app.py

import os
from typing import List
from chainlit.types import AskFileResponse
from aimakerspace.text_utils import CharacterTextSplitter, TextFileLoader, PDFLoader
from aimakerspace.openai_utils.prompts import (
    UserRolePrompt,
    SystemRolePrompt,
    AssistantRolePrompt,
)
from aimakerspace.openai_utils.embedding import EmbeddingModel
from aimakerspace.vectordatabase import VectorDatabase
from aimakerspace.openai_utils.chatmodel import ChatOpenAI
import chainlit as cl

system_template = """\
You are a helpful AI assistant that answers questions based on the provided context. 
Your task is to:
1. Carefully read and understand the context
2. Answer the user's question using ONLY the information from the context
3. If the answer cannot be found in the context, say "I cannot find the answer in the provided context"
4. If you find partial information, share what you found and indicate if more information might be needed

Remember: Only use information from the provided context to answer questions."""
system_role_prompt = SystemRolePrompt(system_template)

user_prompt_template = """\
Context:
{context}

Based on the above context, please answer the following question. If the answer cannot be found in the context, say "I cannot find the answer in the provided context." If you find partial information, share what you found and indicate if more information might be needed.

Question:
{question}

Please provide a clear and concise answer based ONLY on the information in the context above."""
user_role_prompt = UserRolePrompt(user_prompt_template)

class RetrievalAugmentedQAPipeline:
    def __init__(self, llm: ChatOpenAI(), vector_db_retriever: VectorDatabase) -> None:
        self.llm = llm
        self.vector_db_retriever = vector_db_retriever

    async def arun_pipeline(self, user_query: str):
        # Get more contexts with a broader search
        print("\nSearching for relevant contexts...")
        context_list = self.vector_db_retriever.search_by_text(user_query, k=5)  # Increased from 3 to 5
        
        print("\nRetrieved contexts:")
        for i, (context, score) in enumerate(context_list):
            print(f"\nContext {i+1} (score: {score:.3f}):")
            print(context[:500] + "..." if len(context) > 500 else context)  # Show more context
        
        # Limit total context length to approximately 3000 tokens (12000 characters)
        context_prompt = ""
        total_length = 0
        max_length = 12000  # Reduced from 24000 to 12000
        
        # Sort contexts by score before truncating
        sorted_contexts = sorted(context_list, key=lambda x: x[1], reverse=True)
        
        for context, score in sorted_contexts:
            if total_length + len(context) > max_length:
                print(f"\nSkipping context with score {score:.3f} due to length limit")
                continue
            context_prompt += context + "\n"
            total_length += len(context)
            
        print(f"\nUsing {len(context_prompt.split())} words of context")

        formatted_system_prompt = system_role_prompt.create_message()
        formatted_user_prompt = user_role_prompt.create_message(question=user_query, context=context_prompt)

        print("\nFinal messages being sent to the model:")
        print("\nSystem prompt:")
        print(formatted_system_prompt)
        print("\nUser prompt:")
        print(formatted_user_prompt)

        async def generate_response():
            async for chunk in self.llm.astream([formatted_system_prompt, formatted_user_prompt]):
                yield chunk

        return {"response": generate_response(), "context": context_list}

text_splitter = CharacterTextSplitter()


def process_file(file: AskFileResponse):
    import tempfile
    import shutil
    
    print(f"Processing file: {file.name}")
    
    # Create a temporary file with the correct extension
    suffix = f".{file.name.split('.')[-1]}"
    with tempfile.NamedTemporaryFile(delete=False, suffix=suffix) as temp_file:
        # Copy the uploaded file content to the temporary file
        shutil.copyfile(file.path, temp_file.name)
        print(f"Created temporary file at: {temp_file.name}")
        
        # Create appropriate loader
        if file.name.lower().endswith('.pdf'):
            loader = PDFLoader(temp_file.name)
        else:
            loader = TextFileLoader(temp_file.name)
            
        try:
            # Load and process the documents
            documents = loader.load_documents()
            texts = text_splitter.split_texts(documents)
            return texts
        finally:
            # Clean up the temporary file
            try:
                os.unlink(temp_file.name)
            except Exception as e:
                print(f"Error cleaning up temporary file: {e}")


@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 or PDF file to begin!",
            accept=["text/plain", "application/pdf"],
            max_size_mb=2,
            timeout=180,
        ).send()

    file = files[0]
    print(f"Received file: {file.name} ({file.type})")

    msg = cl.Message(
        content=f"Processing `{file.name}`..."
    )
    await msg.send()

    # load the file
    try:
        texts = process_file(file)
        print(f"Successfully processed file. Generated {len(texts)} text chunks")
        print("Sample of first chunk:", texts[0][:200] if texts else "No texts generated")
    except Exception as e:
        print(f"Error processing file: {str(e)}")
        await cl.Message(content=f"Error processing file: {str(e)}").send()
        return

    # Create a dict vector store
    try:
        vector_db = VectorDatabase()
        vector_db = await vector_db.abuild_from_list(texts)
        print("Successfully created vector database")
    except Exception as e:
        print(f"Error creating vector database: {str(e)}")
        await cl.Message(content=f"Error creating vector database: {str(e)}").send()
        return
    
    try:
        chat_openai = ChatOpenAI()
        print("Successfully initialized ChatOpenAI")
    except Exception as e:
        print(f"Error initializing ChatOpenAI: {str(e)}")
        await cl.Message(content=f"Error initializing ChatOpenAI: {str(e)}").send()
        return

    # Create a chain
    retrieval_augmented_qa_pipeline = RetrievalAugmentedQAPipeline(
        vector_db_retriever=vector_db,
        llm=chat_openai
    )
    
    # 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", retrieval_augmented_qa_pipeline)


@cl.on_message
async def main(message):
    chain = cl.user_session.get("chain")
    if not chain:
        await cl.Message(content="Error: Chat session not initialized. Please try uploading the file again.").send()
        return

    msg = cl.Message(content="")
    try:
        result = await chain.arun_pipeline(message.content)
        print(f"Retrieved {len(result['context'])} relevant contexts")
        
        async for stream_resp in result["response"]:
            await msg.stream_token(stream_resp)

        await msg.send()
    except Exception as e:
        print(f"Error in chat pipeline: {str(e)}")
        await cl.Message(content=f"Error processing your question: {str(e)}").send()