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To use the chat engine streaming generator in FastAPI for real-time updates, you would need to create an endpoint that uses the
stream_chat method from the LlamaIndex chat engine. The
stream_chat method returns a generator that yields the chat response in real-time.
Here's a basic example of how you might set this up, based on the information provided in the extracts:
from fastapi import FastAPI
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.llms.openai import OpenAI
app = FastAPI()
# Initialize your LlamaIndex and chat engine
llm = OpenAI(model="gpt-3.5-turbo", temperature=0)
data = SimpleDirectoryReader(input_dir="./data/paul_graham/").load_data()
index = VectorStoreIndex.from_documents(data)
chat_engine = index.as_chat_engine()
@app.get("/stream_chat/{message}")
async def stream_chat(message: str):
streaming_response = chat_engine.stream_chat(message)
for token in streaming_response.response_gen:
yield tokenIn this example, you would replace
"./data/paul_graham/" with the path to your data. The
stream_chat endpoint accepts a message as a path parameter and returns the chat response as a stream.
Please note that this is a simplified example and you might need to adjust it based on your specific needs and setup. For more information on using LlamaIndex and its chat engine, you can refer to the
LlamaIndex documentation ().
For more information on streaming responses with FastAPI, you can refer to the
FastAPI documentation ().
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