update

Dockerfile

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+# 使用官方 Python 基础镜像
+FROM python:3.9-slim
+
+# 创建 appuser 用户和组
+RUN groupadd -r appuser && useradd -r -g appuser appuser
+
+# 设置环境变量
+ENV TRANSFORMERS_CACHE=/app/.cache
+
+# 设置工作目录
+WORKDIR /app
+
+# 将 /app 目录的所有权赋予 appuser
+RUN chown -R appuser:appuser /app
+
+# 复制依赖文件到容器中
+COPY requirements.txt .
+
+# 安装依赖
+RUN pip install --no-cache-dir -r requirements.txt
+
+# 复制项目代码到容器中
+COPY . .
+
+# 切换到 appuser 用户
+USER appuser
+
+# 指定容器启动时运行的命令
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

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+from fastapi import FastAPI, Request
+from pydantic import BaseModel
+from sentence_transformers import SentenceTransformer
+import numpy as np
+
+app = FastAPI()
+
+# Load the Sentence Transformer model
+model = SentenceTransformer("BAAI/bge-large-zh-v1.5")
+
+class EmbeddingRequest(BaseModel):
+    input: str
+
+@app.post("/embeddings")
+async def embeddings(request: EmbeddingRequest):
+    input_text = request.input
+
+    # Calculate embeddings
+    embeddings = model.encode(input_text)
+
+    # Format the embeddings in OpenAI compatible format
+    data = {
+        "object": "list",
+        "data": [
+            {
+                "object": "embedding",
+                "embedding": embeddings.tolist(),
+                "index": 0
+            }
+        ],
+        "model": "BAAI/bge-large-zh-v1.5",
+        "usage": {
+            "prompt_tokens": len(input_text),
+            "total_tokens": len(input_text)
+        }
+    }
+
+    return data

push.sh

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+git add .
+git commit -m "update"
+git push

requirements.txt

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+fastapi
+uvicorn
+transformers
+torch
+numpy