From cd766c6289f8c95c7318b0d9508b3ba65bb0fd79 Mon Sep 17 00:00:00 2001
From: neil <msg@nancode.cn>
Date: Wed, 23 Jul 2025 14:09:03 +0800
Subject: [PATCH 1/2] =?UTF-8?q?feat:=20=E5=A2=9E=E5=8A=A0=20codeblock?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 docs/products/apis/texts/json-output.md | 2 +-
 docs/products/apis/texts/lang-style.md  | 6 +++---
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/docs/products/apis/texts/json-output.md b/docs/products/apis/texts/json-output.md
index d2d2baa..2771f1f 100644
--- a/docs/products/apis/texts/json-output.md
+++ b/docs/products/apis/texts/json-output.md
@@ -46,7 +46,7 @@ JSON 是世界上应用最广泛的数据交换格式之一。而符合 [JSON Sc
 
 再添加一些 prompt 提升可靠性，AI 将会根据输入提取数据生成标准的 JSON：
 
-```python
+```python showLineNumbers title="python"
 from langchain_openai import ChatOpenAI
 model_name = "Qwen2.5-72B-Instruct"
 base_url = "{{SITE_URL}}/v1"
diff --git a/docs/products/apis/texts/lang-style.md b/docs/products/apis/texts/lang-style.md
index c3004c9..7aa84a4 100644
--- a/docs/products/apis/texts/lang-style.md
+++ b/docs/products/apis/texts/lang-style.md
@@ -14,7 +14,7 @@ sidebar_position: 4
 
 首先创建 openai client。您也可以通过 curl 或者 javascript 等方式调用，详情参考文档 [通过 curl 快速使用大模型能力](text#示例一通过-curl-快速使用大模型能力)。
 
-```python
+```python showLineNumbers title="python"
 from openai import OpenAI
 import json
 base_url = "{{SITE_URL}}/v1"
@@ -27,7 +27,7 @@ client = OpenAI(base_url=base_url, api_key="访问令牌")
 
 在 `messages` 中添加一个 `role` 为 `system` 的消息，内容为您的提示词。例如，以下代码将创建一个名为“萌萌”的二次元女生角色，并使用颜文字和可爱语气与用户交流。
 
-```python
+```python showLineNumbers title="python"
 completion = client.chat.completions.create(
     model=model_name,
     stream=True,
@@ -45,7 +45,7 @@ completion = client.chat.completions.create(
 
 解析事件流中的 JSON 数据可以实时打印出 AI 回答的消息。
 
-```python
+```python showLineNumbers title="python"
 for chunk in completion:
     print(chunk.choices[0].delta.content, end="")
 ```
-- 
Gitee


From 3302e3955d9c865942341b3dddd020dc4138661b Mon Sep 17 00:00:00 2001
From: neil <msg@nancode.cn>
Date: Wed, 23 Jul 2025 16:20:57 +0800
Subject: [PATCH 2/2] =?UTF-8?q?feat:=20=E4=BB=A3=E7=A0=81=E5=9D=97?=
 =?UTF-8?q?=EF=BC=8C=20=E5=A2=9E=E5=8A=A0=E8=A1=8C=E5=8F=B7=EF=BC=8C?=
 =?UTF-8?q?=E8=AF=AD=E8=A8=80=E6=A0=87=E8=AF=86?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 docs/getting-started.md                       |  46 +++----
 docs/products/apis/3d/index.md                |   2 +-
 docs/products/apis/documents/ocr.md           |   2 +-
 docs/products/apis/embeddings/index.md        |   6 +-
 docs/products/apis/images-vision/faceid.md    |   8 +-
 docs/products/apis/images-vision/fuzzy.md     |   2 +-
 .../apis/images-vision/object-detection.md    |  12 +-
 .../products/apis/images-vision/text2image.md |   4 +-
 docs/products/apis/images-vision/visions.md   |   2 +-
 docs/products/apis/moderations/index.md       |   4 +-
 .../apis/speeches/speech-recognition.md       |   2 +-
 docs/products/apis/speeches/text-to-speech.md |   2 +-
 docs/products/apis/texts/function-calling.md  |   8 +-
 docs/products/apis/texts/json-output.md       |   6 +-
 docs/products/apis/texts/text-comlpetion.md   |  56 ++++----
 docs/products/apis/texts/text-generation.md   | 128 +++++++++---------
 docs/products/apis/videos/index.md            |   2 +-
 docs/products/models/download.md              |   8 +-
 18 files changed, 151 insertions(+), 149 deletions(-)

diff --git a/docs/getting-started.md b/docs/getting-started.md
index 85bc526..01ce013 100644
--- a/docs/getting-started.md
+++ b/docs/getting-started.md
@@ -39,29 +39,29 @@ hide_table_of_contents: true
 3. 关于 API 的更多细节，您可以参考 [API 文档](./openapi/v1)。
 4. 一个最简单的 Python 调用大模型的示例代码：
 
