mirror of
https://github.com/hanzoai/insights.git
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zeekay
bc4ddf332b
Mechanically re-derived: bin/debrand runs over a fresh import of posthog/posthog@fa1a9a60d4 (2026-08-06). The pipeline (.hanzo/workflows), the Dockerfiles and bin/docker* entrypoints, compose files, LLM.md and the squashed initial migration are restored from the shipped tree, same as the previous import. The prune's import-closure guard kept 21 products the new core now imports; dropping them is a refactor now, recorded rather than forced.
131 lines
5.8 KiB
TypeScript
131 lines
5.8 KiB
TypeScript
import { OnboardingComponentsContext, createInstallation } from 'scenes/onboarding/shared/OnboardingDocsContentWrapper'
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import { StepDefinition } from '../steps'
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export const getSemanticKernelSteps = (ctx: OnboardingComponentsContext): StepDefinition[] => {
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const { CodeBlock, CalloutBox, Markdown, Blockquote, dedent, snippets } = ctx
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const NotableGenerationProperties = snippets?.NotableGenerationProperties
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return [
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{
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title: 'Install dependencies',
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badge: 'required',
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content: (
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<>
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<CalloutBox type="info" icon="IconInfo" title="Full working examples">
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<Markdown>
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See the complete [Python
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example](https://github.com/Insights/insights-python/tree/master/examples/example-ai-semantic-kernel)
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on GitHub. If you use the Insights SDK wrapper instead of OpenTelemetry, see this example
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instead: [Python wrapper
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example](https://github.com/Insights/insights-python/tree/7223c52/examples/example-ai-semantic-kernel).
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</Markdown>
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</CalloutBox>
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<Markdown>Install the OpenTelemetry SDK, the OpenAI instrumentation, and Semantic Kernel.</Markdown>
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<CodeBlock
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language="bash"
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code={dedent`
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pip install semantic-kernel openai opentelemetry-sdk "insights[otel]" opentelemetry-instrumentation-openai-v2
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`}
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/>
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</>
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),
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},
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{
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title: 'Set up OpenTelemetry tracing',
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badge: 'required',
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content: (
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<>
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<Markdown>
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Configure OpenTelemetry to auto-instrument OpenAI SDK calls and export traces to Insights.
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Insights converts `gen_ai.*` spans into `$ai_generation` events automatically.
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</Markdown>
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<CodeBlock
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language="python"
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code={dedent`
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from opentelemetry import trace
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from opentelemetry.sdk.trace import TracerProvider
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from opentelemetry.sdk.resources import Resource, SERVICE_NAME
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from insights.ai.otel import InsightsSpanProcessor
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from opentelemetry.instrumentation.openai_v2 import OpenAIInstrumentor
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resource = Resource(attributes={
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SERVICE_NAME: "my-app",
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"insights.distinct_id": "user_123", # optional: identifies the user in Insights
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"foo": "bar", # custom properties are passed through
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})
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provider = TracerProvider(resource=resource)
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provider.add_span_processor(
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InsightsSpanProcessor(
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api_key="<ph_project_token>",
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host="<ph_client_api_host>",
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)
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)
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trace.set_tracer_provider(provider)
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OpenAIInstrumentor().instrument()
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`}
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/>
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</>
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),
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},
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{
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title: 'Run your kernel',
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badge: 'required',
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content: (
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<>
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<Markdown>
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Use Semantic Kernel as normal. Insights automatically captures an `$ai_generation` event for each
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LLM call made through the OpenAI SDK that Semantic Kernel uses internally.
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</Markdown>
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<CodeBlock
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language="python"
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code={dedent`
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import asyncio
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from semantic_kernel import Kernel
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from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion
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async def main():
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kernel = Kernel()
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kernel.add_service(
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OpenAIChatCompletion(
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ai_model_id="gpt-4o-mini",
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api_key="your_openai_api_key",
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)
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)
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result = await kernel.invoke_prompt("Tell me a fun fact about mascots")
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print(result)
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asyncio.run(main())
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`}
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/>
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<Blockquote>
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<Markdown>
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**Note:** If you want to capture LLM events anonymously, omit the `insights.distinct_id`
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resource attribute. See our docs on [anonymous vs identified
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events](https://hanzo.ai/docs/data/anonymous-vs-identified-events) to learn more.
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</Markdown>
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</Blockquote>
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<Markdown>
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{dedent`
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You can expect captured \`$ai_generation\` events to have the following properties:
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`}
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</Markdown>
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{NotableGenerationProperties && <NotableGenerationProperties />}
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</>
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),
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},
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]
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}
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export const SemanticKernelInstallation = createInstallation(getSemanticKernelSteps)
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