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import gradio as gr
from qdrant_client import models, QdrantClient
from sentence_transformers import SentenceTransformer
from PyPDF2 import PdfReader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
# from langchain.llms import LlamaCpp
from langchain.vectorstores import Qdrant
from qdrant_client.http import models
# from langchain.llms import CTransformers
from ctransformers import AutoModelForCausalLM
# loading the embedding model -
encoder = SentenceTransformer('jinaai/jina-embedding-b-en-v1')
print("embedding model loaded.............................")
print("####################################################")
# loading the LLM
callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])
print("loading the LLM......................................")
# llm = LlamaCpp(
# model_path="TheBloke/Llama-2-7B-Chat-GGUF/llama-2-7b-chat.Q8_0.gguf",
# n_ctx=2048,
# f16_kv=True, # MUST set to True, otherwise you will run into problem after a couple of calls
# callback_manager=callback_manager,
# verbose=True,
# )
llm = AutoModelForCausalLM.from_pretrained("TheBloke/Llama-2-7B-Chat-GGUF",
model_file="llama-2-7b-chat.Q8_0.gguf",
model_type="llama",
# config = ctransformers.hub.AutoConfig,
# hf = True
temperature = 0.2,
max_new_tokens = 1024,
stop = ['\n']
)
print("LLM loaded........................................")
print("################################################################")
def get_chunks(text):
text_splitter = RecursiveCharacterTextSplitter(
# seperator = "\n",
chunk_size = 500,
chunk_overlap = 100,
length_function = len,
)
chunks = text_splitter.split_text(text)
return chunks
pdf_path = './100 Weird Facts About the Human Body.pdf'
reader = PdfReader(pdf_path)
text = ""
num_of_pages = len(reader.pages)
for page in range(num_of_pages):
current_page = reader.pages[page]
text += current_page.extract_text()
chunks = get_chunks(text)
print("Chunks are ready.....................................")
print("######################################################")
qdrant = QdrantClient(path = "./db")
print("db created................................................")
print("#####################################################################")
qdrant.recreate_collection(
collection_name="my_facts",
vectors_config=models.VectorParams(
size=encoder.get_sentence_embedding_dimension(), # Vector size is defined by used model
distance=models.Distance.COSINE,
),
)
print("Collection created........................................")
print("#########################################################")
li = []
for i in range(len(chunks)):
li.append(i)
dic = zip(li, chunks)
dic= dict(dic)
qdrant.upload_records(
collection_name="my_facts",
records=[
models.Record(
id=idx,
vector=encoder.encode(dic[idx]).tolist(),
payload= {dic[idx][:5] : dic[idx]}
) for idx in dic.keys()
],
)
print("Records uploaded........................................")
print("###########################################################")
def chat(question):
# question = input("ask question from pdf.....")
hits = qdrant.search(
collection_name="my_facts",
query_vector=encoder.encode(question).tolist(),
limit=3
)
context = []
for hit in hits:
context.append(list(hit.payload.values())[0])
context = context[0] + context[1] + context[2]
system_prompt = """You are a helpful assistant, you will use the provided context to answer user questions.
Read the given context before answering questions and think step by step. If you can not answer a user question based on
the provided context, inform the user. Do not use any other information for answering user. Provide a detailed answer to the question."""
B_INST, E_INST = "[INST]", "[/INST]"
B_SYS, E_SYS = "<<SYS>>\n", "\n<</SYS>>\n\n"
SYSTEM_PROMPT = B_SYS + system_prompt + E_SYS
instruction = f"""
Context: {context}
User: {question}"""
prompt_template = B_INST + SYSTEM_PROMPT + instruction + E_INST
result = llm(prompt_template)
return result
gr.Interface(
fn = chat,
inputs = gr.Textbox(lines = 10, placeholder = "Enter your question here 👉"),
outputs = gr.Textbox(lines = 10, placeholder = "Your answer will be here soon 🚀"),
title="Q&N with PDF 👩🏻💻📓✍🏻💡",
description="This app facilitates a conversation with PDFs available on https://www.delo.si/assets/media/other/20110728/100%20Weird%20Facts%20About%20the%20Human%20Body.pdf💡",
theme="soft",
examples=["Hello", "what is the speed of human nerve impulses?"],
# cache_examples=True,
).launch()
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