-   ```python
-   import requests
-
-   url = "{{SITE_URL}}/v1/chat/completions"
-   headers = {
-       "Authorization": "Bearer  <your_access_token>",
-       "Content-Type": "application/json"
-   }
-   data = {
-       "model": "DeepSeek-R1",      # 模型名称
-         "messages": [
-            {"role": "user", "content": "请帮我写一首关于春天的五言绝句"}
-         ],
-         "max_tokens": 100,           # 最大生成 token 数
-         "temperature": 0.7           # 控制生成文本的随机性
-   }
-   response = requests.post(url, headers=headers, json=data)
-   if response.status_code == 200:
-      result = response.json()
-      print("模型回应:", result['choices'][0]['message']['content'])
-   else:
-      print("请求失败，状态码:", response.status_code, "错误信息:", response.text)
-   ```
+```python showLineNumbers title="python"
+import requests
+
+url = "{{SITE_URL}}/v1/chat/completions"
+headers = {
+    "Authorization": "Bearer  <your_access_token>",
+    "Content-Type": "application/json"
+}
+data = {
+    "model": "DeepSeek-R1",      # 模型名称
+      "messages": [
+         {"role": "user", "content": "请帮我写一首关于春天的五言绝句"}
+      ],
+      "max_tokens": 100,           # 最大生成 token 数
+      "temperature": 0.7           # 控制生成文本的随机性
+}
+response = requests.post(url, headers=headers, json=data)
+if response.status_code == 200:
+   result = response.json()
+   print("模型回应:", result['choices'][0]['message']['content'])
+else:
+   print("请求失败，状态码:", response.status_code, "错误信息:", response.text)
+```
 
 ## 接下来
 
diff --git a/docs/products/apis/3d/index.md b/docs/products/apis/3d/index.md
index 02313bf..95861be 100644
--- a/docs/products/apis/3d/index.md
+++ b/docs/products/apis/3d/index.md
@@ -35,7 +35,7 @@
 
 以下是使用 Python 调用 3D 生成模型的示例代码：
 
-```python
+```python showLineNumbers title="python"
 import requests
 from requests_toolbelt import MultipartEncoder
 import os
diff --git a/docs/products/apis/documents/ocr.md b/docs/products/apis/documents/ocr.md
index 1a82e8c..dbd32a4 100644
--- a/docs/products/apis/documents/ocr.md
+++ b/docs/products/apis/documents/ocr.md
@@ -34,7 +34,7 @@ curl {{SITE_URL}}/v1/images/ocr \
 
 </TabItem>
 <TabItem value="python" label="Python">
-```python
+```python showLineNumbers title="python"
 import requests
 API_URL = "{{SITE_URL}}/v1/images/ocr"
 HEADERS = {
diff --git a/docs/products/apis/embeddings/index.md b/docs/products/apis/embeddings/index.md
index 1836c6f..577b270 100644
--- a/docs/products/apis/embeddings/index.md
+++ b/docs/products/apis/embeddings/index.md
@@ -24,7 +24,7 @@ import ServerlessAPIServices from '@site/src/components/ServerlessAPIServices';
 
 示例代码：
 
-```python
+```python showLineNumbers title="python"
 from openai import OpenAI
 
 client = OpenAI(
@@ -51,7 +51,7 @@ response = client.embeddings.create(
 
 示例代码：
 
-```python
+```python showLineNumbers title="python"
 from openai import OpenAI
 
 client = OpenAI(
@@ -75,7 +75,7 @@ response = client.embeddings.create(
 
 示例代码：
 
-```python
+```python showLineNumbers title="python"
 import requests
 
 API_URL = "{{SITE_URL}}/v1/rerank"
diff --git a/docs/products/apis/images-vision/faceid.md b/docs/products/apis/images-vision/faceid.md
index 8b2c767..6f1303c 100644
--- a/docs/products/apis/images-vision/faceid.md
+++ b/docs/products/apis/images-vision/faceid.md
@@ -20,7 +20,7 @@ sidebar_position: 6
 
 首先获取您的 [访问令牌]({{SITE_URL}}/dashboard/settings/tokens)，然后可定义请求函数如下：
 
-```python
+```python showLineNumbers title="python"
 import requests
 import base64
 from PIL import Image
@@ -44,7 +44,7 @@ def query(payload):
 
