diff --git a/langgraph_plugin/graph_api/human_in_the_loop/README.md b/langgraph_plugin/graph_api/human_in_the_loop/README.md index f14ada54..d8ff0b48 100644 --- a/langgraph_plugin/graph_api/human_in_the_loop/README.md +++ b/langgraph_plugin/graph_api/human_in_the_loop/README.md @@ -14,7 +14,7 @@ Demonstrates pausing a graph with LangGraph's `interrupt()` and waiting indefini 1. The Workflow starts and the `generate_draft` node produces a response. 2. The `human_review` node calls `interrupt(draft)`, pausing execution. 3. The Workflow stores the draft (visible via the query) and calls `workflow.wait_condition()` — blocking durably until the signal sets `_human_input`. This can wait indefinitely; Temporal persists the state. -4. An external process (UI, CLI, etc.) queries the draft and sends approval via signal. +4. An external process (UI, CLI, etc.) queries the draft and sends the human's feedback via signal. 5. The graph resumes — `interrupt()` returns the signal value and the node completes. ## Running the Sample @@ -25,14 +25,16 @@ Prerequisites: `uv sync --group langgraph` and a running Temporal dev server (`t # Terminal 1: start the worker uv run langgraph_plugin/graph_api/human_in_the_loop/run_worker.py -# Terminal 2: start the workflow (polls for draft, then auto-approves) +# Terminal 2: start the workflow (polls for the draft, then prompts you for feedback) uv run langgraph_plugin/graph_api/human_in_the_loop/run_workflow.py ``` +When the draft is ready, you'll be prompted at the terminal. Type `approve` to accept it as-is, or type revision feedback and the draft will be regenerated by an LLM incorporating your notes. + ## Files | File | Description | |------|-------------| | `workflow.py` | Graph node functions, graph definition, and `ChatbotWorkflow` definition | | `run_worker.py` | Builds graph, registers with `LangGraphPlugin`, starts worker | -| `run_workflow.py` | Starts workflow, polls draft via query, sends approval via signal | +| `run_workflow.py` | Starts workflow, polls draft via query, prompts for human feedback, sends it via signal | diff --git a/langgraph_plugin/graph_api/human_in_the_loop/run_workflow.py b/langgraph_plugin/graph_api/human_in_the_loop/run_workflow.py index 7abfcdcf..c45e2b72 100644 --- a/langgraph_plugin/graph_api/human_in_the_loop/run_workflow.py +++ b/langgraph_plugin/graph_api/human_in_the_loop/run_workflow.py @@ -27,8 +27,11 @@ async def main() -> None: print(f"Draft for review: {draft}") - # Send approval via signal (a UI would trigger this) - await handle.signal(ChatbotWorkflow.provide_feedback, "approve") + # Prompt for human feedback instead of auto-approving. + feedback = await asyncio.to_thread( + input, "Enter 'approve' to accept, or type revision feedback: " + ) + await handle.signal(ChatbotWorkflow.provide_feedback, feedback) result = await handle.result() print(f"Final response: {result}") diff --git a/langgraph_plugin/graph_api/human_in_the_loop/workflow.py b/langgraph_plugin/graph_api/human_in_the_loop/workflow.py index f39275a6..d9e39911 100644 --- a/langgraph_plugin/graph_api/human_in_the_loop/workflow.py +++ b/langgraph_plugin/graph_api/human_in_the_loop/workflow.py @@ -6,6 +6,7 @@ from datetime import timedelta +from langchain.chat_models import init_chat_model from langchain_core.runnables import RunnableConfig from langgraph.checkpoint.memory import InMemorySaver from langgraph.graph import START, StateGraph @@ -20,21 +21,25 @@ class State(TypedDict): async def generate_draft(state: State) -> dict[str, str]: - """Generate a draft response. Replace with an LLM call in production.""" - return { - "value": ( - f"Here's my response to '{state['value']}': " - "The answer is 42. Let me know if this helps!" - ) - } + """Generate a draft response with an LLM.""" + response = await init_chat_model("claude-sonnet-4-6").ainvoke( + f"Please respond concisely to: {state['value']}" + ) + return {"value": str(response.content)} async def human_review(state: State) -> dict[str, str]: - """Present draft to human for review via interrupt.""" + """Present draft to human for review via interrupt; revise with LLM on feedback.""" feedback = interrupt(state["value"]) if feedback == "approve": return {"value": state["value"]} - return {"value": f"[Revised] {state['value']} (incorporating feedback: {feedback})"} + response = await init_chat_model("claude-sonnet-4-6").ainvoke( + "Revise the following draft according to the reviewer's feedback. " + "Output only the revised draft, with no preamble.