Intermediate
⏱ 45 min read
© Gate of AI 2026-04-10
Learn to build a vector database leveraging Claude and ChatGPT API to enhance data retrieval and user interaction.
Prerequisites
- Python 3.9 or higher
- OpenAI API Key and Claude API Key
- Basic understanding of vector databases and embeddings
What We’re Building
In this tutorial, we will create a vector database using Claude and ChatGPT APIs. This database will store embeddings generated from user inputs, enabling efficient retrieval and classification of information. The end result is a robust system capable of handling semantic searches and augmenting large language models with retrieval-augmented generation (RAG).
The system will allow users to input queries and receive contextually relevant information by comparing the embeddings of their queries against those stored in the database. This is particularly useful in applications like chatbots, personalized recommendations, and advanced AI tools.
Setup and Installation
We’ll install necessary libraries and set up our development environment. This includes libraries for interacting with the APIs and managing embeddings.
pip install openai chromadb flaskNext, we need to configure environment variables to securely store our API keys.
# .env file
OPENAI_API_KEY=your_openai_api_key
CLAUDE_API_KEY=your_claude_api_key
Step 1: Setting Up the Flask Backend
We’ll start by creating a Flask application that will handle API requests and responses. This backend will serve as the intermediary between user queries and our vector database.
from flask import Flask, request, jsonify
import openai
import os app = Flask(__name__)
openai.api_key = os.getenv("OPENAI_API_KEY")
@app.route('/query', methods=['POST'])
def query():
data = request.json
query_text = data['query']
response = openai.Embedding.create(input=query_text, model="text-embedding-ada-002")
embedding = response['data'][0]['embedding']
return jsonify({'embedding': embedding})
if __name__ == '__main__':
app.run(debug=True)
This code sets up a basic Flask server with a single endpoint `/query`. When a POST request is made to this endpoint, it generates an embedding for the provided text using OpenAI’s API.
Step 2: Creating the Vector Database with Chroma
Now, we’ll set up Chroma, a vector database, to store and manage our embeddings. This will allow us to perform efficient similarity searches.
from chromadb import Client client = Client()
collection = client.create_collection(name="embeddings")
def add_to_database(embedding, metadata):
collection.add(embedding=embedding, metadata=metadata)
We initialize a Chroma client and create a collection named “embeddings”. The `add_to_database` function will allow us to store embeddings along with any relevant metadata.
Step 3: Integrating Claude for Enhanced Queries
We’ll integrate Claude to enhance the user query process, providing more contextually relevant responses.
import requests CLAUDE_ENDPOINT = "https://api.claude.ai/query"
def enhance_query(query):
response = requests.post(CLAUDE_ENDPOINT, json={'query': query}, headers={'Authorization': f'Bearer {os.getenv("CLAUDE_API_KEY")}'})
return response.json()['enhanced_query']
This function sends a query to Claude’s API, which enhances it by possibly adding more context or correcting it for better results.
Testing Your Implementation
To test our implementation, we will send a sample query through our Flask API and ensure the embedding is correctly stored and retrievable from Chroma.
import requests response = requests.post('http://localhost:5000/query', json={'query': 'What is the latest AI development?'})
print(response.json())
Expect an output containing the embedding vector, which indicates successful processing and storage.
What to Build Next
- Implement a user interface for interacting with the vector database.
- Extend the system to handle multimedia inputs like images and audio using multimodal embeddings.
- Integrate additional AI models to further enhance query understanding and response generation.