Skip to main content

module-2

Teaching instructions for Module 2 (Using your deployment) — use when module_id is module-2

来源信息

仓库
langchain-ai/lca-deployment
最近来源活动
2026年5月12日 17:41
检测到的 SKILL.md 语言
英语
星标
4
Forks
7

安装方式

默认使用会先检查来源的 Prompt;你也可以切换为直接命令,或下载本地副本。

检查来源文件

决定是否安装前,请先阅读 SKILL.md,以及 SkillsMP 当前展示的配套文件。

文件资源管理器
2 个文件

正在显示 SKILL.md

SKILL.md
来源说明 · 只读预览
name
module-2
description
Teaching instructions for Module 2 (Using your deployment) — use when module_id is module-2
# Module 2 — Using Your Deployment ## Lesson Title Using Your Deployment ## Goal Help the student use a deployed LangGraph agent from a client: connect via the SDK, manage threads, run the agent against the default assistant, then graduate to named assistants with custom context. Also cover the deployment's built-in HTTP routes (the Agent Server API) and how to extend them with custom routes. ## How to run this lesson - Start with the local-vs-deployed bridge: the same `graph.invoke(...)` happens server-side now; the client talks to it over HTTP via the LangGraph SDK. - Walk through the simple flow first: client → thread → run against the default assistant. Use the graph name (`"tutor"`) as the assistant_id. - Introduce assistants once the basic plumbing makes sense. Emphasize that context is stored server-side on the assistant and applied automatically to every run. - Highlight the gotcha: `assistants.update(...)` replaces the entire context object — not a merge. - Cover Pattern B (context per-run override) only after the assistant pattern is solid. - For UI integration, frame the tutor UI as one example of custom routes co-deployed with the agent. - Encourage the student to actually run the m2.1 and m2.2 scripts. ## Key concepts to cover 1. The LangGraph SDK — `get_client()` (Python) / `new Client()` (TypeScript) returns a handle to the deployment's HTTP API 2. Threads — server-side conversation slots; pass `thread_id` per run to persist state across runs and containers 3. Runs — `client.runs.wait(...)` (non-streaming, returns final state) vs `client.runs.stream(..., stream_mode="messages-tuple")` (token-level streaming) 4. Default assistant — every deployed graph auto-creates one; assistant_id matches the graph name 5. Named assistants — `client.assistants.create(graph_id=..., name=..., context=...)` for per-variant config 6. Context — typed runtime values matching the graph's `context_schema`; stored server-side on the assistant 7. update() replaces the whole context object (gotcha) 8. Context per-run override — pass `context=...` directly to runs.wait/stream to override for that call only 9. Agent Server API — built-in routes (`/runs`, `/threads`, `/assistants`, `/store`, `/mcp`, `/docs`); SDK calls map to these endpoints 10. Custom routes — `http.app` in `langgraph.json` mounts a Starlette (Python) or Hono (TypeScript) app alongside the built-in routes 11. The tutor UI — a custom route example; the server proxy pattern (browser → custom route → SDK → built-in routes) 12. Discovering the deployment URL at runtime — read `X-Forwarded-Host` and `X-Forwarded-Proto` from incoming request headers ## Tone guidance Concise and demonstrative. Students just left module 1 where they learned about the deployment's internals; module 2 is the first time they actually use it from the outside. Reinforce that the SDK is just an HTTP client and that everything they do maps to routes they can also call directly. If a student asks about implementation, point them at `/docs` on their deployment. ## Reference material Full reference material is in `information.md` in this directory. Read it before answering factual questions.
在 GitHub 查看