Run Supabase 100% LOCALLY for Your AI Agents

Key Concepts

Superbase, local AI, Docker, PG Vector, RAG (Retrieval Augmented Generation), n8n, olama, quadrant, flow wise, open web UI, Docker Compose, environment variables, JWT (JSON Web Token), vector database, file trigger, local file system integration.

Superbase as the Core of Local AI

The video focuses on integrating Superbase, a popular open-source platform, into a local AI package. Superbase is highlighted as a versatile solution due to its PostgreSQL foundation, enabling it to function as both a traditional database for managing conversation history and state, and a vector database for RAG using the PG Vector extension.

• Key Point: Superbase is presented as a central component for AI applications, offering authentication, object storage, and database functionalities.
• Argument: Superbase's utility makes it suitable for various AI projects.
• Quote: "No matter what you are building with AI, there is a place for super base."

Setting Up Superbase Locally with Docker

The video provides a step-by-step guide to self-hosting Superbase locally using Docker, emphasizing its open-source nature and the availability of Docker instructions.

• Process:
  1. Prerequisites: Install Python, Git (or GitHub Desktop), and Docker (or Docker Desktop).
  2. Clone Repository: Clone the provided GitHub repository containing the local AI package.
  3. Configure Environment Variables: Create an .env file, copying the contents of .env.example. Set the PostgreSQL password, dashboard login credentials, and JWT secrets. The video references the official Superbase documentation for generating JWT secrets, anonymous keys, and service role keys.
  4. Start Services: Use the start_services.py script to combine the Superbase Docker Compose file with the local AI package's Docker Compose file. The script handles cloning/updating the Superbase repository and ensures all containers run on the same Docker network.
  5. Architecture-Specific Commands: The video provides different commands based on the user's architecture (Nvidia GPU, AMD GPU on Linux, or Mac).
• Technical Detail: The start_services.py script is crucial for merging the local AI package's Docker Compose file with Superbase's, which consists of multiple containers.
• Example: The video demonstrates setting up environment variables, including generating JWT secrets using the Superbase documentation.

Local AI Package Components and Functionality

The video details the components of the revamped local AI package, including n8n, olama, quadrant, flow wise, open web UI, and Superbase.

• Components:
  • n8n: A low-code platform for building AI agents and workflows.
  • olama: Used for running large language models (LLMs) locally.
  • quadrant: A vector store (kept for speed in certain use cases).
  • flow wise: Another low-code AI agent builder.
  • open web UI: A chat gbt-like interface.
  • Superbase: Replaces the original PostgreSQL database and acts as a vector store.
• Functionality: The package allows users to build and run AI agents entirely locally, leveraging local LLMs and data storage.
• Technical Detail: The Docker Compose file defines the services and their configurations, including port mappings and container names.

Restarting, Updating, and Troubleshooting the Local AI Stack

The video explains how to restart, update, and troubleshoot the local AI stack.

• Restarting: Rerun the start_services.py script. This will tear down and recreate the containers, applying any changes to environment variables.
• Updating: Use Docker Compose commands to pull the latest updates for all containers.
• Troubleshooting: The video includes a troubleshooting section in the README with solutions to common problems encountered when running Superbase locally.
• Important Note: Restarting the containers does not delete existing workflows or data stored in Superbase.

Building a RAG AI Agent with Superbase and Local Files

The video demonstrates building a RAG AI agent using n8n, olama, and Superbase, with a local file trigger.

• Process:
  1. Local File Trigger: The n8n workflow is triggered by files added or changed in a specific local folder.
  2. File Processing: The workflow extracts the text content from the file.
  3. Vector Database Setup: SQL commands are used to create a vector database table in Superbase.
  4. Vector Embedding: The text is converted into vector embeddings using a local embedding model (nomic embed text).
  5. Vector Storage: The embeddings are stored in the Superbase vector database, along with metadata (file path as file ID).
  6. RAG Implementation: The AI agent uses the Superbase vector store to retrieve relevant information based on user queries.
• Technical Detail: The video explains how to connect to the Superbase vector store using the host.docker.internal address.
• Example: The video shows a simple example of querying the AI agent about a to-do list stored in a local text file.
• Data: The vector size is specified as 768, corresponding to the nomic embed text model.

Conclusion

The video provides a comprehensive guide to integrating Superbase into a local AI development environment. By leveraging Docker and a custom Python script, the process is streamlined, allowing developers to build and deploy AI agents with local data storage and processing capabilities. The video emphasizes the importance of community feedback and encourages viewers to contribute to the project's development.