README.md

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title: Api Embedding
emoji: 🐠
colorFrom: green
colorTo: purple
sdk: docker
pinned: false

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

🧠 Unified Embedding API

🧩 Unified API for all your Embedding & Sparse needs — plug and play with any model from Hugging Face or your own fine-tuned versions. This official repository from huggingface space

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🚀 Overview

Unified Embedding API is a modular and open-source RAG-ready API built for developers who want a simple, unified way to access dense, and sparse models.

It’s designed for vector search, semantic retrieval, and AI-powered pipelines — all controlled from a single config.yaml file.

⚠️ Note: This is a development API.
For production deployment, host it on cloud platforms such as Hugging Face TGI, AWS, or GCP.

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🧩 Features

• 🧠 Unified Interface — One API to handle dense, sparse, and reranking models.
• ⚙️ Configurable — Switch models instantly via config.yaml.
• 🔍 Vector DB Ready — Easily integrates with FAISS, Chroma, Qdrant, Milvus, etc.
• 📈 RAG Support — Perfect base for Retrieval-Augmented Generation systems.
• ⚡ Fast & Lightweight — Powered by FastAPI and optimized with async processing.
• 🧰 Extendable — Add your own models or pipelines effortlessly.

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📁 Project Structure

unified-embedding-api/
│
├── core/
│   ├── embedding.py         
│   └── model_manager.py     
├── models/
|   └──model.py
├── app.py                   # Entry point (FastAPI server)
|── config.yaml              # Model + system configuration
├── Dockerfile                 
├── requirements.txt
└── README.md

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🧩 Model Selection

Default configuration is optimized for CPU 2vCPU / 16GB RAM. See MTEB Leaderboard for memory usage reference.

⚠️ If you plan to use larger models like Qwen2-embedding-8B, please upgrade your Space.

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☁️ How to Deploy (Free 🚀)

Deploy your custom Embedding API on Hugging Face Spaces — free, fast, and serverless.

🔧 Steps:

1. Clone this Space Template: 👉 Hugging Face Space — fahmiaziz/api-embedding
2. Edit config.yaml to set your own model names and backend preferences.
3. Push your code — Spaces will automatically rebuild and host your API.

That’s it! You now have a live embedding API endpoint powered by your models.

📘 Tutorial Reference:

• Deploy Applications on Hugging Face Spaces (Official Guide)
• How-to-Sync-Hugging-Face-Spaces-with-a-GitHub-Repository by Ruslanmv

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🧑‍💻 Contributing

Contributions are welcome! Please open an issue or submit a pull request to discuss changes.

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⚠️ License

MIT License © 2025 Developed with ❤️ by the Open-Source Community.

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✨ “Unify your embeddings. Simplify your AI stack.”