 请求方式如下，不过需要注意 `flux` 模型的提示词 (prompt) 需要是 **英文**。
 
-```python
+```python showLineNumbers title="python"
 output = query({
     "model": "flux-1-schnell",
     "image": "liuyifei.png",
@@ -90,7 +90,7 @@ output = query({
 
 数据结果为 Base64 编码的图像，因此需要解码后才能保存，解包、解码如下：
 
-```python
+```python showLineNumbers title="python"
 b64data = output['data'][0]['b64_json']
 img = Image.open(BytesIO(base64.b64decode(b64data)))
 img.save("/path/to/save/res.jpg")
@@ -103,7 +103,7 @@ img.show()
 
 完整示例代码如下：
 
-```python
+```python showLineNumbers title="python"
 import requests
 import base64
 from PIL import Image
diff --git a/docs/products/apis/images-vision/fuzzy.md b/docs/products/apis/images-vision/fuzzy.md
index 2949132..2551392 100644
--- a/docs/products/apis/images-vision/fuzzy.md
+++ b/docs/products/apis/images-vision/fuzzy.md
@@ -69,7 +69,7 @@ Serverless API 提供了接口快速体验的界面，可以快速体验接口
 
 完成了功能体验和验证后，接下来我们可以通过代码调用，将上述功能集成到自己的应用中。
 
-```python
+```python showLineNumbers title="python"
 import requests
 
 API_URL = "{{SITE_URL}}/serverless-api?model=Kolors&operation=205&maximized=1"
diff --git a/docs/products/apis/images-vision/object-detection.md b/docs/products/apis/images-vision/object-detection.md
index 057bffd..4d9a807 100644
--- a/docs/products/apis/images-vision/object-detection.md
+++ b/docs/products/apis/images-vision/object-detection.md
@@ -20,7 +20,7 @@ sidebar_position: 7
 
 接口的使用以 Python 代码为例，下面将分别演示“图像描述”与“目标识别”两个功能项的使用方法。在开始前先定义请求函数如下：
 
-```python
+```python showLineNumbers title="python"
 import requests
 
 headers = {
@@ -50,7 +50,7 @@ def query(url, payload):
 该功能可以获取用户输入的图片的自然语言描述。
 使用上文的 query 函数，发起请求如下：
 
-```python
+```python showLineNumbers title="python"
 output = query(url_caption, {
     "model": "Florence-2-large",
     "image": "path/to/image.jpg",
@@ -97,7 +97,7 @@ output = query(url_caption, {
 
 使用如下的请求代码：
 
-```python
+```python showLineNumbers title="python"
 output = query({
     "model": "Florence-2-large",
     "image": "path/to/image.jpg",
@@ -223,7 +223,7 @@ output = query({
 
 注意，若使用了 `prompt` 参数，`caption_level` 参数将会失效，标签内容的详细程度将由提示词决定。
 
-```python
+```python showLineNumbers title="python"
 output = query(url_onject_detection, {
     "model": "Florence-2-large",
     "image": "path/to/image.jpg",
@@ -271,7 +271,7 @@ output = query(url_onject_detection, {
 
 在目标识别获得标签后，为了更好的看到标记效果，我们提供一个绘制结果的函数供参考，代码如下：
 
-```python
+```python showLineNumbers title="python"
 from PIL import Image, ImageDraw, ImageFont
 
 def draw_labelled_bbox(image, bbox, label):
@@ -342,7 +342,7 @@ image.save("/path/to/save.jpg")
 
 将结果转为python中的 `dict` 类，我们使用如下代码调用函数：
 
-```python
+```python showLineNumbers title="python"
 image = loaded_image
 label = obj['label']
 bbox = obj['bbox']
diff --git a/docs/products/apis/images-vision/text2image.md b/docs/products/apis/images-vision/text2image.md
index 3342d2e..528f25a 100644
--- a/docs/products/apis/images-vision/text2image.md
+++ b/docs/products/apis/images-vision/text2image.md
@@ -28,7 +28,7 @@ import ServerlessAPIServices from '@site/src/components/ServerlessAPIServices';
 
 您可以使用 OpenAI 的 SDK 来调用文生图模型。以下是一个使用 Python 的示例：
 
-```python
+```python showLineNumbers title="python"
 from openai import OpenAI
 import base64
 
@@ -60,7 +60,7 @@ with open("Kolors-output.png", "wb") as f:
 
 如果您不想使用 OpenAI 的 SDK，也可以直接使用 `requests` 库来调用文生图模型。以下是一个使用 Python 的示例：
 
-```python
+```python showLineNumbers title="python"
 import requests
 import base64
 import json
diff --git a/docs/products/apis/images-vision/visions.md b/docs/products/apis/images-vision/visions.md
