docs: Initial import from localai-website (#1312)

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>

docs/content/features/embeddings.md

@@ -0,0 +1,99 @@
+
++++
+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-formers` and models in `huggingface` you can use the `huggingface` embedding backend.
+
+```yaml
+name: text-embedding-ada-002
+backend: huggingface-embeddings
+embeddings: true
+parameters:
+  model: all-MiniLM-L6-v2
+```
+
+The `huggingface` 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 `huggingface` 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 (`pip install -r /path/to/LocalAI/extra/requirements`) and specify the extra backend in the `EXTERNAL_GRPC_BACKENDS` environment variable ( `EXTERNAL_GRPC_BACKENDS="huggingface-embeddings:/path/to/LocalAI/extra/grpc/huggingface/huggingface.py"` ) .
+- The `huggingface` 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 `huggingface` 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/).