LangSmith 可以使用 OpenInference 的 OpenAI 检测功能捕获由 Semantic Kernel 生成的追踪。本指南将向您展示如何自动捕获 Semantic Kernel 应用程序的追踪并将其发送到 LangSmith 进行监控和分析。
使用您首选的包管理器安装所需包
pip install langsmith semantic-kernel openinference-instrumentation-openai
需要 LangSmith Python SDK 版本 langsmith>=0.4.26 才能获得最佳 OpenTelemetry 支持。
export LANGSMITH_API_KEY=<your_langsmith_api_key>
export LANGSMITH_PROJECT=<your_project_name>
export OPENAI_API_KEY=<your_openai_api_key>
from langsmith.integrations.otel import configure
from openinference.instrumentation.openai import OpenAIInstrumentor
# Configure LangSmith tracing
configure(project_name="semantic-kernel-demo")
# Instrument OpenAI calls
OpenAIInstrumentor().instrument()
您无需设置任何 OpenTelemetry 环境变量或手动配置导出器——configure() 会自动处理所有事情。
3. 创建并运行您的 Semantic Kernel 应用程序
配置完成后，您的 Semantic Kernel 应用程序将自动向 LangSmith 发送追踪： 此示例包含一个最小的应用程序，它配置了内核，定义了基于提示的功能，并调用它们以生成追踪活动。import os
import asyncio
from semantic_kernel import Kernel
from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion
from semantic_kernel.prompt_template import InputVariable, PromptTemplateConfig
from openinference.instrumentation.openai import OpenAIInstrumentor
from langsmith.integrations.otel import configure
import dotenv
# Load environment variables
dotenv.load_dotenv(".env.local")
# Configure LangSmith tracing
configure(project_name="semantic-kernel-assistant")
# Instrument OpenAI calls
OpenAIInstrumentor().instrument()
# Configure Semantic Kernel
kernel = Kernel()
kernel.add_service(OpenAIChatCompletion())
# Create a code analysis prompt template
code_analysis_prompt = """
Analyze the following code and provide insights:
Code: {{$code}}
Please provide:
1. A brief summary of what the code does
2. Any potential improvements
3. Code quality assessment
"""
prompt_template_config = PromptTemplateConfig(
template=code_analysis_prompt,
name="code_analyzer",
template_format="semantic-kernel",
input_variables=[
InputVariable(name="code", description="The code to analyze", is_required=True),
],
)
# Add the function to the kernel
code_analyzer = kernel.add_function(
function_name="analyzeCode",
plugin_name="codeAnalysisPlugin",
prompt_template_config=prompt_template_config,
)
# Create a documentation generator
doc_prompt = """
Generate comprehensive documentation for the following function:
{{$function_code}}
Include:
- Purpose and functionality
- Parameters and return values
- Usage examples
- Any important notes
"""
doc_template_config = PromptTemplateConfig(
template=doc_prompt,
name="doc_generator",
template_format="semantic-kernel",
input_variables=[
InputVariable(name="function_code", description="The function code to document", is_required=True),
],
)
doc_generator = kernel.add_function(
function_name="generateDocs",
plugin_name="documentationPlugin",
prompt_template_config=doc_template_config,
)
async def main():
# Example code to analyze
sample_code = """
def fibonacci(n):
if n <= 1:
return n
return fibonacci(n-1) + fibonacci(n-2)
"""
# Analyze the code
analysis_result = await kernel.invoke(code_analyzer, code=sample_code)
print("Code Analysis:")
print(analysis_result)
print("\n" + "="*50 + "\n")
# Generate documentation
doc_result = await kernel.invoke(doc_generator, function_code=sample_code)
print("Generated Documentation:")
print(doc_result)
return {"analysis": str(analysis_result), "documentation": str(doc_result)}
if __name__ == "__main__":
asyncio.run(main())
高级用法
您可以通过设置 span 属性向您的追踪添加自定义元数据
from opentelemetry import trace
# Get the current tracer
tracer = trace.get_tracer(__name__)
async def main():
with tracer.start_as_current_span("semantic_kernel_workflow") as span:
# Add custom metadata
span.set_attribute("langsmith.metadata.workflow_type", "code_analysis")
span.set_attribute("langsmith.metadata.user_id", "developer_123")
span.set_attribute("langsmith.span.tags", "semantic-kernel,code-analysis")
# Your Semantic Kernel code here
result = await kernel.invoke(code_analyzer, code=sample_code)
return result
与其他工具结合使用
您可以通过添加其他 instrumentor（例如 DSPy、AutoGen）并将其初始化为 instrumentor，将 Semantic Kernel 检测与其他 instrumentor 结合使用
from langsmith.integrations.otel import configure
from openinference.instrumentation.openai import OpenAIInstrumentor
from openinference.instrumentation.dspy import DSPyInstrumentor
# Configure LangSmith tracing
configure(project_name="multi-framework-app")
# Initialize multiple instrumentors
OpenAIInstrumentor().instrument()
DSPyInstrumentor().instrument()
# Your application code using multiple frameworks
