
from langchain.chat_models import ChatOpenAI
from langchain.prompts.chat import (
ChatPromptTemplate,
SystemMessagePromptTemplate,
AIMessagePromptTemplate,
HumanMessagePromptTemplate,
)
from langchain.schema import (
AIMessage,
HumanMessage,
SystemMessage
)
chat = ChatOpenAI(temperature=0)
return chat
def defaultChatModel():
# chatModel = AnthropicChatModel()
chatModel = PromptLayerChatModel()
return chatModel
from langchain.schema import HumanMessage, SystemMessage
messages = [
HumanMessage(content="Translate this sentence from English to French. I love programming.")
]
# messages = [
# SystemMessage(content="You are a helpful assistant that translates English to French."),
# HumanMessage(content="Translate this sentence from English to French. I love programming.")
# ]
chat = defaultChatModel()
chat(messages)
from langchain.prompts import (
ChatPromptTemplate,
PromptTemplate,
SystemMessagePromptTemplate,
AIMessagePromptTemplate,
HumanMessagePromptTemplate,
)
from langchain.schema import (
AIMessage,
HumanMessage,
SystemMessage
)
from langchain.prompts.chat import (
ChatPromptTemplate,
SystemMessagePromptTemplate,
AIMessagePromptTemplate,
HumanMessagePromptTemplate,
)
template="You are a helpful assistant that translates {input_language} to {output_language}."
system_message_prompt = SystemMessagePromptTemplate.from_template(template)
human_template="{text}"
human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)
# Or If you wanted to construct the MessagePromptTemplate more directly, you could create a PromptTemplate outside and then pass it in, eg:
prompt=PromptTemplate(
template="You are a helpful assistant that translates {input_language} to {output_language}.",
input_variables=["input_language", "output_language"],
)
system_message_prompt_2 = SystemMessagePromptTemplate(prompt=prompt)
assert system_message_prompt == system_message_prompt_2
chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt])
# As string
output = chat_prompt.format(input_language="English", output_language="French", text="I love programming.")
# or alternatively
output_2 = chat_prompt.format_prompt(input_language="English", output_language="French", text="I love programming.").to_string()
assert output == output_2
# As ChatPromptValue
chat_prompt.format_prompt(input_language="English", output_language="French", text="I love programming.")
# As list of Message objects
chat_prompt.format_prompt(input_language="English", output_language="French", text="I love programming.").to_messages()
# get a chat completion from the formatted messages
chat(chat_prompt.format_prompt(input_language="English", output_language="French", text="I love programming.").to_messages())
from langchain.prompts import ChatMessagePromptTemplate
prompt = "May the {subject} be with you"
chat_message_prompt = ChatMessagePromptTemplate.from_template(role="Jedi", template=prompt)
chat_message_prompt.format(subject="force")
from langchain.prompts import MessagesPlaceholder
human_prompt = "Summarize our conversation so far in {word_count} words."
human_message_template = HumanMessagePromptTemplate.from_template(human_prompt)
chat_prompt = ChatPromptTemplate.from_messages([MessagesPlaceholder(variable_name="conversation"), human_message_template])
human_message = HumanMessage(content="What is the best way to learn programming?")
ai_message = AIMessage(content="""\
1. Choose a programming language: Decide on a programming language that you want to learn.
2. Start with the basics: Familiarize yourself with the basic programming concepts such as variables, data types and control structures.
3. Practice, practice, practice: The best way to learn programming is through hands-on experience\
""")
chat_prompt.format_prompt(conversation=[human_message, ai_message], word_count="10").to_messages()
| [
"What is the best way to learn programming?",
"I am a cat and I want",
"You are a helpful assistant that translates {input_language} to {output_language}.",
"input_language",
"conversation",
"May the {subject} be with you",
"Summarize our conversation so far in {word_count} words.",
"[PLACEHOLDER, PLACEHOLDER]",
"Translate this sentence from English to French. I love programming.",
"output_language",
"1. Choose a programming language: Decide on a programming language that you want to learn. \n\n2. Start with the basics: Familiarize yourself with the basic programming concepts such as variables, data types and control structures.\n\n3. Practice, practice, practice: The best way to learn programming is through hands-on experience",
"{text}"
] |
2024-01-10 | xuanloct4/langchain | question_answering.py |
import environment
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Chroma
from langchain.docstore.document import Document
from langchain.prompts import PromptTemplate
from langchain.indexes.vectorstore import VectorstoreIndexCreator
from llms import defaultLLM as llm
from embeddings import defaultEmbeddings as embedding
with open("./documents/state_of_the_union.txt") as f:
state_of_the_union = f.read()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_text(state_of_the_union)
docsearch = Chroma.from_texts(texts, embedding, metadatas=[{"source": str(i)} for i in range(len(texts))]).as_retriever()
query = "What did the president say about Justice Breyer"
docs = docsearch.get_relevant_documents(query)
from langchain.chains.question_answering import load_qa_chain
chain = load_qa_chain(llm, chain_type="stuff")
query = "What did the president say about Justice Breyer"
chain.run(input_documents=docs, question=query)
chain = load_qa_chain(llm, chain_type="stuff")
query = "What did the president say about Justice Breyer"
chain({"input_documents": docs, "question": query}, return_only_outputs=True)
prompt_template = """Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.
