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LangChain SQL Query Agents

This project demonstrates how to use LangChain to build agents that can process natural language queries and interact with SQL databases. The agents leverage a language model to interpret user queries, translate them into SQL statements, execute these statements against a database, and present the results.

The project includes two example scripts:

  • quote.py: Connects to an SQLite database (example.db) containing quotes and allows users to query them using natural language.
  • country.py: Uses the Chinook sample database to answer queries about music sales data, customers, and countries.

Table of Contents

Architecture


+-----------+      +----------------+      +-------------------+
|  Client   | ---> |  API Gateway   | ---> |     Lambda        |
|  (Request)|      |  (REST API)    |      |   (Invoke Model)  |
+-----------+      +----------------+      +-------------------+
                                         | 
                                         v
                                  +-----------------+
                                  |   GPT           |
                                  |   Model         |
                                  +-----------------+
                                         |
                                         v
                                  +-----------------+
                                  | SQL to Snowflake|
                                  +-----------------+
                                         |
                                         v
                                  +-----------------+
                                  |   Response     |
                                  +-----------------+

Prerequisites

  • Python 3.7 or higher
  • An OpenAI API key
  • Required Python packages (listed in requirements.txt)
pip3 install --upgrade --quiet  langchain-community
pip3 install --upgrade --quiet langchainhub langgraph 
pip3 install -qU langchain-openai
pip3 install snowflake-sqlalchemy

Installation

  1. Clone the repository:

    git clone https://github.com/yourusername/your-repo-name.git
cd your-repo-name

  2. Create and activate a virtual environment (optional but recommended):

    python -m venv venv
source venv/bin/activate  # On Windows, use venv\Scripts\activate

  3. Install the required packages:

    pip install -r requirements.txt

    The requirements.txt file should include:

    langchain
langchain_openai
langchain_community
langgraph
sqlalchemy
requests
python-dotenv

  4. Set up the environment variables:

    Create a .env file in the project directory and add your OpenAI API key:

    OPENAI_API_KEY=your-openai-api-key

Usage

Running quote.py

  1. Prepare the SQLite database:

    • Ensure you have an example.db SQLite database in the project directory.
    • If you don't have one, you can create it using SQLite tools or scripts.

  2. Run the script:

    python quote.py

  3. Interact with the agent:

    • When prompted, enter your natural language query.

    • Example:

      Enter your query (or 'q' to quit): Tell me the quotes which are pending.

Running country.py

  1. Run the script:

    python country.py

  2. Interact with the agent:

    • Enter your natural language query when prompted.

    • Example:

      Enter your query (or 'q' to quit): Which country's customers spent the most?

Examples

Example Queries for quote.py

  • "Tell me the quotes which are pending."

    • Retrieves all quotes from the database with a status of pending.

  • "Show me the pending quotes that were created in February."

    • Finds all pending quotes created in the month of February.

Example Queries for country.py

  • "Which country's customers have spent the most?"

    • Calculates the total spending per country and identifies the country with the highest total.

  • "List the top 5 countries by total sales."

    • Generates a list of the top 5 countries based on total sales amounts.

Customization

  • Changing the Database for quote.py:

    • Replace example.db with your own SQLite database file.
    • Ensure the database schema matches the queries you intend to run.

  • Modifying the Language Model:

    • Adjust the ChatOpenAI initialization to use a different model (e.g., gpt-3.5-turbo or gpt-4).

      llm = ChatOpenAI(model="gpt-3.5-turbo")

  • Customizing the Prompt Template:

    • Modify the system prompt by adjusting the prompt_template or creating your own prompt.

Troubleshooting

  • Module Not Found Errors:

    • Ensure all required packages are installed.
    • If you encounter ModuleNotFoundError, verify the package name and installation.

  • API Errors:

    • Verify that your OpenAI API key is correct and has the necessary permissions.
    • Check your API usage limits and quotas.

  • Database Errors:

    • Ensure the database files are correctly set up and accessible.
    • Verify that the tables and data exist as expected.

License

This project is licensed under the MIT License.

Acknowledgments

  • LangChain for providing tools to build language model applications.
  • The Chinook Database for the sample database used in country.py.