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109 lines
3.2 KiB
Markdown
109 lines
3.2 KiB
Markdown
---
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title: "EmbeddingModels"
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type: docs
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weight: 7
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description: >
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EmbeddingModels represent services that transform text into vector embeddings
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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**, allowing
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[Tools](../tools/_index.md) to automatically convert human-readable text into numerical
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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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## Hidden Parameter Duplication (valueFromParam)
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When building tools for vector ingestion, you often need the same input string
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twice:
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1. To store the original text in a TEXT column.
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1. To generate the vector embedding for a VECTOR column.
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Requesting an Agent (LLM) to output the exact same string twice is inefficient
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and error-prone. The `valueFromParam` field solves this by allowing a parameter
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to inherit its value from another parameter in the same tool.
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### Key Behaviors
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1. Hidden from Manifest: Parameters with valueFromParam set are excluded from
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the tool definition sent to the Agent. The Agent does not know this parameter
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exists.
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1. Auto-Filled: When the tool is executed, the Toolbox automatically copies the
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value from the referenced parameter before processing embeddings.
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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 >}} Use environment variable replacement with the format
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${ENV_NAME} 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 with the `embeddingModel` kind:
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```yaml
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kind: embeddingModel
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name: gemini-model # Name of the embedding model
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type: 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 parameters can be embedded:
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```yaml
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# Vector ingestion tool
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kind: tool
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name: insert_embedding
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type: postgres-sql
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source: my-pg-instance
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description: Insert a new document into the database.
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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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description: The raw text content to be stored in the database.
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- name: vector_string
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type: string
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# This parameter is hidden from the LLM.
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# It automatically copies the value from 'content' and embeds it.
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valueFromParam: content
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embeddedBy: gemini-model
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---
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# Semantic search tool
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kind: tool
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name: search_embedding
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type: postgres-sql
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source: my-pg-instance
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description: Search for documents in the database.
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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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## Types of Embedding Models
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