Create README.md

README.md

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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.