LocalAI/docs/content/features/embeddings.md

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LocalAI supports generating embeddings for text or list of tokens.

For face embeddings specifically, see the Face Recognition feature — it produces 512-d L2-normalized vectors tuned for face similarity.

For the API documentation you can refer to the OpenAI docs: https://platform.openai.com/docs/api-reference/embeddings

Model compatibility

The embedding endpoint is compatible with llama.cpp models, bert.cpp models and sentence-transformers models available in huggingface.

Using Gallery Models

LocalAI provides a model gallery with pre-configured embedding models. To use a gallery model:

1. Ensure the model is available in the gallery (check [Model Gallery]({{%relref "features/model-gallery" %}}))
2. Use the model name directly in your API calls

Example gallery models:

• qwen3-embedding-4b - Qwen3 Embedding 4B model
• qwen3-embedding-8b - Qwen3 Embedding 8B model
• qwen3-embedding-0.6b - Qwen3 Embedding 0.6B model

Example: Using Qwen3-Embedding-4B from Gallery

curl http://localhost:8080/embeddings -X POST -H "Content-Type: application/json" -d '{
  "input": "My text to embed",
  "model": "qwen3-embedding-4b",
  "dimensions": 2560
}'

Manual Setup

Create a YAML config file in the models directory. Specify the backend and the model file.

name: text-embedding-ada-002 # The model name used in the API
parameters:
  model: <model_file>
backend: "<backend>"
embeddings: true

Huggingface embeddings

To use sentence-transformers and models in huggingface you can use the sentencetransformers embedding backend.

name: text-embedding-ada-002
backend: sentencetransformers
embeddings: true
parameters:
  model: all-MiniLM-L6-v2

The sentencetransformers backend uses Python sentence-transformers. For a list of all pre-trained models available see here: https://github.com/UKPLab/sentence-transformers#pre-trained-models

{{% notice note %}}

• The sentencetransformers backend is an optional backend of LocalAI and uses Python. If you are running LocalAI from the containers you are good to go and should be already configured for use.
• For local execution, you also have to specify the extra backend in the EXTERNAL_GRPC_BACKENDS environment variable.
  • Example: EXTERNAL_GRPC_BACKENDS="sentencetransformers:/path/to/LocalAI/backend/python/sentencetransformers/sentencetransformers.py"
• The sentencetransformers backend does support only embeddings of text, and not of tokens. If you need to embed tokens you can use the bert backend or llama.cpp.
• No models are required to be downloaded before using the sentencetransformers backend. The models will be downloaded automatically the first time the API is used.

{{% /notice %}}

Llama.cpp embeddings

Embeddings with llama.cpp are supported with the llama-cpp backend, it needs to be enabled with embeddings set to true.

name: my-awesome-model
backend: llama-cpp
embeddings: true
parameters:
  model: ggml-file.bin

Then you can use the API to generate embeddings:

curl http://localhost:8080/embeddings -X POST -H "Content-Type: application/json" -d '{
  "input": "My text",
  "model": "my-awesome-model"
}' | jq "."

💡 Examples

• Example that uses LLamaIndex and LocalAI as embedding: here.

⚠️ Common Issues and Troubleshooting

Issue: Embedding model not returning correct results

Symptoms:

• Model returns empty or incorrect embeddings
• API returns errors when calling embedding endpoint

Common Causes:

1. Incorrect model filename: Ensure you're using the correct filename from the gallery or your model file location.

   • Gallery models use specific filenames (e.g., Qwen3-Embedding-4B-Q4_K_M.gguf)
   • Check the [Model Gallery]({{%relref "features/model-gallery" %}}) for correct filenames

2. Context size mismatch: Ensure your context_size setting doesn't exceed the model's maximum context length.

   • Qwen3-Embedding-4B: max 32k (32768) context
   • Qwen3-Embedding-8B: max 32k (32768) context
   • Qwen3-Embedding-0.6B: max 32k (32768) context

3. Missing embeddings: true flag: The model configuration must have embeddings: true set.

Correct Configuration Example:

name: qwen3-embedding-4b
backend: llama-cpp
embeddings: true
context_size: 32768
parameters:
  model: Qwen3-Embedding-4B-Q4_K_M.gguf

Issue: Dimension mismatch

Symptoms:

• Returned embedding dimensions don't match expected dimensions

Solution:

• Use the dimensions parameter in your API request to specify the output dimension
• Qwen3-Embedding models support dimensions from 32 to 2560 (4B) or 4096 (8B)

curl http://localhost:8080/embeddings -X POST -H "Content-Type: application/json" -d '{
  "input": "My text",
  "model": "qwen3-embedding-4b",
  "dimensions": 1024
}'

Issue: Model not found

Symptoms:

• API returns 404 or "model not found" error

Solution:

• Ensure the model is properly configured in the models directory
• Check that the model name in your API request matches the name field in the configuration
• For gallery models, ensure the gallery is properly loaded

Qwen3 Embedding Models Specifics

The Qwen3 Embedding series models have these characteristics:

Model	Parameters	Max Context	Max Dimensions	Supported Languages
qwen3-embedding-0.6b	0.6B	32k	1024	100+
qwen3-embedding-4b	4B	32k	2560	100+
qwen3-embedding-8b	8B	32k	4096	100+

All models support:

• User-defined output dimensions (32 to max dimensions)
• Multilingual text embedding (100+ languages)
• Instruction-tuned embedding with custom instructions