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import os
from typing import List
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores.pinecone import Pinecone
from langchain.chains import ConversationalRetrievalChain
from langchain.chat_models import ChatOpenAI
from langchain.memory import ChatMessageHistory, ConversationBufferMemory
from langchain.docstore.document import Document
import pinecone
import chainlit as cl
pinecone.init(
api_key=os.environ.get("PINECONE_API_KEY"),
environment=os.environ.get("PINECONE_ENV"),
)
index_name = "langchain-demo"
embeddings = OpenAIEmbeddings()
welcome_message = "Welcome to the Chainlit Pinecone demo! Ask anything about Shakespeare's King Lear vectorized documents from Pinecone DB."
@cl.on_chat_start
async def start():
await cl.Message(content=welcome_message).send()
docsearch = Pinecone.from_existing_index(
index_name=index_name, embedding=embeddings
)
message_history = ChatMessageHistory()
memory = ConversationBufferMemory(
memory_key="chat_history",
output_key="answer",
chat_memory=message_history,
return_messages=True,
)
chain = ConversationalRetrievalChain.from_llm(
ChatOpenAI(
model_name="gpt-3.5-turbo",
temperature=0,
streaming=True),
chain_type="stuff",
retriever=docsearch.as_retriever(search_kwargs={'k': 3}), # I only want maximum of three document back with the highest similarity score
memory=memory,
return_source_documents=True,
)
cl.user_session.set("chain", chain)
@cl.on_message
async def main(message: cl.Message):
chain = cl.user_session.get("chain") # type: ConversationalRetrievalChain
cb = cl.AsyncLangchainCallbackHandler()
res = await chain.acall(message.content, callbacks=[cb])
answer = res["answer"]
source_documents = res["source_documents"] # type: List[Document]
text_elements = [] # type: List[cl.Text]
if source_documents:
for source_idx, source_doc in enumerate(source_documents):
source_name = f"source_{source_idx}"
# Create the text element referenced in the message
text_elements.append(
cl.Text(content=source_doc.page_content, name=source_name)
)
source_names = [text_el.name for text_el in text_elements]
if source_names:
answer += f"\nSources: {', '.join(source_names)}"
else:
answer += "\nNo sources found"
await cl.Message(content=answer, elements=text_elements).send()
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