Examples
Document QA
In this example, we’re going to build an chatbot QA app. We’ll learn how to:
- Upload a document
- Create vector embeddings from a file
- Create a chatbot app with the ability to display sources used to generate an answer
This example is inspired from the LangChain doc
Prerequisites
This example has extra dependencies. You can install them with:
pip install langchain chromadb tiktoken
Then, you need to go to create an OpenAI key here.
The state of the union file is available here
Conversational Document QA with LangChain
qa.py
import os
from typing import List
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import Chroma
from langchain.chains import (
ConversationalRetrievalChain,
)
from langchain.chat_models import ChatOpenAI
from langchain.docstore.document import Document
from langchain.memory import ChatMessageHistory, ConversationBufferMemory
import chainlit as cl
os.environ["OPENAI_API_KEY"] = "OPENAI_API_KEY"
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
@cl.on_chat_start
async def on_chat_start():
files = None
# Wait for the user to upload a file
while files == None:
files = await cl.AskFileMessage(
content="Please upload a text file to begin!",
accept=["text/plain"],
max_size_mb=20,
timeout=180,
).send()
file = files[0]
msg = cl.Message(content=f"Processing `{file.name}`...")
await msg.send()
with open(file.path, "r", encoding="utf-8") as f:
text = f.read()
# Split the text into chunks
texts = text_splitter.split_text(text)
# Create a metadata for each chunk
metadatas = [{"source": f"{i}-pl"} for i in range(len(texts))]
# Create a Chroma vector store
embeddings = OpenAIEmbeddings()
docsearch = await cl.make_async(Chroma.from_texts)(
texts, embeddings, metadatas=metadatas
)
message_history = ChatMessageHistory()
memory = ConversationBufferMemory(
memory_key="chat_history",
output_key="answer",
chat_memory=message_history,
return_messages=True,
)
# Create a chain that uses the Chroma vector store
chain = ConversationalRetrievalChain.from_llm(
ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0, streaming=True),
chain_type="stuff",
retriever=docsearch.as_retriever(),
memory=memory,
return_source_documents=True,
)
# Let the user know that the system is ready
msg.content = f"Processing `{file.name}` done. 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["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, display="side")
)
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()
Try it out
chainlit run qa.py
You can then upload any .txt
file to the UI and ask questions about it.
If you are using state_of_the_union.txt
you can ask questions like What did the president say about Ketanji Brown Jackson?
.
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