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import chainlit as cl
from chainlit.playground.providers import ChatOpenAI
from dotenv import load_dotenv
from langchain.document_loaders import PyMuPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
import tiktoken
from langchain.prompts import ChatPromptTemplate
from operator import itemgetter
from langchain_core.runnables import RunnablePassthrough
from langchain_core.output_parsers import StrOutputParser
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain.vectorstores import FAISS
from langchain_openai import OpenAIEmbeddings
from langchain.retrievers import MultiQueryRetriever
from langchain.chains.combine_documents import create_stuff_documents_chain
from langchain.chains import create_retrieval_chain
from langchain.prompts import ChatPromptTemplate
from langchain.schema import StrOutputParser
from langchain.schema.runnable import Runnable
from langchain.schema.runnable.config import RunnableConfig
from langchain.retrievers import MultiQueryRetriever
from langchain.chains.combine_documents import create_stuff_documents_chain
from langchain import hub
template = """
you can only answer questions related to what's in the context. If it's not in the context, then you would reply with
'Sorry I have no answer to your particular question. I can only answer things regarding: {context}'
Context:
{context}
Question:
{question}
"""
init_settings = {
"model": "gpt-3.5-turbo",
"temperature": 0,
"max_tokens": 500,
"top_p": 1,
"frequency_penalty": 0,
"presence_penalty": 0,
}
embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
load_dotenv()
def tiktoken_len(text):
tokens = tiktoken.encoding_for_model("gpt-3.5-turbo").encode(
text,
)
return len(tokens)
@cl.on_chat_start
async def main():
model = ChatOpenAI(streaming=True)
prompt = ChatPromptTemplate.from_template(template)
nvida_doc = PyMuPDFLoader('../docs/nvidia-document.pdf')
data = nvida_doc.load()
text_splitter = RecursiveCharacterTextSplitter(
chunk_size = 1700,
chunk_overlap = 0,
length_function = tiktoken_len)
nvidia_doc_chunks = text_splitter.split_documents(data)
vector_store = FAISS.from_documents(nvidia_doc_chunks, embedding=embeddings)
retriever = vector_store.as_retriever()
advanced_retriever = MultiQueryRetriever.from_llm(retriever=retriever, llm=model)
runnable = (
{"context": itemgetter("question") | retriever, "question": itemgetter("question")}
| RunnablePassthrough.assign(context=itemgetter("context"))
| {"response": prompt | model, "context": itemgetter("context")})
# retrieval_qa_prompt = hub.pull("langchain-ai/retrieval-qa-chat")
# document_chain = create_stuff_documents_chain(model, retrieval_qa_prompt)
# runnable = create_retrieval_chain(advanced_retriever, document_chain)
# cl.user_session.set("settings", init_settings)
# cl.user_session.set("nvidia_doc", data)
cl.user_session.set("runnable", runnable)
@cl.on_message
async def on_message(message: cl.Message):
# settings = cl.user_session.get("settings")
# nvida_doc = cl.user_session.get("nvidia_doc")
runnable = cl.user_session.get("runnable")
msg = cl.Message(content="")
# async for chunk in runnable.astream(
# {"question": message.content},
# config=RunnableConfig(callbacks=[cl.LangchainCallbackHandler()]),
# ):
# await msg.stream_token(chunk, True)
# await msg.send()
inputs = {"question": message.content}
result = await runnable.ainvoke(inputs)
msg = cl.Message(content=result["response"].content)
await msg.send()
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