Models Management

The Models Management section provides guidance on how to manage and configure models for analytics and semantic analysis tasks within FlockMTL. These tasks involve processing and analyzing text, embeddings, and other data types using pre-configured models, either system-defined or user-defined, based on specific use cases. Each database is configured with its own model management table during the initial load.

• 1. Model Configuration
• 2. Management Commands
• 3. Global and Local Models
  • Create Models
• 4. SQL Query Examples
  • Semantic Text Completion
  • Semantic Search

1. Model Configuration

Models are stored in a table with the following structure:

Column Name	Description
Model Name	Unique identifier for the model
Model Type	Specific model type (e.g., gpt-4, llama3)
Provider	Source of the model (e.g., openai, azure, ollama)
Model Arguments	JSON configuration parameters. For user-defined models: only tuple_format, batch_size, and model_parameters (which itself is a JSON object for all model params) are allowed. tuple_format can be one of: JSON, XML, or Markdown.

2. Management Commands

• Retrieve all available models

GET
MODELS;

• Retrieve details of a specific model

GET
MODEL 'model_name';

• Create a new user-defined model

-- User-defined model (only tuple_format, batch_size, and model_parameters allowed in JSON)
-- tuple_format can be "JSON", "XML", or "Markdown"
CREATE
MODEL(
    'model_name',
    'model',
    'provider',
    {
        "tuple_format": "JSON",
        "batch_size": 8,
        "model_parameters": {
            "temperature": 0.2,
            "top_p": 0.95
        }
    }
);
CREATE
MODEL(
    'model_name',
    'model',
    'provider',
    {
        "tuple_format": "XML",
        "batch_size": 8,
        "model_parameters": {
            "n": 3,
            "frequency_penalty": 0.1
        }
    }
);
CREATE
MODEL(
    'model_name',
    'model',
    'provider',
    {
        "tuple_format": "Markdown",
        "batch_size": 8
    }
);

• Modify an existing user-defined model

-- Update user-defined model (same JSON rules as CREATE)
-- tuple_format can be "JSON", "XML", or "Markdown"
UPDATE MODEL(
    'model_name',
    'model',
    'provider'
);
UPDATE MODEL(
    'model_name',
    'model',
    'provider',
    {
        "tuple_format": "JSON",
        "batch_size": 8,
        "model_parameters": {
            "temperature": 0.2,
            "top_p": 0.95
        }
    }
);
UPDATE MODEL(
    'model_name',
    'model',
    'provider',
    {
        "tuple_format": "XML",
        "batch_size": 8,
        "model_parameters": {
            "n": 3,
            "frequency_penalty": 0.1
        }
    }
);
UPDATE MODEL(
    'model_name',
    'model',
    'provider',
    {
    "tuple_format": "Markdown",
    "batch_size": 8
    }
);

• Remove a user-defined model

DELETE
MODEL 'model_name';

3. Global and Local Models

Model creation is database specific. If you want it to be available irrespective of the database, use the GLOBAL keyword. LOCAL is the default if not specified.

Create Models

• Create a global model:

CREATE GLOBAL MODEL
('model_name', 'model_type', 'provider'
);
CREATE GLOBAL MODEL
(
    'model_name', 'model_type', 'provider', {
    "tuple_format"
    :
    "JSON",
    "batch_size": 8,
    "model_parameters": {
            "temperature": 0.2,
    "top_p": 0.95
        }
    }
);
CREATE GLOBAL MODEL
(
    'model_name', 'model_type', 'provider', 
    {
        "tuple_format": "XML",
        "batch_size": 8
    }
);
CREATE GLOBAL MODEL
(
    'model_name', 'model_type', 'provider', 
    {
        "tuple_format": "Markdown"
    }
);

• Create a local model (default if no type is specified):

CREATE LOCAL MODEL('model_name', 'model_type', 'provider');
CREATE LOCAL MODEL(
    'model_name', 'model_type', 'provider', 
    {
        "tuple_format": "JSON",
        "batch_size": 8,
        "model_parameters": {
            "temperature": 0.2
        }
    }
);
CREATE LOCAL MODEL
(
    'model_name', 'model_type', 'provider', 
    {
        "tuple_format": "XML",
        "batch_size": 8,
        "model_parameters": {
            "n": 3
        }
    }
);
CREATE LOCAL MODEL
(
    'model_name', 'model_type', 'provider',
    {
        "tuple_format": "Markdown",
        "batch_size": 8,
        "model_parameters": {
            "top_p": 0.95
        }
    }
);
CREATE
MODEL(
    'model_name',
    'model_type',
    'provider'
);
CREATE
MODEL(
    'model_name',
    'model_type',
    'provider',
    {
        "tuple_format": "JSON",
        "batch_size": 8,
        "model_parameters": {
            "temperature": 0.2
        }
    }
);
CREATE
MODEL(
    'model_name',
    'model_type',
    'provider',
    {
        "tuple_format": "XML",
        "batch_size": 8,
        "model_parameters": {
            "n": 3
        }
    }
);
CREATE
MODEL(
    'model_name',
    'model_type',
    'provider',
    {
        "tuple_format": "Markdown",
        "batch_size": 8,
        "model_parameters": {
            "top_p": 0.95
        }
    }
);

• Toggle a model's state between global and local:

UPDATE MODEL 'model_name' TO GLOBAL;
UPDATE MODEL 'model_name' TO LOCAL;

All other queries remain the same for both global and local models.

4. SQL Query Examples

Semantic Text Completion

SELECT llm_complete(
    {'model_name': 'gpt-4'},
    {'prompt_name': 'product-description'},
    {'input_text': product_description}
) AS generated_description
FROM products;

Semantic Search

SELECT llm_complete(
    {'model_name': 'semantic_search_model'},
    {'prompt_name': 'search-query'},
    {'search_query': query}
) AS search_results
FROM search_data;