Embed API (Cohere Compatible)¶

Generate vector embeddings for semantic search and RAG applications with Amazon Bedrock embedding models through a Cohere-compatible interface.

This is an alternate route to the OpenAI-compatible Embeddings API: both are served by the same embedding backends and models, so anything supported there is supported here.

Route Prefix & Base URL

By default, all Cohere-compatible routes are prefixed with /cohere. This means the Embed API is available at /cohere/v2/embed instead of /v2/embed. You can customize this prefix using the COHERE_ROUTES_PREFIX configuration variable documented in Operations Configuration.

The curl examples below use a $BASE variable that must include this prefix — set it to your scheme and host followed by COHERE_ROUTES_PREFIX:

export BASE="https://your-host/cohere"  # <scheme>://<host> + COHERE_ROUTES_PREFIX

Why Choose Embed?¶

Semantic Search
Turn texts and images into dense vectors for similarity search that understands meaning, not just keywords.
Higher RAG Quality
Build retrieval pipelines on high-quality embeddings, with input_type tuning for queries versus documents.
Drop-in Cohere Compatibility
Follows the Cohere v2 Embed API shape. Existing Cohere embed integrations work by changing the base URL.
Private AWS Backend
Served entirely by Bedrock embedding models in your own AWS account — no traffic to third-party endpoints.

Quick Start: Available Endpoints¶

Endpoint	Method	What It Does	Powered By	MCP Tool
`/v2/embed`	`POST`	Transform texts and images into semantic float vectors	Bedrock embedding models	`cohere_embed`
`/v1/embed`	`POST`	Legacy v1 embed for older SDKs and integrations	Bedrock embedding models	`cohere_embed_v1`

Example request:

curl -X POST "$BASE/v2/embed" \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "cohere.embed-multilingual-v3",
    "input_type": "search_document",
    "texts": ["Hello world", "Bonjour le monde"]
  }'

Example response:

{
  "response_type": "embeddings_by_type",
  "id": "0f1b3c6e8d9a4b5c8e7f6a5b4c3d2e1f",
  "embeddings": {"float": [[0.012, -0.034, ...], [0.041, 0.007, ...]]},
  "texts": ["Hello world", "Bonjour le monde"],
  "meta": {
    "api_version": {"version": "2"},
    "billed_units": {"input_tokens": 8}
  }
}

Find compatible models: Call /search_models with route=cohere_embed to discover model IDs that support embeddings — every Bedrock embedding model works, not just Cohere ones.

Feature Compatibility¶

Feature	Status	Notes
Input
`texts`		Full support
`images`		Multimodal models only; data URIs, plus URLs and S3 URIs
`inputs` (fused text + image)		Rejected with 400 — use `texts` or `images` instead
Model Parameters
`input_type`		Applied to Cohere models; no equivalent on other providers
`output_dimension`		Some models support dimension reduction
`truncate`, `max_tokens`		Cohere models only
`embedding_types`		`int8`/`uint8`/`binary`/`ubinary` on Cohere models, `binary` also on Titan Embed v2; `base64` always computed client-side; other combinations return 400
`priority`		Accepted but ignored — request scheduling priority is not applicable on Bedrock
Extra model-specific params		Extra fields are forwarded as additional model request parameters
Output
`images` metadata array		Echoed by models that report image dimensions (e.g. Cohere Embed)
Usage tracking
`billed_units.input_tokens`		Estimated on some models

Legend:

Supported — Fully compatible with the Cohere API
Available on Select Models — Check your model's capabilities
Partial — Supported with limitations
Unsupported — Not available in this implementation
Extra Feature — Enhanced capability beyond the Cohere API

Quantized and Base64 Embedding Types¶

Set embedding_types to request quantized vectors alongside, or instead of, the default float vectors. Bedrock Cohere Embed models natively support int8, uint8, binary, and ubinary; Titan Embed v2 natively supports binary. base64 is always available: it is computed client-side (little-endian float32 bytes, base64-encoded) from the float embedding, matching the Cohere API encoding. Requesting a type not supported by the resolved model returns 400. Only the requested types are populated in the response.

curl -X POST "$BASE/v2/embed" \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "cohere.embed-v4:0",
    "input_type": "search_document",
    "texts": ["Hello world"],
    "embedding_types": ["int8", "base64"]
  }'

{
  "response_type": "embeddings_by_type",
  "id": "0f1b3c6e8d9a4b5c8e7f6a5b4c3d2e1f",
  "embeddings": {
    "int8": [[12, -34, ...]],
    "base64": ["rBgKPw..."]
  },
  "texts": ["Hello world"],
  "meta": {
    "api_version": {"version": "2"},
    "billed_units": {"input_tokens": 4}
  }
}

Cohere v1 Embed API (Legacy)¶

The legacy /v1/embed endpoint is also available for older Cohere SDKs (cohere.Client) and third-party integrations that predate the v2 API. It shares the same Bedrock backend and model support as /v2/embed; new clients should prefer the v2 endpoint.

Differences from the v2 endpoint:

Feature	Status	Notes
Default response shape		Legacy `embeddings_floats`: a plain list of float vectors
`embedding_types`		Any value switches to the `embeddings_by_type` shape; same type support as the v2 endpoint
`input_type`		Optional — forwarded to Cohere models when provided; the backend defaults to `search_document` otherwise
`meta.api_version.version`		Reported as `"1"`

Example request:

curl -X POST "$BASE/v1/embed" \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "cohere.embed-multilingual-v3",
    "input_type": "search_document",
    "texts": ["Hello world", "Bonjour le monde"]
  }'

Example response:

{
  "response_type": "embeddings_floats",
  "id": "0f1b3c6e8d9a4b5c8e7f6a5b4c3d2e1f",
  "embeddings": [[0.012, -0.034, ...], [0.041, 0.007, ...]],
  "texts": ["Hello world", "Bonjour le monde"],
  "meta": {
    "api_version": {"version": "1"},
    "billed_units": {"input_tokens": 8}
  }
}

How It Works¶

Requests are served by the same Bedrock embedding backends as the OpenAI-compatible Embeddings API, with automatic multi-region routing and failover across the regions where the selected model is available.

When both texts and images are provided, embeddings are returned in request order: all texts first, then all images.
Guardrail and performance headers available on the OpenAI-compatible Embeddings API work on this route too.

Billing¶

Requests are billed through Bedrock (per token), not in Cohere search units; billed_units.input_tokens reports the Bedrock-metered input tokens. Usage appears in usage logs and cost tracking as input_tokens.