genai-toolbox/docs/en/resources/embeddingModels/_index.md

title, type, weight, description

title	type	weight	description
EmbeddingModels	docs	2	EmbeddingModels represent services that transform text into vector embeddings for semantic search.

EmbeddingModels represent services that generate vector representations of text data. In the MCP Toolbox, these models enable Semantic Queries, allowing Tools to automatically convert human-readable text into numerical vectors before using them in a query.

This is primarily used in two scenarios:

• Vector Ingestion: Converting a text parameter into a vector string during an INSERT operation.

• Semantic Search: Converting a natural language query into a vector to perform similarity searches.

Example

The following configuration defines an embedding model and applies it to specific tool parameters.

{{< notice tip >}} Use environment variable replacement with the format ${ENV_NAME} instead of hardcoding your API keys into the configuration file. {{< /notice >}}

Step 1 - Define an Embedding Model

Define an embedding model in the embeddingModels section:

embeddingModels:
  gemini-model: # Name of the embedding model
    kind: gemini
    model: gemini-embedding-001
    apiKey: ${GOOGLE_API_KEY}
    dimension: 768

Step 2 - Embed Tool Parameters

Use the defined embedding model, embed your query parameters using the embeddedBy field. Only string-typed parameters can be embedded:

tools:
  # Vector ingestion tool
  insert_embedding:
    kind: postgres-sql
    source: my-pg-instance
    statement: |
      INSERT INTO documents (content, embedding) 
      VALUES ($1, $2);
    parameters:
      - name: content
        type: string
      - name: vector_string
        type: string
        description: The text to be vectorized and stored.
        embeddedBy: gemini-model # refers to the name of a defined embedding model

  # Semantic search tool
  search_embedding:
    kind: postgres-sql
    source: my-pg-instance
    statement: |
      SELECT id, content, embedding <-> $1 AS distance 
      FROM documents
      ORDER BY distance LIMIT 1
    parameters:
      - name: semantic_search_string
        type: string
        description: The search query that will be converted to a vector.
        embeddedBy: gemini-model # refers to the name of a defined embedding model

Kinds of Embedding Models