app.py

import os
from openai import AsyncOpenAI
from RagPipeline import RetrievalAugmentedQAPipeline
from typing import List
from chainlit.types import AskFileResponse
from chainlit.cli import run_chainlit
from aimakerspace.text_utils import CharacterTextSplitter, PdfFileLoader, TextFileLoader
from aimakerspace.openai_utils.prompts import (
    UserRolePrompt,
    SystemRolePrompt,
    AssistantRolePrompt,
)
from aimakerspace.openai_utils.embedding import EmbeddingModel
from aimakerspace.vectordatabase import VectorDatabase, VectorDatabaseOptions
from aimakerspace.openai_utils.chatmodel import ChatOpenAI
import chainlit as cl
from langchain_text_splitters import RecursiveCharacterTextSplitter


# Instrument the OpenAI client
# cl.instrument_openai()

##### Prompt Templates #####
system_template = """\
Use the following context to answer a users question. If you cannot find the answer in the context, say you don't know the answer."""

user_prompt_template = """\
Context:
{context}

Question:
{question}
"""

system_role_prompt = SystemRolePrompt(system_template)
user_role_prompt = UserRolePrompt(user_prompt_template)

### Text Chunking ###

# text_splitter = CharacterTextSplitter()
text_splitter = RecursiveCharacterTextSplitter(
    separators=[
        "\n\n",
        "\n",
        " ",
        ".",
        ",",
        "\u200b",  # Zero-width space
        "\uff0c",  # Fullwidth comma
        "\u3001",  # Ideographic comma
        "\uff0e",  # Fullwidth full stop
        "\u3002",  # Ideographic full stop
        "",
    ],
)

def process_text_file(file: AskFileResponse) -> List[str]:
    import tempfile

    with tempfile.NamedTemporaryFile(
        mode="wb", delete=False, suffix=".txt"
    ) as temp_file:
        temp_file_path = temp_file.name
        temp_file.write(file.content)

    text_loader = TextFileLoader(temp_file_path)
    documents = text_loader.load_documents()
    texts = []
    for doc in documents:
        texts += text_splitter.split_text(doc)
    return texts

def process_pdf_file(file: AskFileResponse) -> List[str]:
    import tempfile
    with tempfile.NamedTemporaryFile(
        mode="wb", delete=False, suffix=".pdf"
    ) as temp_file:
        temp_file_path = temp_file.name
        temp_file.write(file.content)


    pdf_loader = PdfFileLoader(temp_file_path)
    texts = pdf_loader.load_documents() # Also handles splitting the text in this case pages
    return texts

async def send_new_message(content, elemets=None):
    msg = cl.Message(content,elements=elemets)
    await msg.send()
    return msg


@cl.on_chat_start
async def on_chat_start():
    print("On Chat Start")
    # await send_new_message("Welcome to the Chat with Files app!")
    msg = cl.Message(content="Welcome to the Chat with Files app!")
    await msg.send()
    print("After First message")

    files = None

    # Wait for the user to upload a file
    while files == None:

        files = await cl.AskFileMessage(
            content="Please upload a text file to begin!",
            accept=["text/plain", "application/pdf"],
            max_size_mb=10,
            max_files=4,
            timeout=180,
        ).send()
    texts : List[str] = []
    for file in files:
        if file.type == "application/pdf":
            texts += process_pdf_file(file)
        if file.type == "text/plain":
            texts += process_text_file(file)

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

    print(f"Processing {len(texts)} text chunks")
    
    # Create a dict vector store
    vector_db_options =VectorDatabaseOptions.QDRANT
    embedding_model = EmbeddingModel(embeddings_model_name= "text-embedding-3-small",dimensions=1000)
    vector_db = VectorDatabase(vector_db_options,embedding_model)
    vector_db = await vector_db.abuild_from_list(texts)

    chat_openai = ChatOpenAI()

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

    # Let the user know that the system is ready
    msg = cl.Message(content=f"Processing `{file.name}` done. You can now ask questions!")
    await msg.send()

    cl.user_session.set("chain", retrieval_augmented_qa_pipeline)


@cl.on_message
async def main(message: cl.Message):
    msg = cl.Message(content="on message")
    await msg.send()

    chain :RetrievalAugmentedQAPipeline = cl.user_session.get("chain")

    msg = cl.Message(content="")
    result = await chain.arun_pipeline(message.content)

    async for stream_resp in result.get('response'):
        await msg.stream_token(stream_resp)

    await msg.send()
    cl.user_session.set("chain", chain)


if __name__ == "__main__":
    run_chainlit(__file__)