For the complete documentation index, see llms.txt. Markdown versions of all docs pages are available by appending .md to any docs URL.

Embeddings

Send embedding requests through agentgateway using the OpenAI-compatible Embeddings API.

The Embeddings API (/v1/embeddings) creates vector representations of text that you can use for search, retrieval, clustering, and other semantic workflows.

About

Agentgateway supports the OpenAI-compatible Embeddings API. Requests to /v1/embeddings are routed to your configured provider while agentgateway applies the same routing, authentication, observability, and policy framework that you use for other LLM traffic.

Route type configuration

In the simplified llm configuration, agentgateway automatically maps /v1/embeddings requests to the embeddings route type, so no explicit route configuration is required.

# yaml-language-server: $schema=https://agentgateway.dev/schema/config
llm:
  models:
  - name: "*"
    provider: openAI
    params:
      apiKey: "$OPENAI_API_KEY"

To configure the route type explicitly, use the binds/listeners/routes format and set the embeddings route type in the policies.ai.routes map.

# yaml-language-server: $schema=https://agentgateway.dev/schema/config
binds:
- port: 4000
  listeners:
  - routes:
    - backends:
      - ai:
          name: openai
          provider:
            openAI: {}
      policies:
        ai:
          routes:
            "/v1/embeddings": "embeddings"
        backendAuth:
          key: "$OPENAI_API_KEY"

Using the API

Send a request to the /v1/embeddings endpoint. The response includes an embedding vector for each input item.

curl 'http://localhost:4000/v1/embeddings' \
--header 'Content-Type: application/json' \
--data '{
  "model": "text-embedding-3-small",
  "input": [
    "agentgateway routes LLM traffic",
    "embeddings turn text into vectors"
  ]
}'