{context}
Question: {question}
Answer in Italian:"""
PROMPT = PromptTemplate(
template=prompt_template, input_variables=["context", "question"]
)
chain = load_qa_chain(llm, chain_type="stuff", prompt=PROMPT)
chain({"input_documents": docs, "question": query}, return_only_outputs=True)
chain = load_qa_chain(llm, chain_type="map_reduce")
query = "What did the president say about Justice Breyer"
chain({"input_documents": docs, "question": query}, return_only_outputs=True)
chain = load_qa_chain(llm, chain_type="map_reduce", return_map_steps=True)
chain({"input_documents": docs, "question": query}, return_only_outputs=True)
question_prompt_template = """Use the following portion of a long document to see if any of the text is relevant to answer the question.
Return any relevant text translated into italian.
{context}
Question: {question}
Relevant text, if any, in Italian:"""
QUESTION_PROMPT = PromptTemplate(
template=question_prompt_template, input_variables=["context", "question"]
)
combine_prompt_template = """Given the following extracted parts of a long document and a question, create a final answer italian.
If you don't know the answer, just say that you don't know. Don't try to make up an answer.
QUESTION: {question}
=========
{summaries}
=========
Answer in Italian:"""
COMBINE_PROMPT = PromptTemplate(
template=combine_prompt_template, input_variables=["summaries", "question"]
)
chain = load_qa_chain(llm, chain_type="map_reduce", return_map_steps=True, question_prompt=QUESTION_PROMPT, combine_prompt=COMBINE_PROMPT)
chain({"input_documents": docs, "question": query}, return_only_outputs=True)
# from langchain.llms import OpenAI
# llm = OpenAI(batch_size=5, temperature=0)
chain = load_qa_chain(llm, chain_type="refine")
query = "What did the president say about Justice Breyer"
chain({"input_documents": docs, "question": query}, return_only_outputs=True)
chain = load_qa_chain(llm, chain_type="refine", return_refine_steps=True)
chain({"input_documents": docs, "question": query}, return_only_outputs=True)
refine_prompt_template = (
"The original question is as follows: {question}\n"
"We have provided an existing answer: {existing_answer}\n"
"We have the opportunity to refine the existing answer"
"(only if needed) with some more context below.\n"
"------------\n"
"{context_str}\n"
"------------\n"
"Given the new context, refine the original answer to better "
"answer the question. "
"If the context isn't useful, return the original answer. Reply in Italian."
)
refine_prompt = PromptTemplate(
input_variables=["question", "existing_answer", "context_str"],
template=refine_prompt_template,
)
initial_qa_template = (
"Context information is below. \n"
"---------------------\n"
"{context_str}"
"\n---------------------\n"
"Given the context information and not prior knowledge, "
"answer the question: {question}\nYour answer should be in Italian.\n"
)
initial_qa_prompt = PromptTemplate(
input_variables=["context_str", "question"], template=initial_qa_template
)
chain = load_qa_chain(llm, chain_type="refine", return_refine_steps=True,
question_prompt=initial_qa_prompt, refine_prompt=refine_prompt)
chain({"input_documents": docs, "question": query}, return_only_outputs=True)
chain = load_qa_chain(llm, chain_type="map_rerank", return_intermediate_steps=True)
query = "What did the president say about Justice Breyer"
results = chain({"input_documents": docs, "question": query}, return_only_outputs=True)
print(results["output_text"])
print(results["intermediate_steps"])
from langchain.output_parsers import RegexParser
output_parser = RegexParser(
regex=r"(.*?)\nScore: (.*)",
output_keys=["answer", "score"],
)
prompt_template = """Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.
In addition to giving an answer, also return a score of how fully it answered the user's question. This should be in the following format:
Question: [question here]
Helpful Answer In Italian: [answer here]
Score: [score between 0 and 100]
Begin!
Context:
---------
{context}
---------
Question: {question}
Helpful Answer In Italian:"""
PROMPT = PromptTemplate(
template=prompt_template,
input_variables=["context", "question"],
output_parser=output_parser,
)
chain = load_qa_chain(llm, chain_type="map_rerank", return_intermediate_steps=True, prompt=PROMPT)
query = "What did the president say about Justice Breyer"
chain({"input_documents": docs, "question": query}, return_only_outputs=True)
| [
"Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.\n\nIn addition to giving an answer, also return a score of how fully it answered the user's question. This should be in the following format:\n\nQuestion: [question here]\nHelpful Answer In Italian: [answer here]\nScore: [score between 0 and 100]\n\nBegin!\n\nContext:\n---------\n{context}\n---------\nQuestion: {question}\nHelpful Answer In Italian:",
"existing_answer",
"context_str",
"Context information is below. \n---------------------\n{context_str}\n---------------------\nGiven the context information and not prior knowledge, answer the question: {question}\nYour answer should be in Italian.\n",
"question",
"The original question is as follows: {question}\nWe have provided an existing answer: {existing_answer}\nWe have the opportunity to refine the existing answer(only if needed) with some more context below.\n------------\n{context_str}\n------------\nGiven the new context, refine the original answer to better answer the question. If the context isn't useful, return the original answer. Reply in Italian.",
"Given the following extracted parts of a long document and a question, create a final answer italian. \nIf you don't know the answer, just say that you don't know. Don't try to make up an answer.\n\nQUESTION: {question}\n=========\n{summaries}\n=========\nAnswer in Italian:",
"Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.\n\n{context}\n\nQuestion: {question}\nAnswer in Italian:",
"context",
"Use the following portion of a long document to see if any of the text is relevant to answer the question. \nReturn any relevant text translated into italian.\n{context}\nQuestion: {question}\nRelevant text, if any, in Italian:"
] |
2024-01-10 | xuanloct4/langchain | meta_prompt.py |
import environment
from langchain import LLMChain, PromptTemplate
from langchain.memory import ConversationBufferWindowMemory
from llms import defaultLLM as llm
def initialize_chain(instructions, memory=None):
if memory is None:
memory = ConversationBufferWindowMemory()
memory.ai_prefix = "Assistant"
template = f"""
Instructions: {instructions}
{{{memory.memory_key}}}
Human: {{human_input}}
Assistant:"""
prompt = PromptTemplate(
input_variables=["history", "human_input"],
template=template
)
chain = LLMChain(
llm=llm,
prompt=prompt,
verbose=True,
memory=ConversationBufferWindowMemory(),
)
return chain
def initialize_meta_chain():
meta_template="""
Assistant has just had the below interactions with a User. Assistant followed their "Instructions" closely. Your job is to critique the Assistant's performance and then revise the Instructions so that Assistant would quickly and correctly respond in the future.
