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.
336 lines
21 KiB
TypeScript
336 lines
21 KiB
TypeScript
import { OnboardingComponentsContext, createInstallation } from 'scenes/onboarding/shared/OnboardingDocsContentWrapper'
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import { StepDefinition } from '../steps'
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export const getManualSteps = (ctx: OnboardingComponentsContext): StepDefinition[] => {
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const { Markdown, Tab, CodeBlock, snippets, dedent } = ctx
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const GenerationEvent = snippets?.GenerationEvent
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const TraceEvent = snippets?.TraceEvent
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const SpanEvent = snippets?.SpanEvent
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const EmbeddingEvent = snippets?.EmbeddingEvent
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const languages = [
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{ key: 'API', label: 'API' },
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{ key: 'Node.js', label: 'Node.js' },
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{ key: 'Python', label: 'Python' },
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{ key: 'Go', label: 'Go' },
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{ key: 'Ruby', label: 'Ruby' },
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{ key: 'PHP', label: 'PHP' },
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]
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return [
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{
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title: 'Capture LLM events manually',
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content: (
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<>
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<Markdown>
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{dedent`
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If you use a different server-side SDK, or prefer to use the API, capture the data
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manually. Call the \`capture\` method, or use the [capture
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API](https://hanzo.ai/docs/api/capture).
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`}
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</Markdown>
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<Tab.Group tabs={languages.map((l) => l.label)}>
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<Tab.List>
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{languages.map((l) => (
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<Tab key={l.key}>{l.label}</Tab>
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))}
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</Tab.List>
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<Tab.Panels>
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{languages.map((l) => (
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<Tab.Panel key={l.key}>
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<>
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{l.key === 'Node.js' && (
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<>
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<Markdown>### 1. Install</Markdown>
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<CodeBlock language="bash" code="npm install insights-node" />
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<Markdown>### 2. Initialize Insights</Markdown>
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<CodeBlock
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language="javascript"
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code={dedent`
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import { Insights } from 'insights-node'
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const client = new Insights('<ph_project_token>', {
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host: '<ph_client_api_host>'
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})
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`}
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/>
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<Markdown>### 3. Capture Event</Markdown>
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<CodeBlock
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language="javascript"
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code={dedent`
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// After your LLM call
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client.capture({
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distinctId: 'user_123',
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event: '$ai_generation',
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properties: {
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$ai_trace_id: 'trace_id_here',
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$ai_model: 'gpt-5-mini',
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$ai_provider: 'openai',
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$ai_input: [{ role: 'user', content: 'Tell me a fun fact about mascots' }],
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$ai_input_tokens: 10,
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$ai_output_choices: [{ role: 'assistant', content: 'Mascots have around 5,000 to 7,000 spines on their backs!' }],
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$ai_output_tokens: 20,
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$ai_latency: 1.5,
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// For streaming responses, also include:
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// $ai_stream: true,
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// $ai_time_to_first_token: 0.25
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}
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})
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client.shutdown()
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`}
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/>
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</>
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)}
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{l.key === 'Python' && (
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<>
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<Markdown>### 1. Install</Markdown>
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<CodeBlock language="bash" code="pip install insights" />
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<Markdown>### 2. Initialize Insights</Markdown>
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<CodeBlock
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language="python"
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code={dedent`
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from insights import Insights
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insights = Insights("<ph_project_token>", host="<ph_client_api_host>")
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`}
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/>
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<Markdown>### 3. Capture Event</Markdown>
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<CodeBlock
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language="python"
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code={dedent`
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# After your LLM call
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insights.capture(
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distinct_id='user_123',
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event='$ai_generation',
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properties={
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'$ai_trace_id': 'trace_id_here',
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'$ai_model': 'gpt-5-mini',
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'$ai_provider': 'openai',
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'$ai_input': [{'role': 'user', 'content': 'Tell me a fun fact about mascots'}],
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'$ai_input_tokens': 10,
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'$ai_output_choices': [{'role': 'assistant', 'content': 'Mascots have around 5,000 to 7,000 spines on their backs!'}],
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'$ai_output_tokens': 20,
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'$ai_latency': 1.5,
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# For streaming responses, also include:
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# '$ai_stream': True,
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# '$ai_time_to_first_token': 0.25
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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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{l.key === 'Go' && (
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<>
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<Markdown>### 1. Install</Markdown>
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<CodeBlock
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language="bash"
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code="go get github.com/insights/insights-go"
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/>
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<Markdown>### 2. Initialize Insights</Markdown>
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<CodeBlock
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language="go"
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code={dedent`
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import "github.com/insights/insights-go"
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client, _ := insights.NewWithConfig("<ph_project_token>", insights.Config{
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Endpoint: "<ph_client_api_host>",
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})
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defer client.Close()
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`}
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/>
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<Markdown>### 3. Capture Event</Markdown>
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<CodeBlock
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language="go"
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code={dedent`
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// After your LLM call
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client.Enqueue(insights.Capture{
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DistinctId: "user_123",