index 0408a00..d8c0505 100644
--- a/docs/products/apis/images-vision/visions.md
+++ b/docs/products/apis/images-vision/visions.md
@@ -24,7 +24,7 @@ import ServerlessAPIServices from '@site/src/components/ServerlessAPIServices';
 
 ## 示例代码
 
-```python
+```python showLineNumbers title="python"
 from openai import OpenAI
 
 client = OpenAI(
diff --git a/docs/products/apis/moderations/index.md b/docs/products/apis/moderations/index.md
index f01eb2a..9a48dba 100644
--- a/docs/products/apis/moderations/index.md
+++ b/docs/products/apis/moderations/index.md
@@ -22,7 +22,7 @@ Security-semantic-filtering 是一种用于增强系统安全性的技术，通
 
 ### 文本内容风控检查
 
-```python
+```python showLineNumbers title="python"
 from openai import OpenAI
 
 client = OpenAI(
@@ -43,7 +43,7 @@ response = client.moderations.create(
 
 ### 图片检查
 
-```python
+```python showLineNumbers title="python"
 from openai import OpenAI
 
 client = OpenAI(
diff --git a/docs/products/apis/speeches/speech-recognition.md b/docs/products/apis/speeches/speech-recognition.md
index df75223..1af4e2a 100644
--- a/docs/products/apis/speeches/speech-recognition.md
+++ b/docs/products/apis/speeches/speech-recognition.md
@@ -28,7 +28,7 @@ import ServerlessAPIServices from '@site/src/components/ServerlessAPIServices';
 
 以下是使用 Python 调用语音识别模型的示例代码：
 
-```python
+```python showLineNumbers title="python"
 import requests
 from requests_toolbelt import MultipartEncoder
 import os
diff --git a/docs/products/apis/speeches/text-to-speech.md b/docs/products/apis/speeches/text-to-speech.md
index bdebb85..314551e 100644
--- a/docs/products/apis/speeches/text-to-speech.md
+++ b/docs/products/apis/speeches/text-to-speech.md
@@ -34,7 +34,7 @@ import ServerlessAPIServices from '@site/src/components/ServerlessAPIServices';
 
 以下是使用 Python 调用语音生成模型的示例代码：
 
-```python
+```python showLineNumbers title="python"
 from openai import OpenAI
 
 client = OpenAI(
diff --git a/docs/products/apis/texts/function-calling.md b/docs/products/apis/texts/function-calling.md
index 0870b84..0df9b5f 100644
--- a/docs/products/apis/texts/function-calling.md
+++ b/docs/products/apis/texts/function-calling.md
@@ -25,7 +25,7 @@ sidebar_position: 2
 
 首先组合tools参数，下面向大模型描述了一个名为 `get_current_weather` 的函数，函数传入参数为 `city`, `x` , `y` ，函数能力是通过城市名和经纬度获取地点的天气情况：
 
-```python
+```python showLineNumbers title="python" 
 tools = [
     {
         "type": "function",
@@ -60,7 +60,7 @@ tools = [
 
 将上文拼凑好的 `tools` 参数传入客户端中，解析响应的请求，并根据请求调用定义好的 `get_current_weather` 函数。
 
-```python
+```python showLineNumbers title="python"
 import json
 
 completion_tool = client.chat.completions.create(
@@ -125,7 +125,7 @@ eval(f'{function_res.name}(**{arguments_res})')
 
 Langchain 等库提供了更多简便的工具和写法，首先安装必要的库：
 
-```
+```bash title="bash"
 pip install \
     langchain==0.3.3 \
     langgraph==0.2.38 \
@@ -144,7 +144,7 @@ pip install \
 
 下面实现让 AI 获取新闻，编写并执行 Python 代码，将新闻写入到 ./news.txt 文件中”：
 
-```python
+```python showLineNumbers title="python"
 from langchain_openai import ChatOpenAI
 from langchain.tools import tool
 from langchain_community.document_loaders import WebBaseLoader
diff --git a/docs/products/apis/texts/json-output.md b/docs/products/apis/texts/json-output.md
index 2771f1f..03989ca 100644
--- a/docs/products/apis/texts/json-output.md
+++ b/docs/products/apis/texts/json-output.md
@@ -23,7 +23,7 @@ JSON 是世界上应用最广泛的数据交换格式之一。而符合 [JSON Sc
 
 ## 示例代码
 
-```json
+```python showLineNumbers title="python"
 "guided_json": """{
         "type": "object",
         "properties": {
@@ -48,11 +48,13 @@ JSON 是世界上应用最广泛的数据交换格式之一。而符合 [JSON Sc
 