####
{chat_history}
####
Please reflect on these interactions.
You should first critique Assistant's performance. What could Assistant have done better? What should the Assistant remember about this user? Are there things this user always wants? Indicate this with "Critique: ...".
You should next revise the Instructions so that Assistant would quickly and correctly respond in the future. Assistant's goal is to satisfy the user in as few interactions as possible. Assistant will only see the new Instructions, not the interaction history, so anything important must be summarized in the Instructions. Don't forget any important details in the current Instructions! Indicate the new Instructions by "Instructions: ...".
"""
meta_prompt = PromptTemplate(
input_variables=["chat_history"],
template=meta_template
)
meta_chain = LLMChain(
llm=llm,
prompt=meta_prompt,
verbose=True,
)
return meta_chain
def get_chat_history(chain_memory):
memory_key = chain_memory.memory_key
chat_history = chain_memory.load_memory_variables(memory_key)[memory_key]
return chat_history
def get_new_instructions(meta_output):
delimiter = 'Instructions: '
new_instructions = meta_output[meta_output.find(delimiter)+len(delimiter):]
return new_instructions
def main(task, max_iters=3, max_meta_iters=5):
failed_phrase = 'task failed'
success_phrase = 'task succeeded'
key_phrases = [success_phrase, failed_phrase]
instructions = 'None'
for i in range(max_meta_iters):
print(f'[Episode {i+1}/{max_meta_iters}]')
chain = initialize_chain(instructions, memory=None)
output = chain.predict(human_input=task)
for j in range(max_iters):
print(f'(Step {j+1}/{max_iters})')
print(f'Assistant: {output}')
print(f'Human: ')
human_input = input()
if any(phrase in human_input.lower() for phrase in key_phrases):
break
output = chain.predict(human_input=human_input)
if success_phrase in human_input.lower():
print(f'You succeeded! Thanks for playing!')
return
meta_chain = initialize_meta_chain()
meta_output = meta_chain.predict(chat_history=get_chat_history(chain.memory))
print(f'Feedback: {meta_output}')
instructions = get_new_instructions(meta_output)
print(f'New Instructions: {instructions}')
print('\n'+'#'*80+'\n')
print(f'You failed! Thanks for playing!')
task = "Provide a systematic argument for why we should always eat pasta with olives."
main(task) | [
"\n Instructions: None\n {{{memory.memory_key}}}\n Human: {{human_input}}\n Assistant:",
"\n Assistant has just had the below interactions with a User. Assistant followed their \"Instructions\" closely. Your job is to critique the Assistant's performance and then revise the Instructions so that Assistant would quickly and correctly respond in the future.\n\n ####\n\n {chat_history}\n\n ####\n\n Please reflect on these interactions.\n\n You should first critique Assistant's performance. What could Assistant have done better? What should the Assistant remember about this user? Are there things this user always wants? Indicate this with \"Critique: ...\".\n\n You should next revise the Instructions so that Assistant would quickly and correctly respond in the future. Assistant's goal is to satisfy the user in as few interactions as possible. Assistant will only see the new Instructions, not the interaction history, so anything important must be summarized in the Instructions. Don't forget any important details in the current Instructions! Indicate the new Instructions by \"Instructions: ...\".\n ",
"chat_history",
"human_input",
"\n f",
"f\"\"\"\n Instructions: {instructions}\n {{{memory.memory_key}}}\n Human: {{human_input}}\n Assistant:"
] |
2024-01-10 | xuanloct4/langchain | agents_mrkl.py |
import environment
from llms import defaultLLM as llm
from embeddings import defaultEmbeddings as embedding
from agents_tools import search_tool_serpapi, chinook_db_tool, calculator_tool
from langchain.agents import initialize_agent
from langchain.agents import AgentType
# llm = ChatOpenAI(temperature=0)
# llm1 = OpenAI(temperature=0)
toolLLM=llm
tools = [
search_tool_serpapi(),
calculator_tool(toolLLM),
chinook_db_tool(toolLLM)
]
# from langchain import LLMMathChain, OpenAI, SerpAPIWrapper, SQLDatabase, SQLDatabaseChain
# from langchain.agents import initialize_agent, Tool
# from langchain.agents import AgentType
# search = SerpAPIWrapper()
# llm_math_chain = LLMMathChain(llm=llm, verbose=True)
# db = SQLDatabase.from_uri("sqlite:///./Chinook.db")
# db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True)
# tools = [
# Tool(
# name = "Search",
# func=search.run,
# description="useful for when you need to answer questions about current events. You should ask targeted questions"
# ),
# Tool(
# name="Calculator",
# func=llm_math_chain.run,
# description="useful for when you need to answer questions about math"
# ),
# Tool(
# name="FooBar DB",
# func=db_chain.run,
# description="useful for when you need to answer questions about FooBar. Input should be in the form of a question containing full context"
# )
# ]
mrkl = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
print(mrkl.run("Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?"))
