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import os
from typing import List
from chainlit.types import AskFileResponse
from aimakerspace.text_utils import CharacterTextSplitter, PDFFileLoader
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
import asyncio
import nest_asyncio
nest_asyncio.apply()
pdf_loader_NIST = PDFFileLoader("data/NIST.AI.600-1.pdf")
pdf_loader_Blueprint = PDFFileLoader("data/Blueprint-for-an-AI-Bill-of-Rights.pdf")
documents_NIST = pdf_loader_NIST.load_documents()
documents_Blueprint = pdf_loader_Blueprint.load_documents()
text_splitter = CharacterTextSplitter()
split_documents_NIST = text_splitter.split_texts(documents_NIST)
split_documents_Blueprint = text_splitter.split_texts(documents_Blueprint)
RAG_PROMPT_TEMPLATE = """ \
Use the provided context to answer the user's query.
You may not answer the user's query unless there is specific context in the following text.
If you do not know the answer, or cannot answer, please respond with "I don't know".
"""
rag_prompt = SystemRolePrompt(RAG_PROMPT_TEMPLATE)
USER_PROMPT_TEMPLATE = """ \
Context:
{context}
User Query:
{user_query}
"""
user_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):
context_list = self.vector_db_retriever.search_by_text(user_query, k=4)
context_prompt = ""
for context in context_list:
context_prompt += context[0] + "\n"
formatted_system_prompt = rag_prompt.create_message()
formatted_user_prompt = user_prompt.create_message(user_query=user_query, context=context_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}
# ------------------------------------------------------------
@cl.on_chat_start # marks a function that will be executed at the start of a user session
async def start_chat():
# settings = {
# "model": "gpt-3.5-turbo",
# "temperature": 0,
# "max_tokens": 500,
# "top_p": 1,
# "frequency_penalty": 0,
# "presence_penalty": 0,
# }
# Create a dict vector store
vector_db = VectorDatabase()
vector_db = await vector_db.abuild_from_list(split_documents_NIST)
vector_db = await vector_db.abuild_from_list(split_documents_Blueprint)
chat_openai = ChatOpenAI()
# Create a chain
retrieval_augmented_qa_pipeline = RetrievalAugmentedQAPipeline(
vector_db_retriever=vector_db,
llm=chat_openai
)
# cl.user_session.set("settings", settings)
cl.user_session.set("chain", retrieval_augmented_qa_pipeline)
@cl.on_message # marks a function that should be run each time the chatbot receives a message from a user
async def main(message):
chain = cl.user_session.get("chain")
msg = cl.Message(content="")
result = await chain.arun_pipeline(message.content)
async for stream_resp in result["response"]:
await msg.stream_token(stream_resp)
await msg.send() |