diff --git a/docs/docs/how-tos/map-reduce.ipynb b/docs/docs/how-tos/map-reduce.ipynb index 51a2eb14d..fb00f0bfb 100644 --- a/docs/docs/how-tos/map-reduce.ipynb +++ b/docs/docs/how-tos/map-reduce.ipynb @@ -1,286 +1,286 @@ { - "cells": [ - { - "attachments": { - "a108ffc8-6136-4cd7-a6f9-579e41a5a786.png": { - "image/png": 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- } - }, - "cell_type": "markdown", - "id": "95a87145-34d0-4f97-b45f-5c9fd8532c8a", - "metadata": {}, - "source": [ - "# How to create map-reduce branches for parallel execution\n", - "\n", - "[Map-reduce](https://en.wikipedia.org/wiki/MapReduce) operations are essential for efficient task decomposition and parallel processing. This approach involves breaking a task into smaller sub-tasks, processing each sub-task in parallel, and aggregating the results across all of the completed sub-tasks. \n", - "\n", - "Consider this example: given a general topic from the user, generate a list of related subjects, generate a joke for each subject, and select the best joke from the resulting list. In this design pattern, a first node may generate a list of objects (e.g., related subjects) and we want to apply some other node (e.g., generate a joke) to all those objects (e.g., subjects). However, two main challenges arise.\n", - " \n", - "(1) the number of objects (e.g., subjects) may be unknown ahead of time (meaning the number of edges may not be known) when we lay out the graph and (2) the input State to the downstream Node should be different (one for each generated object).\n", - " \n", - "LangGraph addresses these challenges [through its `Send` API](https://langchain-ai.github.io/langgraph/concepts/low_level/#send). By utilizing conditional edges, `Send` can distribute different states (e.g., subjects) to multiple instances of a node (e.g., joke generation). Importantly, the sent state can differ from the core graph's state, allowing for flexible and dynamic workflow management. \n", - "\n", - "![Screenshot 2024-07-12 at 9.45.40 AM.png](attachment:a108ffc8-6136-4cd7-a6f9-579e41a5a786.png)" - ] - }, - { - "cell_type": "markdown", - "id": "66c58b5f", - "metadata": {}, - "source": [ - "## Setup\n", - "\n", - "First, let's install the required packages and set our API keys" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "3eb04cd1", - "metadata": {}, - "outputs": [], - "source": [ - "%%capture --no-stderr\n", - "%pip install -U langchain-anthropic langgraph" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "dc292321", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import getpass\n", - "\n", - "\n", - "def _set_env(name: str):\n", - " if not os.getenv(name):\n", - " os.environ[name] = getpass.getpass(f\"{name}: \")\n", - "\n", - "\n", - "_set_env(\"ANTHROPIC_API_KEY\")" - ] - }, - { - "cell_type": "markdown", - "id": "b87911bb", - "metadata": {}, - "source": [ - "
\n", - "

Set up LangSmith for LangGraph development

\n", - "

\n", - " Sign up for LangSmith to quickly spot issues and improve the performance of your LangGraph projects. LangSmith lets you use trace data to debug, test, and monitor your LLM apps built with LangGraph — read more about how to get started here. \n", - "

\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "b4e782a0", - "metadata": {}, - "source": [ - "## Define the graph" - ] - }, - { - "cell_type": "markdown", - "id": "66803b55", - "metadata": {}, - "source": [ - "
\n", - "

Using Pydantic with LangChain

\n", - "

\n", - " This notebook uses Pydantic v2 BaseModel, which requires langchain-core >= 0.3. Using langchain-core < 0.3 will result in errors due to mixing of Pydantic v1 and v2 BaseModels.\n", - "

\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "0f0f78e4-423d-4e2d-aa1a-01efaec4715f", - "metadata": {}, - "outputs": [], - "source": [ - "import operator\n", - "from typing import Annotated, TypedDict\n", - "\n", - "from langchain_anthropic import ChatAnthropic\n", - "\n", - "from langgraph.constants import Send\n", - "from langgraph.graph import END, StateGraph, START\n", - "\n", - "from pydantic import BaseModel, Field\n", - "\n", - "# Model and prompts\n", - "# Define model and prompts we will use\n", - "subjects_prompt = \"\"\"Generate a comma separated list of between 2 and 5 examples related to: {topic}.\"\"\"\n", - "joke_prompt = \"\"\"Generate a joke about {subject}\"\"\"\n", - "best_joke_prompt = \"\"\"Below are a bunch of jokes about {topic}. Select the best one! Return the ID of the best one.\n", - "\n", - "{jokes}\"\"\"\n", - "\n", - "\n", - "class Subjects(BaseModel):\n", - " subjects: list[str]\n", - "\n", - "\n", - "class Joke(BaseModel):\n", - " joke: str\n", - "\n", - "\n", - "class BestJoke(BaseModel):\n", - " id: int = Field(description=\"Index of the best joke, starting with 0\", ge=0)\n", - "\n", - "\n", - "model = ChatAnthropic(model=\"claude-3-5-sonnet-20240620\")\n", - "\n", - "# Graph components: define the components that will make up the graph\n", - "\n", - "\n", - "# This will be the overall state of the main graph.\n", - "# It will contain a topic (which we expect the user to provide)\n", - "# and then will generate a list of subjects, and then a joke for\n", - "# each subject\n", - "class OverallState(TypedDict):\n", - " topic: str\n", - " subjects: list\n", - " # Notice here we use the operator.add\n", - " # This is because we want combine all the jokes we generate\n", - " # from individual nodes back into one list - this is essentially\n", - " # the \"reduce\" part\n", - " jokes: Annotated[list, operator.add]\n", - " best_selected_joke: str\n", - "\n", - "\n", - "# This will be the state of the node that we will \"map\" all\n", - "# subjects to in order to generate a joke\n", - "class JokeState(TypedDict):\n", - " subject: str\n", - "\n", - "\n", - "# This is the function we will use to generate the subjects of the jokes\n", - "def generate_topics(state: OverallState):\n", - " prompt = subjects_prompt.format(topic=state[\"topic\"])\n", - " response = model.with_structured_output(Subjects).invoke(prompt)\n", - " return {\"subjects\": response.subjects}\n", - "\n", - "\n", - "# Here we generate a joke, given a subject\n", - "def generate_joke(state: JokeState):\n", - " prompt = joke_prompt.format(subject=state[\"subject\"])\n", - " response = model.with_structured_output(Joke).invoke(prompt)\n", - " return {\"jokes\": [response.joke]}\n", - "\n", - "\n", - "# Here we define the logic to map out over the generated subjects\n", - "# We will use this an edge in the graph\n", - "def continue_to_jokes(state: OverallState):\n", - " # We will return a list of `Send` objects\n", - " # Each `Send` object consists of the name of a node in the graph\n", - " # as well as the state to send to that node\n", - " return [Send(\"generate_joke\", {\"subject\": s}) for s in state[\"subjects\"]]\n", - "\n", - "\n", - "# Here we will judge the best joke\n", - "def best_joke(state: OverallState):\n", - " jokes = \"\\n\\n\".join(state[\"jokes\"])\n", - " prompt = best_joke_prompt.format(topic=state[\"topic\"], jokes=jokes)\n", - " response = model.with_structured_output(BestJoke).invoke(prompt)\n", - " return {\"best_selected_joke\": state[\"jokes\"][response.id]}\n", - "\n", - "\n", - "# Construct the graph: here we put everything together to construct our graph\n", - "graph = StateGraph(OverallState)\n", - "graph.add_node(\"generate_topics\", generate_topics)\n", - "graph.add_node(\"generate_joke\", generate_joke)\n", - "graph.add_node(\"best_joke\", best_joke)\n", - "graph.add_edge(START, \"generate_topics\")\n", - "graph.add_conditional_edges(\"generate_topics\", continue_to_jokes, [\"generate_joke\"])\n", - "graph.add_edge(\"generate_joke\", \"best_joke\")\n", - "graph.add_edge(\"best_joke\", END)\n", - "app = graph.compile()" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "37ed1f71-63db-416f-b715-4617b33d4b7f", - "metadata": {}, - "outputs": [ + "cells": [ { - "data": { - "image/jpeg": 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+ } + }, + "cell_type": "markdown", + "id": "95a87145-34d0-4f97-b45f-5c9fd8532c8a", + "metadata": {}, + "source": [ + "# How to create map-reduce branches for parallel execution\n", + "\n", + "[Map-reduce](https://en.wikipedia.org/wiki/MapReduce) operations are essential for efficient task decomposition and parallel processing. This approach involves breaking a task into smaller sub-tasks, processing each sub-task in parallel, and aggregating the results across all of the completed sub-tasks. \n", + "\n", + "Consider this example: given a general topic from the user, generate a list of related subjects, generate a joke for each subject, and select the best joke from the resulting list. In this design pattern, a first node may generate a list of objects (e.g., related subjects) and we want to apply some other node (e.g., generate a joke) to all those objects (e.g., subjects). However, two main challenges arise.\n", + " \n", + "(1) the number of objects (e.g., subjects) may be unknown ahead of time (meaning the number of edges may not be known) when we lay out the graph and (2) the input State to the downstream Node should be different (one for each generated object).\n", + " \n", + "LangGraph addresses these challenges [through its `Send` API](https://langchain-ai.github.io/langgraph/concepts/low_level/#send). By utilizing conditional edges, `Send` can distribute different states (e.g., subjects) to multiple instances of a node (e.g., joke generation). Importantly, the sent state can differ from the core graph's state, allowing for flexible and dynamic workflow management. \n", + "\n", + "![Screenshot 2024-07-12 at 9.45.40 AM.png](attachment:a108ffc8-6136-4cd7-a6f9-579e41a5a786.png)" ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from IPython.display import Image\n", - "\n", - "Image(app.get_graph().draw_mermaid_png())" - ] - }, - { - "cell_type": "markdown", - "id": "4a0026d8", - "metadata": {}, - "source": [ - "## Use the graph" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "fd90cace", - "metadata": {}, - "outputs": [ + }, + { + "cell_type": "markdown", + "id": "66c58b5f", + "metadata": {}, + "source": [ + "## Setup\n", + "\n", + "First, let's install the required packages and set our API keys" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "3eb04cd1", + "metadata": {}, + "outputs": [], + "source": [ + "%%capture --no-stderr\n", + "%pip install -U langchain-anthropic langgraph" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'generate_topics': {'subjects': ['Lions', 'Elephants', 'Penguins', 'Dolphins']}}\n", - "{'generate_joke': {'jokes': [\"Why don't elephants use computers? They're afraid of the mouse!\"]}}\n", - "{'generate_joke': {'jokes': [\"Why don't dolphins use smartphones? Because they're afraid of phishing!\"]}}\n", - "{'generate_joke': {'jokes': [\"Why don't you see penguins in Britain? Because they're afraid of Wales!\"]}}\n", - "{'generate_joke': {'jokes': [\"Why don't lions like fast food? Because they can't catch it!\"]}}\n", - "{'best_joke': {'best_selected_joke': \"Why don't dolphins use smartphones? Because they're afraid of phishing!\"}}\n" - ] + "cell_type": "code", + "execution_count": null, + "id": "dc292321", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import getpass\n", + "\n", + "\n", + "def _set_env(name: str):\n", + " if not os.getenv(name):\n", + " os.environ[name] = getpass.getpass(f\"{name}: \")\n", + "\n", + "\n", + "_set_env(\"ANTHROPIC_API_KEY\")" + ] + }, + { + "cell_type": "markdown", + "id": "b87911bb", + "metadata": {}, + "source": [ + "
\n", + "

