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# 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, ArxivLoader
import bs4
from langchain_core.rate_limiters import InMemoryRateLimiter
from urllib.parse import urljoin
def initialize(arxivcode):
#loader = ArxivLoader(query=str(arxivcode),)
#docs = loader.load()
#retriever = ArxivRetriever(
# load_max_docs=2,
# get_full_documents=True,
#)
#docs = retriever.invoke(str(arxivcode))
#for i in range(len(docs)):
# docs[i].metadata['Published'] = str(docs[i].metadata['Published'])
# Load, chunk and index the contents of the blog.
url = ['https://arxiv.org/abs/%s' % arxivcode]
loader = WebBaseLoader(url)
docs = loader.load()
# LLM model
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.
)
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 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)
def handle_prompt(message, history, arxivcode, rag_chain):
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")
rag_chain = initialize(arxiv_code)
gr.ChatInterface(handle_prompt, type="messages", theme=gr.themes.Soft(),
description=greetingsmessage,
additional_inputs=[arxiv_code, rag_chain]
)
if __name__=='__main__':
demo.launch() |