\n\n" + f"Draft:\n{state['value']}\n\n" + f"Feedback:\n{feedback}" + ) + return {"value": str(response.content)} def make_chatbot_graph() -> StateGraph: diff --git a/tests/langgraph_plugin/human_in_the_loop_test.py b/tests/langgraph_plugin/human_in_the_loop_test.py index ad81e492..2d0b917f 100644 --- a/tests/langgraph_plugin/human_in_the_loop_test.py +++ b/tests/langgraph_plugin/human_in_the_loop_test.py @@ -1,6 +1,7 @@ import asyncio import sys import uuid +from unittest.mock import patch import pytest from temporalio.client import Client @@ -18,36 +19,59 @@ ) +class _FakeMessage: + def __init__(self, content: str) -> None: + self.content = content + + +class _EchoModel: + """Stand-in for a chat model that echoes the prompt back as its response.""" + + async def ainvoke(self, prompt: str) -> _FakeMessage: + return _FakeMessage(prompt) + + +def _fake_init_chat_model(*args: object, **kwargs: object) -> _EchoModel: + return _EchoModel() + + +_patch_llm = lambda: patch( + "langgraph_plugin.graph_api.human_in_the_loop.workflow.init_chat_model", + _fake_init_chat_model, +) + + async def test_human_in_the_loop_approve(client: Client) -> None: task_queue = f"hitl-test-{uuid.uuid4()}" plugin = LangGraphPlugin(graphs={"chatbot": make_chatbot_graph()}) - async with Worker( - client, - task_queue=task_queue, - workflows=[ChatbotWorkflow], - plugins=[plugin], - ): - handle = await client.start_workflow( - ChatbotWorkflow.run, - "test message", - id=f"hitl-{uuid.uuid4()}", + with _patch_llm(): + async with Worker( + client, task_queue=task_queue, - ) - - # Poll for draft to be ready - draft = None - for _ in range(40): - await asyncio.sleep(0.25) - draft = await handle.query(ChatbotWorkflow.get_draft) - if draft is not None: - break - assert draft is not None - assert "test message" in draft - - # Approve - await handle.signal(ChatbotWorkflow.provide_feedback, "approve") - result = await handle.result() + workflows=[ChatbotWorkflow], + plugins=[plugin], + ): + handle = await client.start_workflow( + ChatbotWorkflow.run, + "test message", + id=f"hitl-{uuid.uuid4()}", + task_queue=task_queue, + ) + + # Poll for draft to be ready + draft = None + for _ in range(40): + await asyncio.sleep(0.25) + draft = await handle.query(ChatbotWorkflow.get_draft) + if draft is not None: + break + assert draft is not None + assert "test message" in draft + + # Approve + await handle.signal(ChatbotWorkflow.provide_feedback, "approve") + result = await handle.result() assert result == draft # approved draft returned as-is @@ -56,31 +80,36 @@ async def test_human_in_the_loop_revise(client: Client) -> None: task_queue = f"hitl-revise-test-{uuid.uuid4()}" plugin = LangGraphPlugin(graphs={"chatbot": make_chatbot_graph()}) - async with Worker( - client, - task_queue=task_queue, - workflows=[ChatbotWorkflow], - plugins=[plugin], - ): - handle = await client.start_workflow( - ChatbotWorkflow.run, - "test message", - id=f"hitl-revise-{uuid.uuid4()}", + with _patch_llm(): + async with Worker( + client, task_queue=task_queue, - ) - - # Poll for draft - draft = None - for _ in range(40): - await asyncio.sleep(0.25) - draft = await handle.query(ChatbotWorkflow.get_draft) - if draft is not None: - break - assert draft is not None - - # Send revision feedback - await handle.signal(ChatbotWorkflow.provide_feedback, "please be more concise") - result = await handle.result() - - assert "[Revised]" in result + workflows=[ChatbotWorkflow], + plugins=[plugin], + ): + handle = await client.start_workflow( + ChatbotWorkflow.run, + "test message", + id=f"hitl-revise-{uuid.uuid4()}", + task_queue=task_queue, + ) + + # Poll for draft + draft = None + for _ in range(40): + await asyncio.sleep(0.25) + draft = await handle.query(ChatbotWorkflow.get_draft) + if draft is not None: + break + assert draft is not None + + # Send revision feedback + await handle.signal( + ChatbotWorkflow.provide_feedback, "please be more concise" + ) + result = await handle.result() + + # The revision node feeds the draft and feedback into the LLM; the echo + # stand-in returns the revision prompt, which contains both. assert "please be more concise" in result + assert "test message" in result