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title: Biblos Semantic Search API
emoji: 📖
colorFrom: blue
colorTo: purple
sdk: docker
pinned: false
license: mit
---
# Biblos Semantic Search API
Semantic search over the entire Bible using BGE-large embeddings. This API keeps the model and embeddings in memory for fast responses (~50-100ms after initial load).
## Features
- ✅ Fast semantic search with BGE-large-en-v1.5 embeddings
- ✅ Model stays loaded in memory (no cold starts)
- ✅ Searches entire Bible (Old and New Testament)
- ✅ CORS enabled for easy integration
- ✅ RESTful JSON API with FastAPI
- ✅ Automatic API documentation at `/docs`
## API Endpoints
### `GET /`
Health check and API information
### `GET /health`
Detailed health status and available books
### `POST /search`
Perform semantic search
**Request Body:**
```json
{
"query": "What did Jesus say about love?",
"limit": 10 // Optional: results to return (1-100, default: 10)
}
```
**Response:**
```json
{
"query": "What did Jesus say about love?",
"results": [
{
"book": "jhn",
"chapter": 13,
"testament": "new",
"content": "A new commandment I give to you, that you love one another...",
"similarity": 0.892
}
],
"total_searched": 7957,
"execution_time_ms": 87.3
}
```
## Quick Start
### Using cURL
```bash
curl -X POST https://dssjon-biblos-api.hf.space/search \
-H "Content-Type: application/json" \
-d '{
"query": "faith and works",
"limit": 5
}'
```
### Using JavaScript
```javascript
const response = await fetch('https://dssjon-biblos-api.hf.space/search', {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify({
query: 'faith and works',
limit: 5
})
})
const data = await response.json()
console.log(data.results)
```
### Using Python
```python
import requests
response = requests.post(
'https://dssjon-biblos-api.hf.space/search',
json={
'query': 'faith and works',
'limit': 5
}
)
data = response.json()
print(data['results'])
```
## Interactive Documentation
Visit `/docs` on your deployed Space for interactive Swagger UI documentation where you can test the API directly.
## Performance
- First request: ~2-3 seconds (model loading)
- Subsequent requests: **50-100ms** (model already in memory)
- No cold starts after initial load
- Supports concurrent requests
## Model Information
- **Model:** BAAI/bge-large-en-v1.5
- **Embedding dimensions:** 1024
- **Total Bible passages:** ~31,000
- **Total books:** 66
## Deployment
This Space uses Docker SDK with FastAPI. The model and embeddings are loaded once at startup and kept in memory for fast responses.
## Data
The Bible embeddings are pre-computed and stored in the `data/` directory. See `prepare_data.py` for how to generate embeddings from your own Bible XML source.
## License
MIT License - Free to use for any purpose
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