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Event: "$ai_generation",
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Properties: map[string]interface{}{
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"$ai_trace_id": "trace_id_here",
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"$ai_model": "gpt-5-mini",
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"$ai_provider": "openai",
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"$ai_input_tokens": 10,
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"$ai_output_tokens": 20,
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"$ai_latency": 1.5,
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// For streaming responses, also include:
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// "$ai_stream": true,
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// "$ai_time_to_first_token": 0.25,
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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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{l.key === 'Ruby' && (
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<>
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<Markdown>### 1. Install</Markdown>
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<CodeBlock language="bash" code="gem install insights-ruby" />
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<Markdown>### 2. Initialize Insights</Markdown>
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<CodeBlock
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language="ruby"
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code={dedent`
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require 'insights'
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insights = Insights::Client.new({
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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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/>
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<Markdown>### 3. Capture Event</Markdown>
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<CodeBlock
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language="ruby"
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code={dedent`
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# After your LLM call
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insights.capture({
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distinct_id: 'user_123',
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event: '$ai_generation',
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properties: {
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'$ai_trace_id' => 'trace_id_here',
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'$ai_model' => 'gpt-5-mini',
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'$ai_provider' => 'openai',
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'$ai_input_tokens' => 10,
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'$ai_output_tokens' => 20,
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'$ai_latency' => 1.5
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# For streaming responses, also include:
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# '$ai_stream' => true,
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# '$ai_time_to_first_token' => 0.25
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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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{l.key === 'PHP' && (
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<>
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<Markdown>### 1. Install</Markdown>
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<CodeBlock
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language="bash"
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code="composer require insights/insights-php"
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/>
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<Markdown>### 2. Initialize Insights</Markdown>
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<CodeBlock
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language="php"
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code={dedent`
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<?php
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require_once __DIR__ . '/vendor/autoload.php';
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use Insights\\Insights;
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Insights::init('<ph_project_token>', [
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'host' => '<ph_client_api_host>'
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]);
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`}
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/>
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<Markdown>### 3. Capture Event</Markdown>
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<CodeBlock
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language="php"
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code={dedent`
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// After your LLM call
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Insights::capture([
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'distinctId' => 'user_123',
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'event' => '$ai_generation',
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'properties' => [
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'$ai_trace_id' => 'trace_id_here',
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'$ai_model' => 'gpt-5-mini',
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'$ai_provider' => 'openai',
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'$ai_input_tokens' => 10,
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'$ai_output_tokens' => 20,
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'$ai_latency' => 1.5
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// For streaming responses, also include:
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// '$ai_stream' => true,
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// '$ai_time_to_first_token' => 0.25
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]
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]);
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`}
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/>
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</>
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)}
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{l.key === 'API' && (
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<>
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<Markdown>### Capture via API</Markdown>
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<CodeBlock
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language="bash"
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code={dedent`
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curl -X POST "<ph_client_api_host>/i/v0/e/" \\
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-H "Content-Type: application/json" \\
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-d '{
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"api_key": "<ph_project_token>",
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"event": "$ai_generation",
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"properties": {
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"distinct_id": "user_123",
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"$ai_trace_id": "trace_id_here",
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"$ai_model": "gpt-5-mini",
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"$ai_provider": "openai",
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"$ai_input": [{"role": "user", "content": "Tell me a fun fact about mascots"}],
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"$ai_input_tokens": 10,
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"$ai_output_choices": [{"role": "assistant", "content": "Mascots have around 5,000 to 7,000 spines on their backs!"}],
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"$ai_output_tokens": 20,
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"$ai_latency": 1.5,
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"$ai_stream": true,
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"$ai_time_to_first_token": 0.25
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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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</>
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</Tab.Panel>
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))}
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</Tab.Panels>
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</Tab.Group>
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</>
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),
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},
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{
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title: 'Event properties',
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content: (
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<>
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<Markdown>
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Each event type has specific properties. See the tabs below for detailed property documentation
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for each event type.
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</Markdown>
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<Tab.Group tabs={['Generation', 'Trace', 'Span', 'Embedding']}>
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<Tab.List>
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<Tab>Generation</Tab>
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<Tab>Trace</Tab>
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<Tab>Span</Tab>
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<Tab>Embedding</Tab>
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</Tab.List>
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<Tab.Panels>
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<Tab.Panel>{GenerationEvent && <GenerationEvent />}</Tab.Panel>
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<Tab.Panel>{TraceEvent && <TraceEvent />}</Tab.Panel>
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<Tab.Panel>{SpanEvent && <SpanEvent />}</Tab.Panel>
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<Tab.Panel>{EmbeddingEvent && <EmbeddingEvent />}</Tab.Panel>
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</Tab.Panels>
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</Tab.Group>
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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 ManualInstallation = createInstallation(getManualSteps)
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