在第 1 部分中，我们为代码审查器构建了核心分析工具。现在我们将创建一个可以有效使用这些工具的人工智能助手。我们将逐步介绍每个组件，解释所有组件如何协同工作。
有关 ClientAI 的文档，请参阅此处；有关 Github Repo，请参阅此处。
首先，我们需要让我们的工具可供人工智能系统使用。以下是我们注册它们的方法：
def create_review_tools() -> List[ToolConfig]:
"""Create the tool configurations for code review."""
return [
ToolConfig(
tool=analyze_python_code,
name="code_analyzer",
description=(
"Analyze Python code structure and complexity. "
"Expects a 'code' parameter with the Python code as a string."
),
scopes=["observe"],
),
ToolConfig(
tool=check_style_issues,
name="style_checker",
description=(
"Check Python code style issues. "
"Expects a 'code' parameter with the Python code as a string."
),
scopes=["observe"],
),
ToolConfig(
tool=generate_docstring,
name="docstring_generator",
description=(
"Generate docstring suggestions for Python code. "
"Expects a 'code' parameter with the Python code as a string."
),
scopes=["act"],
),
]
让我们来分解一下这里发生的事情：
每个工具都包装在一个 ToolConfig 对象中，该对象告诉 ClientAI：
我们将工具分为两类：
现在让我们创建我们的人工智能助手。我们将其设计为分步骤工作，模仿人类代码审查者的想法：
class CodeReviewAssistant(Agent):
"""An agent that performs comprehensive Python code review."""
@observe(
name="analyze_structure",
description="Analyze code structure and style",
stream=True,
)
def analyze_structure(self, code: str) -> str:
"""Analyze the code structure, complexity, and style issues."""
self.context.state["code_to_analyze"] = code
return """
Please analyze this Python code structure and style:
The code to analyze has been provided in the context as 'code_to_analyze'.
Use the code_analyzer and style_checker tools to evaluate:
1. Code complexity and structure metrics
2. Style compliance issues
3. Function and class organization
4. Import usage patterns
"""
第一个方法至关重要：
接下来，我们添加改进建议步骤：
@think(
name="suggest_improvements",
description="Suggest code improvements based on analysis",
stream=True,
)
def suggest_improvements(self, analysis_result: str) -> str:
"""Generate improvement suggestions based on the analysis results."""
current_code = self.context.state.get("current_code", "")
return f"""
Based on the code analysis of:
```
{% endraw %}
python
{current_code}
{% raw %}
```
And the analysis results:
{analysis_result}
Please suggest specific improvements for:
1. Reducing complexity where identified
2. Fixing style issues
3. Improving code organization
4. Optimizing import usage
5. Enhancing readability
6. Enhancing explicitness
"""
这个方法：
现在让我们创建一个用户友好的界面。我们将其分解为几个部分：
def main():
# 1. Set up logging
logger = logging.getLogger(__name__)
# 2. Configure Ollama server
config = OllamaServerConfig(
host="127.0.0.1", # Local machine
port=11434, # Default Ollama port
gpu_layers=35, # Adjust based on your GPU
cpu_threads=8, # Adjust based on your CPU
)
第一部分设置错误日志记录，使用合理的默认值配置 Ollama 服务器，并允许自定义 GPU 和 CPU 使用情况。
接下来，我们创建AI客户端和助手：
# Use context manager for Ollama server
with OllamaManager(config) as manager:
# Initialize ClientAI with Ollama
client = ClientAI(
"ollama",
host=f"http://{config.host}:{config.port}"
)
# Create code review assistant with tools
assistant = CodeReviewAssistant(
client=client,
default_model="llama3",
tools=create_review_tools(),
tool_confidence=0.8, # How confident the AI should be before using tools
max_tools_per_step=2, # Maximum tools to use per step
)
此设置的要点：
最后，我们创建交互式循环：
def create_review_tools() -> List[ToolConfig]:
"""Create the tool configurations for code review."""
return [
ToolConfig(
tool=analyze_python_code,
name="code_analyzer",
description=(
"Analyze Python code structure and complexity. "
"Expects a 'code' parameter with the Python code as a string."
),
scopes=["observe"],
),
ToolConfig(
tool=check_style_issues,
name="style_checker",
description=(
"Check Python code style issues. "
"Expects a 'code' parameter with the Python code as a string."
),
scopes=["observe"],
),
ToolConfig(
tool=generate_docstring,
name="docstring_generator",
description=(
"Generate docstring suggestions for Python code. "
"Expects a 'code' parameter with the Python code as a string."
),
scopes=["act"],
),
]
此界面：
让我们将其设为我们能够运行的脚本：
class CodeReviewAssistant(Agent):
"""An agent that performs comprehensive Python code review."""
@observe(
name="analyze_structure",
description="Analyze code structure and style",
stream=True,
)
def analyze_structure(self, code: str) -> str:
"""Analyze the code structure, complexity, and style issues."""
self.context.state["code_to_analyze"] = code
return """
Please analyze this Python code structure and style:
The code to analyze has been provided in the context as 'code_to_analyze'.
Use the code_analyzer and style_checker tools to evaluate:
1. Code complexity and structure metrics
2. Style compliance issues
3. Function and class organization
4. Import usage patterns
"""
让我们看看助手如何处理真实的代码。让我们运行一下：
@think(
name="suggest_improvements",
description="Suggest code improvements based on analysis",
stream=True,
)
def suggest_improvements(self, analysis_result: str) -> str:
"""Generate improvement suggestions based on the analysis results."""
current_code = self.context.state.get("current_code", "")
return f"""
Based on the code analysis of:
```
{% endraw %}
python
{current_code}
{% raw %}
```
And the analysis results:
{analysis_result}
Please suggest specific improvements for:
1. Reducing complexity where identified
2. Fixing style issues
3. Improving code organization
4. Optimizing import usage
5. Enhancing readability
6. Enhancing explicitness
"""
这是一个需要查找问题的示例：
def main():
# 1. Set up logging
logger = logging.getLogger(__name__)
# 2. Configure Ollama server
config = OllamaServerConfig(
host="127.0.0.1", # Local machine
port=11434, # Default Ollama port
gpu_layers=35, # Adjust based on your GPU
cpu_threads=8, # Adjust based on your CPU
)
小助手会多方面分析：
以下是增强助手的一些方法：
通过创建新的工具函数，将其包装为适当的 JSON 格式，将其添加到 create_review_tools() 函数，然后更新助手的提示以使用新工具，可以添加其中的每一个。
要了解有关 ClientAI 的更多信息，请访问文档。
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