update sth. at 2025-01-16 22:40:53

app.py1

@@ -1,59 +0,0 @@
-from fastapi import FastAPI, HTTPException, Request
-from pydantic import BaseModel
-from transformers import AutoTokenizer, AutoModel
-import torch
-from typing import List, Dict
-import uvicorn
-
-# 定义请求和响应模型
-class EmbeddingRequest(BaseModel):
-    input: str
-    model: str = "jinaai/jina-embeddings-v3"
-
-class EmbeddingResponse(BaseModel):
-    status: str
-    embeddings: List[List[float]]
-
-# 创建FastAPI应用
-app = FastAPI(
-    title="Jina Embeddings API",
-    description="Text embedding generation service using jina-embeddings-v3",
-    version="1.0.0"
-)
-
-# 加载模型和分词器
-model_name = "jinaai/jina-embeddings-v3"
-tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
-model = AutoModel.from_pretrained(model_name, trust_remote_code=True)
-
-@app.post("/generate_embeddings", response_model=EmbeddingResponse)
-@app.post("/api/v1/embeddings", response_model=EmbeddingResponse)
-@app.post("/hf/v1/embeddings", response_model=EmbeddingResponse)
-@app.post("/api/v1/chat/completions", response_model=EmbeddingResponse)
-@app.post("/hf/v1/chat/completions", response_model=EmbeddingResponse)
-async def generate_embeddings(request: EmbeddingRequest):
-    try:
-        # 使用分词器处理输入文本
-        inputs = tokenizer(request.input, return_tensors="pt", truncation=True, max_length=512)
-
-        # 生成嵌入
-        with torch.no_grad():
-            embeddings = model(**inputs).last_hidden_state.mean(dim=1)
-
-        return EmbeddingResponse(
-            status="success",
-            embeddings=embeddings.numpy().tolist()
-        )
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))
-
-@app.get("/")
-async def root():
-    return {
-        "status": "active",
-        "model": model_name,
-        "usage": "Send POST request to /generate_embeddings or /api/v1/embeddings or /hf/v1/embeddings"
-    }
-
-if __name__ == "__main__":
-    uvicorn.run(app, host="0.0.0.0", port=7860)