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pdf_txt_qa.py
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pdf_txt_qa.py
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import os
from langchain.document_loaders import PyPDFLoader, TextLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.chains import RetrievalQAWithSourcesChain
from langchain.chat_models import ChatOpenAI
import chainlit as cl
from chainlit.types import AskFileResponse
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
embeddings = OpenAIEmbeddings()
welcome_message = """Welcome to the Chainlit PDF QA demo! To get started:
1. Upload a PDF or text file
2. Ask a question about the 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() as tempfile:
tempfile.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
docsearch = Chroma.from_documents(
docs, embeddings
)
return docsearch
@cl.on_chat_start
async def start():
# Sending an image with the local file path
elements = [
cl.Image(name="image1", display="inline", path="./robot.jpeg")
]
await cl.Message(content="Hello there, Welcome to AskAnyQuery related to Data!", elements=elements).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,
).send()
file = files[0]
msg = cl.Message(content=f"Processing `{file.name}`...")
await msg.send()
# No async implementation in the Pinecone client, fallback to sync
docsearch = await cl.make_async(get_docsearch)(file)
chain = RetrievalQAWithSourcesChain.from_chain_type(
ChatOpenAI(temperature=0, streaming=True),
chain_type="stuff",
retriever=docsearch.as_retriever(max_tokens_limit=4097),
)
# 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):
chain = cl.user_session.get("chain") # type: RetrievalQAWithSourcesChain
cb = cl.AsyncLangchainCallbackHandler(
stream_final_answer=True, answer_prefix_tokens=["FINAL", "ANSWER"]
)
cb.answer_reached = True
res = await chain.acall(message, callbacks=[cb])
answer = res["answer"]
sources = res["sources"].strip()
source_elements = []
# Get the documents from the user session
docs = cl.user_session.get("docs")
metadatas = [doc.metadata for doc in docs]
all_sources = [m["source"] for m in metadatas]
if sources:
found_sources = []
# Add the sources to the message
for source in sources.split(","):
source_name = source.strip().replace(".", "")
# Get the index of the source
try:
index = all_sources.index(source_name)
except ValueError:
continue
text = docs[index].page_content
found_sources.append(source_name)
# Create the text element referenced in the message
source_elements.append(cl.Text(content=text, name=source_name))
if found_sources:
answer += f"\nSources: {', '.join(found_sources)}"
else:
answer += "\nNo sources found"
if cb.has_streamed_final_answer:
cb.final_stream.elements = source_elements
await cb.final_stream.update()
else:
await cl.Message(content=answer, elements=source_elements).send()