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import os
from typing import List
from langchain.document_loaders import PyPDFLoader, TextLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores.pinecone import Pinecone
from langchain.chains import RetrievalQA
from langchain.chat_models import ChatOpenAI
from langchain.memory import ChatMessageHistory, ConversationBufferMemory
from langchain.docstore.document import Document
import pinecone
import chainlit as cl
from chainlit.types import AskFileResponse
pinecone.init(
api_key="2b6aa6bf-2e20-4445-a560-f7dd4952e59e",
environment="gcp-starter",
)
index_name = "skandhaar"
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
embeddings = OpenAIEmbeddings()
namespaces = set()
welcome_message = """Welcome to the Chainlit PDF QA demo! To get started:
1. Upload a PDF or text file
"""
def process_file(file: AskFileResponse):
import tempfile
if file.type == "text/plain":
Loader = TextLoader
elif file.type == "application/pdf":
Loader = PyPDFLoader
with tempfile.NamedTemporaryFile(mode="wb", delete=False) as tempfile:
if file.type == "text/plain":
tempfile.write(file.content)
elif file.type == "application/pdf":
with open(tempfile.name, "wb") as f:
f.write(file.content)
loader = Loader(tempfile.name)
documents = loader.load()
docs = text_splitter.split_documents(documents)
for i, doc in enumerate(docs):
doc.metadata["source"] = f"source_{i}"
return docs
def get_docsearch(file: AskFileResponse):
docs = process_file(file)
# Save data in the user session
cl.user_session.set("docs", docs)
# Create a unique namespace for the file
namespace = str(hash(file.content))
if namespace in namespaces:
docsearch = Pinecone.from_existing_index(
index_name=index_name, embedding=embeddings
)
else:
docsearch = Pinecone.from_documents(
docs, embeddings, index_name=index_name
)
namespaces.add(namespace)
return docsearch
@cl.on_chat_start
async def start():
await cl.Avatar(
name="Chatbot",
url="https://avatars.githubusercontent.com/u/128686189?s=400&u=a1d1553023f8ea0921fba0debbe92a8c5f840dd9&v=4",
).send()
files = None
while files is None:
files = await cl.AskFileMessage(
content=welcome_message,
accept=["text/plain", "application/pdf"],
max_size_mb=20,
timeout=180,
disable_human_feedback=True,
).send()
for file in files:
msg = cl.Message(
content=f"Processing `{file.name}`...", disable_human_feedback=True
)
await msg.send()
# No async implementation in the Pinecone client, fallback to sync
docsearch = await cl.make_async(get_docsearch)(file)
message_history = ChatMessageHistory()
memory = ConversationBufferMemory(
memory_key="chat_history",
output_key="result",
chat_memory=message_history,
return_messages=True,
)
chain = RetrievalQA.from_chain_type(
ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0, streaming=True, openai_api_key="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"),
chain_type="stuff",
retriever=docsearch.as_retriever(),
return_source_documents=True,
)
# Let the user know that the system is ready
msg.content = f"`{file.name}` processed. You can now ask questions!"
await msg.update()
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["result"]
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() |