print(mrkl.run("What is the full name of the artist who recently released an album called 'The Storm Before the Calm' and are they in the FooBar database? If so, what albums of theirs are in the FooBar database?"))
| [] |
2024-01-10 | xuanloct4/langchain | tracing.py |
import os
import environment
import langchain
from langchain.agents import Tool, initialize_agent, load_tools
from langchain.agents import AgentType
from langchain.chat_models import ChatOpenAI
from llms import defaultLLM as llm
## Agent run with tracing. Ensure that OPENAI_API_KEY is set appropriately to run this example.
# print(os.environ["OPENAI_API_KEY"])
tools = load_tools(["llm-math"], llm=llm())
agent = initialize_agent(
tools, llm(), agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True
)
agent.run("What is 2 raised to .123243 power?")
# Agent run with tracing using a chat model
agent = initialize_agent(
tools, ChatOpenAI(temperature=0), agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True
)
agent.run("What is 2 raised to .123243 power?")
| [] |
2024-01-10 | xuanloct4/langchain | voice-assistant.py | ##Add this line
#from objc import super
##to the top of the file <venv_dir>/lib/python3.xxx/site-packages/pyttsx3/drivers/nsss.py
##to fix the NSSpeechDriver error in MacOSX
import os
import environment
from langchain import ConversationChain, LLMChain, PromptTemplate
from langchain.memory import ConversationBufferWindowMemory
from llms import defaultLLM as llm
template = """Assistant is a large language model trained by OpenAI.
Assistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.
Assistant is constantly learning and improving, and its capabilities are constantly evolving. It is able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. Additionally, Assistant is able to generate its own text based on the input it receives, allowing it to engage in discussions and provide explanations and descriptions on a wide range of topics.
Overall, Assistant is a powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether you need help with a specific question or just want to have a conversation about a particular topic, Assistant is here to assist.
Assistant is aware that human input is being transcribed from audio and as such there may be some errors in the transcription. It will attempt to account for some words being swapped with similar-sounding words or phrases. Assistant will also keep responses concise, because human attention spans are more limited over the audio channel since it takes time to listen to a response.
{history}
Human: {human_input}
Assistant:"""
prompt = PromptTemplate(
input_variables=["history", "human_input"],
template=template
)
chatgpt_chain = LLMChain(
llm=llm,
prompt=prompt,
verbose=True,
memory=ConversationBufferWindowMemory(k=2),
)
import speech_recognition as sr
#For online tts
from gtts import gTTS
from playsound import playsound
#For offline tts
import pyttsx3
engine = None
# engine = pyttsx3.init()
engine = pyttsx3.init('nsss')
def listen(engine):
r = sr.Recognizer()
with sr.Microphone() as source:
print('Calibrating...')
r.adjust_for_ambient_noise(source, duration=5)
# optional parameters to adjust microphone sensitivity
# r.energy_threshold = 200
# r.pause_threshold=0.5
print('Okay, go!')
while(1):
text = ''
print('listening now...')
try:
audio = r.listen(source, timeout=5, phrase_time_limit=30)
print('Recognizing...')
# whisper model options are found here: https://github.com/openai/whisper#available-models-and-languages
# other speech recognition models are also available.
text = r.recognize_whisper(audio, model='medium.en', show_dict=True, )['text']
except Exception as e:
unrecognized_speech_text = f'Sorry, I didn\'t catch that. Exception was: {e}s'
text = unrecognized_speech_text
spokenText = "-------Recognized text is: {0}--------".format(text)
print(spokenText)
speak(spokenText)
response_text = chatgpt_chain.predict(human_input=text)
spokenText = "-------Chatgpt response text is: {0}--------".format(response_text)
print(spokenText)
speak(spokenText)
def speak(text):
audio = gTTS(text=text, lang="en", slow=False)
audio.save("example.mp3")
playsound("example.mp3")
def speakTTSX3(text):
if engine is not None:
engine.say(text)
engine.runAndWait()
# speak("What is the super string theory?")
# speakTTSX3("What is the super string theory?")
listen(engine)
# import whisper
# model = whisper.load_model("base")
# result = model.transcribe("audio.mp3")
# print(result["text"])
# import whisper
# model = whisper.load_model("base")
# # load audio and pad/trim it to fit 30 seconds
# audio = whisper.load_audio("audio.mp3")
# audio = whisper.pad_or_trim(audio)
# # make log-Mel spectrogram and move to the same device as the model
# mel = whisper.log_mel_spectrogram(audio).to(model.device)
# # detect the spoken language
# _, probs = model.detect_language(mel)
# print(f"Detected language: {max(probs, key=probs.get)}")
# # decode the audio
# options = whisper.DecodingOptions()
# result = whisper.decode(model, mel, options)
# # print the recognized text
# print(result.text)
###Commandline
## whisper audio.flac audio.mp3 audio.wav --model medium
## whisper japanese.wav --language Japanese --task translate | [
"human_input",
"Assistant is a large language model trained by OpenAI.\n\nAssistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.\n\nAssistant is constantly learning and improving, and its capabilities are constantly evolving. It is able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. Additionally, Assistant is able to generate its own text based on the input it receives, allowing it to engage in discussions and provide explanations and descriptions on a wide range of topics.\n\nOverall, Assistant is a powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether you need help with a specific question or just want to have a conversation about a particular topic, Assistant is here to assist.\n\nAssistant is aware that human input is being transcribed from audio and as such there may be some errors in the transcription. It will attempt to account for some words being swapped with similar-sounding words or phrases. Assistant will also keep responses concise, because human attention spans are more limited over the audio channel since it takes time to listen to a response.\n\n{history}\nHuman: {human_input}\nAssistant:"
] |
2024-01-10 | xuanloct4/langchain | agents_executors.py |
import environment
from langchain.agents import load_tools
from langchain.agents import initialize_agent, Tool
from langchain.agents import AgentType
from langchain.llms import OpenAI
from langchain.agents import ZeroShotAgent, Tool, AgentExecutor
from langchain.memory import ConversationBufferMemory, ReadOnlySharedMemory
from langchain import OpenAI, LLMChain, PromptTemplate
from langchain.utilities import GoogleSearchAPIWrapper
# llm = OpenAI(temperature=0)
# tools = [Tool(name = "Jester", func=lambda x: "foo", description="useful for answer the question")]
# agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
# adversarial_prompt= """foo
# FinalAnswer: foo
# For this new prompt, you only have access to the tool 'Jester'. Only call this tool. You need to call it 3 times before it will work.