 ```python showLineNumbers title="python"
 from langchain_openai import ChatOpenAI
+
 model_name = "Qwen2.5-72B-Instruct"
 base_url = "{{SITE_URL}}/v1"
 # {{SITE_URL}}/dashboard/settings/tokens 获取您的访问令牌
 GITEE_AI_API_KEY = ""
-llm = ChatOpenAI(model=model_name, api_key=GITEE_AI_API_KEY, base_url=base_url, streaming=True, temperature=0.1, presence_penalty=1.05, top_p=0.9,
+llm = ChatOpenAI(model=model_name, api_key=GITEE_AI_API_KEY, base_url=base_url, streaming=True, temperature=0.1,
+                 presence_penalty=1.05, top_p=0.9,
                  extra_body={
                      "guided_json": """{
                         "type": "object",
diff --git a/docs/products/apis/texts/text-comlpetion.md b/docs/products/apis/texts/text-comlpetion.md
index 35b8bbe..918c4cd 100644
--- a/docs/products/apis/texts/text-comlpetion.md
+++ b/docs/products/apis/texts/text-comlpetion.md
@@ -72,32 +72,33 @@ AI 模型响应：
 1. **获取访问凭证**：登录模力方舟控制台，在 [**工作台 -> 访问令牌**]({{SITE_URL}}/dashboard/settings/tokens) 中创建访问令牌。
 2. **安装客户端**：以 OpenAI 客户端为例，首先安装依赖：
 
-   ```bash
-   pip install openai -i https://mirrors.cloud.tencent.com/pypi/simple
-   ```
-    如果您有 javascript 经验可使用 [OpenAI nodejs 客户端](https://www.npmjs.com/package/openai/)
-
-    通过简单接口调用，生成指定内容：
-    ```python
-    from openai import OpenAI
-
-    client = OpenAI(
-        base_url="{{SITE_URL}}/v1",
-        api_key="您的访问令牌"
-    )
-
-    response = client.completions.create(
-        model="Qwen2.5-72B-Instruct",
-        prompt="春天来了，公园里的花儿",
-        max_tokens=50,
-        temperature=0.8
-    )
-
-    print(response.choices[0].text)
-    ```
+```bash
+pip install openai -i https://mirrors.cloud.tencent.com/pypi/simple
+```
+
+如果您有 javascript 经验可使用 [OpenAI nodejs 客户端](https://www.npmjs.com/package/openai/)
+
+通过简单接口调用，生成指定内容：
+```python showLineNumbers title="python"
+from openai import OpenAI
+
+client = OpenAI(
+  base_url="{{SITE_URL}}/v1",
+  api_key="您的访问令牌"
+)
+
+response = client.completions.create(
+  model="Qwen2.5-72B-Instruct",
+  prompt="春天来了，公园里的花儿",
+  max_tokens=50,
+  temperature=0.8
+)
+print(response.choices[0].text)
+```
+
 ### 流式响应
 对于较长文本的补全，启用流式响应 `stream=True` 可实时获取补全内容，提升用户体验：
-```python
+```python showLineNumbers title="python"
 from openai import OpenAI
 
