Actually query the database, print the response

flask_app/app.py

@@ -1,9 +1,31 @@
+from dotenv import load_dotenv
+load_dotenv()
+
 from flask import Flask, request, jsonify
 from flask_cors import CORS, cross_origin
+import os
+from pymongo.mongo_client import MongoClient
+from pymongo.server_api import ServerApi
+from llama_index.vector_stores.mongodb import MongoDBAtlasVectorSearch
+from llama_index.indices.vector_store.base import VectorStoreIndex
+
+# Create a new client and connect to the server
+client = MongoClient(os.getenv("MONGODB_URI"), server_api=ServerApi('1'))
+
+# connect to Atlas as a vector store
+store = MongoDBAtlasVectorSearch(
+    client,
+    db_name=os.getenv('MONGODB_DATABASE'), # this is the database where you stored your embeddings
+    collection_name=os.getenv('MONGODB_VECTORS'), # this is where your embeddings were stored in 2_load_and_index.py
+    index_name=os.getenv('MONGODB_VECTOR_INDEX') # this is the name of the index you created after loading your data
+)
+index = VectorStoreIndex.from_vector_store(store)
+
 app = Flask(__name__)
 cors = CORS(app)
 app.config['CORS_HEADERS'] = 'Content-Type'
 
+# This is just so you can easily tell the app is running
 @app.route('/')
 def hello_world():
     return 'Hello, World!'
@@ -13,7 +35,10 @@ def hello_world():
 def process_form():
     query = request.form.get('query')
     if query is not None:
-        # Do something with query if necessary
-        return jsonify({"response": "bar"})
+        # query your data!
+        # here we have customized the number of documents returned per query to 20, because tweets are really short
+        query_engine = index.as_query_engine(similarity_top_k=20)
+        response = query_engine.query(query)
+        return jsonify({"response": str(response)})
     else:
         return jsonify({"error": "query field is missing"}), 400

next_app/app/page.js

@@ -5,9 +5,11 @@ import React, { useState } from 'react';
 
 export default function Home() {
   const [query, setQuery] = useState('');
+  const [responseText, setResponseText] = useState('');
 
   const handleSubmit = async (event) => {
     event.preventDefault();
+    setResponseText('Thinking...')
 
     const formData = new FormData();
     formData.append('query', query);
@@ -20,6 +22,7 @@ export default function Home() {
     if (response.ok) {
       const responseData = await response.json();
       console.log(responseData);
+      setResponseText(responseData.response)
     } else {
       console.error('Failed to submit:', response.statusText);
     }
@@ -39,6 +42,9 @@ export default function Home() {
           <button type="submit">Submit</button>
         </form>
       </div>
+      <div>
+        {responseText}
+      </div>
     </main>
   )
 }

requirements.txt

@@ -0,0 +1,60 @@
+aiohttp==3.8.6
+aiosignal==1.3.1
+aiostream==0.5.2
+annotated-types==0.6.0
+anyio==3.7.1
+async-timeout==4.0.3
+attrs==23.1.0
+certifi==2023.7.22
+charset-normalizer==3.3.1
+click==8.1.7
+dataclasses-json==0.5.14
+Deprecated==1.2.14
+dnspython==2.4.2
+Flask==2.2.5
+Flask-Cors==4.0.0
+frozenlist==1.4.0
+fsspec==2023.10.0
+greenlet==3.0.1
+gunicorn==21.2.0
+idna==3.4
+itsdangerous==2.1.2
+Jinja2==3.1.2
+joblib==1.3.2
+jsonpatch==1.33
+jsonpointer==2.4
+langchain==0.0.325
+langsmith==0.0.53
+llama-index==0.8.55
+MarkupSafe==2.1.3
+marshmallow==3.20.1
+multidict==6.0.4
+mypy-extensions==1.0.0
+nest-asyncio==1.5.8
+nltk==3.8.1
+numpy==1.26.1
+openai==0.28.1
+packaging==23.2
+pandas==2.1.2
+pydantic==2.4.2
+pydantic_core==2.10.1
+pymongo==4.5.0
+python-dateutil==2.8.2
+python-dotenv==1.0.0
+pytz==2023.3.post1
+PyYAML==6.0.1
+regex==2023.10.3
+requests==2.31.0
+six==1.16.0
+sniffio==1.3.0
+SQLAlchemy==2.0.22
+tenacity==8.2.3
+tiktoken==0.5.1
+tqdm==4.66.1
+typing-inspect==0.9.0
+typing_extensions==4.8.0
+tzdata==2023.3
+urllib3==1.26.18
+Werkzeug==2.2.3
+wrapt==1.15.0
+yarl==1.9.2