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import os
import chainlit as cl
from dotenv import load_dotenv
# LangChain imports for retrieval and generation
from langchain.document_loaders import WebBaseLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import FAISS
from langchain.chains import RetrievalQA
from langchain.llms import OpenAI
# Load environment variables (e.g., OPENAI_API_KEY)
load_dotenv()
# Global variable to store our QA chain.
qa_chain = None
@cl.on_chat_start
async def start_chat():
"""
When the chat starts, load the document using WebBaseLoader, split it into chunks,
create embeddings, build a vector store, and finally initialize a RetrievalQA chain.
This chain will serve as the backend for our RAG system.
"""
global qa_chain
# URL to crawl (German Wikipedia page on Künstliche Intelligenz)
url = "https://de.wikipedia.org/wiki/K%C3%BCnstliche_Intelligenz"
# Retrieve the document from the webpage
loader = WebBaseLoader(url)
documents = loader.load() # returns a list of Document objects
# Split the document into manageable chunks for better retrieval
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
docs = text_splitter.split_documents(documents)
# Create embeddings (make sure your OPENAI_API_KEY is set in your environment)
embeddings = OpenAIEmbeddings()
# Build a vector store from the documents using FAISS
vectorstore = FAISS.from_documents(docs, embeddings)
# Configure the retriever: retrieve the top 3 most relevant chunks
retriever = vectorstore.as_retriever(search_kwargs={"k": 3})
# Set up the language model (using OpenAI LLM here) with desired parameters
llm = OpenAI(temperature=0)
# Create a RetrievalQA chain that first retrieves relevant context and then generates an answer.
qa_chain = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever)
await cl.Message(
content="✅ Document loaded and processed successfully! "
"You can now ask me questions about 'Künstliche Intelligenz'."
).send()
@cl.on_message
async def process_question(message: cl.Message):
"""
When a message is received, use the QA chain to process the query. The chain:
1. Retrieves relevant document chunks.
2. Augments your query with the retrieved context.
3. Generates an answer via the language model.
"""
global qa_chain
if qa_chain is None:
await cl.Message(content="❌ The document has not been loaded yet.").send()
return
# Get the user's query
query = message.content.strip()
# Process the query using the RetrievalQA chain
result = qa_chain.run(query)
# Send the answer back to the user
await cl.Message(content=result).send()
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