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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()
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