RAGAPI / app.py
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from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from flask import Flask, request,abort,jsonify
from g4f.client import Client
from os import getenv
from requests import get as reqget
from re import search
app = Flask(__name__)
emb = HuggingFaceEmbedding(getenv("embmodel").strip())
embcache:dict[str,list[float]] = {}
chatcache:dict[str,str] = {}
transcache: dict[tuple[str, str], str] = {}
@app.get("/")
def index():
return "Hello World!"
@app.post("/api")
def api():
text = request.data.decode().strip()
typeofapi = request.headers.get("type")
if not text or not typeofapi:
abort(400,"text and type is required")
if typeofapi == "embedding":
if text in embcache:
return embcache.get(text)
result = emb.get_query_embedding(text)
embcache[text] = result
return jsonify(result)
elif typeofapi == "chat":
if text in chatcache:
return chatcache.get(text)
response:str = Client().chat.completions.create(max_tokens=2024,model="gpt-4o-mini",messages=[{"role": "user", "content": text}]).choices[0].message.content
chatcache[text] = response
return response
elif typeofapi == "translate_to_en":
srclang: str = request.headers.get("srclang")
if not srclang:
abort(400,"srclang is required")
if (srclang,text) in transcache:
return transcache.get((srclang,text))
if search(r"[A-Za-z]", text): # transliration
origtxt = text
text = reqget(f"https://inputtools.google.com/request?itc={srclang}-t-i0-und&num=1&text={text}").json()[1][0][1][0]
response:str = "".join([i[0] for i in reqget(f'https://translate.googleapis.com/translate_a/single?client=gtx&sl={srclang}&tl=en&dt=t&q={text}').json()[0]])
if origtxt:
transcache[(srclang,origtxt)] = response
transcache[(srclang,text)] = response
return response
else:
abort(400,"type is invalid")
@app.get("/data")
def data():
return jsonify({"embcache": embcache, "chatcache": chatcache, "transcache": transcache})
app.run(host="0.0.0.0", port=7860)