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---
title: PansGPT Qwen3 Embedding API
emoji: 🚀
colorFrom: blue
colorTo: green
sdk: docker
sdk_version: 4.44.0
app_file: app.py
pinned: false
license: mit
app_port: 7860
short_description: Embedding model
---
# PansGPT Qwen3 Embedding API
A stable, Docker-based API for generating text embeddings using the Qwen3-Embedding-0.6B model. This space provides a reliable service for the PansGPT application.
## Features
- **Single Text Embedding**: Generate embeddings for individual texts
- **Batch Processing**: Process multiple texts efficiently
- **Similarity Calculation**: Compute cosine similarity between embeddings
- **Docker-based**: Stable deployment with containerization
- **Health Monitoring**: Built-in health check endpoints
- **Fallback Support**: Automatic fallback to sentence-transformers if needed
## API Endpoints
### 1. Single Text Embedding
```bash
POST /api/predict
Content-Type: application/json
{
"data": ["Your text here"]
}
```
### 2. Batch Text Embedding
```bash
POST /api/predict
Content-Type: application/json
{
"data": [["Text 1", "Text 2", "Text 3"]]
}
```
### 3. Health Check
```bash
GET /health
```
## Usage Examples
### Python
```python
import requests
import json
# Single text embedding
response = requests.post(
"https://ojochegbeng-pansgpt.hf.space/api/predict",
json={"data": ["Hello, world!"]}
)
embedding = response.json()["data"][0]
# Batch embedding
response = requests.post(
"https://ojochegbeng-pansgpt.hf.space/api/predict",
json={"data": [["Text 1", "Text 2", "Text 3"]]}
)
embeddings = response.json()["data"][0]
```
### JavaScript
```javascript
// Single text embedding
const response = await fetch("https://ojochegbeng-pansgpt.hf.space/api/predict", {
method: "POST",
headers: { "Content-Type": "application/json" },
body: JSON.stringify({ data: ["Hello, world!"] })
});
const embedding = (await response.json()).data[0];
// Batch embedding
const response = await fetch("https://ojochegbeng-pansgpt.hf.space/api/predict", {
method: "POST",
headers: { "Content-Type": "application/json" },
body: JSON.stringify({ data: [["Text 1", "Text 2", "Text 3"]] })
});
const embeddings = (await response.json()).data[0];
```
## Model Information
- **Base Model**: Qwen3-Embedding-0.6B
- **Embedding Dimension**: 1024 (Qwen3) or 384 (fallback)
- **Max Input Length**: 512 tokens
- **Device**: Auto-detects CUDA/CPU
## Docker Configuration
This space uses Docker for stable deployment:
- **Base Image**: Python 3.11-slim
- **Port**: 7860
- **Health Check**: Built-in monitoring
- **Non-root User**: Security best practices
## Performance
- **Single Text**: ~100-500ms (depending on hardware)
- **Batch Processing**: Optimized for multiple texts
- **Memory Usage**: ~2-4GB RAM
- **Concurrent Requests**: Supports multiple simultaneous requests
## Integration with PansGPT
This API is specifically designed for the PansGPT application:
1. **Stable Connection**: Docker-based deployment eliminates connection issues
2. **Consistent Performance**: Reliable response times
3. **Error Handling**: Comprehensive error handling and fallbacks
4. **Monitoring**: Built-in health checks for monitoring
## Support
For issues or questions:
- Check the health endpoint first: `/health`
- Review the logs for error details
- Ensure your input format matches the expected structure
---
**Note**: This space is optimized for stability and reliability. The Docker-based deployment ensures consistent performance for the PansGPT application. |