mirror of
https://github.com/langchain-ai/langgraph.git
synced 2026-09-13 21:27:52 +02:00
docs: add note for langgraph cloud checkpointers (#1011)
This commit is contained in:
Vadym Barda
+178
-27
@@ -38,14 +38,6 @@
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"</div> "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "0c3fde0a",
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"metadata": {},
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"outputs": [],
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"source": [""]
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},
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{
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"cell_type": "markdown",
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"id": "7cbd446a-808f-4394-be92-d45ab818953c",
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@@ -62,7 +54,10 @@
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"id": "af4ce0ba-7596-4e5f-8bf8-0b0bd6e62833",
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"metadata": {},
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"outputs": [],
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"source": ["%%capture --no-stderr\n%pip install --quiet -U langgraph langchain_anthropic"]
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"source": [
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"%%capture --no-stderr\n",
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"%pip install --quiet -U langgraph langchain_anthropic"
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]
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},
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{
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"cell_type": "markdown",
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@@ -78,7 +73,18 @@
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"id": "c903a1cf-2977-4e2d-ad7d-8b3946821d89",
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"metadata": {},
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"outputs": [],
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"source": ["import getpass\nimport os\n\n\ndef _set_env(var: str):\n if not os.environ.get(var):\n os.environ[var] = getpass.getpass(f\"{var}: \")\n\n\n_set_env(\"ANTHROPIC_API_KEY\")"]
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"source": [
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"import getpass\n",
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"import os\n",
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"\n",
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"\n",
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"def _set_env(var: str):\n",
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" if not os.environ.get(var):\n",
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" os.environ[var] = getpass.getpass(f\"{var}: \")\n",
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"\n",
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"\n",
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"_set_env(\"ANTHROPIC_API_KEY\")"
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]
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},
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{
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"cell_type": "markdown",
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@@ -94,7 +100,10 @@
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"id": "95e25aec-7c9f-4a63-b143-225d0e9a79c3",
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"metadata": {},
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"outputs": [],
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"source": ["os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n_set_env(\"LANGCHAIN_API_KEY\")"]
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"source": [
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"os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
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"_set_env(\"LANGCHAIN_API_KEY\")"
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]
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},
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{
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"cell_type": "markdown",
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@@ -112,7 +121,22 @@
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"id": "14619607",
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"metadata": {},
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"outputs": [],
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"source": ["from typing import Annotated\n\nfrom typing_extensions import TypedDict\n\nfrom langgraph.graph.message import add_messages\n\n# Add messages essentially does this with more\n# robust handling\n# def add_messages(left: list, right: list):\n# return left + right\n\n\nclass State(TypedDict):\n messages: Annotated[list, add_messages]"]
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"source": [
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"from typing import Annotated\n",
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"\n",
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"from typing_extensions import TypedDict\n",
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"\n",
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"from langgraph.graph.message import add_messages\n",
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"\n",
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"# Add messages essentially does this with more\n",
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"# robust handling\n",
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"# def add_messages(left: list, right: list):\n",
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"# return left + right\n",
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"\n",
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"\n",
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"class State(TypedDict):\n",
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" messages: Annotated[list, add_messages]"
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]
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},
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{
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"cell_type": "markdown",
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@@ -132,7 +156,19 @@
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"id": "d7ef57dd-5d6e-4ad3-9377-a92201c1310e",
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"metadata": {},
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"outputs": [],
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"source": ["from langchain_core.tools import tool\n\n\n@tool\ndef search(query: str):\n \"\"\"Call to surf the web.\"\"\"\n # This is a placeholder for the actual implementation\n return [\"The answer to your question lies within.\"]\n\n\ntools = [search]"]
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"source": [
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"from langchain_core.tools import tool\n",
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"\n",
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"\n",
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"@tool\n",
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"def search(query: str):\n",
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" \"\"\"Call to surf the web.\"\"\"\n",
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" # This is a placeholder for the actual implementation\n",
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" return [\"The answer to your question lies within.\"]\n",
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"\n",
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"\n",
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"tools = [search]"
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]
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},
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{
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"cell_type": "markdown",
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@@ -149,7 +185,11 @@
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"id": "5cf3331e-ccb3-41c8-aeb9-a840a94d41e7",
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"metadata": {},
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"outputs": [],
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"source": ["from langgraph.prebuilt import ToolNode\n\ntool_node = ToolNode(tools)"]
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"source": [
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"from langgraph.prebuilt import ToolNode\n",
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"\n",
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"tool_node = ToolNode(tools)"
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]
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},
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{
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"cell_type": "markdown",
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@@ -179,7 +219,13 @@
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"id": "892b54b9-75f0-4804-9ed0-88b5e5532989",
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"metadata": {},
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"outputs": [],
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"source": ["from langchain_openai import ChatOpenAI\n\n# We will set streaming=True so that we can stream tokens\n# See the streaming section for more information on this.\nmodel = ChatOpenAI(temperature=0, streaming=True)"]
