Hugging Face Text Embedding Server
- Python
- JavaScript
Chroma provides a convenient wrapper for HuggingFace Text Embedding Server, a standalone server that provides text embeddings via a REST API. You can read more about it here.
Setting Up The Server
To run the embedding server locally you can run the following command from the root of the Chroma repository. The docker compose command will run Chroma and the embedding server together.
docker compose -f examples/server_side_embeddings/huggingface/docker-compose.yml up -d
or
docker run -p 8001:80 -d -rm --name huggingface-embedding-server ghcr.io/huggingface/text-embeddings-inference:cpu-0.3.0 --model-id BAAI/bge-small-en-v1.5 --revision -main
The above docker command will run the server with the BAAI/bge-small-en-v1.5 model. You can find more information about running the server in docker here.
Usage
This embedding function relies on the requests python package, which you can install with pip install requests.
from chromadb.utils.embedding_functions import HuggingFaceEmbeddingServer
huggingface_ef = HuggingFaceEmbeddingServer(url="http://localhost:8001/embed")
The embedding model is configured on the server side. Check the docker-compose file in examples/server_side_embeddings/huggingface/docker-compose.yml for an example of how to configure the server.
import {HuggingFaceEmbeddingServerFunction} from 'chromadb';
const embedder = new HuggingFaceEmbeddingServerFunction({url:"http://localhost:8001/embed"})
// use directly
const embeddings = embedder.generate(["document1","document2"])
// pass documents to query for .add and .query
const collection = await client.createCollection({name: "name", embeddingFunction: embedder})
const collection = await client.getCollection({name: "name", embeddingFunction: embedder})