Set up LangSmith for LangGraph development

\n", + "

\n", + " Sign up for LangSmith to quickly spot issues and improve the performance of your LangGraph projects. LangSmith lets you use trace data to debug, test, and monitor your LLM apps built with LangGraph — read more about how to get started here. \n", + "

\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "b4e782a0", + "metadata": {}, + "source": [ + "## Define the graph" + ] + }, + { + "cell_type": "markdown", + "id": "66803b55", + "metadata": {}, + "source": [ + "
\n", + "

Using Pydantic with LangChain

\n", + "

\n", + " This notebook uses Pydantic v2 BaseModel, which requires langchain-core >= 0.3. Using langchain-core < 0.3 will result in errors due to mixing of Pydantic v1 and v2 BaseModels.\n", + "

\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "0f0f78e4-423d-4e2d-aa1a-01efaec4715f", + "metadata": {}, + "outputs": [], + "source": [ + "import operator\n", + "from typing import Annotated, TypedDict\n", + "\n", + "from langchain_anthropic import ChatAnthropic\n", + "\n", + "from langgraph.types import Send\n", + "from langgraph.graph import END, StateGraph, START\n", + "\n", + "from pydantic import BaseModel, Field\n", + "\n", + "# Model and prompts\n", + "# Define model and prompts we will use\n", + "subjects_prompt = \"\"\"Generate a comma separated list of between 2 and 5 examples related to: {topic}.\"\"\"\n", + "joke_prompt = \"\"\"Generate a joke about {subject}\"\"\"\n", + "best_joke_prompt = \"\"\"Below are a bunch of jokes about {topic}. Select the best one! Return the ID of the best one.\n", + "\n", + "{jokes}\"\"\"\n", + "\n", + "\n", + "class Subjects(BaseModel):\n", + " subjects: list[str]\n", + "\n", + "\n", + "class Joke(BaseModel):\n", + " joke: str\n", + "\n", + "\n", + "class BestJoke(BaseModel):\n", + " id: int = Field(description=\"Index of the best joke, starting with 0\", ge=0)\n", + "\n", + "\n", + "model = ChatAnthropic(model=\"claude-3-5-sonnet-20240620\")\n", + "\n", + "# Graph components: define the components that will make up the graph\n", + "\n", + "\n", + "# This will be the overall state of the main graph.\n", + "# It will contain a topic (which we expect the user to provide)\n", + "# and then will generate a list of subjects, and then a joke for\n", + "# each subject\n", + "class OverallState(TypedDict):\n", + " topic: str\n", + " subjects: list\n", + " # Notice here we use the operator.add\n", + " # This is because we want combine all the jokes we generate\n", + " # from individual nodes back into one list - this is essentially\n", + " # the \"reduce\" part\n", + " jokes: Annotated[list, operator.add]\n", + " best_selected_joke: str\n", + "\n", + "\n", + "# This will be the state of the node that we will \"map\" all\n", + "# subjects to in order to generate a joke\n", + "class JokeState(TypedDict):\n", + " subject: str\n", + "\n", + "\n", + "# This is the function we will use to generate the subjects of the jokes\n", + "def generate_topics(state: OverallState):\n", + " prompt = subjects_prompt.format(topic=state[\"topic\"])\n", + " response = model.with_structured_output(Subjects).invoke(prompt)\n", + " return {\"subjects\": response.subjects}\n", + "\n", + "\n", + "# Here we generate a joke, given a subject\n", + "def generate_joke(state: JokeState):\n", + " prompt = joke_prompt.format(subject=state[\"subject\"])\n", + " response = model.with_structured_output(Joke).invoke(prompt)\n", + " return {\"jokes\": [response.joke]}\n", + "\n", + "\n", + "# Here we define the logic to map out over the generated subjects\n", + "# We will use this an edge in the graph\n", + "def continue_to_jokes(state: OverallState):\n", + " # We will return a list of `Send` objects\n", + " # Each `Send` object consists of the name of a node in the graph\n", + " # as well as the state to send to that node\n", + " return [Send(\"generate_joke\", {\"subject\": s}) for s in state[\"subjects\"]]\n", + "\n", + "\n", + "# Here we will judge the best joke\n", + "def best_joke(state: OverallState):\n", + " jokes = \"\\n\\n\".join(state[\"jokes\"])\n", + " prompt = best_joke_prompt.format(topic=state[\"topic\"], jokes=jokes)\n", + " response = model.with_structured_output(BestJoke).invoke(prompt)\n", + " return {\"best_selected_joke\": state[\"jokes\"][response.id]}\n", + "\n", + "\n", + "# Construct the graph: here we put everything together to construct our graph\n", + "graph = StateGraph(OverallState)\n", + "graph.add_node(\"generate_topics\", generate_topics)\n", + "graph.add_node(\"generate_joke\", generate_joke)\n", + "graph.add_node(\"best_joke\", best_joke)\n", + "graph.add_edge(START, \"generate_topics\")\n", + "graph.add_conditional_edges(\"generate_topics\", continue_to_jokes, [\"generate_joke\"])\n", + "graph.add_edge(\"generate_joke\", \"best_joke\")\n", + "graph.add_edge(\"best_joke\", END)\n", + "app = graph.compile()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "37ed1f71-63db-416f-b715-4617b33d4b7f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/jpeg": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import Image\n", + "\n", + "Image(app.get_graph().draw_mermaid_png())" + ] + }, + { + "cell_type": "markdown", + "id": "4a0026d8", + "metadata": {}, + "source": [ + "## Use the graph" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "fd90cace", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'generate_topics': {'subjects': ['Lions', 'Elephants', 'Penguins', 'Dolphins']}}\n", + "{'generate_joke': {'jokes': [\"Why don't elephants use computers? They're afraid of the mouse!\"]}}\n", + "{'generate_joke': {'jokes': [\"Why don't dolphins use smartphones? Because they're afraid of phishing!\"]}}\n", + "{'generate_joke': {'jokes': [\"Why don't you see penguins in Britain? Because they're afraid of Wales!\"]}}\n", + "{'generate_joke': {'jokes': [\"Why don't lions like fast food? Because they can't catch it!\"]}}\n", + "{'best_joke': {'best_selected_joke': \"Why don't dolphins use smartphones? Because they're afraid of phishing!\"}}\n" + ] + } + ], + "source": [ + "# Call the graph: here we call it to generate a list of jokes\n", + "for s in app.stream({\"topic\": \"animals\"}):\n", + " print(s)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" } - ], - "source": [ - "# Call the graph: here we call it to generate a list of jokes\n", - "for s in app.stream({\"topic\": \"animals\"}):\n", - " print(s)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.9" - } - }, - "nbformat": 4, - "nbformat_minor": 5 + "nbformat": 4, + "nbformat_minor": 5 } diff --git a/docs/docs/reference/graphs.md b/docs/docs/reference/graphs.md index b2f4acdf2..14392ba2c 100644 --- a/docs/docs/reference/graphs.md +++ b/docs/docs/reference/graphs.md @@ -29,7 +29,7 @@ handler: python ## StreamMode -::: langgraph.pregel.StreamMode +::: langgraph.types.StreamMode ## Constants @@ -69,8 +69,12 @@ builder.add_conditional_edges("my_node", my_condition) ## Send -::: langgraph.constants.Send +::: langgraph.types.Send + +## Interrupt + +::: langgraph.types.Interrupt ## RetryPolicy -::: langgraph.pregel.types.RetryPolicy +::: langgraph.types.RetryPolicy diff --git a/libs/langgraph/langgraph/constants.py b/libs/langgraph/langgraph/constants.py index e8719e664..bde74c438 100644 --- a/libs/langgraph/langgraph/constants.py +++ b/libs/langgraph/langgraph/constants.py @@ -1,133 +1,109 @@ -from dataclasses import dataclass from types import MappingProxyType -from typing import Any, Literal, Mapping +from typing import Any, Mapping +from langgraph.types import Interrupt, Send # noqa: F401 + +# Interrupt, Send re-exported for backwards compatibility + + +# --- Empty read-only containers --- +EMPTY_MAP: Mapping[str, Any] = MappingProxyType({}) +EMPTY_SEQ: tuple[str, ...] = tuple() + +# --- Public constants --- +TAG_HIDDEN = "langsmith:hidden" +# tag to hide a node/edge from certain tracing/streaming environments +START = "__start__" +# the first (maybe virtual) node in graph-style Pregel +END = "__end__" +# the last (maybe virtual) node in graph-style Pregel + +# --- Reserved write keys --- INPUT = "__input__" +# for values passed as input to the graph +INTERRUPT = "__interrupt__" +# for dynamic interrupts raised by nodes +ERROR = "__error__" +# for errors raised by nodes +NO_WRITES = "__no_writes__" +# marker to signal node didn't write anything +SCHEDULED = "__scheduled__" +# marker to signal node was scheduled (in distributed mode) +TASKS = "__pregel_tasks" +# for Send objects returned by nodes/edges, corresponds to PUSH below + +# --- Reserved config.configurable keys --- CONFIG_KEY_SEND = "__pregel_send" +# holds the `write` function that accepts writes to state/edges/reserved keys CONFIG_KEY_READ = "__pregel_read" +# holds the `read` function that returns a copy of the current state CONFIG_KEY_CHECKPOINTER = "__pregel_checkpointer" +# holds a `BaseCheckpointSaver` passed from parent graph to child graphs CONFIG_KEY_STREAM = "__pregel_stream" +# holds a `StreamProtocol` passed from parent graph to child graphs CONFIG_KEY_STREAM_WRITER = "__pregel_stream_writer" +# holds a `StreamWriter` for stream_mode=custom CONFIG_KEY_STORE = "__pregel_store" +# holds a `BaseStore` made available to managed values CONFIG_KEY_RESUMING = "__pregel_resuming" +# holds a boolean indicating if subgraphs should resume from a previous checkpoint CONFIG_KEY_TASK_ID = "__pregel_task_id" +# holds the task ID for the current task CONFIG_KEY_DEDUPE_TASKS = "__pregel_dedupe_tasks" +# holds a boolean indicating if tasks should be deduplicated (for distributed mode) CONFIG_KEY_ENSURE_LATEST = "__pregel_ensure_latest" +# holds a boolean indicating whether to assert the requested checkpoint is the latest +# (for distributed mode) CONFIG_KEY_DELEGATE = "__pregel_delegate" -# this one part of public API so more readable +# holds a boolean indicating whether to delegate subgraphs (for distributed mode) CONFIG_KEY_CHECKPOINT_MAP = "checkpoint_map" -INTERRUPT = "__interrupt__" -ERROR = "__error__" -NO_WRITES = "__no_writes__" -SCHEDULED = "__scheduled__" -TASKS = "__pregel_tasks" # for backwards compat, this is the original name of PUSH +# holds a mapping of checkpoint_ns -> checkpoint_id for parent graphs +CONFIG_KEY_CHECKPOINT_ID = "checkpoint_id" +# holds the current checkpoint_id, if any +CONFIG_KEY_CHECKPOINT_NS = "checkpoint_ns" +# holds the current checkpoint_ns, "" for root graph + +# --- Other constants --- PUSH = "__pregel_push" +# denotes push-style tasks, ie. those created by Send objects PULL = "__pregel_pull" +# denotes pull-style tasks, ie. those triggered by edges RUNTIME_PLACEHOLDER = "__pregel_runtime_placeholder__" +# placeholder for managed values replaced at runtime +NS_SEP = "|" +# for checkpoint_ns, separates each level (ie. graph|subgraph|subsubgraph) +NS_END = ":" +# for checkpoint_ns, for each level, separates the namespace from the task_id + RESERVED = { - SCHEDULED, + TAG_HIDDEN, + # reserved write keys + INPUT, INTERRUPT, ERROR, NO_WRITES, + SCHEDULED, TASKS, - PUSH, - PULL, + # reserved config.configurable keys CONFIG_KEY_SEND, CONFIG_KEY_READ, CONFIG_KEY_CHECKPOINTER, - CONFIG_KEY_CHECKPOINT_MAP, CONFIG_KEY_STREAM, CONFIG_KEY_STREAM_WRITER, CONFIG_KEY_STORE, + CONFIG_KEY_CHECKPOINT_MAP, CONFIG_KEY_RESUMING, CONFIG_KEY_TASK_ID, CONFIG_KEY_DEDUPE_TASKS, CONFIG_KEY_ENSURE_LATEST, CONFIG_KEY_DELEGATE, - INPUT, + CONFIG_KEY_CHECKPOINT_MAP, + CONFIG_KEY_CHECKPOINT_ID, + CONFIG_KEY_CHECKPOINT_NS, + # other constants + PUSH, + PULL, RUNTIME_PLACEHOLDER, + NS_SEP, + NS_END, } -TAG_HIDDEN = "langsmith:hidden" - -START = "__start__" -END = "__end__" - -NS_SEP = "|" -NS_END = ":" - -EMPTY_MAP: Mapping[str, Any] = MappingProxyType({}) - - -class Send: - """A message or packet to send to a specific node in the graph. - - The `Send` class is used within a `StateGraph`'s conditional edges to - dynamically invoke a node with a custom state at the next step. - - Importantly, the sent state can differ from the core graph's state, - allowing for flexible and dynamic workflow management. - - One such example is a "map-reduce" workflow where your graph invokes - the same node multiple times in parallel with different states, - before aggregating the results back into the main graph's state. - - Attributes: - node (str): The name of the target node to send the message to. - arg (Any): The state or message to send to the target node. - - Examples: - >>> from typing import Annotated - >>> import operator - >>> class OverallState(TypedDict): - ... subjects: list[str] - ... jokes: Annotated[list[str], operator.add] - ... - >>> from langgraph.constants import Send - >>> from langgraph.graph import END, START - >>> def continue_to_jokes(state: OverallState): - ... return [Send("generate_joke", {"subject": s}) for s in state['subjects']] - ... - >>> from langgraph.graph import StateGraph - >>> builder = StateGraph(OverallState) - >>> builder.add_node("generate_joke", lambda state: {"jokes": [f"Joke about {state['subject']}"]}) - >>> builder.add_conditional_edges(START, continue_to_jokes) - >>> builder.add_edge("generate_joke", END) - >>> graph = builder.compile() - >>> - >>> # Invoking with two subjects results in a generated joke for each - >>> graph.invoke({"subjects": ["cats", "dogs"]}) - {'subjects': ['cats', 'dogs'], 'jokes': ['Joke about cats', 'Joke about dogs']} - """ - - node: str - arg: Any - - def __init__(self, /, node: str, arg: Any) -> None: - """ - Initialize a new instance of the Send class. - - Args: - node (str): The name of the target node to send the message to. - arg (Any): The state or message to send to the target node. - """ - self.node = node - self.arg = arg - - def __hash__(self) -> int: - return hash((self.node, self.arg)) - - def __repr__(self) -> str: - return f"Send(node={self.node!r}, arg={self.arg!r})" - - def __eq__(self, value: object) -> bool: - return ( - isinstance(value, Send) - and self.node == value.node - and self.arg == value.arg - ) - - -@dataclass -class Interrupt: - value: Any - when: Literal["during"] = "during" diff --git a/libs/langgraph/langgraph/errors.py b/libs/langgraph/langgraph/errors.py index ec84e0b28..63bc8aff6 100644 --- a/libs/langgraph/langgraph/errors.py +++ b/libs/langgraph/langgraph/errors.py @@ -1,7 +1,9 @@ from typing import Any, Sequence -from langgraph.checkpoint.base import EmptyChannelError -from langgraph.constants import Interrupt +from langgraph.checkpoint.base import EmptyChannelError # noqa: F401 +from langgraph.types import Interrupt + +# EmptyChannelError re-exported for backwards compatibility class GraphRecursionError(RecursionError): @@ -24,13 +26,14 @@ class GraphRecursionError(RecursionError): class InvalidUpdateError(Exception): - """Raised when attempting to update a channel with an invalid sequence of updates.""" + """Raised when attempting to update a channel with an invalid set of updates.""" pass class GraphInterrupt(Exception): - """Raised when a subgraph is interrupted.""" + """Raised when a subgraph is interrupted, suppressed by the root graph. + Never raised directly, or surfaced to the user.""" def __init__(self, interrupts: Sequence[Interrupt] = ()) -> None: super().__init__(interrupts) @@ -44,7 +47,7 @@ def __init__(self, value: Any) -> None: class GraphDelegate(Exception): - """Raised when a graph is delegated.""" + """Raised when a graph is delegated (for distributed mode).""" def __init__(self, *args: dict[str, Any]) -> None: super().__init__(*args) @@ -57,22 +60,22 @@ class EmptyInputError(Exception): class TaskNotFound(Exception): - """Raised when the executor is unable to find a task.""" + """Raised when the executor is unable to find a task (for distributed mode).""" pass class CheckpointNotLatest(Exception): - """Raised when the checkpoint is not the latest version.""" + """Raised when the checkpoint is not the latest version (for distributed mode).""" + + pass + + +class MultipleSubgraphsError(Exception): + """Raised when multiple subgraphs are called inside the same node.""" pass -__all__ = [ - "GraphRecursionError", - "InvalidUpdateError", - "GraphInterrupt", - "NodeInterrupt", - "EmptyInputError", - "EmptyChannelError", -] +_SEEN_CHECKPOINT_NS: set[str] = set() +"""Used for subgraph detection.""" diff --git a/libs/langgraph/langgraph/graph/graph.py b/libs/langgraph/langgraph/graph/graph.py index c5a043ee7..e957a15b9 100644 --- a/libs/langgraph/langgraph/graph/graph.py +++ b/libs/langgraph/langgraph/graph/graph.py @@ -26,7 +26,6 @@ from typing_extensions import Self from langgraph.channels.ephemeral_value import EphemeralValue -from langgraph.checkpoint.base import BaseCheckpointSaver from langgraph.constants import ( END, NS_END, @@ -38,8 +37,8 @@ from langgraph.errors import InvalidUpdateError from langgraph.pregel import Channel, Pregel from langgraph.pregel.read import PregelNode -from langgraph.pregel.types import All from langgraph.pregel.write import ChannelWrite, ChannelWriteEntry +from langgraph.types import All, Checkpointer from langgraph.utils.runnable import RunnableCallable, coerce_to_runnable logger = logging.getLogger(__name__) @@ -406,7 +405,7 @@ def validate(self, interrupt: Optional[Sequence[str]] = None) -> Self: def compile( self, - checkpointer: Optional[BaseCheckpointSaver] = None, + checkpointer: Checkpointer = None, interrupt_before: Optional[Union[All, list[str]]] = None, interrupt_after: Optional[Union[All, list[str]]] = None, debug: bool = False, diff --git a/libs/langgraph/langgraph/graph/state.py b/libs/langgraph/langgraph/graph/state.py index cee0fb849..bc0762c80 100644 --- a/libs/langgraph/langgraph/graph/state.py +++ b/libs/langgraph/langgraph/graph/state.py @@ -32,7 +32,6 @@ from langgraph.channels.ephemeral_value import EphemeralValue from langgraph.channels.last_value import LastValue from langgraph.channels.named_barrier_value import NamedBarrierValue -from langgraph.checkpoint.base import BaseCheckpointSaver from langgraph.constants import NS_END, NS_SEP, TAG_HIDDEN from langgraph.errors import InvalidUpdateError from langgraph.graph.graph import END, START, Branch, CompiledGraph, Graph, Send @@ -45,9 +44,9 @@ is_writable_managed_value, ) from langgraph.pregel.read import ChannelRead, PregelNode -from langgraph.pregel.types import All, RetryPolicy from langgraph.pregel.write import SKIP_WRITE, ChannelWrite, ChannelWriteEntry from langgraph.store.base import BaseStore +from langgraph.types import All, Checkpointer, RetryPolicy from langgraph.utils.fields import get_field_default from langgraph.utils.pydantic import create_model from langgraph.utils.runnable import coerce_to_runnable @@ -400,7 +399,7 @@ def add_edge(self, start_key: Union[str, list[str]], end_key: str) -> Self: def compile( self, - checkpointer: Optional[BaseCheckpointSaver] = None, + checkpointer: Checkpointer = None, *, store: Optional[BaseStore] = None, interrupt_before: Optional[Union[All, list[str]]] = None, @@ -413,7 +412,7 @@ def compile( streamed, batched, and run asynchronously. Args: - checkpointer (Optional[BaseCheckpointSaver]): An optional checkpoint saver object. + checkpointer (Checkpointer): An optional checkpoint saver object. This serves as a fully versioned "memory" for the graph, allowing the graph to be paused and resumed, and replayed from any point. interrupt_before (Optional[Sequence[str]]): An optional list of node names to interrupt before. diff --git a/libs/langgraph/langgraph/prebuilt/chat_agent_executor.py b/libs/langgraph/langgraph/prebuilt/chat_agent_executor.py index 1e2209bd7..c5c64cd24 100644 --- a/libs/langgraph/langgraph/prebuilt/chat_agent_executor.py +++ b/libs/langgraph/langgraph/prebuilt/chat_agent_executor.py @@ -16,13 +16,13 @@ from langchain_core.tools import BaseTool from langgraph._api.deprecation import deprecated_parameter -from langgraph.checkpoint.base import BaseCheckpointSaver from langgraph.graph import StateGraph from langgraph.graph.graph import CompiledGraph from langgraph.graph.message import add_messages from langgraph.managed import IsLastStep from langgraph.prebuilt.tool_executor import ToolExecutor from langgraph.prebuilt.tool_node import ToolNode +from langgraph.types import Checkpointer # We create the AgentState that we will pass around @@ -132,7 +132,7 @@ def create_react_agent( state_schema: Optional[StateSchemaType] = None, messages_modifier: Optional[MessagesModifier] = None, state_modifier: Optional[StateModifier] = None, - checkpointer: Optional[BaseCheckpointSaver] = None, + checkpointer: Checkpointer = None, interrupt_before: Optional[list[str]] = None, interrupt_after: Optional[list[str]] = None, debug: bool = False, diff --git a/libs/langgraph/langgraph/pregel/__init__.py b/libs/langgraph/langgraph/pregel/__init__.py index f1a9aa578..f78d072d6 100644 --- a/libs/langgraph/langgraph/pregel/__init__.py +++ b/libs/langgraph/langgraph/pregel/__init__.py @@ -83,11 +83,11 @@ from langgraph.pregel.read import PregelNode from langgraph.pregel.retry import RetryPolicy from langgraph.pregel.runner import PregelRunner -from langgraph.pregel.types import All, StateSnapshot, StreamMode from langgraph.pregel.utils import get_new_channel_versions from langgraph.pregel.validate import validate_graph, validate_keys from langgraph.pregel.write import ChannelWrite, ChannelWriteEntry from langgraph.store.base import BaseStore +from langgraph.types import All, Checkpointer, StateSnapshot, StreamMode from langgraph.utils.config import ( ensure_config, merge_configs, @@ -197,7 +197,7 @@ class Pregel(Runnable[Union[dict[str, Any], Any], Union[dict[str, Any], Any]]): debug: bool """Whether to print debug information during execution. Defaults to False.""" - checkpointer: Optional[BaseCheckpointSaver] = None + checkpointer: Checkpointer = None """Checkpointer used to save and load graph state. Defaults to None.""" store: Optional[BaseStore] = None @@ -281,7 +281,7 @@ def config_specs(self) -> list[ConfigurableFieldSpec]: [spec for node in self.nodes.values() for spec in node.config_specs] + ( self.checkpointer.config_specs - if self.checkpointer is not None + if isinstance(self.checkpointer, BaseCheckpointSaver) else [] ) + ( @@ -1059,6 +1059,8 @@ def _defaults( Union[All, Sequence[str]], Optional[BaseCheckpointSaver], ]: + if config["recursion_limit"] < 1: + raise ValueError("recursion_limit must be at least 1") debug = debug if debug is not None else self.debug if output_keys is None: output_keys = self.stream_channels_asis @@ -1072,12 +1074,16 @@ def _defaults( if CONFIG_KEY_TASK_ID in config.get("configurable", {}): # if being called as a node in another graph, always use values mode stream_mode = ["values"] - if CONFIG_KEY_CHECKPOINTER in config.get("configurable", {}): - checkpointer: Optional[BaseCheckpointSaver] = config["configurable"][ - CONFIG_KEY_CHECKPOINTER - ] + if self.checkpointer is False: + checkpointer: Optional[BaseCheckpointSaver] = None + elif CONFIG_KEY_CHECKPOINTER in config.get("configurable", {}): + checkpointer = config["configurable"][CONFIG_KEY_CHECKPOINTER] else: checkpointer = self.checkpointer + if checkpointer and not config.get("configurable"): + raise ValueError( + f"Checkpointer requires one or more of the following 'configurable' keys: {[s.id for s in checkpointer.config_specs]}" + ) return ( debug, set(stream_mode), @@ -1193,12 +1199,6 @@ def output() -> Iterator: run_id=config.get("run_id"), ) try: - if config["recursion_limit"] < 1: - raise ValueError("recursion_limit must be at least 1") - if self.checkpointer and not config.get("configurable"): - raise ValueError( - f"Checkpointer requires one or more of the following 'configurable' keys: {[s.id for s in self.checkpointer.config_specs]}" - ) # assign defaults ( debug, @@ -1414,12 +1414,6 @@ def output() -> Iterator: None, ) try: - if config["recursion_limit"] < 1: - raise ValueError("recursion_limit must be at least 1") - if self.checkpointer and not config.get("configurable"): - raise ValueError( - f"Checkpointer requires one or more of the following 'configurable' keys: {[s.id for s in self.checkpointer.config_specs]}" - ) # assign defaults ( debug, diff --git a/libs/langgraph/langgraph/pregel/algo.py b/libs/langgraph/langgraph/pregel/algo.py index 8a63d4cc6..f9b0096c8 100644 --- a/libs/langgraph/langgraph/pregel/algo.py +++ b/libs/langgraph/langgraph/pregel/algo.py @@ -33,6 +33,7 @@ CONFIG_KEY_READ, CONFIG_KEY_SEND, CONFIG_KEY_TASK_ID, + EMPTY_SEQ, INTERRUPT, NO_WRITES, NS_END, @@ -50,15 +51,16 @@ from langgraph.pregel.log import logger from langgraph.pregel.manager import ChannelsManager from langgraph.pregel.read import PregelNode -from langgraph.pregel.types import All, PregelExecutableTask, PregelTask +from langgraph.types import All, PregelExecutableTask, PregelTask from langgraph.utils.config import merge_configs, patch_config GetNextVersion = Callable[[Optional[V], BaseChannel], V] -EMPTY_SEQ: tuple[str, ...] = tuple() - class WritesProtocol(Protocol): + """Protocol for objects containing writes to be applied to checkpoint. + Implemented by PregelTaskWrites and PregelExecutableTask.""" + @property def name(self) -> str: ... @@ -70,6 +72,9 @@ def triggers(self) -> Sequence[str]: ... class PregelTaskWrites(NamedTuple): + """Simplest implementation of WritesProtocol, for usage with writes that + don't originate from a runnable task, eg. graph input, update_state, etc.""" + name: str writes: Sequence[tuple[str, Any]] triggers: Sequence[str] @@ -80,6 +85,7 @@ def should_interrupt( interrupt_nodes: Union[All, Sequence[str]], tasks: Iterable[PregelExecutableTask], ) -> list[PregelExecutableTask]: + """Check if the graph should be interrupted based on current state.""" version_type = type(next(iter(checkpoint["channel_versions"].values()), None)) null_version = version_type() # type: ignore[misc] seen = checkpoint["versions_seen"].get(INTERRUPT, {}) @@ -117,6 +123,9 @@ def local_read( select: Union[list[str], str], fresh: bool = False, ) -> Union[dict[str, Any], Any]: + """Function injected under CONFIG_KEY_READ in task config, to read current state. + Used by conditional edges to read a copy of the state with reflecting the writes + from that node only.""" if isinstance(select, str): managed_keys = [] for c, _ in task.writes: @@ -153,6 +162,8 @@ def local_write( managed: ManagedValueMapping, writes: Sequence[tuple[str, Any]], ) -> None: + """Function injected under CONFIG_KEY_SEND in task config, to write to channels. + Validates writes and forwards them to `commit` function.""" for chan, value in writes: if chan == TASKS: if not isinstance(value, Send): @@ -169,6 +180,7 @@ def local_write( def increment(current: Optional[int], channel: BaseChannel) -> int: + """Default channel versioning function, increments the current int version.""" return current + 1 if current is not None else 1 @@ -178,6 +190,9 @@ def apply_writes( tasks: Iterable[WritesProtocol], get_next_version: Optional[GetNextVersion], ) -> dict[str, list[Any]]: + """Apply writes from a set of tasks (usually the tasks from a Pregel step) + to the checkpoint and channels, and return managed values writes to be applied + externally.""" # update seen versions for task in tasks: checkpoint["versions_seen"].setdefault(task.name, {}).update( @@ -297,6 +312,9 @@ def prepare_next_tasks( checkpointer: Optional[BaseCheckpointSaver] = None, manager: Union[None, ParentRunManager, AsyncParentRunManager] = None, ) -> Union[dict[str, PregelTask], dict[str, PregelExecutableTask]]: + """Prepare the set of tasks that will make up the next Pregel step. + This is the union of all PUSH tasks (Sends) and PULL tasks (nodes triggered + by edges).""" tasks: dict[str, Union[PregelTask, PregelExecutableTask]] = {} # Consume pending packets for idx, _ in enumerate(checkpoint["pending_sends"]): @@ -348,6 +366,8 @@ def prepare_single_task( checkpointer: Optional[BaseCheckpointSaver] = None, manager: Union[None, ParentRunManager, AsyncParentRunManager] = None, ) -> Union[None, PregelTask, PregelExecutableTask]: + """Prepares a single task for the next Pregel step, given a task path, which + uniquely identifies a PUSH or PULL task within the graph.""" checkpoint_id = UUID(checkpoint["id"]).bytes configurable = config.get("configurable", {}) parent_ns = configurable.get("checkpoint_ns", "") @@ -568,6 +588,7 @@ def _proc_input( *, for_execution: bool, ) -> Iterator[Any]: + """Prepare input for a PULL task, based on the process's channels and triggers.""" # If all trigger channels subscribed by this process are not empty # then invoke the process with the values of all non-empty channels if isinstance(proc.channels, dict): diff --git a/libs/langgraph/langgraph/pregel/debug.py b/libs/langgraph/langgraph/pregel/debug.py index 56f1eb9c6..982182842 100644 --- a/libs/langgraph/langgraph/pregel/debug.py +++ b/libs/langgraph/langgraph/pregel/debug.py @@ -22,7 +22,7 @@ from langgraph.checkpoint.base import Checkpoint, CheckpointMetadata, PendingWrite from langgraph.constants import ERROR, INTERRUPT, TAG_HIDDEN from langgraph.pregel.io import read_channels -from langgraph.pregel.types import PregelExecutableTask, PregelTask, StateSnapshot +from langgraph.types import PregelExecutableTask, PregelTask, StateSnapshot class TaskPayload(TypedDict): @@ -84,6 +84,7 @@ class DebugOutputCheckpoint(DebugOutputBase): def map_debug_tasks( step: int, tasks: Iterable[PregelExecutableTask] ) -> Iterator[DebugOutputTask]: + """Produce "task" events for stream_mode=debug.""" ts = datetime.now(timezone.utc).isoformat() for task in tasks: if task.config is not None and TAG_HIDDEN in task.config.get("tags", []): @@ -107,6 +108,7 @@ def map_debug_task_results( task_tup: tuple[PregelExecutableTask, Sequence[tuple[str, Any]]], stream_keys: Union[str, Sequence[str]], ) -> Iterator[DebugOutputTaskResult]: + """Produce "task_result" events for stream_mode=debug.""" stream_channels_list = ( [stream_keys] if isinstance(stream_keys, str) else stream_keys ) @@ -135,6 +137,7 @@ def map_debug_checkpoint( tasks: Iterable[PregelExecutableTask], pending_writes: list[PendingWrite], ) -> Iterator[DebugOutputCheckpoint]: + """Produce "checkpoint" events for stream_mode=debug.""" yield { "type": "checkpoint", "timestamp": checkpoint["ts"], @@ -213,6 +216,7 @@ def tasks_w_writes( pending_writes: Optional[list[PendingWrite]], states: Optional[dict[str, Union[RunnableConfig, StateSnapshot]]], ) -> tuple[PregelTask, ...]: + """Apply writes / subgraph states to tasks to be returned in a StateSnapshot.""" pending_writes = pending_writes or [] return tuple( PregelTask( diff --git a/libs/langgraph/langgraph/pregel/executor.py b/libs/langgraph/langgraph/pregel/executor.py index 46f1c3f64..691098b7a 100644 --- a/libs/langgraph/langgraph/pregel/executor.py +++ b/libs/langgraph/langgraph/pregel/executor.py @@ -39,6 +39,13 @@ def __call__( class BackgroundExecutor(ContextManager): + """A context manager that runs sync tasks in the background. + Uses a thread pool executor to delegate tasks to separate threads. + On exit, + - cancels any (not yet started) tasks with `__cancel_on_exit__=True` + - waits for all tasks to finish + - re-raises the first exception from tasks with `__reraise_on_exit__=True`""" + def __init__(self, config: RunnableConfig) -> None: self.stack = ExitStack() self.executor = self.stack.enter_context(get_executor_for_config(config)) @@ -49,7 +56,7 @@ def submit( # type: ignore[valid-type] fn: Callable[P, T], *args: P.args, __name__: Optional[str] = None, # currently not used in sync version - __cancel_on_exit__: bool = False, + __cancel_on_exit__: bool = False, # for sync, can cancel only if not started __reraise_on_exit__: bool = True, **kwargs: P.kwargs, ) -> concurrent.futures.Future[T]: @@ -101,6 +108,14 @@ def __exit__( class AsyncBackgroundExecutor(AsyncContextManager): + """A context manager that runs async tasks in the background. + Uses the current event loop to delegate tasks to asyncio tasks. + On exit, + - cancels any tasks with `__cancel_on_exit__=True` + - waits for all tasks to finish + - re-raises the first exception from tasks with `__reraise_on_exit__=True` + ignoring CancelledError""" + def __init__(self) -> None: self.context_not_supported = sys.version_info < (3, 11) self.tasks: dict[asyncio.Task, tuple[bool, bool]] = {} diff --git a/libs/langgraph/langgraph/pregel/io.py b/libs/langgraph/langgraph/pregel/io.py index ad2252c9d..ef9822641 100644 --- a/libs/langgraph/langgraph/pregel/io.py +++ b/libs/langgraph/langgraph/pregel/io.py @@ -3,9 +3,9 @@ from langchain_core.runnables.utils import AddableDict from langgraph.channels.base import BaseChannel, EmptyChannelError -from langgraph.constants import ERROR, INTERRUPT, TAG_HIDDEN +from langgraph.constants import EMPTY_SEQ, ERROR, INTERRUPT, TAG_HIDDEN from langgraph.pregel.log import logger -from langgraph.pregel.types import PregelExecutableTask +from langgraph.types import PregelExecutableTask def read_channel( @@ -97,9 +97,6 @@ def __radd__(self, other: dict[str, Any]) -> "AddableUpdatesDict": raise TypeError("AddableUpdatesDict does not support right-side addition") -EMPTY_SEQ: tuple[str, ...] = tuple() - - def map_output_updates( output_channels: Union[str, Sequence[str]], tasks: list[tuple[PregelExecutableTask, Sequence[tuple[str, Any]]]], diff --git a/libs/langgraph/langgraph/pregel/loop.py b/libs/langgraph/langgraph/pregel/loop.py index 49baa1846..45b8798af 100644 --- a/libs/langgraph/langgraph/pregel/loop.py +++ b/libs/langgraph/langgraph/pregel/loop.py @@ -44,6 +44,7 @@ CONFIG_KEY_RESUMING, CONFIG_KEY_STREAM, CONFIG_KEY_TASK_ID, + EMPTY_SEQ, ERROR, INPUT, INTERRUPT, @@ -53,10 +54,12 @@ TASKS, ) from langgraph.errors import ( + _SEEN_CHECKPOINT_NS, CheckpointNotLatest, EmptyInputError, GraphDelegate, GraphInterrupt, + MultipleSubgraphsError, ) from langgraph.managed.base import ( ManagedValueMapping, @@ -93,21 +96,20 @@ ) from langgraph.pregel.manager import AsyncChannelsManager, ChannelsManager from langgraph.pregel.read import PregelNode -from langgraph.pregel.types import All, PregelExecutableTask, StreamMode from langgraph.pregel.utils import get_new_channel_versions from langgraph.store.base import BaseStore from langgraph.store.batch import AsyncBatchedStore +from langgraph.types import All, PregelExecutableTask, StreamMode from langgraph.utils.config import patch_configurable V = TypeVar("V") P = ParamSpec("P") +StreamChunk = tuple[tuple[str, ...], str, Any] + INPUT_DONE = object() INPUT_RESUMING = object() -EMPTY_SEQ = () SPECIAL_CHANNELS = (ERROR, INTERRUPT, SCHEDULED) -StreamChunk = tuple[tuple[str, ...], str, Any] - class StreamProtocol: __slots__ = ("modes", "__call__") @@ -195,6 +197,7 @@ def __init__( specs: Mapping[str, Union[BaseChannel, ManagedValueSpec]], output_keys: Union[str, Sequence[str]], stream_keys: Union[str, Sequence[str]], + check_subgraphs: bool = True, debug: bool = False, ) -> None: self.stream = stream @@ -220,6 +223,11 @@ def __init__( self.config = patch_configurable( self.config, {"checkpoint_ns": "", "checkpoint_id": None} ) + if check_subgraphs and self.is_nested and self.checkpointer is not None: + if self.config["configurable"]["checkpoint_ns"] in _SEEN_CHECKPOINT_NS: + raise MultipleSubgraphsError + else: + _SEEN_CHECKPOINT_NS.add(self.config["configurable"]["checkpoint_ns"]) if ( CONFIG_KEY_CHECKPOINT_MAP in self.config["configurable"] and self.config["configurable"].get("checkpoint_ns") @@ -634,6 +642,7 @@ def __init__( specs: Mapping[str, Union[BaseChannel, ManagedValueSpec]], output_keys: Union[str, Sequence[str]] = EMPTY_SEQ, stream_keys: Union[str, Sequence[str]] = EMPTY_SEQ, + check_subgraphs: bool = True, debug: bool = False, ) -> None: super().__init__( @@ -646,6 +655,7 @@ def __init__( specs=specs, output_keys=output_keys, stream_keys=stream_keys, + check_subgraphs=check_subgraphs, debug=debug, ) self.stack = ExitStack() @@ -755,6 +765,7 @@ def __init__( specs: Mapping[str, Union[BaseChannel, ManagedValueSpec]], output_keys: Union[str, Sequence[str]] = EMPTY_SEQ, stream_keys: Union[str, Sequence[str]] = EMPTY_SEQ, + check_subgraphs: bool = True, debug: bool = False, ) -> None: super().__init__( @@ -767,6 +778,7 @@ def __init__( specs=specs, output_keys=output_keys, stream_keys=stream_keys, + check_subgraphs=check_subgraphs, debug=debug, ) self.store = AsyncBatchedStore(self.store) if self.store else None diff --git a/libs/langgraph/langgraph/pregel/messages.py b/libs/langgraph/langgraph/pregel/messages.py index 7c3f90b10..d0ae539e2 100644 --- a/libs/langgraph/langgraph/pregel/messages.py +++ b/libs/langgraph/langgraph/pregel/messages.py @@ -24,6 +24,9 @@ class StreamMessagesHandler(BaseCallbackHandler, _StreamingCallbackHandler): + """A callback handler that implements stream_mode=messages. + Collects messages from (1) chat model stream events and (2) node outputs.""" + def __init__(self, stream: Callable[[StreamChunk], None]): self.stream = stream self.metadata: dict[UUID, Meta] = {} diff --git a/libs/langgraph/langgraph/pregel/metadata.py b/libs/langgraph/langgraph/pregel/metadata.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/libs/langgraph/langgraph/pregel/read.py b/libs/langgraph/langgraph/pregel/read.py index 3ad988b89..097e76fa2 100644 --- a/libs/langgraph/langgraph/pregel/read.py +++ b/libs/langgraph/langgraph/pregel/read.py @@ -31,6 +31,9 @@ class ChannelRead(RunnableCallable): + """Implements the logic for reading state from CONFIG_KEY_READ. + Usable both as a runnable as well as a static method to call imperatively.""" + channel: Union[str, list[str]] fresh: bool = False @@ -108,21 +111,39 @@ def do_read( class PregelNode(Runnable): + """A node in a Pregel graph. This won't be invoked as a runnable by the graph + itself, but instead acts as a container for the components necessary to make + a PregelExecutableTask for a node.""" + channels: Union[list[str], Mapping[str, str]] + """The channels that will be passed as input to `bound`. + If a list, the node will be invoked with the first of that isn't empty. + If a dict, the keys are the names of the channels, and the values are the keys + to use in the input to `bound`.""" triggers: list[str] + """If any of these channels is written to, this node will be triggered in + the next step.""" mapper: Optional[Callable[[Any], Any]] + """A function to transform the input before passing it to `bound`.""" writers: list[Runnable] + """A list of writers that will be executed after `bound`, responsible for + taking the output of `bound` and writing it to the appropriate channels.""" bound: Runnable[Any, Any] + """The main logic of the node. This will be invoked with the input from + `channels`.""" retry_policy: Optional[RetryPolicy] + """The retry policy to use when invoking the node.""" tags: Optional[Sequence[str]] + """Tags to attach to the node for tracing.""" metadata: Optional[Mapping[str, Any]] + """Metadata to attach to the node for tracing.""" def __init__( self, @@ -151,7 +172,7 @@ def copy(self, update: dict[str, Any]) -> PregelNode: @cached_property def flat_writers(self) -> list[Runnable]: - """Get writers with optimizations applied.""" + """Get writers with optimizations applied. Dedupes consecutive ChannelWrites.""" writers = self.writers.copy() while ( len(writers) > 1 @@ -170,6 +191,7 @@ def flat_writers(self) -> list[Runnable]: @cached_property def node(self) -> Optional[Runnable[Any, Any]]: + """Get a runnable that combines `bound` and `writers`.""" writers = self.flat_writers if self.bound is DEFAULT_BOUND and not writers: return None diff --git a/libs/langgraph/langgraph/pregel/retry.py b/libs/langgraph/langgraph/pregel/retry.py index 90ccaa7d0..33c60d875 100644 --- a/libs/langgraph/langgraph/pregel/retry.py +++ b/libs/langgraph/langgraph/pregel/retry.py @@ -5,8 +5,8 @@ from typing import Optional, Sequence from langgraph.constants import CONFIG_KEY_RESUMING -from langgraph.errors import GraphInterrupt -from langgraph.pregel.types import PregelExecutableTask, RetryPolicy +from langgraph.errors import _SEEN_CHECKPOINT_NS, GraphInterrupt +from langgraph.types import PregelExecutableTask, RetryPolicy from langgraph.utils.config import patch_configurable logger = logging.getLogger(__name__) @@ -71,6 +71,13 @@ def run_with_retry( ) # signal subgraphs to resume (if available) config = patch_configurable(config, {CONFIG_KEY_RESUMING: True}) + # clear checkpoint_ns seen (for subgraph detection) + if checkpoint_ns := config["configurable"].get("checkpoint_ns"): + _SEEN_CHECKPOINT_NS.discard(checkpoint_ns) + finally: + # clear checkpoint_ns seen (for subgraph detection) + if checkpoint_ns := config["configurable"].get("checkpoint_ns"): + _SEEN_CHECKPOINT_NS.discard(checkpoint_ns) async def arun_with_retry( @@ -137,3 +144,10 @@ async def arun_with_retry( ) # signal subgraphs to resume (if available) config = patch_configurable(config, {CONFIG_KEY_RESUMING: True}) + # clear checkpoint_ns seen (for subgraph detection) + if checkpoint_ns := config["configurable"].get("checkpoint_ns"): + _SEEN_CHECKPOINT_NS.discard(checkpoint_ns) + finally: + # clear checkpoint_ns seen (for subgraph detection) + if checkpoint_ns := config["configurable"].get("checkpoint_ns"): + _SEEN_CHECKPOINT_NS.discard(checkpoint_ns) diff --git a/libs/langgraph/langgraph/pregel/runner.py b/libs/langgraph/langgraph/pregel/runner.py index 14e84352f..b8392b613 100644 --- a/libs/langgraph/langgraph/pregel/runner.py +++ b/libs/langgraph/langgraph/pregel/runner.py @@ -18,10 +18,14 @@ from langgraph.errors import GraphDelegate, GraphInterrupt from langgraph.pregel.executor import Submit from langgraph.pregel.retry import arun_with_retry, run_with_retry -from langgraph.pregel.types import PregelExecutableTask, RetryPolicy +from langgraph.types import PregelExecutableTask, RetryPolicy class PregelRunner: + """Responsible for executing a set of Pregel tasks concurrently, committing + their writes, yielding control to caller when there is output to emit, and + interrupting other tasks if appropriate.""" + def __init__( self, *, @@ -215,6 +219,8 @@ def commit( def _should_stop_others( done: Union[set[concurrent.futures.Future[Any]], set[asyncio.Future[Any]]], ) -> bool: + """Check if any task failed, if so, cancel all other tasks. + GraphInterrupts are not considered failures.""" for fut in done: if fut.cancelled(): return True @@ -227,6 +233,7 @@ def _should_stop_others( def _exception( fut: Union[concurrent.futures.Future[Any], asyncio.Future[Any]], ) -> Optional[BaseException]: + """Return the exception from a future, without raising CancelledError.""" if fut.cancelled(): if isinstance(fut, asyncio.Future): return asyncio.CancelledError() @@ -245,6 +252,7 @@ def _panic_or_proceed( timeout_exc_cls: Type[Exception] = TimeoutError, panic: bool = True, ) -> None: + """Cancel remaining tasks if any failed, re-raise exception if panic is True.""" done: set[Union[concurrent.futures.Future[Any], asyncio.Future[Any]]] = set() inflight: set[Union[concurrent.futures.Future[Any], asyncio.Future[Any]]] = set() for fut, val in futs.items(): diff --git a/libs/langgraph/langgraph/pregel/types.py b/libs/langgraph/langgraph/pregel/types.py index d34845483..7a72b88c9 100644 --- a/libs/langgraph/langgraph/pregel/types.py +++ b/libs/langgraph/langgraph/pregel/types.py @@ -1,124 +1,25 @@ -from collections import deque -from typing import Any, Callable, Literal, NamedTuple, Optional, Sequence, Type, Union - -from langchain_core.runnables import Runnable, RunnableConfig - -from langgraph.checkpoint.base import CheckpointMetadata -from langgraph.constants import Interrupt - - -def default_retry_on(exc: Exception) -> bool: - import httpx - import requests - - if isinstance(exc, ConnectionError): - return True - if isinstance( - exc, - ( - ValueError, - TypeError, - ArithmeticError, - ImportError, - LookupError, - NameError, - SyntaxError, - RuntimeError, - ReferenceError, - StopIteration, - StopAsyncIteration, - OSError, - ), - ): - return False - if isinstance(exc, httpx.HTTPStatusError): - return 500 <= exc.response.status_code < 600 - if isinstance(exc, requests.HTTPError): - return 500 <= exc.response.status_code < 600 if exc.response else True - return True - - -class RetryPolicy(NamedTuple): - """Configuration for retrying nodes.""" - - initial_interval: float = 0.5 - """Amount of time that must elapse before the first retry occurs. In seconds.""" - backoff_factor: float = 2.0 - """Multiplier by which the interval increases after each retry.""" - max_interval: float = 128.0 - """Maximum amount of time that may elapse between retries. In seconds.""" - max_attempts: int = 3 - """Maximum number of attempts to make before giving up, including the first.""" - jitter: bool = True - """Whether to add random jitter to the interval between retries.""" - retry_on: Union[ - Type[Exception], Sequence[Type[Exception]], Callable[[Exception], bool] - ] = default_retry_on - """List of exception classes that should trigger a retry, or a callable that returns True for exceptions that should trigger a retry.""" - - -class CachePolicy(NamedTuple): - """Configuration for caching nodes.""" - - pass - - -class PregelTask(NamedTuple): - id: str - name: str - path: tuple[Union[str, int], ...] - error: Optional[Exception] = None - interrupts: tuple[Interrupt, ...] = () - state: Union[None, RunnableConfig, "StateSnapshot"] = None - - -class PregelExecutableTask(NamedTuple): - name: str - input: Any - proc: Runnable - writes: deque[tuple[str, Any]] - config: RunnableConfig - triggers: list[str] - retry_policy: Optional[RetryPolicy] - cache_policy: Optional[CachePolicy] - id: str - path: tuple[Union[str, int], ...] - scheduled: bool = False - - -class StateSnapshot(NamedTuple): - """Snapshot of the state of the graph at the beginning of a step.""" - - values: Union[dict[str, Any], Any] - """Current values of channels""" - next: tuple[str, ...] - """The name of the node to execute in each task for this step.""" - config: RunnableConfig - """Config used to fetch this snapshot""" - metadata: Optional[CheckpointMetadata] - """Metadata associated with this snapshot""" - created_at: Optional[str] - """Timestamp of snapshot creation""" - parent_config: Optional[RunnableConfig] - """Config used to fetch the parent snapshot, if any""" - tasks: tuple[PregelTask, ...] - """Tasks to execute in this step. If already attempted, may contain an error.""" - - -All = Literal["*"] - -StreamMode = Literal["values", "updates", "debug", "messages", "custom"] -"""How the stream method should emit outputs. - -- 'values': Emit all values of the state for each step. -- 'updates': Emit only the node name(s) and updates - that were returned by the node(s) **after** each step. -- 'debug': Emit debug events for each step. -- 'messages': Emit LLM messages token-by-token. -- 'custom': Emit custom output `write: StreamWriter` kwarg of each node. -""" - -StreamWriter = Callable[[Any], None] -"""Callable that accepts a single argument and writes it to the output stream. -Always injected into nodes if requested, -but it's a no-op when not using stream_mode="custom".""" +"""Re-export types moved to langgraph.types""" + +from langgraph.types import ( + All, + CachePolicy, + PregelExecutableTask, + PregelTask, + RetryPolicy, + StateSnapshot, + StreamMode, + StreamWriter, + default_retry_on, +) + +__all__ = [ + "All", + "CachePolicy", + "PregelExecutableTask", + "PregelTask", + "RetryPolicy", + "StateSnapshot", + "StreamMode", + "StreamWriter", + "default_retry_on", +] diff --git a/libs/langgraph/langgraph/pregel/utils.py b/libs/langgraph/langgraph/pregel/utils.py index c6dc064d3..3a29e5ed1 100644 --- a/libs/langgraph/langgraph/pregel/utils.py +++ b/libs/langgraph/langgraph/pregel/utils.py @@ -4,7 +4,7 @@ def get_new_channel_versions( previous_versions: ChannelVersions, current_versions: ChannelVersions ) -> ChannelVersions: - """Get new channel versions.""" + """Get subset of current_versions that are newer than previous_versions.""" if previous_versions: version_type = type(next(iter(current_versions.values()), None)) null_version = version_type() # type: ignore[misc] diff --git a/libs/langgraph/langgraph/pregel/validate.py b/libs/langgraph/langgraph/pregel/validate.py index 232014240..cf957dc07 100644 --- a/libs/langgraph/langgraph/pregel/validate.py +++ b/libs/langgraph/langgraph/pregel/validate.py @@ -3,7 +3,7 @@ from langgraph.channels.base import BaseChannel from langgraph.constants import RESERVED from langgraph.pregel.read import PregelNode -from langgraph.pregel.types import All +from langgraph.types import All def validate_graph( @@ -17,7 +17,7 @@ def validate_graph( ) -> None: for chan in channels: if chan in RESERVED: - raise ValueError(f"Channel names {RESERVED} are reserved") + raise ValueError(f"Channel names {chan} are reserved") subscribed_channels = set[str]() for name, node in nodes.items(): diff --git a/libs/langgraph/langgraph/pregel/write.py b/libs/langgraph/langgraph/pregel/write.py index 2adcab757..c2795c67c 100644 --- a/libs/langgraph/langgraph/pregel/write.py +++ b/libs/langgraph/langgraph/pregel/write.py @@ -1,6 +1,5 @@ from __future__ import annotations -import asyncio from typing import ( Any, Callable, @@ -22,30 +21,29 @@ TYPE_SEND = Callable[[Sequence[tuple[str, Any]]], None] R = TypeVar("R", bound=Runnable) - SKIP_WRITE = object() PASSTHROUGH = object() class ChannelWriteEntry(NamedTuple): channel: str + """Channel name to write to.""" value: Any = PASSTHROUGH + """Value to write, or PASSTHROUGH to use the input.""" skip_none: bool = False + """Whether to skip writing if the value is None.""" mapper: Optional[Callable] = None + """Function to transform the value before writing.""" class ChannelWrite(RunnableCallable): + """Implements th logic for sending writes to CONFIG_KEY_SEND. + Can be used as a runnable or as a static method to call imperatively.""" + writes: list[Union[ChannelWriteEntry, Send]] - """ - Sequence of write entries, each of which is a tuple of: - - channel name - - runnable to map input, or None to use the input, or any other value to use instead - - whether to skip writing if the mapped value is None - """ + """Sequence of write entries or Send objects to write.""" require_at_least_one_of: Optional[Sequence[str]] - """ - If defined, at least one of these channels must be written to. - """ + """If defined, at least one of these channels must be written to.""" def __init__( self, @@ -145,6 +143,7 @@ def do_write( @staticmethod def is_writer(runnable: Runnable) -> bool: + """Used by PregelNode to distinguish between writers and other runnables.""" return ( isinstance(runnable, ChannelWrite) or getattr(runnable, "_is_channel_writer", False) is True @@ -152,13 +151,9 @@ def is_writer(runnable: Runnable) -> bool: @staticmethod def register_writer(runnable: R) -> R: + """Used to mark a runnable as a writer, so that it can be detected by is_writer. + Instances of ChannelWrite are automatically marked as writers.""" # using object.__setattr__ to work around objects that override __setattr__ # eg. pydantic models and dataclasses object.