piglets 0.1.12

A modular text-to-SQL toolkit.

🐷 piglets

A modular library of text-to-SQL tools.

Status

piglets is currently an alpha-stage package. The API is expected to evolve before 1.0.

Get started

Install

venv

pip install piglets

uv

uv add piglets

Install the optional dependency for the model provider you use. For OpenAI:

venv

pip install "piglets[openai]"

uv

uv add "piglets[openai]"

Other provider extras include anthropic, google_genai, google_vertexai, bedrock, cohere, mistralai, groq, ollama, and openrouter.

Install the optional dependency for the database backend you use. For BigQuery:

venv

pip install "piglets[bigquery]"

uv

uv add "piglets[bigquery]"

Logical planning

Use gpt-5.2 to generate 3 logical plans from a natural language query.

from piglets import LogicalPlanner

# initialise a logical planner
logical_planner = LogicalPlanner('gpt-5.2')

# generate 3 logical plan samples and aggregate them
logical_plan = logical_planner.plan(
    natural_language_query="What was the average number of piglets per week for Q4 2025?",
    num_samples=3,
)

# print the aggregated logical plan
for i, step in enumerate(logical_plan.logical_steps):
    print(f"Step {i + 1}: ")
    print(step)

# inspect the candidate plans used to create the aggregate
print(f"Aggregated from {len(logical_plan.sample_plans)} sample plans.")

>>> Step 1:
>>> 1. Identify all piglet birth (or piglet addition) events with their event dates and piglet counts.
>>> Step 2:
>>> 2. Filter the events to the Q4 2025 date range (Oct 1, 2025 through Dec 31, 2025).
>>> Step 3:
>>> 3. Assign each event to a calendar week within that quarter using a consistent week definition (e.g., week starting Monday or Sunday).
>>> Aggregated from 3 sample plans.
...

Database connector

Use DatabaseConnector to inspect a supported database and return a typed schema.

from piglets import DatabaseConnector

database_connector = DatabaseConnector(
    database_type="bigquery",
    bq_dataset="my_bigquery_dataset",
)

database = database_connector.get_database_schema()

print(database.name)
for table in database.tables:
    print(table.name)
    for column in table.columns:
        print(f"- {column.name} ({column.data_type})")

BigQuery connections use the GOOGLE_CLOUD_PROJECT_ID environment variable by default. You can also pass gcp_project_id directly:

database_connector = DatabaseConnector(
    database_type="bigquery",
    database_name="my_bigquery_dataset",
    gcp_project_id="my-gcp-project",
)

Supported Databases

Database type	database_type value	Install requirement	Notes
SQLite	sqlite	Included by default	Uses SQLAlchemy's built-in SQLite dialect.
MySQL	mysql	SQLAlchemy dialect included by default	Requires a compatible MySQL DBAPI driver.
PostgreSQL	postgresql	SQLAlchemy dialect included by default	Requires a compatible PostgreSQL DBAPI driver.
Oracle	oracle	SQLAlchemy dialect included by default	Requires a compatible Oracle DBAPI driver.
Microsoft SQL Server	mssql	SQLAlchemy dialect included by default	Requires a compatible SQL Server DBAPI driver.
BigQuery	bigquery	piglets[bigquery]	Uses GOOGLE_CLOUD_PROJECT_ID or gcp_project_id for the GCP project.
Snowflake	snowflake	piglets[snowflake]	Uses SNOWFLAKE_ACCOUNT, SNOWFLAKE_USER and SNOWFLAKE_PASSWORD environment variables.

Dual-pathway pruning

Use Pruner to reduce a database schema with both preservation and deletion signals. The preservation pathway selects tables and columns that look useful for the query. The deletion pathway removes tables and columns that look irrelevant. dual_pathway_pruning() combines both paths into a final Database schema.

from piglets import DatabaseConnector, LogicalPlanner, Pruner

question = "Which tags saw the largest increase in average answer score from 2022 to 2023, considering only questions with at least 5 answers?"

logical_planner = LogicalPlanner("gpt-5.2")
logical_plan = logical_planner.plan(
    natural_language_query=question,
    num_samples=3,
)

database_connector = DatabaseConnector(
    database_type="bigquery",
    bq_dataset="stack_overflow",
)
database = database_connector.get_database_schema()

pruner = Pruner(model_name="gpt-5.2")
pruned_database = pruner.dual_pathway_pruning(
    natural_language_query=question,
    database=database,
    logical_plan=logical_plan,
)

print(pruned_database.export_as_string())