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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,
StorageContext,
load_index_from_storage,
)
from llama_index.llms import OpenAI
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(input_files=["hitchhikers.pdf"]).load_data()
index = GPTVectorStoreIndex.from_documents(documents)
index.storage_context.persist()
@cl.on_chat_start
async def factory():
llm_predictor = LLMPredictor(
llm=OpenAI(
temperature=0,
model="ft:gpt-3.5-turbo-0613:personal::7rG4voK4",
streaming=True,
context_window=2048,
),
)
service_context = ServiceContext.from_defaults(
llm_predictor=llm_predictor,
chunk_size=512,
callback_manager=CallbackManager([cl.LlamaIndexCallbackHandler()]),
)
query_engine = index.as_query_engine(
service_context=service_context,
streaming=True,
)
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)
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.response_txt
await response_message.send() |