app.py

import os
import openai

from llama_index.query_engine.retriever_query_engine import RetrieverQueryEngine
from llama_index.callbacks.base import CallbackManager
from llama_index import (
    LLMPredictor,
    ServiceContext,
    SimpleDirectoryReader,
    StorageContext,
    load_index_from_storage,
)
from langchain.chat_models import ChatOpenAI
from llama_index.llms import OpenAI
from llama_index import VectorStoreIndex
import chainlit as cl


openai.api_key = os.environ.get("OPENAI_API_KEY")

# try:
#     # rebuild storage context
#     storage_context = StorageContext.from_defaults(persist_dir="./storage")
#     # load index
#     index = load_index_from_storage(storage_context)
# except:
#     from llama_index import GPTVectorStoreIndex, SimpleDirectoryReader

#     documents = SimpleDirectoryReader("./data").load_data()
#     index = GPTVectorStoreIndex.from_documents(documents)
#     index.storage_context.persist()
documents = SimpleDirectoryReader(
    input_files=["hitchhikers.pdf"]
).load_data()

index = VectorStoreIndex.from_documents(documents)


@cl.on_chat_start
async def factory():
    # llm_predictor = LLMPredictor(
    #     llm=ChatOpenAI(
    #         temperature=0,
    #         model_name="gpt-3.5-turbo",
    #         streaming=True,
    #     ),
    # )
    # service_context = ServiceContext.from_defaults(
    #     llm_predictor=llm_predictor,
    #     chunk_size=512,
    #     callback_manager=CallbackManager([cl.LlamaIndexCallbackHandler()]),
    # )

    gpt_35_context = ServiceContext.from_defaults(
    llm=OpenAI(model="gpt-3.5-turbo", temperature=0.3),
    context_window=2048,  # limit the context window artifically to test refine process
     callback_manager=CallbackManager([cl.LlamaIndexCallbackHandler()]),
    )

    query_engine = index.as_query_engine(
        service_context=gpt_35_context
    )

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


@cl.on_message
async def main(message):
    query_engine = cl.user_session.get("query_engine")  # type: RetrieverQueryEngine
    response = await cl.make_async(query_engine.query)(message)
    print(response)
    response_message = cl.Message(content="")

    # for token in response.response_gen:
    #     await response_message.stream_token(token=token)

    # if response.response_txt:
    response_message.content = response

    await response_message.send()