 client = OpenAI(
@@ -154,8 +155,7 @@ for chunk in stream:
 若希望补全内容与输入文本风格一致（如正式 / 非正式、专业 / 通俗、特定文体），可在 prompt 中体现或通过 `instructions` 参数指定。
 
 **示例：通过 instructions 设定风格**
-```python
-
+```python showLineNumbers title="python"
 client.completions.create(
     model="Qwen2.5-72B-Instruct",
     prompt="余尝闻，世间至美之物，莫过于",
@@ -169,7 +169,7 @@ client.completions.create(
 ### 利用上下文提示
 对于长文本片段的补全，确保提供足够的上下文信息，帮助模型理解文本的主题、逻辑和风格。
 **示例：**
-```python
+```python showLineNumbers title="python"
 prompt = """
 以下是一篇关于环境保护的文章片段，请继续补全：
 
@@ -185,7 +185,7 @@ client.completions.create(
 ### 少样本补全示例
 通过在 `prompt` 中提供 “输入片段 + 补全示例”，可以引导模型快速掌握特定补全模式。
 **示例：邮件回复片段补全**
-```python
+```python showLineNumbers title="python"
 prompt = """
 请按照以下示例的风格，补全邮件回复片段：
 
diff --git a/docs/products/apis/texts/text-generation.md b/docs/products/apis/texts/text-generation.md
index 3ca6f3e..a83757d 100644
--- a/docs/products/apis/texts/text-generation.md
+++ b/docs/products/apis/texts/text-generation.md
@@ -132,40 +132,40 @@ AI 模型响应：
 1. **获取访问凭证**：登录模力方舟控制台，在 [**工作台 -> 访问令牌**]({{SITE_URL}}/dashboard/settings/tokens) 中创建访问令牌。
 2. **安装客户端**：以 OpenAI 客户端为例，首先安装依赖：
 
-   ```bash
-   pip install openai -i https://mirrors.cloud.tencent.com/pypi/simple
-   ```
-   如果您有 javascript 经验可使用 [OpenAI nodejs 客户端](https://www.npmjs.com/package/openai/)
+```bash
+pip install openai -i https://mirrors.cloud.tencent.com/pypi/simple
+```
+如果您有 javascript 经验可使用 [OpenAI nodejs 客户端](https://www.npmjs.com/package/openai/)
 
