We're building a Database Memory Agent with RAG (Retrieval Augmented Generation) capabilities that integrates MongoDB Atlas Vector Search for semantic document retrieval, Voyage AI for embeddings, and OpenAI for intelligent responses. The agent uses tools (vector search and calculator) to answer questions from uploaded documents and perform calculations, with context-aware memory across conversations.

We use:

MongoDB Atlas Vector Search for semantic search and document storage
Voyage AI for generating embeddings (voyage-3-large model)
OpenAI for LLM responses (gpt-4o)
Streamlit to wrap the logic in an interactive UI

Set Up

Prerequisites

You must have the following:

One of the following MongoDB cluster types:
- An Atlas cluster running MongoDB version 6.0.11, 7.0.2, or later. Ensure that your IP address (Internet Protocol address) is included in your Atlas project's access list.
- A local Atlas deployment created using the Atlas CLI. To learn more, see Create a Local Atlas Deployment.
- A MongoDB Community or Enterprise cluster with Search and Vector Search installed.
A Voyage AI API key.
An OpenAI API key.

Configure Environment Variables

Copy .env.example to .env and configure the following environment variables:

MONGODB_URI="<mongodb-connection-string>"
VOYAGE_API_KEY="<your-voyage-api-key>"
OPENAI_API_KEY="<your-openai-api-key>"

Replace <mongodb-connection-string> with the connection string for your Atlas cluster or local Atlas deployment.

Atlas Cluster:

Your connection string should use the following format:

mongodb+srv://<db_username>:<db_password>@<clusterName>.<hostname>.mongodb.net

To learn more, see Connect to a Cluster via Drivers.

Local or Self-Managed:

Your connection string should use the following format:

mongodb://localhost:<port-number>/?directConnection=true

To learn more, see Connection Strings.

Install Dependencies

uv sync

Run the Application

Run the application with:

streamlit run app.py

Or use the CLI version:

python main.py

Get your Voyage AI API keys here

Get your OpenAI API keys here

Project Structure

database-memory-agent/
├── .env                 # Environment variables (create from .env.example)
├── config.py            # MongoDB and API configuration
├── ingest_data.py       # PDF ingestion and vector index creation
├── tools.py             # Agent tools (vector search, calculator)
├── memory.py            # Chat history storage
├── planning.py          # Agent planning and response generation
├── app.py              # Streamlit web application
├── main.py             # CLI application
├── pyproject.toml      # Project dependencies
└── README.md           # This file

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Contribution

Contributions are welcome! Feel free to fork this repository and submit pull requests with your improvements.

README.md

Database Memory Agent