PipableAI 🚀

About

PipableAI is a Python package that simplifies the process of querying a database. It provides a high-level interface for connecting to a remote database server, generating and executing natural language-based data search queries mapped to database queries using a language model.

Current Features

• Natural Language Queries: Express database queries in plain English.
• PostgreSQL Integration: Seamlessly connects to PostgreSQL databases.
• Language Model Mapping: Translates natural language queries into SQL queries.
• Structured Results: Returns query results in a structured format for easy processing.

Installation

To install PipableAI, you can use pip3, Python's package manager. Open your terminal or command prompt and run the following command:

pip3 install pipableai

Note: PipableAI requires Python 3.7 or higher.

If you prefer to install PipableAI from source, you can clone the GitHub repository and install it using setup.py. Navigate to the project directory and run:

python3 setup.py install

This will install PipableAI locally on your system.

Usage

PipableAI simplifies the process of connecting to a remote db servers, generating SQL 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.

Import Pipable:

To start using PipableAI, import the necessary classes:

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

Initialize Pipable:

Create an instance of Pipable by providing the required database configuration and LLM 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-pipllm-api-url.com")

# Create a Pipable instance
pipable_instance = Pipable(database_connector=database_connector, llm_api_client=llm_api_client)

Generate and Execute Queries:

Generate SQL queries using the language model and execute them on the database. For Better Performance one can pass the tables names which shold be used in the query

When table_names is None or not passed in:

question = "List all employees."

# Generate the query
try:
    result_df = pipable_instance.ask(question)
    print("Query Result:")
    print(result_df)
except Exception as e:
    print(f"Error: {e}")

# Generate and execute the query
try:
    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:

table_names = ["table1", "table2", "table3"]
question = "List all employees."

# Generate and execute the query
try:
    result_df = pipable_instance.ask_and_execute(question, table_names)
    print("Query Result:")
    print(result_df)
except Exception as e:
    print(f"Error: {e}")

table_names = ["table1", "table2", "table3"]
question = "List all employees."

# Generate the query
try:
    result_query = pipable_instance.ask(question, table_names)
    print("Query Result:")
    print(result_query)
except Exception as e:
    print(f"Error: {e}")

When table_names is an empty list:

table_names = []
question = "List all employees."

# Generate and execute the query
try:
    result_df = pipable_instance.ask_and_execute(question, table_names)
    print("Query Result:")
    print(result_df)
except Exception as e:
    print(f"Error: {e}")

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

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.

Additional Information:

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

Example

For a complete example, refer to the example/ directory in this repository.

Documentation

For detailed usage instructions and examples, please refer to the official documentation.

Contributing

We welcome contributions from the community! To contribute to Pipable, follow these steps:

1. Fork the repository
2. Create a new branch: git checkout -b feature-name
3. Make changes and commit: git commit -m 'Description of changes'
4. Push to the branch: git push origin feature-name
5. Submit a pull request

Please read our Contribution Guidelines for more details.

License

This project is licensed under the Apache 2.0 - see the LICENSE file for details.