Spaces:
Sleeping
Sleeping
Bui Vu Duc Nghia commited on
Commit ·
b7846e0
1
Parent(s): f17fb45
feat: initialize embedding service
Browse files- Dockerfile +18 -0
- README.md +30 -6
- app.py +34 -0
- requirements.txt +4 -0
Dockerfile
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FROM python:3.10-slim
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WORKDIR /app
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# Cài system deps tối thiểu
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RUN apt-get update && apt-get install -y \
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git \
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&& rm -rf /var/lib/apt/lists/*
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COPY requirements.txt .
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RUN pip install --no-cache-dir -r requirements.txt
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COPY app.py .
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EXPOSE 7860
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CMD ["python", "app.py"]
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README.md
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---
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title:
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emoji:
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colorFrom: blue
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colorTo:
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sdk: docker
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short_description: Voicebot Embedding
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---
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-
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---
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title: Text EmbeddinG
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emoji: 🔢
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colorFrom: blue
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colorTo: purple
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sdk: docker
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app_port: 7860
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---
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# Text Embedding API
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This Space provides a simple REST API to generate text embeddings using:
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**Model:** `Alibaba-NLP/gte-multilingual-base`
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## 🚀 Features
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- Multilingual text embedding
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- SentenceTransformers-based
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- FastAPI + Docker
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- Ready for RAG / Vector DB (Qdrant, FAISS, Milvus…)
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---
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## 📡 API Endpoints
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### `POST /embed`
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Generate embedding for input text.
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**Request**
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```json
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{
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"text": "Xin chào, đây là một câu tiếng Việt"
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}
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app.py
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from fastapi import FastAPI
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from pydantic import BaseModel
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from sentence_transformers import SentenceTransformer
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import uvicorn
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MODEL_NAME = "Alibaba-NLP/gte-multilingual-base"
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app = FastAPI(title="Text Embedding API")
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model = SentenceTransformer(MODEL_NAME)
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class EmbedRequest(BaseModel):
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text: str
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class EmbedResponse(BaseModel):
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embedding: list[float]
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dim: int
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model: str
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@app.post("/embed", response_model=EmbedResponse)
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def embed(req: EmbedRequest):
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embedding = model.encode(req.text, normalize_embeddings=True)
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return {
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"embedding": embedding.tolist(),
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"dim": len(embedding),
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"model": MODEL_NAME
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}
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@app.get("/")
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def health():
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return {"status": "ok", "model": MODEL_NAME}
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if __name__ == "__main__":
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uvicorn.run(app, host="0.0.0.0", port=7860)
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requirements.txt
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fastapi
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uvicorn
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sentence-transformers
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torch
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