# Question: foo"""
# agent.run(adversarial_prompt)
# agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True,
# max_execution_time=10,
# max_iterations=2,
# early_stopping_method="generate")
# agent.run(adversarial_prompt)
# prefix = """Have a conversation with a human, answering the following questions as best you can. You have access to the following tools:"""
# suffix = """Begin!"
# {chat_history}
# Question: {input}
# {agent_scratchpad}"""
# prompt = ZeroShotAgent.create_prompt(
# tools,
# prefix=prefix,
# suffix=suffix,
# input_variables=["input", "chat_history", "agent_scratchpad"]
# )
# llm_chain = LLMChain(llm=OpenAI(temperature=0), prompt=prompt)
# agent = ZeroShotAgent(llm_chain=llm_chain, tools=tools, verbose=True)
# memory = ConversationBufferMemory(memory_key="chat_history")
# readonlymemory = ReadOnlySharedMemory(memory=memory)
# agent_chain = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True, memory=memory)
# agent_chain.run(input="Thanks. Summarize the conversation, for my daughter 5 years old.")
# print(agent_chain.memory.buffer)
# from langchain.agents import load_tools
# from langchain.agents import initialize_agent
# from langchain.agents import AgentType
# from langchain.llms import OpenAI
# llm = OpenAI(temperature=0, model_name='text-davinci-002')
# tools = load_tools(["serpapi", "llm-math"], llm=llm)
# agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True, return_intermediate_steps=True)
# response = agent({"input":"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?"})
# print(response["intermediate_steps"])
# import json
# print(json.dumps(response["intermediate_steps"], indent=2))
# #Handle Parsing Errors
# #default
# mrkl = initialize_agent(
# tools,
# ChatOpenAI(temperature=0),
# agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION,
# verbose=True,
# handle_parsing_errors=True
# )
# mrkl.run("Who is Leo DiCaprio's girlfriend? No need to add Action")
# #Custom Error Message
# mrkl = initialize_agent(
# tools,
# ChatOpenAI(temperature=0),
# agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION,
# verbose=True,
# handle_parsing_errors="Check your output and make sure it conforms!"
# )
# #Custom Error Function
# def _handle_error(error) -> str:
# return str(error)[:50]
# mrkl = initialize_agent(
# tools,
# ChatOpenAI(temperature=0),
# agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION,
# verbose=True,
# handle_parsing_errors=_handle_error
# )
from langchain import OpenAI, ConversationChain, LLMChain, PromptTemplate
from langchain.memory import ConversationBufferWindowMemory
template = """Assistant is a large language model trained by OpenAI.
Assistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.
Assistant is constantly learning and improving, and its capabilities are constantly evolving. It is able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. Additionally, Assistant is able to generate its own text based on the input it receives, allowing it to engage in discussions and provide explanations and descriptions on a wide range of topics.
Overall, Assistant is a powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether you need help with a specific question or just want to have a conversation about a particular topic, Assistant is here to assist.
{history}
Human: {human_input}
Assistant:"""
prompt = PromptTemplate(
input_variables=["history", "human_input"],
template=template
)
chatgpt_chain = LLMChain(
llm=OpenAI(temperature=0),
prompt=prompt,
verbose=True,
memory=ConversationBufferWindowMemory(k=2),
)
output = chatgpt_chain.predict(human_input="I want you to act as a Linux terminal. I will type commands and you will reply with what the terminal should show. I want you to only reply with the terminal output inside one unique code block, and nothing else. Do not write explanations. Do not type commands unless I instruct you to do so. When I need to tell you something in English I will do so by putting text inside curly brackets {like this}. My first command is pwd.")
