Spaces:
Runtime error
Runtime error
File size: 3,445 Bytes
e7dbb12 bde2b54 e7dbb12 bde2b54 e7dbb12 bde2b54 e7dbb12 bde2b54 e7dbb12 bde2b54 e7dbb12 bde2b54 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 |
# https://python.langchain.com/docs/tutorials/rag/
import gradio as gr
from langchain import hub
from langchain_chroma import Chroma
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough
from langchain_mistralai import MistralAIEmbeddings
from langchain_community.embeddings import HuggingFaceInstructEmbeddings
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_mistralai import ChatMistralAI
from langchain_community.document_loaders import PyPDFLoader
import requests
from pathlib import Path
from langchain_community.document_loaders import WebBaseLoader
from langchain_community.retrievers import ArxivRetriever
import bs4
from langchain_core.rate_limiters import InMemoryRateLimiter
from urllib.parse import urljoin
rate_limiter = InMemoryRateLimiter(
requests_per_second=0.1, # <-- MistralAI free. We can only make a request once every second
check_every_n_seconds=0.01, # Wake up every 100 ms to check whether allowed to make a request,
max_bucket_size=10, # Controls the maximum burst size.
)
retriever = ArxivRetriever(
load_max_docs=2,
get_ful_documents=True,
)
# LLM model
llm = ChatMistralAI(model="mistral-large-latest", rate_limiter=rate_limiter)
# Embeddings
embed_model = "sentence-transformers/multi-qa-distilbert-cos-v1"
# embed_model = "nvidia/NV-Embed-v2"
embeddings = HuggingFaceInstructEmbeddings(model_name=embed_model)
# embeddings = MistralAIEmbeddings()
def initialize(arxivcode):
docs = retriever.invoke(arxiv)
docs[0].metadata['Published'] = str(doc[0].metadata['Published'])
def format_docs(docs):
return "\n\n".join(doc.page_content for doc in docs)
def RAG(llm, docs, embeddings):
# Split text
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
splits = text_splitter.split_documents(docs)
# Create vector store
vectorstore = Chroma.from_documents(documents=splits, embedding=embeddings)
# Retrieve and generate using the relevant snippets of the documents
retriever = vectorstore.as_retriever()
# Prompt basis example for RAG systems
prompt = hub.pull("rlm/rag-prompt")
# Create the chain
rag_chain = (
{"context": retriever | format_docs, "question": RunnablePassthrough()}
| prompt
| llm
| StrOutputParser()
)
return rag_chain
return RAG(llm, docs, embeddings)
rag_chain = None
def handle_prompt(message, history, arxivcode):
if rag_chain is None:
# initialize RAG chain
# RAG chain
rag_chain = initialize(arxivcode)
try:
# Stream output
out=""
for chunk in rag_chain.stream(message):
out += chunk
yield out
except:
raise gr.Error("Requests rate limit exceeded")
greetingsmessage = "Hi, I'm your personal arXiv reader. Ask me questions about the arXiv paper above"
with gr.Blocks() as demo:
arxiv_code = gr.Textbox("", label="arxiv.number")
gr.ChatInterface(handle_prompt, type="messages", theme=gr.themes.Soft(),
description=greetingsmessage,
additional_inputs=[arxiv_code]
)
if __name__ == "__main__":
demo.launch() |