mirror of https://github.com/mudler/LocalAI.git
103 lines
3.4 KiB
Markdown
103 lines
3.4 KiB
Markdown
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disableToc = false
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title = "🧠 Embeddings"
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weight = 13
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LocalAI supports generating embeddings for text or list of tokens.
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For the API documentation you can refer to the OpenAI docs: https://platform.openai.com/docs/api-reference/embeddings
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## Model compatibility
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The embedding endpoint is compatible with `llama.cpp` models, `bert.cpp` models and sentence-transformers models available in huggingface.
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## Manual Setup
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Create a `YAML` config file in the `models` directory. Specify the `backend` and the model file.
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```yaml
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name: text-embedding-ada-002 # The model name used in the API
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parameters:
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model: <model_file>
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backend: "<backend>"
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embeddings: true
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# .. other parameters
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```
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## Bert embeddings
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To use `bert.cpp` models you can use the `bert` embedding backend.
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An example model config file:
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```yaml
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name: text-embedding-ada-002
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parameters:
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model: bert
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backend: bert-embeddings
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embeddings: true
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# .. other parameters
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```
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The `bert` backend uses [bert.cpp](https://github.com/skeskinen/bert.cpp) and uses `ggml` models.
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For instance you can download the `ggml` quantized version of `all-MiniLM-L6-v2` from https://huggingface.co/skeskinen/ggml:
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```bash
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wget https://huggingface.co/skeskinen/ggml/resolve/main/all-MiniLM-L6-v2/ggml-model-q4_0.bin -O models/bert
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```
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To test locally (LocalAI server running on `localhost`),
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you can use `curl` (and `jq` at the end to prettify):
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```bash
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curl http://localhost:8080/embeddings -X POST -H "Content-Type: application/json" -d '{
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"input": "Your text string goes here",
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"model": "text-embedding-ada-002"
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}' | jq "."
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```
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## Huggingface embeddings
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To use `sentence-transformers` and models in `huggingface` you can use the `sentencetransformers` embedding backend.
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```yaml
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name: text-embedding-ada-002
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backend: sentencetransformers
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embeddings: true
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parameters:
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model: all-MiniLM-L6-v2
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```
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The `sentencetransformers` backend uses Python [sentence-transformers](https://github.com/UKPLab/sentence-transformers). For a list of all pre-trained models available see here: https://github.com/UKPLab/sentence-transformers#pre-trained-models
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{{% alert note %}}
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- 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.
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- If you are running `LocalAI` manually you must install the python dependencies (`make prepare-extra-conda-environments`). This requires `conda` to be installed.
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- For local execution, you also have to specify the extra backend in the `EXTERNAL_GRPC_BACKENDS` environment variable.
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- Example: `EXTERNAL_GRPC_BACKENDS="sentencetransformers:/path/to/LocalAI/backend/python/sentencetransformers/sentencetransformers.py"`
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- 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`.
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- 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.
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{{% /alert %}}
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## Llama.cpp embeddings
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Embeddings with `llama.cpp` are supported with the `llama` backend.
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```yaml
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name: my-awesome-model
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backend: llama
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embeddings: true
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parameters:
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model: ggml-file.bin
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# ...
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```
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## 💡 Examples
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- Example that uses LLamaIndex and LocalAI as embedding: [here](https://github.com/go-skynet/LocalAI/tree/master/examples/query_data/).
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