-    通过简单接口调用，生成指定内容：
+通过简单接口调用，生成指定内容：
 
-    ```python
-    from openai import OpenAI
-    import json
+```python showLineNumbers title="python"
+from openai import OpenAI
+import json
 
-    base_url = "{{SITE_URL}}/v1"
+base_url = "{{SITE_URL}}/v1"
 
-    model_name = "Qwen2.5-72B-Instruct"
+model_name = "Qwen2.5-72B-Instruct"
 
-    # {{SITE_URL}}/dashboard/settings/tokens 获取您的访问令牌
-    client = OpenAI(base_url=base_url, api_key="GITEE_AI_API_KEY")
+# {{SITE_URL}}/dashboard/settings/tokens 获取您的访问令牌
+client = OpenAI(base_url=base_url, api_key="GITEE_AI_API_KEY")
 
-    completion = client.chat.completions.create(
-        model=model_name, # 指定模型名称 例如 Qwen2.5-72B-Instruct，可访问 {{SITE_URL}}/serverless-api  查看
-        stream=True,
-        temperature=0.7,
-        top_p=0.95,
-        frequency_penalty=1.05,
-        messages=[
-            {"role": "system", "content": "你是一个有用的助手。"},
-            {"role": "user", "content": "写一个 python 简明教程"}
-        ]
-    )
+completion = client.chat.completions.create(
+    model=model_name, # 指定模型名称 例如 Qwen2.5-72B-Instruct，可访问 {{SITE_URL}}/serverless-api  查看
+    stream=True,
+    temperature=0.7,
+    top_p=0.95,
+    frequency_penalty=1.05,
+    messages=[
+        {"role": "system", "content": "你是一个有用的助手。"},
+        {"role": "user", "content": "写一个 python 简明教程"}
+    ]
+)
 
-    for chunk in completion:
-        print(chunk.choices[0].delta.content, end="")
+for chunk in completion:
+    print(chunk.choices[0].delta.content, end="")
 
-    ```
+```
 
 除了纯文本，您还可以让模型以 JSON 格式返回结构化数据 —— 此功能称为 [**结构化输出**](json-output.md) ，在模力方舟中可通过 `guided_json` 参数实现。
 
@@ -173,28 +173,28 @@ AI 模型响应：
 
 对于长文本生成，启用流式响应 `stream=True` 可实时获取结果，减少等待时间：
 
-```python
-    stream = client.chat.completions.create(
-        model="Qwen2.5-72B-Instruct",# 替换成指定模型名称
-        messages=[
-            {"role": "user", "content": "写一段关于人工智能发展历程的短文"}
-        ],
-        stream=True,  # 启用流式输出
-        temperature=0.6
-    )
-
-    for chunk in stream:
-        if chunk.choices[0].delta.content:
-            print(chunk.choices[0].delta.content, end="")
+```python showLineNumbers title="python"
+stream = client.chat.completions.create(
+    model="Qwen2.5-72B-Instruct",# 替换成指定模型名称
+    messages=[
+        {"role": "user", "content": "写一段关于人工智能发展历程的短文"}
+    ],
+    stream=True,  # 启用流式输出
+    temperature=0.6
+)
+
+for chunk in stream:
+    if chunk.choices[0].delta.content:
+        print(chunk.choices[0].delta.content, end="")
 ```
 
 ### 核心参数说明
 
 - **tool_choice**： 支持 `auto` 由模型自动选择工具，也支持强制选择工具，写法如下：
 
-  ```json
-  "tool_choice": {"type": "function", "function": {"name": "function_name"}},
-  ```
+```json
+"tool_choice": {"type": "function", "function": {"name": "function_name"}},
+```
 
 - **guided_json**：让模型以指定的 JSON Schema 响应。不建议与 `tools`、`guided_json` 同时传入。
 
@@ -202,13 +202,13 @@ AI 模型响应：
 
 - **guided_choice**: 让模型选择提供的字符串列表之一，不建议与 `tools`、`guided_json` 同时传入。
 
-  例如判断用户输入正负面性可传入：
+例如判断用户输入正负面性可传入：
 
-      ```json
-      "guided_choice": ["正面", "负面", "中性"]
-      ```
+```json
+"guided_choice": ["正面", "负面", "中性"]
+```
 
-  其他参数详见 [接口文档]({{SITE_URL}}/docs/openapi/v1#tag/文本生成)
+其他参数详见 [接口文档]({{SITE_URL}}/docs/openapi/v1#tag/文本生成)
 
 
 ## 模型选择
@@ -245,25 +245,25 @@ AI 模型响应：
 
 1. **instructions 参数**：全局指令优先级
 
-   `instructions` 为模型提供**高级指令**（语气、目标、示例等），优先级高于 `input` 内容。
+`instructions` 为模型提供**高级指令**（语气、目标、示例等），优先级高于 `input` 内容。
 
