Unleashing the Power of MindsDB and Ollama: A Comprehensive Guide to Advanced Natural Language Processing

In this tutorial, I’ll explore how to integrate MindsDB with Ollama, a powerful natural language processing tool. MindsDB provides a platform for machine learning automation, while Ollama offers advanced capabilities for text-based tasks. By combining these tools, we can create robust models for various natural language processing tasks.

We’ll learn how to seamlessly integrate MindsDB with Ollama for advanced natural language processing tasks. Also understanding the process of setting up models and engines for text-based interactions.

Prerequisites:

Before getting started, ensure you have the following:

1. Python installed on your system.
2. Docker installed (for Docker network setup).
3. Access to MindsDB and Ollama APIs.

Step 1: Setup Localhost Network (Option 1a):

Setting up a local network to connect MindsDB and Ollama:

CREATE ML_ENGINE ollama_engine
FROM ollama;

CREATE MODEL ollama_model
PREDICT completion
USING
   engine = 'ollama_engine',
   model_name = 'model-name',

Step 1: Setup Localhost Network (Option 1b):

If you explicitly need to call the local URL to set up the network to connect MindsDB and Ollama:

CREATE ML_ENGINE ollama_engine
FROM ollama;

CREATE MODEL ollama_model
PREDICT completion
USING
   engine = 'ollama_engine',
   model_name = 'model-name',
   ollama_serve_url = http://127.0.0.1:11434; -- http://localhost:11434/api/tags

Step 1: Setup Docker Network (Option 2):

If you’re using Docker, set up a network to connect MindsDB and Ollama:

CREATE ML_ENGINE ollama_engine
FROM ollama;

CREATE MODEL ollama_model
PREDICT completion
USING
   engine = 'ollama_engine',
   model_name = 'model-name',
   ollama_serve_url = 'http://host.docker.internal:11434';

Step 1: Setup Service Discovery (Option 3):

For service discovery setup:

CREATE ML_ENGINE ollama_engine
FROM ollama;

CREATE MODEL ollama_model
PREDICT completion
USING
   engine = 'ollama_engine',
   model_name = 'model-name',
   ollama_serve_url = '<service_discovery_url>';

Step 2: Create Ollama Models:

Next, create Ollama models for different tasks:

CREATE MODEL llama3_model
PREDICT completion
USING
  engine = 'ollama_engine',
  model_name = 'llama3',
  ollama_serve_url = 'http://host.docker.internal:11434';

CREATE MODEL llama3_model
PREDICT completion
USING
  engine = 'ollama_engine',
  model_name = 'llama3',
  prompt_template = 'Answer using exactly five words: {{text}}';

Step 3: Execute Queries:

Execute queries using the created models:

SELECT text, completion
FROM llama_model
WHERE text = 'Who are you?'

Step 4: Celebrate!:

Pat yourself on the back, call a friend, better yet phone your newly created AI, you’re an AI engineer!!!

Happy coding!

I hope I provided a basic but comprehensive overview of integrating MindsDB with Ollama, you can seamlessly set up models and engines for efficient text-based interactions, enhancing their ability to tackle various natural language processing tasks.

From setting up Docker networks to creating Ollama models and executing queries, this should guide you on harnessing the power of MindsDB and Ollama for your NLP projects.

Thanks for stopping by, hope you learned something, I look forward to seeing the incredible creations and discoveries that emerge from your MindsDB and Ollama projects. Happy coding!