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1 change: 0 additions & 1 deletion .translation-init

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112 changes: 56 additions & 56 deletions docs/cn/guides/00-products/01-dee/10-enterprise-features.md

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28 changes: 14 additions & 14 deletions docs/cn/guides/51-ai-functions/01-external-functions.md
Original file line number Diff line number Diff line change
@@ -1,17 +1,17 @@
---
title: 使用外部函数自定义 AI/ML
title: 使用外部函数(External Functions)自定义 AI/ML
---

# 使用外部函数自定义 AI/ML
# 使用外部函数(External Functions)自定义 AI/ML

通过将 Databend 与您自己的基础设施连接,构建强大的 AI/ML 能力。外部函数(External Function)让您能够部署自定义模型、利用 GPU 加速,并与任何 ML 框架集成,同时确保数据安全
通过将 Databend 与您自己的基础设施连接,构建强大的 AI/ML 能力。外部函数(External Functions)让您能够部署自定义模型、利用 GPU 加速,并与任何 ML 框架集成,同时保持数据安全

## 核心能力

| 功能 | 优势 |
|---------|----------|
| **自定义模型** | 使用任何开源或专有的 AI/ML 模型 |
| **GPU 加速** | 部署在配备 GPU 的机器上以加快推理速度 |
| **GPU 加速** | 在配备 GPU 的机器上部署以实现更快的推理 |
| **数据隐私** | 将数据保留在您的基础设施内 |
| **可扩展性** | 独立扩展和资源优化 |
| **灵活性** | 支持任何编程语言和 ML 框架 |
Expand All @@ -25,31 +25,31 @@ title: 使用外部函数自定义 AI/ML
## 示例:文本嵌入函数

```python
# 简单的嵌入 UDF 服务器演示
# Simple embedding UDF server demo
from databend_udf import udf, UDFServer
from sentence_transformers import SentenceTransformer

# 加载预训练模型
model = SentenceTransformer('all-mpnet-base-v2') # 768 维向量
# Load pre-trained model
model = SentenceTransformer('all-mpnet-base-v2') # 768-dimensional vectors

@udf(
input_types=["STRING"],
result_type="ARRAY(FLOAT)",
)
def ai_embed_768(inputs: list[str], headers) -> list[list[float]]:
"""为输入文本生成 768 维嵌入"""
"""为输入文本生成 768 维嵌入向量"""
try:
# 单批次处理输入
# 在单个批次中处理输入
embeddings = model.encode(inputs)
# 转换为列表格式
return [embedding.tolist() for embedding in embeddings]
except Exception as e:
print(f"Error generating embeddings: {e}")
# 如果出错,则返回空列表
# 出错时返回空列表
return [[] for _ in inputs]

if __name__ == '__main__':
print("正在端口 8815 上启动嵌入 UDF 服务器...")
print("Starting embedding UDF server on port 8815...")
server = UDFServer("0.0.0.0:8815")
server.add_function(ai_embed_768)
server.serve()
Expand All @@ -63,7 +63,7 @@ CREATE OR REPLACE FUNCTION ai_embed_768 (STRING)
HANDLER = 'ai_embed_768'
ADDRESS = 'https://your-ml-server.example.com';

-- 在查询中使用自定义嵌入
-- 在查询中使用自定义嵌入函数
SELECT
id,
title,
Expand All @@ -78,5 +78,5 @@ LIMIT 5;

## 了解更多

- **[外部函数指南](/guides/ai-functions/external-functions)** - 完整的设置和部署说明
- **[Databend Cloud](https://databend.cn)** - 使用免费账户试用外部函数
- **[外部函数(External Functions)指南](/guides/ai-functions/external-functions)** - 完整的设置和部署说明
- **[Databend Cloud](https://databend.cn)** - 使用免费账户试用外部函数(External Functions)
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