Semantic search that works inside PostgreSQL. Upload documents and images, then search them with natural language.
Instead of setting up a separate vector database like Pinecone or Weaviate, Pany uses your existing PostgreSQL database with the pgvector extension.
📖 Technical Architecture - Detailed system design and implementation
pip install pany
panyGo to http://localhost:8000 to upload files and start searching.
- Python 3.9+
- PostgreSQL with pgvector extension
- 4GB RAM minimum
# Custom database
export DATABASE_URL="postgresql://user:pass@localhost:5432/mydb"
# Custom port
pany --port 8080
# Or use Docker
git clone https://github.com/laxmanclo/pany.cloud.git
cd pany.cloud
docker-compose up -dUpload stuff: Drop PDFs, images, text files into the web interface Search naturally: Type "find the red car" or "contract with Microsoft" Get results: Finds semantically similar content, not just keyword matches
Works with text and images. You can search for images using text descriptions, or upload an image to find similar ones.
from pany import PanyClient
client = PanyClient()
# Upload files
client.upload_file("product_catalog.pdf", project_id="store")
client.upload_file("car_photo.jpg", project_id="store")
# Search with text
results = client.search("red sedan", project_id="store")
# Search with image
results = client.search_by_image("my_car.jpg", project_id="store")The main advantage over other vector databases is that you can join search results with your existing data:
SELECT
products.name,
products.price,
search.similarity_score
FROM products
JOIN semantic_search('comfortable shoes', 0.7) search
ON products.id = search.content_id
WHERE products.in_stock = true
ORDER BY search.similarity_score DESC;This is impossible with most vector databases because they're separate systems.
Because you probably already have PostgreSQL running your app. Instead of:
- Setting up another database (Pinecone, Weaviate, etc.)
- Keeping data in sync between systems
- Learning new APIs and query languages
- Paying monthly fees
You just add pgvector to your existing database and you're done. contact me for another db: laxmansrivastacc@gmail.com(a simple mail works, come on call or anything of the sort.)
REST endpoints for integration:
# Upload content
curl -X POST http://localhost:8000/embeddings \
-F "file=@document.pdf" \
-F "project_id=myproject"
# Search
curl -X POST http://localhost:8000/search \
-H "Content-Type: application/json" \
-d '{"query": "find something", "project_id": "myproject"}'- Embedding: Uses CLIP model to convert text and images into vectors
- Storage: Saves vectors in PostgreSQL using pgvector extension
- Search: Finds similar vectors using cosine similarity
- Results: Returns content ranked by similarity score
The CLIP model means you can search across different types of content - find images with text queries, or text with image queries.
- Product search: Upload product catalog, let customers search "red leather shoes"
- Document management: Search PDFs and docs with natural language
- Media libraries: Find images by describing what's in them
- Knowledge bases: Semantic search through support docs
DATABASE_URL="postgresql://user:pass@host:5432/db"
HOST="0.0.0.0"
PORT="8000"
EMBEDDING_MODEL="all-MiniLM-L6-v2"
OPENAI_API_KEY="sk-..." # optional
UPLOAD_DIR="./uploads"git clone https://github.com/laxmanclo/pany.cloud.git
cd pany.cloud
pip install -r requirements.txt
python -m pany.mainRun tests: pytest tests/
Fork the repo, make changes, submit PR.
Code style: use Black formatter, add type hints, write tests.
