app.py

import tiktoken
import os
from qdrant_client import QdrantClient
from langchain_qdrant import QdrantVectorStore
from qdrant_client.http.models import Distance, VectorParams
from typing import List
from chainlit.types import AskFileResponse
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_community.document_loaders import PyMuPDFLoader
from langchain_community.vectorstores import Qdrant
from langchain_openai.llms import OpenAI
from langchain_openai.chat_models import ChatOpenAI
from langchain_openai.embeddings import OpenAIEmbeddings
from langchain.chains.summarize import load_summarize_chain
from langchain.chains.conversation.memory import ConversationSummaryBufferMemory
from langchain.chains.conversation.base import ConversationChain
from langchain_core.runnables import RunnableParallel, RunnablePassthrough
from langchain_core.messages import SystemMessage, AIMessage, HumanMessage
from langchain_core.prompts import (ChatMessagePromptTemplate, SystemMessagePromptTemplate, 
                                    AIMessagePromptTemplate, HumanMessagePromptTemplate)
from langchain_community.chat_message_histories import ChatMessageHistory
from langchain_core.chat_history import BaseChatMessageHistory
from langchain_core.runnables.history import RunnableWithMessageHistory
from langchain_core.prompts.chat import ChatPromptTemplate, MessagesPlaceholder
from langchain_core.pydantic_v1 import BaseModel, Field
from langchain.output_parsers import PydanticOutputParser
from langchain.output_parsers import OutputFixingParser
import chainlit as cl

from dotenv import load_dotenv; _ = load_dotenv()

RAG_PROMPT = """
Please answer the question below using the provided context. If the question cannnot be answered
using the context, politely state that you can't answer that question.

Question:
{question}

Context:
{context}
"""

def get_rag_chain():
    """Fetches a simple RAG chain"""
    prompt = ChatPromptTemplate.from_template(RAG_PROMPT)
    embedding = OpenAIEmbeddings(model='text-embedding-3-small')
    retriever = QdrantVectorStore.from_existing_collection(
        collection_name='ai_ethics_te3_small',
        embedding=embedding,
        url=os.environ.get('QDRANT_DB'),
        api_key=os.environ.get('QDRANT_API_KEY')
    ).as_retriever()
    llm = ChatOpenAI(model='gpt-4o', temperature=0)
    rag_chain = ({'context': retriever, 'question': RunnablePassthrough()}
             | prompt
             | llm)
    return rag_chain


@cl.on_chat_start
async def on_chat_start():
    """Initialization of the application"""
    msg = cl.Message(
        content="", disable_human_feedback=True
    )
    await msg.send()
    chain = get_rag_chain()
    # Let the user know that the system is ready
    msg.content = """
    I'm ready to answer any of your questions about the framework for [AI Bill of Rights](https://www.whitehouse.gov/wp-content/uploads/2022/10/Blueprint-for-an-AI-Bill-of-Rights.pdf)
    and [NIST AI Risk Management Framework](https://www.whitehouse.gov/wp-content/uploads/2022/10/Blueprint-for-an-AI-Bill-of-Rights.pdf)
    Ask away!
    """
    await msg.update()

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


@cl.on_message
async def main(message):
    """Run on every user message"""
    chain = cl.user_session.get("chain")

    msg = cl.Message(content="")
    async for resp in chain.astream(message.content):
        await msg.stream_token(resp.content)

    await msg.send()