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| from fastapi import FastAPI | |
| from pydantic import BaseModel | |
| from sentence_transformers import SentenceTransformer | |
| import uvicorn | |
| MODEL_NAME = "Alibaba-NLP/gte-multilingual-base" | |
| app = FastAPI(title="Text Embedding API") | |
| model = SentenceTransformer(MODEL_NAME, trust_remote_code=True) | |
| class EmbedRequest(BaseModel): | |
| text: str | |
| class EmbedResponse(BaseModel): | |
| embedding: list[float] | |
| dim: int | |
| model: str | |
| def embed(req: EmbedRequest): | |
| embedding = model.encode(req.text, normalize_embeddings=True) | |
| return { | |
| "embedding": embedding.tolist(), | |
| "dim": len(embedding), | |
| "model": MODEL_NAME | |
| } | |
| def health(): | |
| return {"status": "ok", "model": MODEL_NAME} | |
| if __name__ == "__main__": | |
| uvicorn.run(app, host="0.0.0.0", port=7860) | |