anything-llm/server/storage/models/README.md

## Native models used by AnythingLLM

This folder is specifically created as a local cache and storage folder that is used for native models that can run on a CPU.

Currently, AnythingLLM uses this folder for the following parts of the application.

### Embedding
When your embedding engine preference is `native` we will use the ONNX **all-MiniLM-L6-v2** model built by [Xenova on HuggingFace.co](https://huggingface.co/Xenova/all-MiniLM-L6-v2). This model is a quantized and WASM version of the popular [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) which produces a 384-dimension vector.

If you are using the `native` embedding engine your vector database should be configured to accept 384-dimension models if that parameter is directly editable (Pinecone only).

### Text generation (LLM selection)
_in progress_
Add built-in embedding engine into AnythingLLM (#411) * Implement use of native embedder (all-Mini-L6-v2) stop showing prisma queries during dev * Add native embedder as an available embedder selection * wrap model loader in try/catch * print progress on download * Update to progress output for embedder * move embedder selection options to component * forgot import * add Data privacy alert updates for local embedder 2023-12-06 19:36:22 +01:00			`## Native models used by AnythingLLM`

			`This folder is specifically created as a local cache and storage folder that is used for native models that can run on a CPU.`

			`Currently, AnythingLLM uses this folder for the following parts of the application.`

			`### Embedding`
			When your embedding engine preference is `native` we will use the ONNX all-MiniLM-L6-v2 model built by [Xenova on HuggingFace.co](https://huggingface.co/Xenova/all-MiniLM-L6-v2). This model is a quantized and WASM version of the popular [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) which produces a 384-dimension vector.

			If you are using the `native` embedding engine your vector database should be configured to accept 384-dimension models if that parameter is directly editable (Pinecone only).

			`### Text generation (LLM selection)`
			`_in progress_`