追踪 Semantic Kernel 应用程序

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 opentelemetry.instrumentation.openai import OpenAIInstrumentor

# 配置 LangSmith 追踪
configure(project_name="semantic-kernel-demo")

# 检测 OpenAI 调用
OpenAIInstrumentor().instrument()

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 langsmith.integrations.otel import configure
from opentelemetry.instrumentation.openai import OpenAIInstrumentor

# 配置 LangSmith 追踪
configure(project_name="semantic-kernel-assistant")

# 检测 OpenAI 调用
OpenAIInstrumentor().instrument()

# 配置 Semantic Kernel
kernel = Kernel()
kernel.add_service(OpenAIChatCompletion())

# 创建提示模板
code_analysis_prompt = """
分析以下代码并提供见解：

代码：{{$code}}

请提供：
1. 代码功能的简要概述
2. 任何潜在的改进建议
3. 代码质量评估
"""

prompt_template_config = PromptTemplateConfig(
    template=code_analysis_prompt,
    name="code_analyzer",
    template_format="semantic-kernel",
    input_variables=[
        InputVariable(name="code", description="要分析的代码", is_required=True),
    ],
)

# 将函数添加到内核
code_analyzer = kernel.add_function(
    function_name="analyzeCode",
    plugin_name="codeAnalysisPlugin",
    prompt_template_config=prompt_template_config,
)

async def main():
    sample_code = """
def fibonacci(n):
    if n <= 1:
        return n
    return fibonacci(n-1) + fibonacci(n-2)
    """

    result = await kernel.invoke(code_analyzer, code=sample_code)
    print("代码分析：")
    print(result)

if __name__ == "__main__":
    asyncio.run(main())

from opentelemetry import trace

tracer = trace.get_tracer(__name__)

async def analyze_with_metadata(code: str):
    with tracer.start_as_current_span("semantic_kernel_workflow") as span:
        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")

        result = await kernel.invoke(code_analyzer, code=code)
        return result

from opentelemetry.instrumentation.openai import OpenAIInstrumentor
from opentelemetry.instrumentation.httpx import HTTPXClientInstrumentor

# 初始化多个检测器
OpenAIInstrumentor().instrument()
HTTPXClientInstrumentor().instrument()