--- title: Embed API - AWS Bedrock Vector Embeddings (Cohere Compatible) description: Generate vector embeddings with AWS Bedrock embedding models using a Cohere-compatible API. Semantic search, RAG, and multimodal embeddings through the Cohere v2 Embed interface. keywords: Cohere embed API, vector embeddings AWS, semantic search API, RAG embeddings, Cohere compatible API, AWS Bedrock embeddings, input_type embeddings --- # Embed API (Cohere Compatible) Generate vector embeddings for semantic search and RAG applications with AWS Bedrock embedding models through a Cohere-compatible interface. This is an alternate route to the [OpenAI-compatible Embeddings API](api_openai_embeddings.md): both are served by the same embedding backends and models, so anything supported there is supported here. !!! info "Route Prefix Configuration" 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](operations_configuration.md#cohere-routes-prefix). The `curl` examples below use a `$BASE` variable that **must include this prefix** — set it to your scheme and host followed by `COHERE_ROUTES_PREFIX`: ```bash export BASE="https://your-host/cohere" # :// + COHERE_ROUTES_PREFIX ``` ## Quick Start: Available Endpoints | Endpoint | Method | What It Does | Powered By | MCP Tool | |-------------|--------|--------------------------------------------------------|------------------------------|-------------------| | `/v2/embed` | POST | Transform texts and images into semantic float vectors | AWS Bedrock Embedding Models | `cohere_embed` | | `/v1/embed` | POST | Legacy v1 embed for older SDKs and integrations | AWS Bedrock Embedding Models | `cohere_embed_v1` | **Example request:** ```bash curl -X POST "$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:** ```json { "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`](api_search_models.md) with `mcp_tool=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` | :material-check-circle:{ .success } | Full support | | `images` (data URIs) | :material-cog:{ .model-dep } | Multimodal models only; URLs and S3 URIs also accepted | | `inputs` (fused text + image) | :material-close-circle:{ .unsupported } | Use `texts` or `images` instead | | **Model Parameters** | | | | `input_type` | :material-cog:{ .model-dep } | Applied to Cohere models; no equivalent on other providers | | `output_dimension` | :material-cog:{ .model-dep } | Some models support dimension reduction | | `truncate`, `max_tokens` | :material-cog:{ .model-dep } | Cohere models only | | `embedding_types` | :material-close-circle:{ .unsupported } | Only `float` embeddings are returned | | `priority` | :material-close-circle:{ .unsupported } | Accepted but ignored — not applicable on AWS Bedrock | | Extra model-specific params | :material-plus-circle:{ .extra-feature } | Extra fields are forwarded as additional model request parameters | | **Output** | | | | `images` metadata array | :material-close-circle:{ .unsupported } | Image dimensions are not echoed in the response | | **Usage tracking** | | | | `billed_units.input_tokens` | :material-cog:{ .model-dep } | Estimated on some models |
**Legend:** * :material-check-circle:{ .success } **Supported** — Fully compatible with the Cohere API * :material-cog:{ .model-dep } **Available on Select Models** — Check your model's capabilities * :material-close-circle:{ .unsupported } **Unsupported** — Not available in this implementation * :material-plus-circle:{ .extra-feature } **Extra Feature** — Enhanced capability beyond the Cohere API
## 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 AWS 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 | :material-check-circle:{ .success } | Legacy `embeddings_floats`: a plain list of float vectors | | `embedding_types` | :material-check-circle:{ .success } | `["float"]` switches to the `embeddings_by_type` shape; other types return 400 | | `input_type` | :material-check-circle:{ .success } | Optional — forwarded to Cohere models when provided; the backend defaults to `search_document` otherwise | | `meta.api_version.version` | :material-check-circle:{ .success } | Reported as `"1"` |
**Example request:** ```bash 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:** ```json { "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} } } ``` ## Notes - Requests are billed through AWS Bedrock (per token), not in Cohere search units; `billed_units.input_tokens` reports the Bedrock-metered input tokens. - 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](api_openai_embeddings.md#available-request-headers) work on this route too.