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First part of the implementation to support semantic search in tools. Second part: https://github.com/googleapis/genai-toolbox/pull/2151
85 lines
2.3 KiB
Markdown
85 lines
2.3 KiB
Markdown
---
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title: "EmbeddingModels"
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type: docs
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weight: 2
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description: >
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EmbeddingModels represent services that transform text into vector embeddings for semantic search.
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---
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EmbeddingModels represent services that generate vector representations of text
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data. In the MCP Toolbox, these models enable **Semantic Queries**,
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allowing [Tools](../tools/) to automatically convert human-readable text into
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numerical vectors before using them in a query.
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This is primarily used in two scenarios:
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- **Vector Ingestion**: Converting a text parameter into a vector string during
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an `INSERT` operation.
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- **Semantic Search**: Converting a natural language query into a vector to
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perform similarity searches.
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## Example
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The following configuration defines an embedding model and applies it to
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specific tool parameters.
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{{< notice tip >}}
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Use environment variable replacement with the format ${ENV_NAME}
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instead of hardcoding your API keys into the configuration file.
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{{< /notice >}}
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### Step 1 - Define an Embedding Model
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Define an embedding model in the `embeddingModels` section:
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```yaml
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embeddingModels:
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gemini-model: # Name of the embedding model
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kind: gemini
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model: gemini-embedding-001
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apiKey: ${GOOGLE_API_KEY}
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dimension: 768
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```
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### Step 2 - Embed Tool Parameters
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Use the defined embedding model, embed your query parameters using the
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`embeddedBy` field. Only string-typed
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parameters can be embedded:
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```yaml
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tools:
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# Vector ingestion tool
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insert_embedding:
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kind: postgres-sql
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source: my-pg-instance
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statement: |
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INSERT INTO documents (content, embedding)
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VALUES ($1, $2);
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parameters:
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- name: content
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type: string
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- name: vector_string
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type: string
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description: The text to be vectorized and stored.
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embeddedBy: gemini-model # refers to the name of a defined embedding model
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# Semantic search tool
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search_embedding:
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kind: postgres-sql
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source: my-pg-instance
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statement: |
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SELECT id, content, embedding <-> $1 AS distance
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FROM documents
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ORDER BY distance LIMIT 1
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parameters:
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- name: semantic_search_string
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type: string
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description: The search query that will be converted to a vector.
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embeddedBy: gemini-model # refers to the name of a defined embedding model
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```
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## Kinds of Embedding Models
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