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from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.document_loaders import PyMuPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import Chroma
from langchain.chains import RetrievalQAWithSourcesChain
from langchain.chat_models import ChatOpenAI
from typing import Any, List, Mapping, Optional
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.prompts.chat import (
ChatPromptTemplate,
SystemMessagePromptTemplate,
HumanMessagePromptTemplate,
)
import os
import arxiv
import chainlit as cl
from chainlit import user_session
@cl.langchain_factory(use_async=True)
async def init():
arxiv_query = None
# Wait for the user to ask an Arxiv question
while arxiv_query == None:
arxiv_query = await cl.AskUserMessage(
content="Please enter a topic to begin!", timeout=15
).send()
# Obtain the top 3 results from Arxiv for the query
search = arxiv.Search(
query=arxiv_query["content"],
max_results=3,
sort_by=arxiv.SortCriterion.Relevance,
)
await cl.Message(content="Downloading and chunking articles...").send()
# download each of the pdfs
pdf_data = []
for result in search.results():
loader = PyMuPDFLoader(result.pdf_url)
loaded_pdf = loader.load()
for document in loaded_pdf:
document.metadata["source"] = result.entry_id
document.metadata["file_path"] = result.pdf_url
document.metadata["title"] = result.title
pdf_data.append(document)
# Create a Chroma vector store
embeddings = OpenAIEmbeddings(
disallowed_special=(),
)
# If operation takes too long, make_async allows to run in a thread
# docsearch = await cl.make_async(Chroma.from_documents)(pdf_data, embeddings)
docsearch = Chroma.from_documents(pdf_data, embeddings)
# custom SageMaker Model
class Llama2SageMaker(LLM):
max_new_tokens: int = 256
top_p: float = 0.9
temperature: float = 0.1
@property
def _llm_type(self) -> str:
return "Llama2SageMaker"
def _call(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
) -> str:
if stop is not None:
raise ValueError("stop kwargs are not permitted.")
json_body = {
"inputs" : [
[{"role" : "user", "content" : prompt}]
],
"parameters" : {
"max_new_tokens" : self.max_new_tokens,
"top_p" : self.top_p,
"temperature" : self.temperature
}
}
response = requests.post(model_api_gateway, json=json_body)
return response.json()[0]["generation"]["content"]
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
return {
"max_new_tokens" : self.max_new_tokens,
"top_p" : self.top_p,
"temperature" : self.temperature
}
# set our llm to the custom Llama2SageMaker endpoint model
llm = Llama2SageMaker()
# Create a chain that uses the Chroma vector store
chain = RetrievalQAWithSourcesChain.from_chain_type(
llm=llm,
chain_type="stuff",
retriever=docsearch.as_retriever(),
return_source_documents=True,
)
# Let the user know that the system is ready
await cl.Message(
content=f"We found a few papers about `{arxiv_query['content']}` you can now ask questions!"
).send()
return chain
@cl.langchain_postprocess
async def process_response(res):
answer = res["answer"]
source_elements_dict = {}
source_elements = []
for idx, source in enumerate(res["source_documents"]):
title = source.metadata["title"]
if title not in source_elements_dict:
source_elements_dict[title] = {
"page_number": [source.metadata["page"]],
"url": source.metadata["file_path"],
}
else:
source_elements_dict[title]["page_number"].append(source.metadata["page"])
# sort the page numbers
source_elements_dict[title]["page_number"].sort()
for title, source in source_elements_dict.items():
# create a string for the page numbers
page_numbers = ", ".join([str(x) for x in source["page_number"]])
text_for_source = f"Page Number(s): {page_numbers}\nURL: {source['url']}"
source_elements.append(
cl.Text(name=title, content=text_for_source, display="inline")
)
await cl.Message(content=answer, elements=source_elements).send() |