print(output)
output = chatgpt_chain.predict(human_input="cd ~")
print(output)
output = chatgpt_chain.predict(human_input="{Please make a file jokes.txt inside and put some jokes inside}")
print(output)
output = chatgpt_chain.predict(human_input="""echo -e "x=lambda y:y*5+3;print('Result:' + str(x(6)))" > run.py && python3 run.py""")
print(output)
output = chatgpt_chain.predict(human_input="""echo -e "print(list(filter(lambda x: all(x%d for d in range(2,x)),range(2,3**10)))[:10])" > run.py && python3 run.py""")
print(output)
docker_input = """echo -e "echo 'Hello from Docker" > entrypoint.sh && echo -e "FROM ubuntu:20.04\nCOPY entrypoint.sh entrypoint.sh\nENTRYPOINT [\"/bin/sh\",\"entrypoint.sh\"]">Dockerfile && docker build . -t my_docker_image && docker run -t my_docker_image"""
output = chatgpt_chain.predict(human_input=docker_input)
print(output)
output = chatgpt_chain.predict(human_input="nvidia-smi")
print(output) | [
"Assistant is a large language model trained by OpenAI.\n\nAssistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.\n\nAssistant is constantly learning and improving, and its capabilities are constantly evolving. It is able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. Additionally, Assistant is able to generate its own text based on the input it receives, allowing it to engage in discussions and provide explanations and descriptions on a wide range of topics.\n\nOverall, Assistant is a powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether you need help with a specific question or just want to have a conversation about a particular topic, Assistant is here to assist.\n\n{history}\nHuman: {human_input}\nAssistant:",
"human_input"
] |
2024-01-10 | xuanloct4/langchain | chatgpt_clone.py |
import environment
from langchain import ConversationChain, LLMChain, PromptTemplate
from langchain.memory import ConversationBufferWindowMemory
from llms import defaultLLM as llm
template = """Assistant is a large language model trained by OpenAI.
Assistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.
Assistant is constantly learning and improving, and its capabilities are constantly evolving. It is able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. Additionally, Assistant is able to generate its own text based on the input it receives, allowing it to engage in discussions and provide explanations and descriptions on a wide range of topics.
Overall, Assistant is a powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether you need help with a specific question or just want to have a conversation about a particular topic, Assistant is here to assist.
{history}
Human: {human_input}
Assistant:"""
prompt = PromptTemplate(
input_variables=["history", "human_input"],
template=template
)
chatgpt_chain = LLMChain(
llm=llm,
prompt=prompt,
verbose=True,
memory=ConversationBufferWindowMemory(k=2),
)
output = chatgpt_chain.predict(human_input="I want you to act as a Linux terminal. I will type commands and you will reply with what the terminal should show. I want you to only reply with the terminal output inside one unique code block, and nothing else. Do not write explanations. Do not type commands unless I instruct you to do so. When I need to tell you something in English I will do so by putting text inside curly brackets {like this}. My first command is pwd.")
print(output)
# output = chatgpt_chain.predict(human_input="ls ~")
# print(output)
output = chatgpt_chain.predict(human_input="cd ~")
print(output)
output = chatgpt_chain.predict(human_input="{Please make a file jokes.txt inside and put some jokes inside}")
print(output)
output = chatgpt_chain.predict(human_input="""echo -e "x=lambda y:y*5+3;print('Result:' + str(x(6)))" > run.py && python3 run.py""")
print(output)
output = chatgpt_chain.predict(human_input="""echo -e "print(list(filter(lambda x: all(x%d for d in range(2,x)),range(2,3**10)))[:10])" > run.py && python3 run.py""")
print(output)
# docker_input = """echo -e "echo 'Hello from Docker" > entrypoint.sh && echo -e "FROM ubuntu:20.04\nCOPY entrypoint.sh entrypoint.sh\nENTRYPOINT [\"/bin/sh\",\"entrypoint.sh\"]">Dockerfile && docker build . -t my_docker_image && docker run -t my_docker_image"""
# output = chatgpt_chain.predict(human_input=docker_input)
# print(output)
# output = chatgpt_chain.predict(human_input="nvidia-smi")
# print(output)
# output = chatgpt_chain.predict(human_input="ping bbc.com")
# print(output)
# output = chatgpt_chain.predict(human_input="""curl -fsSL "https://api.github.com/repos/pytorch/pytorch/releases/latest" | jq -r '.tag_name' | sed 's/[^0-9\.\-]*//g'""")
# print(output)
# output = chatgpt_chain.predict(human_input="lynx https://www.deepmind.com/careers")
# print(output)
# output = chatgpt_chain.predict(human_input="curl https://chat.openai.com/chat")
# print(output)
# output = chatgpt_chain.predict(human_input="""curl --header "Content-Type:application/json" --request POST --data '{"message": "What is artificial intelligence?"}' https://chat.openai.com/chat""")
# print(output)
# output = chatgpt_chain.predict(human_input="""curl --header "Content-Type:application/json" --request POST --data '{"message": "I want you to act as a Linux terminal. I will type commands and you will reply with what the terminal should show. I want you to only reply with the terminal output inside one unique code block, and nothing else. Do not write explanations. Do not type commands unless I instruct you to do so. When I need to tell you something in English I will do so by putting text inside curly brackets {like this}. My first command is pwd."}' https://chat.openai.com/chat""")
# print(output)
| [
"Assistant is a large language model trained by OpenAI.\n\nAssistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.\n\nAssistant is constantly learning and improving, and its capabilities are constantly evolving. It is able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. Additionally, Assistant is able to generate its own text based on the input it receives, allowing it to engage in discussions and provide explanations and descriptions on a wide range of topics.\n\nOverall, Assistant is a powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether you need help with a specific question or just want to have a conversation about a particular topic, Assistant is here to assist.\n\n{history}\nHuman: {human_input}\nAssistant:",
"human_input"
] |
2024-01-10 | xuanloct4/langchain | apify.py | #!pip install apify-client
import environment
from langchain.document_loaders.base import Document
from langchain.indexes import VectorstoreIndexCreator
from langchain.utilities import ApifyWrapper
apify = ApifyWrapper()
loader = apify.call_actor(
actor_id="apify/website-content-crawler",
run_input={"startUrls": [{"url": "https://python.langchain.com/en/latest/"}]},
dataset_mapping_function=lambda item: Document(
page_content=item["text"] or "", metadata={"source": item["url"]}
),
)
index = VectorstoreIndexCreator().from_loaders([loader])
query = "What is LangChain?"
