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.
221 lines
11 KiB
TypeScript
221 lines
11 KiB
TypeScript
import { OnboardingComponentsContext, createInstallation } from 'scenes/onboarding/shared/OnboardingDocsContentWrapper'
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import { StepDefinition } from '../steps'
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export const getLangChainSteps = (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
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[Node.js](https://github.com/Insights/insights-js/tree/main/examples/example-ai-langchain) and
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[Python](https://github.com/Insights/insights-python/tree/master/examples/example-ai-langchain)
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examples on GitHub.
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</Markdown>
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</CalloutBox>
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<Markdown>Install the Insights SDK and LangChain with OpenAI.</Markdown>
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<CodeBlock
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blocks={[
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{
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language: 'bash',
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file: 'Python',
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code: dedent`
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pip install insights "langchain>=1.0" langchain-core langchain-openai
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`,
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},
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{
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language: 'bash',
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file: 'Node',
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code: dedent`
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npm install insights-node @hanzo/ai langchain@^1.0 @langchain/core @langchain/openai zod
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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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{
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title: 'Configure Insights',
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badge: 'required',
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content: (
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<>
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<Markdown>
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{dedent`
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Create a Insights client once, then build a callback handler for each request or
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conversation. \`distinct_id\` ties each call to a user, and \`$ai_session_id\` groups calls in one conversation.
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`}
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</Markdown>
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<CodeBlock
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blocks={[
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{
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language: 'python',
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file: 'Python',
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code: dedent`
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from insights import Insights
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from insights.ai.langchain import CallbackHandler
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insights = Insights("<ph_project_token>", host="<ph_client_api_host>")
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def create_handler(user_id: str, session_id: str) -> CallbackHandler:
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return CallbackHandler(
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client=insights,
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distinct_id=user_id,
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properties={"$ai_session_id": session_id},
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)
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`,
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},
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{
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language: 'typescript',
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file: 'Node',
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code: dedent`
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import { Insights } from 'insights-node'
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import { LangChainCallbackHandler } from '@hanzo/ai/langchain'
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const insights = new Insights('<ph_project_token>', { host: '<ph_client_api_host>' })
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function createHandler(userId: string, sessionId: string): LangChainCallbackHandler {
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return new LangChainCallbackHandler({
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client: insights,
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distinctId: userId,
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properties: { $ai_session_id: sessionId },
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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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<Blockquote>
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<Markdown>
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**Note:** If you want to capture LLM events anonymously, omit `distinct_id`/`distinctId`
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when constructing the handler. 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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</>
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),
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},
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{
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title: 'Call LangChain',
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badge: 'required',
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content: (
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<>
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<Markdown>
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{dedent`
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Build your agent once and build the handler on each turn to track the relevant sessions. Traces, generations, and spans (tool calls) are automatically captured.
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`}
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</Markdown>
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<CodeBlock
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blocks={[
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{
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language: 'python',
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file: 'Python',
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code: dedent`
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from langchain_openai import ChatOpenAI
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from langchain_core.tools import tool
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from langchain.agents import create_agent
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@tool
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def get_weather(city: str) -> str:
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"""Get the weather for a given city."""
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return f"It's always sunny in {city}!"
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model = ChatOpenAI(openai_api_key="your_openai_api_key")
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agent = create_agent(model, tools=[get_weather])
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def ask(user_input: str, user_id: str, conversation_id: str) -> str:
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handler = create_handler(user_id=user_id, session_id=conversation_id)
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result = agent.invoke(
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{"messages": [{"role": "user", "content": user_input}]},
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config={"callbacks": [handler]},
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)
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return result["messages"][-1].content
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ask("What's the weather in Paris?", "user_123", "conversation-abc")
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`,
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},
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{
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language: 'typescript',
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file: 'Node',
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code: dedent`
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import { ChatOpenAI } from '@langchain/openai'
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import { tool } from '@langchain/core/tools'
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import { createAgent } from 'langchain'
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import { z } from 'zod'
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const getWeather = tool(
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(input) => \`It's always sunny in \${input.city}!\`,
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{
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name: 'get_weather',
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description: 'Get the weather for a given city',
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schema: z.object({
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city: z.string().describe('The city to get the weather for'),
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}),
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}
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)
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const model = new ChatOpenAI({ apiKey: 'your_openai_api_key' })
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const agent = createAgent({ model, tools: [getWeather] })
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async function ask(userInput: string, userId: string, conversationId: string): Promise<string> {
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const handler = createHandler(userId, conversationId)
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const result = await agent.invoke(
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{ messages: [{ role: 'user', content: userInput }] },
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{ callbacks: [handler] }
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)
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return result.messages[result.messages.length - 1].content
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}
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await ask("What's the weather in Paris?", 'user_123', 'conversation-abc')
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`,
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},
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]}
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/>
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<Blockquote>
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<Markdown>
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{dedent`
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**Using LangChain 0.x?** LangChain built agents with \`AgentExecutor\` before 1.0.
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Everything else on this page is the same on either version, including the handler
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and the properties it sets. See LangChain's
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[migration guide](https://docs.langchain.com/oss/python/migrate/langchain-v1) to move
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to \`create_agent\`.
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`}
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</Markdown>
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</Blockquote>
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<Markdown>
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Insights automatically captures an `$ai_generation` event along with these properties:
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</Markdown>
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{NotableGenerationProperties && <NotableGenerationProperties />}
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<Markdown>
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{dedent`
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The handler also builds a trace hierarchy automatically based on how you structure your
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agent. Pass the same \`$ai_session_id\` to every handler you construct for a
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conversation, to group its calls into one session. Pass \`trace_id\`/\`traceId\` too, to
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control the top-level trace ID instead of letting Insights generate one.
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`}
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</Markdown>
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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 LangChainInstallation = createInstallation(getLangChainSteps)
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