README.md

---
title: FastEmbed EN Embeddings
emoji: 🚀
colorFrom: blue
colorTo: green
sdk: docker
pinned: false
license: apache-2.0
---

# FastEmbed Code Embeddings Server

CPU-optimized embedding server using **FastEmbed** with ONNX quantized models.

## Models

Models:
- Dense: BAAI/bge-base-en-v1.5	 (768 dim)
- Sparse: Qdrant/bm25 (BM25, 0.01GB)
- Reranker: jinaai/jina-reranker-v1-turbo-en	 (0.13GB)

**Total: ~0.78 GB** - Fits easily in CPU Basic (2 vCPU, 16GB RAM)

## API Endpoints

### Dense Embeddings
```bash
curl -X POST https://YOUR_SPACE.hf.space/v1/embeddings \
  -H "Content-Type: application/json" \
  -d '{"input": ["def hello(): pass", "class Foo: ..."], "model": "code-embed"}'
```

### Sparse BM25 Embeddings
```bash
curl -X POST https://YOUR_SPACE.hf.space/v1/sparse/embeddings \
  -H "Content-Type: application/json" \
  -d '{"input": ["search query", "document text"]}'
```

### Hybrid Search Embeddings
```bash
curl -X POST https://YOUR_SPACE.hf.space/v1/hybrid/embeddings \
  -H "Content-Type: application/json" \
  -d '{"input": ["code snippet"]}'
```

### Reranking
```bash
curl -X POST https://YOUR_SPACE.hf.space/v1/rerank \
  -H "Content-Type: application/json" \
  -d '{"query": "python async function", "documents": ["doc1", "doc2", "doc3"]}'
```

## Features

- **ONNX Runtime**: Optimized CPU inference, no PyTorch overhead
- **Model Caching**: Models loaded once, reused across requests
- **Hybrid Search**: Dense + sparse (BM25) for better retrieval
- **Code-Optimized**: `jina-embeddings-v2-base-code` specifically trained for code

## Performance

Compared to PyTorch-based SentenceTransformers:
- **5-10x faster** on CPU
- **5x smaller** model footprint
- **Lower latency**: ONNX quantization + caching