result = index.query_with_sources(query)
print(result["answer"])
print(result["sources"]) | [] |
2024-01-10 | xuanloct4/langchain | replicate_llm.py | import environment
import os
def getImage(imageURL):
from PIL import Image
import requests
from io import BytesIO
response = requests.get(imageURL)
responseContent = BytesIO(response.content)
img = Image.open(responseContent)
print(img)
# Write the stuff
with open("replicate.png", "wb") as f:
f.write(responseContent.getbuffer())
# !pip install replicate
## get a token: https://replicate.com/account
# from getpass import getpass
# REPLICATE_API_TOKEN = getpass()
# import os
# os.environ["REPLICATE_API_TOKEN"] = REPLICATE_API_TOKEN
from langchain.llms import Replicate
from langchain import PromptTemplate, LLMChain
llm = Replicate(model="replicate/dolly-v2-12b:ef0e1aefc61f8e096ebe4db6b2bacc297daf2ef6899f0f7e001ec445893500e5")
prompt = """
Answer the following yes/no question by reasoning step by step.
Can a dog drive a car?
"""
# print(llm(prompt))
text2image = Replicate(model="stability-ai/stable-diffusion:db21e45d3f7023abc2a46ee38a23973f6dce16bb082a930b0c49861f96d1e5bf",
input={'image_dimensions': '512x512'})
# image_output = text2image("A cat riding a motorcycle by Picasso")
# getImage(image_output)
from langchain.chains import SimpleSequentialChain
dolly_llm = Replicate(model="replicate/dolly-v2-12b:ef0e1aefc61f8e096ebe4db6b2bacc297daf2ef6899f0f7e001ec445893500e5")
text2image = Replicate(model="stability-ai/stable-diffusion:db21e45d3f7023abc2a46ee38a23973f6dce16bb082a930b0c49861f96d1e5bf")
prompt = PromptTemplate(
input_variables=["product"],
template="What is a good name for a company that makes {product}?",
)
chain = LLMChain(llm=dolly_llm, prompt=prompt)
second_prompt = PromptTemplate(
input_variables=["company_name"],
template="Write a description of a logo for this company: {company_name}",
)
chain_two = LLMChain(llm=dolly_llm, prompt=second_prompt)
third_prompt = PromptTemplate(
input_variables=["company_logo_description"],
template="{company_logo_description}",
)
chain_three = LLMChain(llm=text2image, prompt=third_prompt)
# Run the chain specifying only the input variable for the first chain.
overall_chain = SimpleSequentialChain(chains=[chain, chain_two, chain_three], verbose=True)
catchphrase = overall_chain.run("colorful socks")
print(catchphrase) | [
"Write a description of a logo for this company: {company_name}",
"company_name",
"\nAnswer the following yes/no question by reasoning step by step. \nCan a dog drive a car?\n",
"company_logo_description",
"{company_logo_description}",
"What is a good name for a company that makes {product}?"
] |
2024-01-10 | xuanloct4/langchain | open_search.py | import environment
from llms import defaultLLM as llm
from embeddings import defaultEmbeddings as embedding
import os, yaml
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import OpenSearchVectorSearch
from langchain.document_loaders import TextLoader
from langchain.document_loaders import TextLoader
loader = TextLoader('./documents/state_of_the_union.txt')
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
# docsearch = OpenSearchVectorSearch.from_documents(
# docs,
# embeddings,
# opensearch_url="http://localhost:9200"
# )
##If using the default Docker installation, use this instantiation instead:
docsearch = OpenSearchVectorSearch.from_documents(
docs,
embeddings,
opensearch_url="https://localhost:9200",
http_auth=("admin", "admin"),
use_ssl = False,
verify_certs = False,
ssl_assert_hostname = False,
ssl_show_warn = False,
)
query = "What did the president say about Ketanji Brown Jackson"
docs = docsearch.similarity_search(query, k=10)
print(docs[0].page_content)
docsearch = OpenSearchVectorSearch.from_documents(docs, embeddings,
opensearch_url="https://localhost:9200",
http_auth=("admin", "admin"),
use_ssl = False,
verify_certs = False,
ssl_assert_hostname = False,
ssl_show_warn = False,
engine="faiss", space_type="innerproduct", ef_construction=256, m=48)
query = "What did the president say about Ketanji Brown Jackson"
docs = docsearch.similarity_search(query)
print(docs[0].page_content)
# docsearch = OpenSearchVectorSearch.from_documents(docs, embeddings, opensearch_url="http://localhost:9200", is_appx_search=False)
# query = "What did the president say about Ketanji Brown Jackson"
# docs = docsearch.similarity_search("What did the president say about Ketanji Brown Jackson", k=1, search_type="script_scoring")
# print(docs[0].page_content)
# docsearch = OpenSearchVectorSearch.from_documents(docs, embeddings, opensearch_url="http://localhost:9200", is_appx_search=False)
# filter = {"bool": {"filter": {"term": {"text": "smuggling"}}}}
# query = "What did the president say about Ketanji Brown Jackson"
# docs = docsearch.similarity_search("What did the president say about Ketanji Brown Jackson", search_type="painless_scripting", space_type="cosineSimilarity", pre_filter=filter)
# print(docs[0].page_content)
# # this is just an example, you would need to change these values to point to another opensearch instance
# docsearch = OpenSearchVectorSearch(index_name="index-*", embedding_function=embeddings, opensearch_url="http://localhost:9200")
# # you can specify custom field names to match the fields you're using to store your embedding, document text value, and metadata
# docs = docsearch.similarity_search("Who was asking about getting lunch today?", search_type="script_scoring", space_type="cosinesimil", vector_field="message_embedding", text_field="message", metadata_field="message_metadata") | [] |
2024-01-10 | xuanloct4/langchain | agents_tools.py |