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"source": [
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"from langchain_openai import ChatOpenAI\n",
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"\n",
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"# We will set streaming=True so that we can stream tokens\n",
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"# See the streaming section for more information on this.\n",
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"model = ChatOpenAI(temperature=0, streaming=True)"
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]
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},
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{
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"cell_type": "markdown",
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@@ -197,7 +243,9 @@
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"id": "cd3cbae5-d92c-4559-a4aa-44721b80d107",
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"metadata": {},
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"outputs": [],
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"source": ["bound_model = model.bind_tools(tools)"]
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"source": [
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"bound_model = model.bind_tools(tools)"
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]
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},
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{
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"cell_type": "markdown",
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@@ -232,7 +280,27 @@
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"id": "3b541bb9-900c-40d0-964d-7b5dfee30667",
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"metadata": {},
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"outputs": [],
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"source": ["# Define the function that determines whether to continue or not\nfrom typing import Literal\n\n\ndef should_continue(state: State) -> Literal[\"action\", \"__end__\"]:\n \"\"\"Return the next node to execute.\"\"\"\n last_message = state[\"messages\"][-1]\n # If there is no function call, then we finish\n if not last_message.tool_calls:\n return \"__end__\"\n # Otherwise if there is, we continue\n return \"action\"\n\n\n# Define the function that calls the model\ndef call_model(state: State):\n response = model.invoke(state[\"messages\"])\n # We return a list, because this will get added to the existing list\n return {\"messages\": response}"]
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"source": [
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"# Define the function that determines whether to continue or not\n",
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"from typing import Literal\n",
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"\n",
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"\n",
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"def should_continue(state: State) -> Literal[\"action\", \"__end__\"]:\n",
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" \"\"\"Return the next node to execute.\"\"\"\n",
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" last_message = state[\"messages\"][-1]\n",
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" # If there is no function call, then we finish\n",
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" if not last_message.tool_calls:\n",
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" return \"__end__\"\n",
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" # Otherwise if there is, we continue\n",
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" return \"action\"\n",
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"\n",
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"\n",
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"# Define the function that calls the model\n",
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"def call_model(state: State):\n",
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" response = model.invoke(state[\"messages\"])\n",
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" # We return a list, because this will get added to the existing list\n",
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" return {\"messages\": response}"
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]
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},
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{
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"cell_type": "markdown",
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@@ -248,7 +316,33 @@
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"id": "812b4e70-4956-4415-8880-db48b3dcbad2",
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"metadata": {},
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"outputs": [],
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"source": ["from langgraph.graph import StateGraph, START\n\n# Define a new graph\nworkflow = StateGraph(State)\n\n# Define the two nodes we will cycle between\nworkflow.add_node(\"agent\", call_model)\nworkflow.add_node(\"action\", tool_node)\n\n# Set the entrypoint as `agent`\n# This means that this node is the first one called\nworkflow.add_edge(START, \"agent\")\n\n# We now add a conditional edge\nworkflow.add_conditional_edges(\n # First, we define the start node. We use `agent`.\n # This means these are the edges taken after the `agent` node is called.\n \"agent\",\n # Next, we pass in the function that will determine which node is called next.\n should_continue,\n)\n\n# We now add a normal edge from `tools` to `agent`.\n# This means that after `tools` is called, `agent` node is called next.\nworkflow.add_edge(\"action\", \"agent\")"]
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"source": [
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"from langgraph.graph import StateGraph, START\n",
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"\n",
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"# Define a new graph\n",
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"workflow = StateGraph(State)\n",
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"\n",
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"# Define the two nodes we will cycle between\n",
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"workflow.add_node(\"agent\", call_model)\n",
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"workflow.add_node(\"action\", tool_node)\n",
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"\n",
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"# Set the entrypoint as `agent`\n",
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"# This means that this node is the first one called\n",
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"workflow.add_edge(START, \"agent\")\n",
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"\n",
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"# We now add a conditional edge\n",
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"workflow.add_conditional_edges(\n",
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" # First, we define the start node. We use `agent`.\n",
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" # This means these are the edges taken after the `agent` node is called.\n",
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" \"agent\",\n",
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" # Next, we pass in the function that will determine which node is called next.\n",
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" should_continue,\n",
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")\n",
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"\n",
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"# We now add a normal edge from `tools` to `agent`.\n",
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"# This means that after `tools` is called, `agent` node is called next.\n",
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"workflow.add_edge(\"action\", \"agent\")"
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]
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},
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{
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"cell_type": "markdown",
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@@ -266,7 +360,11 @@
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"id": "6845ed6a-d155-4105-9160-28849877248b",
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"metadata": {},
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"outputs": [],
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"source": ["from langgraph.checkpoint.sqlite import SqliteSaver\n\nmemory = SqliteSaver.from_conn_string(\":memory:\")"]
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"source": [
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"from langgraph.checkpoint.sqlite import SqliteSaver\n",
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"\n",
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"memory = SqliteSaver.from_conn_string(\":memory:\")"
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]
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},
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{
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"cell_type": "code",
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@@ -274,7 +372,25 @@
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"id": "79d29875-8aa8-434c-9f20-1c58346a6249",
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"metadata": {},
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"outputs": [],
