langchain-looker-agent 0.1.5

A LangChain SQL agent for querying Looker via its Open SQL Interface (JDBC/Avatica).

LangChain Looker SQL Agent

Connect LangChain to your Looker instance for conversational data querying using Looker's Open SQL Interface and its governed semantic layer.

This project provides a Python package, langchain-looker-agent, that allows you to build LangChain agents capable of interacting with your Looker data via its Avatica JDBC driver. Ask questions in natural language, and the agent will translate them into Looker-specific SQL, execute them, and provide answers based on your curated LookML models.

Key Features

• Leverage Looker's Semantic Layer: Queries use your existing LookML models, ensuring consistent metrics and business logic.
• Data Governance: Respects Looker's permission model through JDBC authentication.
• Looker SQL Syntax Aware: The agent is prompted to use backticked identifiers (`model_name`.`explore_name`, `view_name.field_name`), AGGREGATE() for measures, and other Calcite/Looker SQL nuances.
• Conversational Interaction: Supports multi-turn conversations (when used with memory).
• Metadata Richness: Informs the LLM with Looker-specific field metadata (labels, descriptions, categories) and filters hidden fields.
• Standard LangChain Integration: Built with core LangChain components for easy use.

Quickstart (Using the Library)

This example shows how to use the langchain-looker-agent library in your own Python project after installing it.

1. Install Prerequisites

   sudo apt-get update
   sudo apt-get install -y openjdk-11-jdk --no-install-recommends
   java -version # Verify installation
   export JAVA_HOME=$(java -XshowSettings:properties -version 2>&1 > /dev/null | grep 'java.home' | awk '{print $3}')
   echo $JAVA_HOME
   mkdir drivers
   cd drivers
   wget https://github.com/looker-open-source/calcite-avatica/releases/download/avatica-1.26.0-looker/avatica-1.26.0-looker.jar
   pip install langchain-looker-agent langchain-openai python-dotenv
   

2. Install the package:

   pip install langchain-looker-agent
   

3. Set Environment Variables: Ensure the following are set in your environment or a .env file (see .env.example):

   • OPENAI_API_KEY (or your chosen LLM's API key)
   • LOOKER_INSTANCE_URL (e.g., https://yourcompany.cloud.looker.com)
   • LOOKML_MODEL_NAME (e.g., analytics)
   • LOOKER_CLIENT_ID
   • LOOKER_CLIENT_SECRET
   • LOOKER_JDBC_DRIVER_PATH (absolute path to your avatica-...-looker.jar)
   • JAVA_HOME (pointing to your JRE/JDK root)

4. Example Python Script:

   import os
   from dotenv import load_dotenv
   from langchain_looker_agent import LookerSQLDatabase, LookerSQLToolkit, create_looker_sql_agent
   from langchain_openai import ChatOpenAI # Or your preferred LLM
   
   load_dotenv() # Loads .env file from current directory or parent
   
   # Initialize components
   llm = ChatOpenAI(model="gpt-4o", temperature=0)
   db = LookerSQLDatabase(
       looker_instance_url=os.environ["LOOKER_INSTANCE_URL"],
       lookml_model_name=os.environ["LOOKML_MODEL_NAME"],
       client_id=os.environ["LOOKER_CLIENT_ID"],
       client_secret=os.environ["LOOKER_CLIENT_SECRET"],
       jdbc_driver_path=os.environ["LOOKER_JDBC_DRIVER_PATH"],
       sample_rows_in_table_info=0 # Keep it minimal for quickstart
   )
   toolkit = LookerSQLToolkit(db=db)
   agent_executor = create_looker_sql_agent(llm=llm, toolkit=toolkit, verbose=False)
   
   # Ask a question
   question = "How many explores are available in the model?"
   response = agent_executor.invoke({"input": question, "chat_history": []})
   print(f"Question: {question}")
   print(f"Answer: {response.get('output')}")
   
   db.close()
   

Running the Example Notebooks

This repository includes a Jupyter Notebook to demonstrate setup, testing, and conversational use.

• For detailed setup instructions for running the example notebooks (including Java, Python environment, and JDBC driver placement), please see: EXAMPLES_SETUP.md
• Notebooks available:
  • examples/looker_langchain_sql_agent_tests.ipynb: Thorough tests of all components.

Technical Details

For a deeper dive into the implementation, JDBC interaction, specific Looker SQL syntax handled, and design choices (like why a custom LookerSQLDatabase wrapper was used over a new SQLAlchemy dialect), please refer to:

• TECHNICAL_DETAILS.md

Core Python Module

The main logic is encapsulated in the langchain_looker_agent package, primarily in src/langchain_looker_agent/agent.py. This includes:

• LookerSQLDatabase: Manages JDBC connection and Looker-specific metadata/querying.
• LookerSQLToolkit: Bundles LangChain tools.
• create_looker_sql_agent: Factory for the ReAct agent.

Troubleshooting

Common issues and solutions are discussed in the EXAMPLES_SETUP.md and TECHNICAL_DETAILS.md. Key areas include:

• JAVA_HOME configuration.
• Correct path to the Looker JDBC driver JAR.
• Looker credentials and permissions for the SQL Interface.
• Python package import errors (ensure pip install -e . or package installation).

License

This project is licensed under the Apache 2.0 License. See the LICENSE file for details.