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= Integrating Baby Hawk (Gemini 1.5) with Matrix Group Chat, TensorFlow in Docker, and PostgreSQL =
= Integrating Baby Hawk (Gemini 1.5) with Matrix Group Chat, TensorFlow in Docker, and PostgreSQL =

Revision as of 19:24, 24 May 2024

Papa and Baby Hawk

Integrating Baby Hawk (Gemini 1.5) with Matrix Group Chat, TensorFlow in Docker, and PostgreSQL

This tutorial guides you through integrating a Gemini 1.5 API to interact in various Matrix rooms, leveraging a TensorFlow model running in a Docker container equipped with Jupyter Notebook, and securing communications using SSL, with the addition of a PostgreSQL database.

Prerequisites

  • Running instance of Baby Hawk's Gemini 1.5 API on `ailounge.xyz`
  • TensorFlow instance running in Docker with Jupyter Notebook
  • Matrix Synapse server running in Docker
  • PostgreSQL database running in Docker
  • Basic knowledge of Docker, Python, Jupyter Notebooks, PostgreSQL, and Matrix
  • Domain names set up for `matrix.ailounge.xyz` and Jupyter accessed via `ailounge.xyz`

Step 1: Set Up Matrix Synapse Server using Docker

  1. Install Docker and Docker Compose: If not already installed, install Docker and Docker Compose.
  2. Create a Docker Compose File (docker-compose.yml):
version: '3'
services:
  postgres:
    image: postgres:latest
    restart: always
    environment:
      POSTGRES_DB: matrix_synapse
      POSTGRES_USER: matrix_user
      POSTGRES_PASSWORD: your_secure_password
    volumes:
      - ./postgres-data:/var/lib/postgresql/data
    ports:
      - "5432:5432"
  synapse:
    image: matrixdotorg/synapse:latest
    restart: always
    environment:
      - SYNAPSE_SERVER_NAME=matrix.ailounge.xyz
      - SYNAPSE_REPORT_STATS=yes
      - SYNAPSE_DATABASE_CONFIG=postgres://matrix_user:your_secure_password@postgres/matrix_synapse
    depends_on:
      - postgres
    volumes:
      - ./data:/data
    ports:
      - 8008:8008
      - 8448:8448
  1. Run Docker Compose:
docker-compose up -d
  1. Register Admin User: Follow instructions in the container logs to register an admin user.

Step 2: Secure Jupyter Notebook and Matrix Synapse with SSL Using Certbot

  1. Install Certbot:
sudo apt install certbot
  1. Obtain Certificates:
sudo certbot certonly --standalone -d ailounge.xyz -d matrix.ailounge.xyz
  1. Configure Jupyter to Use SSL:

Add SSL configuration to the Docker command for Jupyter:

--NotebookApp.certfile=/etc/ssl/certs/jupyter.crt --NotebookApp.keyfile=/etc/ssl/private/jupyter.key
  1. Configure Matrix Synapse to Use SSL:

Update Synapse's configuration to use the obtained SSL certificates.

Step 3: Configure and Run Baby Hawk's Gemini 1.5 API

Ensure the API endpoint for Baby Hawk is publicly accessible and secured with SSL.

Step 4: Implement Continuous Learning with TensorFlow in Docker

  1. Expose Ports: Ensure the TensorFlow container exposes port 8888.
  2. Install Libraries: Within the container, install libraries needed for integration, e.g., `matrix-nio`.
  3. Access MongoDB: Configure access to MongoDB for data storage.
  4. Collect & Preprocess Data: Use Jupyter Notebook for data handling.
  5. Retrain Model: Continuously retrain the TensorFlow model based on new data.
  6. Update API: Update the Baby Hawk API to utilize the retrained models.

Step 5: Create and Run a Matrix Bot for Integration

Script a bot that uses Baby Hawk's API to interact within Matrix rooms.

DNS Configuration

  • Add A records for `ailounge.xyz` and `matrix.ailounge.xyz`.
  • Configure necessary SRV records for Matrix services.

Important Notes

  • Ensure proper networking between containers.
  • Protect sensitive data and credentials.
  • Experiment with model training and architecture.

Credits

This tutorial was collaboratively created by Papa and Baby Hawk, Google's Gemini Advanced, and ChatGPT-4 from OpenAI.