Adding Support for Other Large LLM Models

This project integrates support for various Large Language Models (LLMs) including Ollama, LM Studio, and Gemini (Google AI Studio). The configuration is managed via a configuration.yaml file to ensure flexibility and easy adjustments. Additionally, Ollama and LM Studio are set up to run as localhost services for enhanced control and performance.

Features

• Multiple LLM Support: Leverage the capabilities of Ollama, LM Studio, and Gemini.

• Flexible Configuration: Manage model configurations through a configuration.yaml file.

• Local Hosting: Run Ollama and LM Studio locally for better control and performance.

• Scalability: Easily add or remove models by updating the configuration.yaml file.

Steps to Implement

1. Clone the Repository Clone the project's repository to your local machine to get started with the integration.

2. Environment Setup Set up a Python virtual environment and install the required dependencies to ensure a clean and isolated setup.

3. Configuration File Create and populate a configuration.yaml file with the necessary API keys and settings for Ollama, LM Studio, and Google AI Studio.

4. Initialize Models Write scripts to initialize and load the models from the specified platforms using the configurations from the configuration.yaml file.

5. Use Models Implement functionality to utilize the loaded models for generating responses or performing tasks as per your application's requirements.

6. Host Locally Use Docker or similar tools to host the models locally. Set up Docker containers for Ollama and LM Studio to run them as localhost services. This provides enhanced control, security, and performance.

Example Configuration:-

ollama:

host: http://host.docker.internal:11434/v1

lm studio:

host: http://host.docker.internal:1234/v1

Gemini:

host: https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-flash:generateContent?key=GEMINI_API_KEY