---
title: "EmbeddingModels"
type: docs
weight: 2
description: >
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](../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.
## Hidden Parameter Duplication (valueFromParam)
When building tools for vector ingestion, you often need the same input string
twice:
1. To store the original text in a TEXT column.
1. To generate the vector embedding for a VECTOR column.
Requesting an Agent (LLM) to output the exact same string twice is inefficient
and error-prone. The `valueFromParam` field solves this by allowing a parameter
to inherit its value from another parameter in the same tool.
### Key Behaviors
1. Hidden from Manifest: Parameters with valueFromParam set are excluded from
the tool definition sent to the Agent. The Agent does not know this parameter
exists.
1. Auto-Filled: When the tool is executed, the Toolbox automatically copies the
value from the referenced parameter before processing embeddings.
## 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:
```yaml
kind: embeddingModels
name: gemini-model # Name of the embedding model
type: 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:
```yaml
# Vector ingestion tool
kind: tools
name: insert_embedding
type: postgres-sql
source: my-pg-instance
statement: |
INSERT INTO documents (content, embedding)
VALUES ($1, $2);
parameters:
- name: content
type: string
description: The raw text content to be stored in the database.
- name: vector_string
type: string
# This parameter is hidden from the LLM.
# It automatically copies the value from 'content' and embeds it.
valueFromParam: content
embeddedBy: gemini-model
---
# Semantic search tool
kind: tools
name: search_embedding
type: 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