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"source": ["# Finally, we compile it!\n# This compiles it into a LangChain Runnable,\n# meaning you can use it as you would any other runnable\napp = workflow.compile(checkpointer=memory)"]
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"source": [
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"# Finally, we compile it!\n",
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"# This compiles it into a LangChain Runnable,\n",
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"# meaning you can use it as you would any other runnable\n",
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"app = workflow.compile(checkpointer=memory)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "7654ebcc-2179-41b4-92d1-6666f6f8634f",
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"metadata": {},
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"source": [
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"<div class=\"admonition tip\">\n",
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" <p class=\"admonition-title\">Note</p>\n",
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" <p>\n",
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" If you're using LangGraph Cloud, you <strong>don't need</strong> to pass checkpointer when compiling the graph, since it's done automatically.\n",
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" </p>\n",
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"</div>"
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]
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},
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{
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"cell_type": "code",
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@@ -293,7 +409,15 @@
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"output_type": "display_data"
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}
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],
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"source": ["from IPython.display import Image, display\n\ntry:\n display(Image(app.get_graph().draw_mermaid_png()))\nexcept Exception:\n # This requires some extra dependencies and is optional\n pass"]
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"source": [
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"from IPython.display import Image, display\n",
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"\n",
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"try:\n",
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" display(Image(app.get_graph().draw_mermaid_png()))\n",
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"except Exception:\n",
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" # This requires some extra dependencies and is optional\n",
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" pass"
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]
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},
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{
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"cell_type": "markdown",
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@@ -324,7 +448,14 @@
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]
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}
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],
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"source": ["from langchain_core.messages import HumanMessage\n\nconfig = {\"configurable\": {\"thread_id\": \"2\"}}\ninput_message = HumanMessage(content=\"hi! I'm bob\")\nfor event in app.stream({\"messages\": [input_message]}, config, stream_mode=\"values\"):\n event[\"messages\"][-1].pretty_print()"]
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"source": [
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"from langchain_core.messages import HumanMessage\n",
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"\n",
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"config = {\"configurable\": {\"thread_id\": \"2\"}}\n",
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"input_message = HumanMessage(content=\"hi! I'm bob\")\n",
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"for event in app.stream({\"messages\": [input_message]}, config, stream_mode=\"values\"):\n",
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" event[\"messages\"][-1].pretty_print()"
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]
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},
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{
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"cell_type": "code",
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@@ -345,7 +476,11 @@
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]
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}
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],
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"source": ["input_message = HumanMessage(content=\"what is my name?\")\nfor event in app.stream({\"messages\": [input_message]}, config, stream_mode=\"values\"):\n event[\"messages\"][-1].pretty_print()"]
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"source": [
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"input_message = HumanMessage(content=\"what is my name?\")\n",
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"for event in app.stream({\"messages\": [input_message]}, config, stream_mode=\"values\"):\n",
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" event[\"messages\"][-1].pretty_print()"
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]
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},
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{
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"cell_type": "markdown",
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@@ -374,7 +509,15 @@
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]
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}
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],
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"source": ["input_message = HumanMessage(content=\"what is my name?\")\nfor event in app.stream(\n {\"messages\": [input_message]},\n {\"configurable\": {\"thread_id\": \"3\"}},\n stream_mode=\"values\",\n):\n event[\"messages\"][-1].pretty_print()"]
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"source": [
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"input_message = HumanMessage(content=\"what is my name?\")\n",
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"for event in app.stream(\n",
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" {\"messages\": [input_message]},\n",
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" {\"configurable\": {\"thread_id\": \"3\"}},\n",
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" stream_mode=\"values\",\n",
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"):\n",
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" event[\"messages\"][-1].pretty_print()"
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]
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},
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{
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"cell_type": "markdown",
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@@ -403,7 +546,15 @@
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]
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}
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],
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"source": ["input_message = HumanMessage(content=\"You forgot??\")\nfor event in app.stream(\n {\"messages\": [input_message]},\n {\"configurable\": {\"thread_id\": \"2\"}},\n stream_mode=\"values\",\n):\n event[\"messages\"][-1].pretty_print()"]
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"source": [
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"input_message = HumanMessage(content=\"You forgot??\")\n",
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"for event in app.stream(\n",
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" {\"messages\": [input_message]},\n",
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" {\"configurable\": {\"thread_id\": \"2\"}},\n",
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" stream_mode=\"values\",\n",
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"):\n",
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" event[\"messages\"][-1].pretty_print()"
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]
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},
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{
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"cell_type": "code",
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@@ -411,7 +562,7 @@
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"id": "eb20430f",
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"metadata": {},
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"outputs": [],
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"source": [""]
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"source": []
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}
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],
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"metadata": {
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@@ -430,7 +581,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.2"
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"version": "3.11.9"
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}
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},
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"nbformat": 4,
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