Delete app_v1.py

app_v1.py

@@ -1,113 +0,0 @@
-from langchain_openai import ChatOpenAI
-from langchain.prompts import ChatPromptTemplate
-from langchain.schema import StrOutputParser
-from langchain.schema.runnable import Runnable
-from langchain.schema.runnable.config import RunnableConfig
-from typing import cast
-from dotenv import load_dotenv
-import os
-from langchain_community.document_loaders import PyMuPDFLoader
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain_openai.embeddings import OpenAIEmbeddings
-from langchain_community.vectorstores import Qdrant
-from langchain_core.runnables import RunnablePassthrough, RunnableParallel
-import chainlit as cl
-from pathlib import Path
-from sentence_transformers import SentenceTransformer  # Ensure this import is correct
-
-load_dotenv()
-
-os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY")
-
-@cl.on_chat_start
-async def on_chat_start():
-    model = ChatOpenAI(streaming=True)
-
-    # Load documents
-    ai_framework_document = PyMuPDFLoader(file_path="https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.600-1.pdf").load()
-    ai_blueprint_document = PyMuPDFLoader(file_path="https://www.whitehouse.gov/wp-content/uploads/2022/10/Blueprint-for-an-AI-Bill-of-Rights.pdf").load()
-    
-    RAG_PROMPT = """\
-    Given a provided context and question, you must answer the question based only on context.
-
-    Context: {context}
-    Question: {question}
-    """
-
-    rag_prompt = ChatPromptTemplate.from_template(RAG_PROMPT)
-
-    sentence_text_splitter = RecursiveCharacterTextSplitter(
-        chunk_size=500,
-        chunk_overlap=100,
-        separators=["\n\n", "\n", ".", "!", "?"]
-    )
-
-    def metadata_generator(document, name, splitter):
-        collection = splitter.split_documents(document)
-        for doc in collection:
-            doc.metadata["source"] = name
-        return collection
-
-    sentence_framework = metadata_generator(ai_framework_document, "AI Framework", sentence_text_splitter)
-    sentence_blueprint = metadata_generator(ai_blueprint_document, "AI Blueprint", sentence_text_splitter)
-
-    sentence_combined_documents = sentence_framework + sentence_blueprint
-
-    # Initialize the SentenceTransformer model properly
-    embedding_model = SentenceTransformer('Cheselle/finetuned-arctic-sentence')
-
-    # Create the Qdrant vector store using the initialized embedding model
-    sentence_vectorstore = Qdrant.from_documents(
-        documents=sentence_combined_documents,
-        embedding=embedding_model,  # Ensure this is an instance
-        location=":memory:",
-        collection_name="AI Policy"
-    )
-
-    sentence_retriever = sentence_vectorstore.as_retriever()
-    
-    # Set the retriever and prompt into session for reuse
-    cl.user_session.set("runnable", model)
-    cl.user_session.set("retriever", sentence_retriever)
-    cl.user_session.set("prompt_template", rag_prompt)
-
-
-@cl.on_message
-async def on_message(message: cl.Message):
-    # Get the stored model, retriever, and prompt
-    model = cast(ChatOpenAI, cl.user_session.get("runnable"))
-    retriever = cl.user_session.get("retriever")
-    prompt_template = cl.user_session.get("prompt_template")
-
-    # Log the message content
-    print(f"Received message: {message.content}")
-
-    # Retrieve relevant context from documents based on the user's message
-    relevant_docs = retriever.get_relevant_documents(message.content)
-    print(f"Retrieved {len(relevant_docs)} documents.")
-
-    if not relevant_docs:
-        print("No relevant documents found.")
-        await cl.Message(content="Sorry, I couldn't find any relevant documents.").send()
-        return
-
-    context = "\n\n".join([doc.page_content for doc in relevant_docs])
-
-    # Log the context to check
-    print(f"Context: {context}")
-
-    # Construct the final RAG prompt
-    final_prompt = prompt_template.format(context=context, question=message.content)
-    print(f"Final prompt: {final_prompt}")
-
-    # Initialize a streaming message
-    msg = cl.Message(content="")
-
-    # Stream the response from the model
-    async for chunk in model.astream(
-        final_prompt,
-        config=RunnableConfig(callbacks=[cl.LangchainCallbackHandler()]),
-    ):
-        await msg.stream_token(chunk.content)
-
-    await msg.send()