MIT License
Report bugs at https://github.com/laxmanclo/pany.cloud/issues
Contact: laxmansrivastacc@gmail.com
### **Customer Support: Instant Knowledge Base**
**Problem**: Agents spend 10+ minutes finding answers, customers wait
**Solution**: Semantic search across all documentation
```sql
-- Find answers across all support docs
SELECT document, answer, similarity
FROM semantic_search('password reset not working', 'support_docs')
WHERE similarity > 0.8;
┌─────────────────┐ ┌──────────────────┐ ┌─────────────────┐
│ Web Widget │ │ FastAPI │ │ PostgreSQL │
│ Dashboard │◄──►│ + CLIP Model │◄──►│ + pgvector │
│ REST API │ │ Embedding Gen │ │ Vector Store │
└─────────────────┘ └──────────────────┘ └─────────────────┘
Why This Stack Wins:
- PostgreSQL: ACID compliance, backups, joins, familiar
- CLIP: Multi-modal embeddings (text ↔ images)
- FastAPI: Modern async Python, auto-documentation
- Docker: One-command deployment anywhere
- Upload speed: ~2MB/sec document processing
- Throughput: 100+ concurrent searches
- Storage: ~1KB per document embedding
- Accuracy: 85-95% relevance for semantic queries
- Small team (1-10 users): 2GB RAM, 2 cores
- Medium business (10-100 users): 8GB RAM, 4 cores
- Large organization (100+ users): 16GB+ RAM, 8+ cores
- Database: Scales linearly with PostgreSQL
# Database
POSTGRES_HOST=localhost
POSTGRES_PORT=5432
POSTGRES_DB=pany
POSTGRES_USER=pany
POSTGRES_PASSWORD=your-secure-password
# API
API_HOST=0.0.0.0
API_PORT=8000
MAX_FILE_SIZE_MB=50
# Embeddings
EMBEDDING_MODEL=clip-ViT-B-32
VECTOR_DIMENSIONS=512# embedding-service/services/custom_embedding.py
from sentence_transformers import SentenceTransformer
class CustomEmbeddingService:
def __init__(self):
self.model = SentenceTransformer('your-custom-model')
def embed_text(self, text: str) -> List[float]:
return self.model.encode(text).tolist()// Minimal integration
<script src="http://your-domain/widget.js"></script>
<div id="pany-search"></div>
// Custom styling
<script>
PanyWidget.init({
container: '#my-search',
placeholder: 'Search our knowledge base...',
theme: 'dark',
maxResults: 10
});
</script>import { PanySearch } from '@pany/react';
function App() {
return (
<PanySearch
apiUrl="http://localhost:8000"
projectId="my-project"
placeholder="Search documents..."
/>
);
}from pany import PanyClient
client = PanyClient(base_url="http://localhost:8000")
# Upload file
result = client.upload_file("document.pdf", project_id="my-project")
# Search
results = client.search("password reset instructions", project_id="my-project")version: '3.8'
services:
pany-api:
image: pany/pany:latest
ports:
- "8000:8000"
environment:
- POSTGRES_HOST=postgres
depends_on:
- postgres
postgres:
image: pgvector/pgvector:pg15
environment:
POSTGRES_DB: pany
POSTGRES_USER: pany
POSTGRES_PASSWORD: secure-password
volumes:
- postgres_data:/var/lib/postgresql/dataapiVersion: apps/v1
kind: Deployment
metadata:
name: pany-api
spec:
replicas: 3
selector:
matchLabels:
app: pany-api
template:
metadata:
labels:
app: pany-api
spec:
containers:
- name: pany-api
image: pany/pany:latest
ports:
- containerPort: 8000- Configure secure database passwords
- Set up SSL/TLS certificates
- Configure reverse proxy (nginx/traefik)
- Set up monitoring and logging
- Configure backup strategy
- Set resource limits and scaling rules
<<<<<<< HEAD
Do star :D and support if it made your life easier, and also report issues, and always welcome to fix whats broken :)
=======
### **Code Style**
- Use **Black** for code formatting
- Follow **PEP 8** conventions
- Add **type hints** for all functions
- Write **docstrings** for public APIs
- Add **tests** for new features
### **Pull Request Process**
1. Fork the repository
2. Create a feature branch (`git checkout -b feature/amazing-feature`)
3. Make your changes with tests
4. Ensure CI passes
5. Submit a pull request
## 📝 License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## 🛣️ Roadmap
### **v1.0 - Core Features** ✅
- Multi-modal search with CLIP
- PostgreSQL + pgvector backend
- REST API with documentation
- Docker deployment
- Web dashboard
### **v1.1 - Developer Experience** 🔄
- [ ] React/Vue component libraries
- [ ] Python/JavaScript SDKs
- [ ] Kubernetes deployment manifests
- [ ] Performance optimization
- [ ] Advanced filtering and facets
### **v1.2 - Enterprise Features** 📋
- [ ] Authentication and authorization
- [ ] Multi-tenancy support
- [ ] Advanced analytics dashboard
- [ ] Webhook system
- [ ] Plugin architecture
### **v2.0 - AI Features** 🤖
- [ ] Question answering over documents
- [ ] Document summarization
- [ ] Custom model fine-tuning
- [ ] Advanced RAG (Retrieval Augmented Generation)
- [ ] Multi-language support
## 📞 Support
- **Documentation:** [docs.pany.ai](https://docs.pany.ai)(when ready)
- **Community:** [Discord](when ready)
- **Issues:** [GitHub Issues](https://github.com/laxmanclo/pany.cloud/issues)
- **Email:** laxmansrivastacc@gmail.com
## 🌟 Show Your Support
If Pany helps your project, consider:
- ⭐ Star this repository
- 🐛 Report bugs and request features
- 📝 Contribute code or documentation
- 💬 Share your use case in discussions
- 🎉 Spread the word!
---
**Transform your content into searchable intelligence. Upload, search, discover. It's that simple.**
*Built with ❤️ for developers who need semantic search without the complexity.*
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