/
main.py
54 lines (43 loc) · 1.46 KB
/
main.py
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import os
from flask import Flask, render_template, request
from flask_limiter import Limiter
from flask_limiter.util import get_remote_address
from llama_index import StorageContext, load_index_from_storage
import openai
openai.api_key = os.getenv("OPENAI_API_KEY")
app = Flask(__name__)
limiter = Limiter(
get_remote_address,
app=app,
default_limits=["100 per minute"],
storage_uri=os.getenv("FLASK_LIMITER_STORAGE_URI")
)
# rebuild storage context
storage_context = StorageContext.from_defaults(persist_dir="./storage")
# load index
index = load_index_from_storage(storage_context)
query_engine = index.as_query_engine()
@app.route("/")
def routing_index():
return render_template("main.html")
@app.route("/help")
def routing_help():
return render_template("help.html")
@app.route("/limitcheck")
@limiter.limit("5 per minute")
def routing_limitcheck():
return "ok"
@app.route("/api/search", methods=["POST"])
@limiter.limit("100 per day")
def routing_apiSearch():
req = request.get_json()
query = req['query']
if (query == ""):
return {"error": "need query parameter"}, 400
response = openai.Completion.create(
prompt= "Translate to English: " + req['query'],
model="text-davinci-003"
)
queryEnglish = response["choices"][0]["text"].strip().strip("Title").strip("タイトル").strip(":").strip()
response = query_engine.query(queryEnglish)
return {"llama_index_response": response}