app.py

import os
import gradio as gr
from llama_index.core import StorageContext, load_index_from_storage, Settings
from llama_index.llms.azure_openai import AzureOpenAI
from llama_index.embeddings.azure_openai import AzureOpenAIEmbedding
from dotenv import load_dotenv

load_dotenv(override=True)

api_key = os.getenv("AZURE_OPENAI_API_KEY")
api_version = "2024-05-01-preview"
azure_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT")

llm = AzureOpenAI(
    model="gpt-4o",
    deployment_name="gpt-4o",
    api_key=api_key,
    azure_endpoint=azure_endpoint,
    api_version=api_version,
)

# You need to deploy your own embedding model as well as your own chat completion model
embed_model = AzureOpenAIEmbedding(
    model="text-embedding-3-small",
    deployment_name="text-embedding-3-small",
    api_key=api_key,
    azure_endpoint=azure_endpoint,
    api_version=api_version,
)

Settings.llm = llm
Settings.embed_model = embed_model

# rebuild storage context
storage_context = StorageContext.from_defaults(persist_dir="./index")

# load index
index = load_index_from_storage(storage_context)

query_engine = index.as_query_engine(similarity_top_k=10)

# Function to handle chat messages with history
def echo(message, history):
    context = "\n".join([f"User: {user_msg}\nBot: {bot_msg}" for user_msg, bot_msg in history])
    full_context = f"{context}\nUser: {message}"
    response = query_engine.query(full_context).response
    history.append((message, response))
    return response # history

demo = gr.ChatInterface(
    fn=echo, 
    examples=[
        "光源氏はどのような人物ですか？",
        "夕顔はどのような人物ですか？"
    ], 
    title="Llama Index Chatbot",
)
demo.launch()