Usage

Pipable simplifies the process of connecting to a remote database server, generating database queries using a language model, and executing them. This section provides a step-by-step guide on how to use PipableAI effectively in your Python projects.

1. Import Pipable:

   To start using PipableAI, import the necessary classes and interfaces:

   from pipableai import Pipable
   from pipableai.llm_client.pipllm import PipLlmApiClient
   from pipableai.core.postgresql_connector import PostgresConfig, PostgresConnector
   

2. Initialize Pipable:

   Create an instance of Pipable by providing the required database configuration and PipLLM API base URL:

   # Define PostgreSQL configuration
   postgres_config = PostgresConfig(
       host="your_postgres_host",
       port=5432,  # Replace with your port number
       database="your_database_name",
       user="your_username",
       password="your_password",
   )
   
   # Initialize the database connector and LLM API client
   database_connector = PostgresConnector(postgres_config)
   llm_api_client = PipLlmApiClient(api_base_url="https://your-llm-api-url.com")
   
   # Create a Pipable instance
   pipable_instance = Pipable(database_connector=database_connector, llm_api_client=llm_api_client)
   

3. Generate and Execute Queries:

   Generate SQL queries using the language model and execute them on the database:

   • When table_names is an empty list:

   # Generate a query using the language model
   table_names = []
   question = "List all employees."
   try:
       # Generate and execute the query
       result_df = pipable_instance.ask_and_execute(question, table_names)
       print("Query Result:")
       print(result_df)
   except Exception as e:
       print(f"Error: {e}")
   

   • When table_names is None or not passed in:

   # Generate a query using the language model
   table_names = None
   question = "List all employees."
   try:
       # Generate and execute the query
       result_df = pipable_instance.ask_and_execute(question)
       print("Query Result:")
       print(result_df)
   except Exception as e:
       print(f"Error: {e}")
   

   • When table_names is populated with correct table names:

   # Generate and Execute a query using the language model
   table_names = ["table1", "table2", "table3"]
   question = "List all employees."
   try:
       # Generate and execute the query
       result_df = pipable_instance.ask_and_execute(question, table_names)
       print("Query Result:")
       print(result_df)
   except Exception as e:
       print(f"Error: {e}")
   
   # Generate a query using the language model
   table_names = ["table1", "table2", "table3"]
   question = "List all employees."
   try:
       # Generate and execute the query
       result_query = pipable_instance.ask(question, table_names)
       print("Query Result:")
       print(result_query)
   except Exception as e:
       print(f"Error: {e}")
   

   Handle exceptions appropriately to ensure graceful error handling in your application.

4. Disconnect from the Database:

   Close the connection to the PostgreSQL server after executing the queries:

   pipable_instance.disconnect()
   

   or

   database_connector.disconnect()
   

   Ensure that you disconnect from the database to release resources when the queries are completed.

5. Additional Information:

   • Check the interfaces: DatabaseConnectorInterface and LlmApiClientInterface for more details on the methods and functionalities provided by Pipable.

This guide outlines the fundamental steps for using Pipable to interact with a PostgreSQL database. Be sure to customize the error handling and query generation logic based on your specific use case to create a robust and reliable application.

Feel free to explore additional features and methods provided by Pipable to further enhance your database interaction and query generation capabilities.