__setattr__(runnable, "_is_channel_writer", True) return runnable - - -def _mk_future(val: Any) -> asyncio.Future: - fut: asyncio.Future[Any] = asyncio.Future() - fut.set_result(val) - return fut diff --git a/libs/langgraph/langgraph/types.py b/libs/langgraph/langgraph/types.py new file mode 100644 index 000000000..f8a8a74c6 --- /dev/null +++ b/libs/langgraph/langgraph/types.py @@ -0,0 +1,214 @@ +from collections import deque +from dataclasses import dataclass +from typing import ( + Any, + Callable, + Literal, + NamedTuple, + Optional, + Sequence, + Type, + Union, +) + +from langchain_core.runnables import Runnable, RunnableConfig + +from langgraph.checkpoint.base import BaseCheckpointSaver, CheckpointMetadata + +All = Literal["*"] +"""Special value to indicate that graph should interrupt on all nodes.""" + +Checkpointer = Union[None, Literal[False], BaseCheckpointSaver] +"""Type of the checkpointer to use for a subgraph. False disables checkpointing, +even if the parent graph has a checkpointer. None inherits checkpointer.""" + +StreamMode = Literal["values", "updates", "debug", "messages", "custom"] +"""How the stream method should emit outputs. + +- 'values': Emit all values of the state for each step. +- 'updates': Emit only the node name(s) and updates + that were returned by the node(s) **after** each step. +- 'debug': Emit debug events for each step. +- 'messages': Emit LLM messages token-by-token. +- 'custom': Emit custom output `write: StreamWriter` kwarg of each node. +""" + +StreamWriter = Callable[[Any], None] +"""Callable that accepts a single argument and writes it to the output stream. +Always injected into nodes if requested as a keyword argument, but it's a no-op +when not using stream_mode="custom".""" + + +def default_retry_on(exc: Exception) -> bool: + import httpx + import requests + + if isinstance(exc, ConnectionError): + return True + if isinstance( + exc, + ( + ValueError, + TypeError, + ArithmeticError, + ImportError, + LookupError, + NameError, + SyntaxError, + RuntimeError, + ReferenceError, + StopIteration, + StopAsyncIteration, + OSError, + ), + ): + return False + if isinstance(exc, httpx.HTTPStatusError): + return 500 <= exc.response.status_code < 600 + if isinstance(exc, requests.HTTPError): + return 500 <= exc.response.status_code < 600 if exc.response else True + return True + + +class RetryPolicy(NamedTuple): + """Configuration for retrying nodes.""" + + initial_interval: float = 0.5 + """Amount of time that must elapse before the first retry occurs. In seconds.""" + backoff_factor: float = 2.0 + """Multiplier by which the interval increases after each retry.""" + max_interval: float = 128.0 + """Maximum amount of time that may elapse between retries. In seconds.""" + max_attempts: int = 3 + """Maximum number of attempts to make before giving up, including the first.""" + jitter: bool = True + """Whether to add random jitter to the interval between retries.""" + retry_on: Union[ + Type[Exception], Sequence[Type[Exception]], Callable[[Exception], bool] + ] = default_retry_on + """List of exception classes that should trigger a retry, or a callable that returns True for exceptions that should trigger a retry.""" + + +class CachePolicy(NamedTuple): + """Configuration for caching nodes.""" + + pass + + +@dataclass +class Interrupt: + value: Any + when: Literal["during"] = "during" + + +class PregelTask(NamedTuple): + id: str + name: str + path: tuple[Union[str, int], ...] + error: Optional[Exception] = None + interrupts: tuple[Interrupt, ...] = () + state: Union[None, RunnableConfig, "StateSnapshot"] = None + + +class PregelExecutableTask(NamedTuple): + name: str + input: Any + proc: Runnable + writes: deque[tuple[str, Any]] + config: RunnableConfig + triggers: list[str] + retry_policy: Optional[RetryPolicy] + cache_policy: Optional[CachePolicy] + id: str + path: tuple[Union[str, int], ...] + scheduled: bool = False + + +class StateSnapshot(NamedTuple): + """Snapshot of the state of the graph at the beginning of a step.""" + + values: Union[dict[str, Any], Any] + """Current values of channels""" + next: tuple[str, ...] + """The name of the node to execute in each task for this step.""" + config: RunnableConfig + """Config used to fetch this snapshot""" + metadata: Optional[CheckpointMetadata] + """Metadata associated with this snapshot""" + created_at: Optional[str] + """Timestamp of snapshot creation""" + parent_config: Optional[RunnableConfig] + """Config used to fetch the parent snapshot, if any""" + tasks: tuple[PregelTask, ...] + """Tasks to execute in this step. If already attempted, may contain an error.""" + + +class Send: + """A message or packet to send to a specific node in the graph. + + The `Send` class is used within a `StateGraph`'s conditional edges to + dynamically invoke a node with a custom state at the next step. + + Importantly, the sent state can differ from the core graph's state, + allowing for flexible and dynamic workflow management. + + One such example is a "map-reduce" workflow where your graph invokes + the same node multiple times in parallel with different states, + before aggregating the results back into the main graph's state. + + Attributes: + node (str): The name of the target node to send the message to. + arg (Any): The state or message to send to the target node. + + Examples: + >>> from typing import Annotated + >>> import operator + >>> class OverallState(TypedDict): + ... subjects: list[str] + ... jokes: Annotated[list[str], operator.add] + ... + >>> from langgraph.types import Send + >>> from langgraph.graph import END, START + >>> def continue_to_jokes(state: OverallState): + ... return [Send("generate_joke", {"subject": s}) for s in state['subjects']] + ... + >>> from langgraph.graph import StateGraph + >>> builder = StateGraph(OverallState) + >>> builder.add_node("generate_joke", lambda state: {"jokes": [f"Joke about {state['subject']}"]}) + >>> builder.add_conditional_edges(START, continue_to_jokes) + >>> builder.add_edge("generate_joke", END) + >>> graph = builder.compile() + >>> + >>> # Invoking with two subjects results in a generated joke for each + >>> graph.invoke({"subjects": ["cats", "dogs"]}) + {'subjects': ['cats', 'dogs'], 'jokes': ['Joke about cats', 'Joke about dogs']} + """ + + __slots__ = ("node", "arg") + + node: str + arg: Any + + def __init__(self, /, node: str, arg: Any) -> None: + """ + Initialize a new instance of the Send class. + + Args: + node (str): The name of the target node to send the message to. + arg (Any): The state or message to send to the target node. + """ + self.node = node + self.arg = arg + + def __hash__(self) -> int: + return hash((self.node, self.arg)) + + def __repr__(self) -> str: + return f"Send(node={self.node!r}, arg={self.arg!r})" + + def __eq__(self, value: object) -> bool: + return ( + isinstance(value, Send) + and self.node == value.node + and self.arg == value.arg + ) diff --git a/libs/langgraph/langgraph/utils/runnable.py b/libs/langgraph/langgraph/utils/runnable.py index bfc2a627b..d81f86e34 100644 --- a/libs/langgraph/langgraph/utils/runnable.py +++ b/libs/langgraph/langgraph/utils/runnable.py @@ -35,7 +35,7 @@ from typing_extensions import TypeGuard from langgraph.constants import CONFIG_KEY_STREAM_WRITER -from langgraph.pregel.types import StreamWriter +from langgraph.types import StreamWriter from langgraph.utils.config import ( ensure_config, get_async_callback_manager_for_config, diff --git a/libs/langgraph/langgraph/version.py b/libs/langgraph/langgraph/version.py index 3368893c0..f5cb757f5 100644 --- a/libs/langgraph/langgraph/version.py +++ b/libs/langgraph/langgraph/version.py @@ -1,4 +1,4 @@ -"""Main entrypoint into package.""" +"""Exports package version.""" from importlib import metadata diff --git a/libs/langgraph/tests/any_str.py b/libs/langgraph/tests/any_str.py index 9a1977a8c..5643a00fb 100644 --- a/libs/langgraph/tests/any_str.py +++ b/libs/langgraph/tests/any_str.py @@ -1,6 +1,28 @@ import re from typing import Any, Sequence, Union +from typing_extensions import Self + + +class FloatBetween(float): + def __new__(cls, min_value: float, max_value: float) -> Self: + return super().__new__(cls, min_value) + + def __init__(self, min_value: float, max_value: float) -> None: + super().__init__() + self.min_value = min_value + self.max_value = max_value + + def __eq__(self, other: object) -> bool: + return ( + isinstance(other, float) + and other >= self.min_value + and other <= self.max_value + ) + + def __hash__(self) -> int: + return hash((float(self), self.min_value, self.max_value)) + class AnyStr(str): def __init__(self, prefix: Union[str, re.Pattern] = "") -> None: diff --git a/libs/langgraph/tests/test_pregel.py b/libs/langgraph/tests/test_pregel.py index 3e43ef28f..605f00747 100644 --- a/libs/langgraph/tests/test_pregel.py +++ b/libs/langgraph/tests/test_pregel.py @@ -52,8 +52,8 @@ CheckpointTuple, ) from langgraph.checkpoint.memory import MemorySaver -from langgraph.constants import ERROR, PULL, PUSH, Interrupt, Send -from langgraph.errors import InvalidUpdateError, NodeInterrupt +from langgraph.constants import ERROR, PULL, PUSH +from langgraph.errors import InvalidUpdateError, MultipleSubgraphsError, NodeInterrupt from langgraph.graph import END, Graph from langgraph.graph.graph import START from langgraph.graph.message import MessageGraph, add_messages @@ -70,9 +70,9 @@ StateSnapshot, ) from langgraph.pregel.retry import RetryPolicy -from langgraph.pregel.types import PregelTask, StreamWriter from langgraph.store.memory import MemoryStore -from tests.any_str import AnyDict, AnyStr, AnyVersion, UnsortedSequence +from langgraph.types import Interrupt, PregelTask, Send, StreamWriter +from tests.any_str import AnyDict, AnyStr, AnyVersion, FloatBetween, UnsortedSequence from tests.conftest import ALL_CHECKPOINTERS_SYNC, SHOULD_CHECK_SNAPSHOTS from tests.fake_chat import FakeChatModel from tests.fake_tracer import FakeTracer @@ -1861,7 +1861,12 @@ def test_invoke_two_processes_two_in_join_two_out(mocker: MockerFixture) -> None assert [*executor.map(app.invoke, [2] * 100)] == [[13, 13]] * 100 -def test_invoke_join_then_call_other_pregel(mocker: MockerFixture) -> None: +@pytest.mark.parametrize("checkpointer_name", ALL_CHECKPOINTERS_SYNC) +def test_invoke_join_then_call_other_pregel( + mocker: MockerFixture, request: pytest.FixtureRequest, checkpointer_name: str +) -> None: + checkpointer = request.getfixturevalue(f"checkpointer_{checkpointer_name}") + add_one = mocker.Mock(side_effect=lambda x: x + 1) add_10_each = mocker.Mock(side_effect=lambda x: [y + 10 for y in x]) @@ -1912,6 +1917,17 @@ def test_invoke_join_then_call_other_pregel(mocker: MockerFixture) -> None: with ThreadPoolExecutor() as executor: assert [*executor.map(app.invoke, [[2, 3]] * 10)] == [27] * 10 + # add checkpointer + app.checkpointer = checkpointer + # subgraph is called twice in the same node, through .map(), so raises + with pytest.raises(MultipleSubgraphsError): + app.invoke([2, 3], {"configurable": {"thread_id": "1"}}) + + # set inner graph checkpointer NeverCheckpoint + inner_app.checkpointer = False + # subgraph still called twice, but checkpointing for inner graph is disabled + assert app.invoke([2, 3], {"configurable": {"thread_id": "1"}}) == 27 + def test_invoke_two_processes_one_in_two_out(mocker: MockerFixture) -> None: add_one = mocker.Mock(side_effect=lambda x: x + 1) @@ -8580,22 +8596,22 @@ def outer_2(state: State): assert chunks == [ # arrives before "inner" finishes ( - 0.0, + FloatBetween(0.0, 0.1), ( (AnyStr("inner:"),), {"inner_1": {"my_key": "got here", "my_other_key": ""}}, ), ), - (0.2, ((), {"outer_1": {"my_key": " and parallel"}})), + (FloatBetween(0.2, 0.3), ((), {"outer_1": {"my_key": " and parallel"}})), ( - 0.5, + FloatBetween(0.5, 0.6), ( (AnyStr("inner:"),), {"inner_2": {"my_key": " and there", "my_other_key": "got here"}}, ), ), - (0.5, ((), {"inner": {"my_key": "got here and there"}})), - (0.5, ((), {"outer_2": {"my_key": " and back again"}})), + (FloatBetween(0.5, 0.6), ((), {"inner": {"my_key": "got here and there"}})), + (FloatBetween(0.5, 0.6), ((), {"outer_2": {"my_key": " and back again"}})), ] diff --git a/libs/langgraph/tests/test_pregel_async.py b/libs/langgraph/tests/test_pregel_async.py index 640658399..d17925aca 100644 --- a/libs/langgraph/tests/test_pregel_async.py +++ b/libs/langgraph/tests/test_pregel_async.py @@ -51,8 +51,8 @@ CheckpointTuple, ) from langgraph.checkpoint.memory import MemorySaver -from langgraph.constants import ERROR, PULL, PUSH, Interrupt, Send -from langgraph.errors import InvalidUpdateError, NodeInterrupt +from langgraph.constants import ERROR, PULL, PUSH +from langgraph.errors import InvalidUpdateError, MultipleSubgraphsError, NodeInterrupt from langgraph.graph import END, Graph, StateGraph from langgraph.graph.graph import START from langgraph.graph.message import MessageGraph, add_messages @@ -68,9 +68,9 @@ StateSnapshot, ) from langgraph.pregel.retry import RetryPolicy -from langgraph.pregel.types import PregelTask, StreamWriter from langgraph.store.memory import MemoryStore -from tests.any_str import AnyDict, AnyStr, AnyVersion, UnsortedSequence +from langgraph.types import Interrupt, PregelTask, Send, StreamWriter +from tests.any_str import AnyDict, AnyStr, AnyVersion, FloatBetween, UnsortedSequence from tests.conftest import ( ALL_CHECKPOINTERS_ASYNC, ALL_CHECKPOINTERS_ASYNC_PLUS_NONE, @@ -2080,7 +2080,10 @@ async def test_invoke_two_processes_two_in_join_two_out(mocker: MockerFixture) - ] -async def test_invoke_join_then_call_other_pregel(mocker: MockerFixture) -> None: +@pytest.mark.parametrize("checkpointer_name", ALL_CHECKPOINTERS_ASYNC) +async def test_invoke_join_then_call_other_pregel( + mocker: MockerFixture, checkpointer_name: str +) -> None: add_one = mocker.Mock(side_effect=lambda x: x + 1) add_10_each = mocker.Mock(side_effect=lambda x: [y + 10 for y in x]) @@ -2133,6 +2136,18 @@ async def test_invoke_join_then_call_other_pregel(mocker: MockerFixture) -> None 27 for _ in range(10) ] + async with awith_checkpointer(checkpointer_name) as checkpointer: + # add checkpointer + app.checkpointer = checkpointer + # subgraph is called twice in the same node, through .map(), so raises + with pytest.raises(MultipleSubgraphsError): + await app.ainvoke([2, 3], {"configurable": {"thread_id": "1"}}) + + # set inner graph checkpointer NeverCheckpoint + inner_app.checkpointer = False + # subgraph still called twice, but checkpointing for inner graph is disabled + assert await app.ainvoke([2, 3], {"configurable": {"thread_id": "1"}}) == 27 + async def test_invoke_two_processes_one_in_two_out(mocker: MockerFixture) -> None: add_one = mocker.Mock(side_effect=lambda x: x + 1) @@ -7187,22 +7202,22 @@ async def outer_2(state: State): assert chunks == [ # arrives before "inner" finishes ( - 0.0, + FloatBetween(0.0, 0.1), ( (AnyStr("inner:"),), {"inner_1": {"my_key": "got here", "my_other_key": ""}}, ), ), - (0.2, ((), {"outer_1": {"my_key": " and parallel"}})), + (FloatBetween(0.2, 0.3), ((), {"outer_1": {"my_key": " and parallel"}})), ( - 0.5, + FloatBetween(0.5, 0.6), ( (AnyStr("inner:"),), {"inner_2": {"my_key": " and there", "my_other_key": "got here"}}, ), ), - (0.5, ((), {"inner": {"my_key": "got here and there"}})), - (0.5, ((), {"outer_2": {"my_key": " and back again"}})), + (FloatBetween(0.5, 0.6), ((), {"inner": {"my_key": "got here and there"}})), + (FloatBetween(0.5, 0.6), ((), {"outer_2": {"my_key": " and back again"}})), ] diff --git a/libs/langgraph/tests/test_tracing_interops.py b/libs/langgraph/tests/test_tracing_interops.py index 9bc8750b5..5b458394b 100644 --- a/libs/langgraph/tests/test_tracing_interops.py +++ b/libs/langgraph/tests/test_tracing_interops.py @@ -5,11 +5,14 @@ from unittest.mock import MagicMock import langsmith as ls +import pytest from langchain_core.runnables import RunnableConfig from langchain_core.tracers import LangChainTracer from langgraph.graph import StateGraph +pytestmark = pytest.mark.anyio + def _get_mock_client(**kwargs: Any) -> ls.Client: mock_session = MagicMock() @@ -52,6 +55,7 @@ def wait_for( raise ValueError(f"Callable did not return within {total_time}") +@pytest.mark.skip("This test times out in CI") async def test_nested_tracing(): lt_py_311 = sys.version_info < (3, 11) mock_client = _get_mock_client() @@ -76,7 +80,7 @@ async def child_node(state: State) -> State: child_builder = StateGraph(State) child_builder.add_node(child_node) child_builder.add_edge("__start__", "child_node") - child_graph = child_builder.compile() + child_graph = child_builder.compile().with_config(run_name="child_graph") parent_builder = StateGraph(State) parent_builder.add_node(parent_node) @@ -101,7 +105,7 @@ def get_posts(): # If the callbacks weren't propagated correctly, we'd # end up with broken dotted_orders parent_run = next(data for data in posts if data["name"] == "parent_node") - child_run = next(data for data in posts if data["name"] == "child_node") + child_run = next(data for data in posts if data["name"] == "child_graph") traceable_run = next(data for data in posts if data["name"] == "some_traceable") assert child_run["dotted_order"].startswith(traceable_run["dotted_order"]) diff --git a/libs/scheduler-kafka/README.md b/libs/scheduler-kafka/README.md index 637a337dd..fd65d7f3c 100644 --- a/libs/scheduler-kafka/README.md +++ b/libs/scheduler-kafka/README.md @@ -95,7 +95,7 @@ You can pass any of the following values as `kwargs` to either `KafkaOrchestrato - batch_max_n (int): Maximum number of messages to include in a single batch. Default: 10. - batch_max_ms (int): Maximum time in milliseconds to wait for messages to include in a batch. Default: 1000. -- retry_policy (langgraph.pregel.types.RetryPolicy): Controls which graph-level errors will be retried when processing messages. A good use for this is to retry database errors thrown by the checkpointer. Defaults to None. +- retry_policy (langgraph.types.RetryPolicy): Controls which graph-level errors will be retried when processing messages. A good use for this is to retry database errors thrown by the checkpointer. Defaults to None. ### Connection settings diff --git a/libs/scheduler-kafka/langgraph/scheduler/kafka/executor.py b/libs/scheduler-kafka/langgraph/scheduler/kafka/executor.py index 9cbb6bf83..c803239e8 100644 --- a/libs/scheduler-kafka/langgraph/scheduler/kafka/executor.py +++ b/libs/scheduler-kafka/langgraph/scheduler/kafka/executor.py @@ -25,7 +25,6 @@ ) from langgraph.pregel.manager import AsyncChannelsManager, ChannelsManager from langgraph.pregel.runner import PregelRunner -from langgraph.pregel.types import RetryPolicy from langgraph.scheduler.kafka.retry import aretry, retry from langgraph.scheduler.kafka.types import ( AsyncConsumer, @@ -38,6 +37,7 @@ Sendable, Topics, ) +from langgraph.types import RetryPolicy from langgraph.utils.config import patch_configurable diff --git a/libs/scheduler-kafka/langgraph/scheduler/kafka/orchestrator.py b/libs/scheduler-kafka/langgraph/scheduler/kafka/orchestrator.py index 1ad9c5c5b..39e7b755b 100644 --- a/libs/scheduler-kafka/langgraph/scheduler/kafka/orchestrator.py +++ b/libs/scheduler-kafka/langgraph/scheduler/kafka/orchestrator.py @@ -24,7 +24,6 @@ from langgraph.pregel import Pregel from langgraph.pregel.executor import BackgroundExecutor, Submit from langgraph.pregel.loop import AsyncPregelLoop, SyncPregelLoop -from langgraph.pregel.types import RetryPolicy from langgraph.scheduler.kafka.retry import aretry, retry from langgraph.scheduler.kafka.types import ( AsyncConsumer, @@ -37,6 +36,7 @@ Producer, Topics, ) +from langgraph.types import RetryPolicy from langgraph.utils.config import patch_configurable @@ -158,6 +158,7 @@ async def attempt(self, msg: MessageToOrchestrator) -> None: specs=graph.channels, output_keys=graph.output_channels, stream_keys=graph.stream_channels, + check_subgraphs=False, ) as loop: if loop.tick( input_keys=graph.input_channels, @@ -347,6 +348,7 @@ def attempt(self, msg: MessageToOrchestrator) -> None: specs=graph.channels, output_keys=graph.output_channels, stream_keys=graph.stream_channels, + check_subgraphs=False, ) as loop: if loop.tick( input_keys=graph.input_channels, diff --git a/libs/scheduler-kafka/langgraph/scheduler/kafka/retry.py b/libs/scheduler-kafka/langgraph/scheduler/kafka/retry.py index bb80047f8..74dbe3e27 100644 --- a/libs/scheduler-kafka/langgraph/scheduler/kafka/retry.py +++ b/libs/scheduler-kafka/langgraph/scheduler/kafka/retry.py @@ -6,7 +6,7 @@ from typing_extensions import ParamSpec -from langgraph.pregel.types import RetryPolicy +from langgraph.types import RetryPolicy logger = logging.getLogger(__name__) P = ParamSpec("P")