mirror of
https://github.com/run-llama/mongodb-demo.git
synced 2026-07-01 20:44:05 -04:00
Laurie Voss
45 lines
1.7 KiB
Python
45 lines
1.7 KiB
Python
from dotenv import load_dotenv
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load_dotenv()
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from flask import Flask, request, jsonify
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from flask_cors import CORS, cross_origin
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import os
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from pymongo.mongo_client import MongoClient
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from pymongo.server_api import ServerApi
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from llama_index.vector_stores.mongodb import MongoDBAtlasVectorSearch
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from llama_index.indices.vector_store.base import VectorStoreIndex
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# Create a new client and connect to the server
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client = MongoClient(os.getenv("MONGODB_URI"), server_api=ServerApi('1'))
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# connect to Atlas as a vector store
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store = MongoDBAtlasVectorSearch(
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client,
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db_name=os.getenv('MONGODB_DATABASE'), # this is the database where you stored your embeddings
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collection_name=os.getenv('MONGODB_VECTORS'), # this is where your embeddings were stored in 2_load_and_index.py
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index_name=os.getenv('MONGODB_VECTOR_INDEX') # this is the name of the index you created after loading your data
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)
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index = VectorStoreIndex.from_vector_store(store)
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app = Flask(__name__)
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cors = CORS(app)
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app.config['CORS_HEADERS'] = 'Content-Type'
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# This is just so you can easily tell the app is running
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@app.route('/')
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def hello_world():
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return 'Hello, World!'
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@app.route('/process_form', methods=['POST'])
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@cross_origin()
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def process_form():
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query = request.form.get('query')
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if query is not None:
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# query your data!
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# here we have customized the number of documents returned per query to 20, because tweets are really short
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query_engine = index.as_query_engine(similarity_top_k=20)
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response = query_engine.query(query)
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return jsonify({"response": str(response)})
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else:
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return jsonify({"error": "query field is missing"}), 400
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