đ§ Embeddings
LocalAI supports generating embeddings for text or list of tokens.
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.
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
# .. other parameters
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
The
sentencetransformersbackend is an optional backend of LocalAI and uses Python. If you are runningLocalAIfrom the containers you are good to go and should be already configured for use.If you are running
LocalAImanually you must install the python dependencies (make prepare-extra-conda-environments). This requirescondato be installed.For local execution, you also have to specify the extra backend in the
EXTERNAL_GRPC_BACKENDSenvironment variable.- Example:
EXTERNAL_GRPC_BACKENDS="sentencetransformers:/path/to/LocalAI/backend/python/sentencetransformers/sentencetransformers.py"
- Example:
The
sentencetransformersbackend does support only embeddings of text, and not of tokens. If you need to embed tokens you can use thebertbackend orllama.cpp.No models are required to be downloaded before using the
sentencetransformersbackend. The models will be downloaded automatically the first time the API is used.
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 "."
đĄ Examples
- Example that uses LLamaIndex and LocalAI as embedding: here.
Last updated 27 Nov 2024, 16:34 +0100 .