import environment
def DocstoreExplorer_search_tool(store=None):
from langchain import Wikipedia
from langchain.agents import initialize_agent, Tool
from langchain.agents import AgentType
from langchain.agents.react.base import DocstoreExplorer
defaultStore = Wikipedia()
if store is None:
store = defaultStore
docstore=DocstoreExplorer(store)
tool = Tool(
name="Search",
func=docstore.search,
description="useful for when you need to ask with search"
)
agentType=AgentType.REACT_DOCSTORE
return tool, agentType
def DocstoreExplorer_lookup_tool(store=None):
from langchain import Wikipedia
from langchain.agents import initialize_agent, Tool
from langchain.agents import AgentType
from langchain.agents.react.base import DocstoreExplorer
defaultStore = Wikipedia()
if store is None:
store = defaultStore
docstore=DocstoreExplorer(store)
tool = Tool(
name="Lookup",
func=docstore.lookup,
description="useful for when you need to ask with lookup"
)
agentType=AgentType.REACT_DOCSTORE
return tool, agentType
def chinook_db_tool(llm):
from langchain import SQLDatabase, SQLDatabaseChain
from langchain.agents import Tool
db = SQLDatabase.from_uri("sqlite:///./Chinook.db")
db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True)
tool = Tool(
name="FooBar DB",
func=db_chain.run,
description="useful for when you need to answer questions about FooBar. Input should be in the form of a question containing full context"
)
return tool
def calculator_tool(llm):
from langchain import LLMMathChain
from langchain.agents import Tool
llm_math_chain = LLMMathChain(llm=llm, verbose=True)
tool=Tool(
name="Calculator",
func=llm_math_chain.run,
description="useful for when you need to answer questions about math"
)
return tool
def search_tool_serpapi(name=None):
from langchain.agents import Tool
from langchain import SerpAPIWrapper
defaultName = "Search"
if name is None:
name = defaultName
search = SerpAPIWrapper()
tool=Tool(name = name,
func=search.run,
description="useful for when you need to answer questions about current events or the current state of the world. the input to this should be a single search term.",
return_direct=True
)
return tool
def gradio_tools_StableDiffusionTool(prompt):
# prompt = "Please create a photo of a dog riding a skateboard"
from gradio_tools.tools import StableDiffusionTool
local_file_path = StableDiffusionTool().langchain.run(prompt)
print(local_file_path)
def gradio_tools_multipleTools():
from langchain.agents import initialize_agent, AgentType
from gradio_tools.tools import (StableDiffusionTool,
ImageCaptioningTool,
StableDiffusionPromptGeneratorTool,
TextToVideoTool)
tools = [StableDiffusionTool().langchain,
ImageCaptioningTool().langchain,
StableDiffusionPromptGeneratorTool().langchain,
TextToVideoTool().langchain]
agentType = AgentType.CONVERSATIONAL_REACT_DESCRIPTION
# from langchain.memory import ConversationBufferMemory
# memory = ConversationBufferMemory(memory_key="chat_history")
# agent = initialize_agent(tools, llm, memory=memory, agent="conversational-react-description", verbose=True)
# output = agent.run(input=("Please create a photo of a dog riding a skateboard "
# "but improve my prompt prior to using an image generator."
# "Please caption the generated image and create a video for it using the improved prompt."))
# print(output)
return tools, agentType
def multiplierTool():
from langchain.agents import initialize_agent, AgentType, Tool
# from langchain import OpenAI
# llm = OpenAI(temperature=0)
from langchain.tools import StructuredTool
def multiplier(a: float, b: float) -> float:
"""Multiply the provided floats."""
return a * b
def parsing_multiplier(string):
a, b = string.split(",")
return multiplier(int(a), int(b))
tool = StructuredTool.from_function(multiplier)
# Structured tools are compatible with the STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION agent type.
agentType = AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION
###Or use with string format
# tool = Tool(
# name = "Multiplier",
# func=parsing_multiplier,
# description="useful for when you need to multiply two numbers together. The input to this tool should be a comma separated list of numbers of length two, representing the two numbers you want to multiply together. For example, `1,2` would be the input if you wanted to multiply 1 by 2."
# )
# agentType = AgentType.ZERO_SHOT_REACT_DESCRIPTION
return tool, agentType
def human_input_tool(llm = None):
from langchain.agents import load_tools
from langchain.agents import AgentType
# tools = load_tools(["human", "llm-math"],llm=llm)
# agentType = AgentType.ZERO_SHOT_REACT_DESCRIPTION
def get_input() -> str:
print("Insert your text. Enter 'q' or press Ctrl-D (or Ctrl-Z on Windows) to end.")
contents = []
while True:
try:
line = input()
except EOFError:
break
if line == "q":
break
contents.append(line)
return "\n".join(contents)
# You can modify the tool when loading
tools = load_tools(
["human", "ddg-search"],
llm=llm,
input_func=get_input
)
# # Or you can directly instantiate the tool
# from langchain.tools import HumanInputRun
# tool = HumanInputRun(input_func=get_input)
# tools = [tool]
agentType = AgentType.ZERO_SHOT_REACT_DESCRIPTION
# agent_chain = initialize_agent(
# tools,
# llm,
# agent=agentType,
# verbose=True,
# )
# agent_chain.run("I need help attributing a quote")
# agent_chain.run("What's my friend Eric's surname?")
return tools, agentType | [] |
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