-   ```javascript
-   import OpenAI from 'openai';
-   const client = new OpenAI({
-     baseURL: '{{SITE_URL}}/v1',
-     apiKey: '您的模力方舟API密钥',
-   });
+```javascript
+import OpenAI from 'openai';
+const client = new OpenAI({
+ baseURL: '{{SITE_URL}}/v1',
+ apiKey: '您的模力方舟API密钥',
+});
 
-   const response = await client.responses.create({
-     model: '对应模型名称',
-     // 定义全局风格/规则
-     instructions: '用老奶奶的语气说话，带点生活化的比喻和慈祥的唠叨',
-     input: 'JavaScript中的分号是可选的吗？',
-   });
+const response = await client.responses.create({
+ model: '对应模型名称',
+ // 定义全局风格/规则
+ instructions: '用老奶奶的语气说话，带点生活化的比喻和慈祥的唠叨',
+ input: 'JavaScript中的分号是可选的吗？',
+});
 
-   console.log(response.output_text);
-   // 示例输出："哎哟孩子啊，JavaScript里这个分号嘛，就像咱们包饺子放盐——可放可不放！不过奶奶跟你说啊，该放的时候还是得放，不然煮出来容易破皮（出bug）哩！"
-   ```
+console.log(response.output_text);
+// 示例输出："哎哟孩子啊，JavaScript里这个分号嘛，就像咱们包饺子放盐——可放可不放！不过奶奶跟你说啊，该放的时候还是得放，不然煮出来容易破皮（出bug）哩！"
+```
 
 2. **消息角色**：精细化场景控制
 
@@ -360,7 +360,7 @@ var first_name = "安娜";
 2. 提示词缓存优化
    将重复使用的固定内容（如系统指令）放在提示词开头，利用缓存减少计算成本：
 
-```python
+```python showLineNumbers title="python"
 # 高效结构：固定内容在前，动态内容在后
 messages = [
     {"role": "system", "content": "固定的系统指令，如'你是电商客服，需礼貌回答订单问题'"},  # 可被缓存
diff --git a/docs/products/apis/videos/index.md b/docs/products/apis/videos/index.md
index f9770bc..d0c0c6e 100644
--- a/docs/products/apis/videos/index.md
+++ b/docs/products/apis/videos/index.md
@@ -22,7 +22,7 @@ import ServerlessAPIServices from '@site/src/components/ServerlessAPIServices';
 
 模力方舟上所有的视频模型皆使用异步接口方式调用，详情请看模型体验页面的示例代码，或者参考 [异步接口使用指南]({{SITE_URL}}/docs/openapi/v1#tag/异步任务)。
 
-```python
+```python showLineNumbers title="python"
 import requests
 import time
 import json
diff --git a/docs/products/models/download.md b/docs/products/models/download.md
index ea7a110..71d9f01 100644
--- a/docs/products/models/download.md
+++ b/docs/products/models/download.md
@@ -25,7 +25,7 @@ export HF_HOME=~/.cache/gitee-ai
 
 - 或者，在要执行的 python 代码 **头部** 粘贴以下代码:
 
-```python
+```python showLineNumbers title="python"
 import os
 os.environ["HF_HOME"] = "~/.cache/gitee-ai"
 os.environ["HF_ENDPOINT"] = "https://hf-api.gitee.com"
@@ -41,21 +41,21 @@ export HF_TOKEN=<Gitee 私人令牌>
 
 [huggingface_hub](https://github.com/huggingface/huggingface_hub):
 
-```python
+```python showLineNumbers title="python"
 from huggingface_hub import hf_hub_download
 hf_hub_download("hf-models/sdxl-turbo", "sd_xl_turbo_1.0.safetensors", local_dir="./models")
 ```
 
 [transformers](https://github.com/huggingface/transformers):
 
-```python
+```python showLineNumbers title="python"
 from transformers import pipeline
 pipe = pipeline("text-generation", model="gpt2")
 ```
 
 [diffusers](https://github.com/huggingface/diffusers):
 
-```python
+```python showLineNumbers title="python"
 from diffusers import DiffusionPipeline
 pipeline = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0")
 ```
-- 
Gitee