sqlseed-ai 0.2.1

AI-powered data generation plugin for sqlseed

sqlseed-ai

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AI-powered data generation plugin for sqlseed.

LLM-driven schema analysis, self-correcting config generation, and template pool assistance. Supports multiple backends: Google AI Studio (Gemma 4 Native Function Calling), LM Studio, Ollama, and any OpenAI-compatible API (OpenRouter, OpenAI, DeepSeek, etc.).

Installation

pip install sqlseed-ai

Quick Start

# Set API key (or use GOOGLE_API_KEY for Google AI Studio)
export SQLSEED_AI_API_KEY="your-api-key"

# Generate AI-suggested YAML config
sqlseed ai-suggest app.db --table users --output users.yaml

# With self-correction (3 rounds by default)
sqlseed ai-suggest app.db --table users --output users.yaml --verify

# Specify model (defaults to Gemma 4 26B via Google AI Studio)
sqlseed ai-suggest app.db --table users -o users.yaml --model gemma-4-26b-it

# Use local LM Studio
sqlseed ai-suggest app.db --table users -o users.yaml --backend lm_studio --model google/gemma-4-e4b

# Skip cache
sqlseed ai-suggest app.db --table users -o users.yaml --no-cache

Features

Schema Analyzer

SchemaAnalyzer extracts rich context from your database (columns, indexes, sample data, foreign keys, data distribution) and builds a structured prompt for LLM analysis. Returns column-level generation configs as JSON.

Self-Correcting Refiner

AiConfigRefiner validates LLM output against actual schema:

1. LLM generates column config
2. Refiner checks for unknown generators, type mismatches, expression errors
3. If errors found, sends correction request back to LLM
4. Up to 3 retry rounds, then raises AISuggestionFailedError

Auto Model Selection

When using the google_ai_studio backend (default), the GemmaModel enum provides pre-configured Gemma 4 variants. The model is selected based on the backend:

1. Google AI Studio: Defaults to gemma-4-26b-it (recommended balance of quality and speed).
2. LM Studio / Ollama: User must specify a loaded model via --model or SQLSEED_AI_MODEL.
3. OpenAI-compatible (OpenRouter, DeepSeek, etc.): User must specify both --model and --base-url.

For OpenRouter free models, set:

export SQLSEED_AI_BACKEND=openai_compat
export SQLSEED_AI_BASE_URL=https://openrouter.ai/api/v1
export SQLSEED_AI_MODEL=<free-model-name>

Skip auto-selection by specifying --model or SQLSEED_AI_MODEL.

When using the google_ai_studio backend, the GemmaModel enum provides pre-configured Gemma 4 variants:

Enum Value	Model ID	Description
GemmaModel.GEMMA_4_2B	gemma-4-2b	Lightweight, fast inference
GemmaModel.GEMMA_4_4B	gemma-4-4b	Balanced speed and quality
GemmaModel.GEMMA_4_26B	gemma-4-26b	High quality, recommended
GemmaModel.GEMMA_4_31B	gemma-4-31b	Best quality, largest model

The AIBackend enum selects the API backend:

Enum Value	Backend	Default Base URL
AIBackend.GOOGLE_AI_STUDIO	Google AI Studio	https://generativelanguage.googleapis.com/v1beta/openai/
AIBackend.LM_STUDIO	LM Studio	http://localhost:1234/v1
AIBackend.OLLAMA	Ollama	http://localhost:11434/v1

Template Pool

When sqlseed fills a table with skip_ai=False, the plugin pre-generates candidate values for columns that can't be mapped to a deterministic generator (via sqlseed_pre_generate_templates hook).

File Caching

AI configs cached in platform-specific cache directory (~/Library/Caches/sqlseed/ai_configs/ on macOS, ~/.cache/sqlseed/ai_configs/ on Linux, %LOCALAPPDATA%/sqlseed/ai_configs/ on Windows) with schema hash validation. Schema changes auto-invalidate cache. Use --no-cache to skip. Override with SQLSEED_CACHE_DIR environment variable.

Configuration

Environment Variables

Variable	Fallback	Default	Description
SQLSEED_AI_API_KEY	OPENAI_API_KEY	—	API key (required)
SQLSEED_AI_BASE_URL	OPENAI_BASE_URL	(auto by backend)	API endpoint
SQLSEED_AI_MODEL	—	gemma-4-26b-it	Model name
SQLSEED_AI_TIMEOUT	—	60	API timeout (seconds)
SQLSEED_AI_BACKEND	—	google_ai_studio	AI backend: google_ai_studio, lm_studio, ollama, openai_compat
GOOGLE_API_KEY	—	—	Google AI Studio API key (required when backend is google_ai_studio)

CLI Options

--model, -m       Model name (overrides auto-selection)
--api-key         API key (overrides env)
--base-url        API base URL (overrides env)
--max-retries     Self-correction rounds (default: 3, 0=disable)
--verify/--no-verify  Toggle self-correction (default: verify)
--no-cache        Skip file cache
--timeout         API timeout in seconds (default: 120)

Plugin Hooks

This plugin registers via [project.entry-points."sqlseed"] and implements:

Hook	Purpose
sqlseed_ai_analyze_table	LLM-driven table analysis, returns column configs
sqlseed_pre_generate_templates	Pre-generate candidate values for complex columns
sqlseed_register_providers	Placeholder (no-op, entry-point registration)
sqlseed_register_column_mappers	Placeholder (no-op, entry-point registration)

Requirements

• Python >= 3.10
• sqlseed >= 0.1.0
• openai >= 1.0
• google-generativeai >= 0.8
• An OpenAI-compatible API key or Google AI Studio API key

Gemma 4 Integration

When using the google_ai_studio backend, sqlseed-ai leverages Gemma 4 Native Function Calling for structured schema analysis:

GEMMA_TOOLS

The plugin defines a GEMMA_TOOLS function declaration that tells Gemma 4 how to respond with structured column configs. Instead of parsing free-form text, the model is instructed to call a generate_column_config function with typed parameters (column name, generator, parameters, etc.), ensuring output conforms to the expected schema.

Native Function Calling Mechanism

1. Tool Definition: GEMMA_TOOLS declares a generate_column_config function with a strict JSON Schema describing each parameter (column_name, generator_name, parameters, nullable, etc.).
2. Request: The schema context and analysis prompt are sent to the Gemma 4 model with tools=[GEMMA_TOOLS] and tool_config set to force a function call.
3. Response Parsing: The model returns a FunctionCall object instead of plain text. The plugin extracts the structured args directly — no regex or fragile parsing needed.
4. Validation: The extracted args are passed through the same AiConfigRefiner pipeline for self-correction.

This approach significantly improves reliability over text-based LLM output parsing, as the model is constrained to produce well-formed, schema-compliant responses.

License

AGPL-3.0-or-later