> ## Documentation Index
> Fetch the complete documentation index at: https://docs.salad.com/llms.txt
> Use this file to discover all available pages before exploring further.

# BGE-M3 Embeddings with Text Embeddings Inference

> Serve BAAI/bge-m3 embeddings with Hugging Face Text Embeddings Inference for RAG, semantic search, document retrieval, and similarity search.

*Last Updated: June 29, 2026*

<Tip>Deploy from the [SaladCloud Portal](https://portal.salad.com).</Tip>

## Overview

This recipe runs [`BAAI/bge-m3`](https://huggingface.co/BAAI/bge-m3) with
[Hugging Face Text Embeddings Inference](https://github.com/huggingface/text-embeddings-inference) on a Salad GPU. It
serves dense embeddings for RAG, semantic search, document retrieval, and similarity search workloads.

BGE-M3 is useful when you need one embedding model that handles multilingual content, short queries, and longer
documents. The model supports more than 100 languages, accepts inputs up to 8192 tokens, and returns 1024-dimensional
dense vectors that can be stored in a vector database.

The BGE-M3 model family also supports sparse and ColBERT-style retrieval modes through FlagEmbedding. This Salad recipe
is focused on the dense embedding endpoint exposed by TEI because that is the interface most RAG frameworks and vector
search systems expect.

## Quick Start

1. Open the [SaladCloud Portal](https://portal.salad.com).
2. Deploy the **BGE-M3 Embeddings** recipe.
3. Enter a **Container Group Name**.
4. Decide whether to enable **Require Container Gateway Authentication**:
   * Enabled: requests must include your SaladCloud API key.
   * Disabled: anyone with the URL can call the embedding service.
5. Deploy and wait for the first startup to finish.

<Callout variation="note">
  The model is downloaded from Hugging Face at startup, so it can take several minutes before the deployment becomes
  ready.
</Callout>

Once the container is ready, call `/embed` for TEI's native embedding API or `/v1/embeddings` for the OpenAI-compatible
API.

## Defaults

The recipe comes preconfigured with these defaults:

* Server: Hugging Face Text Embeddings Inference
* Model ID: `BAAI/bge-m3`
* Container image: `ghcr.io/huggingface/text-embeddings-inference:89-1.9`
* Command equivalent: `text-embeddings-router --model-id BAAI/bge-m3 --port 3000`
* Container port: `3000`
* Host bind: `::`
* Data type: `float16`
* Max batch tokens: `16384`
* Max client batch size: `32`
* Readiness probe: `GET /health`
* Authentication: enabled by default

## API Endpoints

Useful TEI endpoints include:

* `GET /health` - readiness probe and health check
* `GET /info` - model and server metadata
* `GET /docs` - Swagger documentation
* `POST /embed` - TEI native dense embedding endpoint
* `POST /v1/embeddings` - OpenAI-compatible embeddings endpoint
* `POST /similarity` - TEI similarity endpoint

## Authentication

**Require Container Gateway Authentication** is available in the deployment form and is enabled by default.

* Enabled: every request must include the `Salad-Api-Key` header.
* Disabled: anyone with the deployment URL can call the API.

If you enable authentication, see [Sending Requests](/container-engine/how-to-guides/gateway/sending-requests) for the
header format.

## Example Request

Use `/embed` to generate dense vectors with TEI's native API:

```bash theme={null}
curl https://<your-dns>.salad.cloud/embed \
  -X POST \
  -H 'Content-Type: application/json' \
  -H 'Salad-Api-Key: <api-key>' \
  -d '{
    "inputs": [
      "BGE-M3 is useful for multilingual semantic search.",
      "Text embeddings help retrieve documents for RAG applications."
    ],
    "normalize": true
  }'
```

If you disabled authentication during deployment, omit the `Salad-Api-Key` header.

Each returned vector should contain 1024 floating-point values.

## OpenAI-Compatible Request

Use `/v1/embeddings` if your client expects an OpenAI-compatible embeddings API:

```bash theme={null}
curl https://<your-dns>.salad.cloud/v1/embeddings \
  -X POST \
  -H 'Content-Type: application/json' \
  -H 'Salad-Api-Key: <api-key>' \
  -d '{
    "model": "BAAI/bge-m3",
    "input": [
      "What is BGE-M3 good for?",
      "Use embeddings for document retrieval and similarity search."
    ],
    "encoding_format": "float"
  }'
```

If you disabled authentication during deployment, omit the `Salad-Api-Key` header.

## Test A Deployment

Check health:

```bash theme={null}
curl https://<your-dns>.salad.cloud/health
```

Check model metadata:

```bash theme={null}
curl https://<your-dns>.salad.cloud/info
```

Confirm the embedding dimension:

```bash theme={null}
curl -s https://<your-dns>.salad.cloud/embed \
  -X POST \
  -H 'Content-Type: application/json' \
  -d '{"inputs":["hello world"],"normalize":true}' \
  | jq '.[0] | length'
```

The expected result is `1024`.

## Tuning Notes

* Keep `MODEL_ID` set to `BAAI/bge-m3` unless you intentionally want to repurpose the recipe.
* BGE-M3 supports long inputs, but long batches use more VRAM. Lower `MAX_BATCH_TOKENS` if replicas run out of memory.
* Increase `MAX_CLIENT_BATCH_SIZE` only after load testing your expected request shape.
* If you want private or gated Hugging Face models in a customized deployment, add `HF_TOKEN` in Advanced Configuration.

## Source Code

* [<Icon icon="github" size="24" /> Recipe Source](https://github.com/SaladTechnologies/salad-recipes/tree/master/recipes/tei-bge-m3)
* [Hugging Face Text Embeddings Inference](https://github.com/huggingface/text-embeddings-inference)
* [BAAI/bge-m3 model card](https://huggingface.co/BAAI/bge-m3)
* [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding)