Delete app1.1.py

app1.1.py

@@ -1,85 +0,0 @@
-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
-from langchain.embeddings import HuggingFaceEmbeddings
-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)
-    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
-    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()