Spaces:
Running
Running
| 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 | |
| --- | |
| ## 🚀 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**. | |
| --- | |
| ## 🧩 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. | |
| --- | |
| ## 📁 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 | |
| ``` | |
| --- | |
| ## 🧩 Model Selection | |
| Default configuration is optimized for **CPU 2vCPU / 16GB RAM**. See [MTEB Leaderboard](https://huggingface.co/spaces/mteb/leaderboard) for memory usage reference. | |
| ⚠️ If you plan to use larger models like `Qwen2-embedding-8B`, please upgrade your Space. | |
| --- | |
| ## ☁️ 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](https://huggingface.co/spaces/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)](https://huggingface.co/blog/HemanthSai7/deploy-applications-on-huggingface-spaces) | |
| - [How-to-Sync-Hugging-Face-Spaces-with-a-GitHub-Repository by Ruslanmv](https://github.com/ruslanmv/How-to-Sync-Hugging-Face-Spaces-with-a-GitHub-Repository?tab=readme-ov-file) | |
| --- | |
| ## 🧑💻 Contributing | |
| Contributions are welcome! | |
| Please open an issue or submit a pull request to discuss changes. | |
| --- | |
| ## ⚠️ License | |
| MIT License © 2025 | |
| Developed with ❤️ by the Open-Source Community. | |
| --- | |
| > ✨ “Unify your embeddings. Simplify your AI stack.” | |