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Explore Assistant Cloud Function

This cloud function provides an API for generating Looker queries using the Vertex AI Gemini Pro model. It allows users to input natural language descriptions of data queries, which the function then converts into Looker Explore URLs or query suggestions using a generative AI model.

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How It Works

The cloud function integrates with Vertex AI and utilizes the GenerativeModel class to generate content based on input parameters. Here’s a step-by-step breakdown of its operation:

  1. Environment Setup: The function initializes with the Vertex AI environment, requiring PROJECT and REGION environment variables to be set for connecting to the Vertex AI services.

  2. Query Generation: The core functionality lies in the generate_looker_query function, which accepts a natural language query (contents) and optional parameters (parameters) to customize the AI generation process.

  3. Parameter Configuration: It uses default parameters for the AI model (like temperature, max_output_tokens, top_p, top_k) which can be overridden by the request’s parameters.

  4. Model Invocation: The Gemini Pro model is then invoked to generate the Looker query based on the provided contents and parameters.

  5. Logging: Metadata from the generation process, including input and output token counts, is logged for monitoring and debugging purposes.

  6. API Endpoints: The function can be deployed as a Flask web server or as a Google Cloud Function. It handles POST requests to the main endpoint, where it expects a JSON payload with contents and optional parameters.

  7. CORS Handling: Cross-Origin Resource Sharing (CORS) is configured to allow web clients from different origins to interact with the API.

  8. Execution Environment: When executed, the script checks if it’s running in a Google Cloud Function environment and acts accordingly; otherwise, it starts a Flask web server for local development or testing.

  9. Endpoint Security: We are using a simple shared secret approach to securing the endpoint. The request body is checked against the supplied signature in the X-Signature header. We aren’t yet guarding against replay attacks with nonces.

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Local Development

To set up and run the function locally, follow these steps:

  1. Create a virtual environment and activate it:

    python3 -m venv .venv
source .venv/bin/activate

  2. Navigate to the project directory and install the required dependencies:

    cd explore-assistant-cloud-function
pip3 install -r requirements.txt

  3. Run the function locally by executing the main script:

    PROJECT=XXXX LOCATION=us-central-1 VERTEX_CF_AUTH_TOKEN=$(cat ../.vertex_cf_auth_token) python main.py

  4. Test calling the endpoint locally with a custom query and parameter declaration

      python test.py

This setup allows developers to test and modify the function in a local environment before deploying it to a cloud function service.

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Explore Assistant Cloud Function

This cloud function provides an API for generating Looker queries using the Vertex AI Gemini Pro model. It allows users to input natural language descriptions of data queries, which the function then converts into Looker Explore URLs or query suggestions using a generative AI model.

​
How It Works

The cloud function integrates with Vertex AI and utilizes the GenerativeModel class to generate content based on input parameters. Here’s a step-by-step breakdown of its operation:

  1. Environment Setup: The function initializes with the Vertex AI environment, requiring PROJECT and REGION environment variables to be set for connecting to the Vertex AI services.

  2. Query Generation: The core functionality lies in the generate_looker_query function, which accepts a natural language query (contents) and optional parameters (parameters) to customize the AI generation process.

  3. Parameter Configuration: It uses default parameters for the AI model (like temperature, max_output_tokens, top_p, top_k) which can be overridden by the request’s parameters.

  4. Model Invocation: The Gemini Pro model is then invoked to generate the Looker query based on the provided contents and parameters.

  5. Logging: Metadata from the generation process, including input and output token counts, is logged for monitoring and debugging purposes.

  6. API Endpoints: The function can be deployed as a Flask web server or as a Google Cloud Function. It handles POST requests to the main endpoint, where it expects a JSON payload with contents and optional parameters.

  7. CORS Handling: Cross-Origin Resource Sharing (CORS) is configured to allow web clients from different origins to interact with the API.

  8. Execution Environment: When executed, the script checks if it’s running in a Google Cloud Function environment and acts accordingly; otherwise, it starts a Flask web server for local development or testing.

  9. Endpoint Security: We are using a simple shared secret approach to securing the endpoint. The request body is checked against the supplied signature in the X-Signature header. We aren’t yet guarding against replay attacks with nonces.

​
Local Development

To set up and run the function locally, follow these steps:

  1. Create a virtual environment and activate it:

    python3 -m venv .venv
source .venv/bin/activate

  2. Navigate to the project directory and install the required dependencies:

    cd explore-assistant-cloud-function
pip3 install -r requirements.txt

  3. Run the function locally by executing the main script:

    PROJECT=XXXX LOCATION=us-central-1 VERTEX_CF_AUTH_TOKEN=$(cat ../.vertex_cf_auth_token) python main.py

  4. Test calling the endpoint locally with a custom query and parameter declaration

      python test.py

This setup allows developers to test and modify the function in a local environment before deploying it to a cloud function service.