updating app.py; author: Enrico Collautti enricoll@bu.edu

app1.1.py

@@ -0,0 +1,83 @@
+from dotenv import load_dotenv  # Import dotenv to load environment variables
+import os
+import chainlit as cl
+from langchain.chains import RetrievalQA
+from langchain_community.vectorstores import FAISS
+from langchain_community.embeddings import OpenAIEmbeddings
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.chat_models import ChatOpenAI
+from langchain.schema import Document
+import json
+
+# Load environment variables from .env file
+load_dotenv()
+
+# Get the OpenAI API key from the environment
+OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
+
+if not OPENAI_API_KEY:
+    raise ValueError("OPENAI_API_KEY is not set. Please add it to your .env file.")
+
+# Global variables for vector store and QA chain
+vector_store = None
+qa_chain = None
+
+# Step 1: Load and Process JSON Data
+def load_json_file(file_path):
+    with open(file_path, "r", encoding="utf-8") as file:
+        data = json.load(file)
+    return data
+
+def setup_vector_store_from_json(json_data):
+    # Create Document objects with URLs and content
+    documents = [Document(page_content=item["content"], metadata={"url": item["url"]}) for item in json_data]
+
+    # Create embeddings and store them in FAISS
+    embeddings = OpenAIEmbeddings(openai_api_key=OPENAI_API_KEY)
+    vector_store = FAISS.from_documents(documents, embeddings)
+    return vector_store
+
+def setup_qa_chain(vector_store):
+    retriever = vector_store.as_retriever(search_kwargs={"k": 3})
+    llm = ChatOpenAI(model="gpt-3.5-turbo", openai_api_key=OPENAI_API_KEY)
+    qa_chain = RetrievalQA.from_chain_type(llm=llm, retriever=retriever, return_source_documents=True)
+    return qa_chain
+
+# Initialize Chainlit: Preload data when the chat starts
+@cl.on_chat_start
+async def chat_start():
+    global vector_store, qa_chain
+
+    # Load and preprocess the JSON file
+    json_data = load_json_file("football_players.json")
+    vector_store = setup_vector_store_from_json(json_data)
+    qa_chain = setup_qa_chain(vector_store)
+
+    # Send a welcome message
+    await cl.Message(content="Welcome to the RAG app! Ask me any question based on the knowledge base.").send()
+
+# Process user queries
+@cl.on_message
+async def main(message: cl.Message):
+    global qa_chain
+
+    # Ensure the QA chain is ready
+    if qa_chain is None:
+        await cl.Message(content="The app is still initializing. Please wait a moment and try again.").send()
+        return
+
+    # Get query from the user and run the QA chain
+    query = message.content
+    response = qa_chain({"query": query})
+
+    # Extract the answer and source documents
+    answer = response["result"]
+    sources = response["source_documents"]
+
+    # Format and send the response
+    await cl.Message(content=f"**Answer:** {answer}").send()
+    if sources:
+        await cl.Message(content="**Sources:**").send()
+        for i, doc in enumerate(sources, 1):
+            url = doc.metadata.get("url", "No URL available")
+            await cl.Message(content=f"**Source {i}:** {doc.page_content}\n**URL:** {url}").send()