LLM extension

The llm extension helps you run LLM-based workflows directly within Ladybug using Cypher.

Currently, this extension provides the following function:

• CREATE_EMBEDDING: Create embeddings from text

Note

We plan to add more functions to this extension in the future, such as sending queries to LLMs directly from Ladybug.

Usage

Please see Install an extension and Load an extension first before getting started.

Create embeddings

The CREATE_EMBEDDING function allows you to create embeddings from text using the following providers:

• Amazon Bedrock
• Google Vertex
• Google Gemini
• Ollama
• OpenAI
• Voyage AI

The general form of the function is shown below. It returns an embedding of type LIST[FLOAT].

CREATE_EMBEDDING(
    // Required arguments
    <PROMPT>,
    <PROVIDER>,
    <MODEL>
    // Provider-specific arguments
    <DIMENSIONS>,
    <REGION>,
    <ENDPOINT>
);

Required arguments:

• PROMPT: The input text to embed.
  • Type: STRING
• PROVIDER: The embedding provider.
  • Type: STRING
• MODEL: The identifier of the embedding model.
  • Type: STRING

Additional provider-specific arguments:

• DIMENSIONS: The size of the embedding vector.
  • Type: INT64
  • Note: This value must be positive.
• REGION: The region to send requests to.
  • Type: STRING
  • Note: This value is required when the provider supports it.
• ENDPOINT: The endpoint to send requests to.
  • Type: STRING
  • Default: http://localhost:11434

The additional arguments that you can use depends on the provider. The following table shows the arguments associated with each provider:

Provider	dimensions	region	endpoint
amazon-bedrock	-	Required	-
google-vertex	Optional	Required	-
google-gemini	-	-	-
openai	Optional	-	-
voyageai	Optional	-	-
ollama	-	-	Optional

Note

If an unsupported model, dimension, region, or endpoint is specified, the API call to the provider will fail. Check the provider’s API documentation for the currently supported options.

Configuration

To allow API calls to the providers, you must set the appropriate environment variables:

Provider	Required Environment Variables
amazon-bedrock	AWS_ACCESS_KEY, AWS_SECRET_ACCESS_KEY
google-vertex	GOOGLE_CLOUD_PROJECT_ID, GOOGLE_VERTEX_ACCESS_KEY
google-gemini	GOOGLE_GEMINI_API_KEY
openai	OPENAI_API_KEY
voyageai	VOYAGE_API_KEY
ollama	OLLAMA_URL

Note

OLLAMA_URL is optional for ollama. You can also choose to configure the endpoint as an argument to the CREATE_EMBEDDING function, as explained above.

Examples

// Required REGION
RETURN CREATE_EMBEDDING("Hello world", "amazon-bedrock", "amazon.titan-embed-text-v1", "us-east-1");

RETURN CREATE_EMBEDDING("Hello world", "google-gemini", "gemini-embedding-exp-03-07");

// Required REGION
RETURN CREATE_EMBEDDING("Hello world", "google-vertex", "gemini-embedding-001", "us-central1");
// Optional DIMENSIONS and required REGION.
RETURN CREATE_EMBEDDING("Hello world", "google-vertex", "gemini-embedding-001", 256, "us-central1");

RETURN CREATE_EMBEDDING("Hello world", "ollama", "nomic-embed-text");
// Optional ENDPOINT
RETURN CREATE_EMBEDDING("Hello world", "ollama", "nomic-embed-text", "http://endpoint.example.com:8000");

RETURN CREATE_EMBEDDING( "Hello world", "openai", "text-embedding-3-small");
// Optional DIMENSIONS
RETURN CREATE_EMBEDDING( "Hello world", "openai", "text-embedding-3-small", 512);

RETURN CREATE_EMBEDDING( "Hello world", "voyageai", "voyage-3-large");
// Optional DIMENSIONS
RETURN CREATE_EMBEDDING( "Hello world", "voyageai", "voyage-3-large", 512);

Storing embeddings in a vector index

The CREATE_EMBEDDING function can be used together with the vector extension to store and search embeddings directly in Ladybug. Refer to the vector search docs for an example.