mirror of
https://github.com/mudler/LocalAI.git
synced 2024-06-07 19:40:48 +00:00
2d64d8b444
* Update docs for new requirements.txt path Signed-off-by: Marcus Köhler <khler.marcus@gmail.com> * Fix typo (.PONY -> .PHONY) in python backend makefiles Signed-off-by: Marcus Köhler <khler.marcus@gmail.com> --------- Signed-off-by: Marcus Köhler <khler.marcus@gmail.com>
103 lines
3.4 KiB
Markdown
103 lines
3.4 KiB
Markdown
|
|
+++
|
|
disableToc = false
|
|
title = "🧠 Embeddings"
|
|
weight = 2
|
|
+++
|
|
|
|
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.
|
|
|
|
```yaml
|
|
name: text-embedding-ada-002 # The model name used in the API
|
|
parameters:
|
|
model: <model_file>
|
|
backend: "<backend>"
|
|
embeddings: true
|
|
# .. other parameters
|
|
```
|
|
|
|
## Bert embeddings
|
|
|
|
To use `bert.cpp` models you can use the `bert` embedding backend.
|
|
|
|
An example model config file:
|
|
|
|
```yaml
|
|
name: text-embedding-ada-002
|
|
parameters:
|
|
model: bert
|
|
backend: bert-embeddings
|
|
embeddings: true
|
|
# .. other parameters
|
|
```
|
|
|
|
The `bert` backend uses [bert.cpp](https://github.com/skeskinen/bert.cpp) and uses `ggml` models.
|
|
|
|
For instance you can download the `ggml` quantized version of `all-MiniLM-L6-v2` from https://huggingface.co/skeskinen/ggml:
|
|
|
|
```bash
|
|
wget https://huggingface.co/skeskinen/ggml/resolve/main/all-MiniLM-L6-v2/ggml-model-q4_0.bin -O models/bert
|
|
```
|
|
|
|
To test locally (LocalAI server running on `localhost`),
|
|
you can use `curl` (and `jq` at the end to prettify):
|
|
|
|
```bash
|
|
curl http://localhost:8080/embeddings -X POST -H "Content-Type: application/json" -d '{
|
|
"input": "Your text string goes here",
|
|
"model": "text-embedding-ada-002"
|
|
}' | jq "."
|
|
```
|
|
|
|
## Huggingface embeddings
|
|
|
|
To use `sentence-transformers` and models in `huggingface` you can use the `sentencetransformers` embedding backend.
|
|
|
|
```yaml
|
|
name: text-embedding-ada-002
|
|
backend: sentencetransformers
|
|
embeddings: true
|
|
parameters:
|
|
model: all-MiniLM-L6-v2
|
|
```
|
|
|
|
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
|
|
|
|
{{% 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.
|
|
- If you are running `LocalAI` manually you must install the python dependencies (`make prepare-extra-conda-environments`). This requires `conda` to be installed.
|
|
- 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` backend.
|
|
|
|
```yaml
|
|
name: my-awesome-model
|
|
backend: llama
|
|
embeddings: true
|
|
parameters:
|
|
model: ggml-file.bin
|
|
# ...
|
|
```
|
|
|
|
## 💡 Examples
|
|
|
|
- Example that uses LLamaIndex and LocalAI as embedding: [here](https://github.com/go-skynet/LocalAI/tree/master/examples/query_data/).
|