diff --git a/examples/persistence.ipynb b/examples/persistence.ipynb index 7fd7b4e65..b99767a2c 100644 --- a/examples/persistence.ipynb +++ b/examples/persistence.ipynb @@ -38,14 +38,6 @@ " " ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "0c3fde0a", - "metadata": {}, - "outputs": [], - "source": [""] - }, { "cell_type": "markdown", "id": "7cbd446a-808f-4394-be92-d45ab818953c", @@ -62,7 +54,10 @@ "id": "af4ce0ba-7596-4e5f-8bf8-0b0bd6e62833", "metadata": {}, "outputs": [], - "source": ["%%capture --no-stderr\n%pip install --quiet -U langgraph langchain_anthropic"] + "source": [ + "%%capture --no-stderr\n", + "%pip install --quiet -U langgraph langchain_anthropic" + ] }, { "cell_type": "markdown", @@ -78,7 +73,18 @@ "id": "c903a1cf-2977-4e2d-ad7d-8b3946821d89", "metadata": {}, "outputs": [], - "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\")"] + "source": [ + "import getpass\n", + "import os\n", + "\n", + "\n", + "def _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\")" + ] }, { "cell_type": "markdown", @@ -94,7 +100,10 @@ "id": "95e25aec-7c9f-4a63-b143-225d0e9a79c3", "metadata": {}, "outputs": [], - "source": ["os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n_set_env(\"LANGCHAIN_API_KEY\")"] + "source": [ + "os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n", + "_set_env(\"LANGCHAIN_API_KEY\")" + ] }, { "cell_type": "markdown", @@ -112,7 +121,22 @@ "id": "14619607", "metadata": {}, "outputs": [], - "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]"] + "source": [ + "from typing import Annotated\n", + "\n", + "from typing_extensions import TypedDict\n", + "\n", + "from 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", + "\n", + "class State(TypedDict):\n", + " messages: Annotated[list, add_messages]" + ] }, { "cell_type": "markdown", @@ -132,7 +156,19 @@ "id": "d7ef57dd-5d6e-4ad3-9377-a92201c1310e", "metadata": {}, "outputs": [], - "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]"] + "source": [ + "from langchain_core.tools import tool\n", + "\n", + "\n", + "@tool\n", + "def 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", + "\n", + "tools = [search]" + ] }, { "cell_type": "markdown", @@ -149,7 +185,11 @@ "id": "5cf3331e-ccb3-41c8-aeb9-a840a94d41e7", "metadata": {}, "outputs": [], - "source": ["from langgraph.prebuilt import ToolNode\n\ntool_node = ToolNode(tools)"] + "source": [ + "from langgraph.prebuilt import ToolNode\n", + "\n", + "tool_node = ToolNode(tools)" + ] }, { "cell_type": "markdown", @@ -179,7 +219,13 @@ "id": "892b54b9-75f0-4804-9ed0-88b5e5532989", "metadata": {}, "outputs": [], - "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)"] + "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.\n", + "model = ChatOpenAI(temperature=0, streaming=True)" + ] }, { "cell_type": "markdown", @@ -197,7 +243,9 @@ "id": "cd3cbae5-d92c-4559-a4aa-44721b80d107", "metadata": {}, "outputs": [], - "source": ["bound_model = model.bind_tools(tools)"] + "source": [ + "bound_model = model.bind_tools(tools)" + ] }, { "cell_type": "markdown", @@ -232,7 +280,27 @@ "id": "3b541bb9-900c-40d0-964d-7b5dfee30667", "metadata": {}, "outputs": [], - "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}"] + "source": [ + "# Define the function that determines whether to continue or not\n", + "from typing import Literal\n", + "\n", + "\n", + "def 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\n", + "def 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}" + ] }, { "cell_type": "markdown", @@ -248,7 +316,33 @@ "id": "812b4e70-4956-4415-8880-db48b3dcbad2", "metadata": {}, "outputs": [], - "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\")"] + "source": [ + "from langgraph.graph import StateGraph, START\n", + "\n", + "# Define a new graph\n", + "workflow = StateGraph(State)\n", + "\n", + "# Define the two nodes we will cycle between\n", + "workflow.add_node(\"agent\", call_model)\n", + "workflow.add_node(\"action\", tool_node)\n", + "\n", + "# Set the entrypoint as `agent`\n", + "# This means that this node is the first one called\n", + "workflow.add_edge(START, \"agent\")\n", + "\n", + "# We now add a conditional edge\n", + "workflow.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.\n", + "workflow.add_edge(\"action\", \"agent\")" + ] }, { "cell_type": "markdown", @@ -266,7 +360,11 @@ "id": "6845ed6a-d155-4105-9160-28849877248b", "metadata": {}, "outputs": [], - "source": ["from langgraph.checkpoint.sqlite import SqliteSaver\n\nmemory = SqliteSaver.from_conn_string(\":memory:\")"] + "source": [ + "from langgraph.checkpoint.sqlite import SqliteSaver\n", + "\n", + "memory = SqliteSaver.from_conn_string(\":memory:\")" + ] }, { "cell_type": "code", @@ -274,7 +372,25 @@ "id": "79d29875-8aa8-434c-9f20-1c58346a6249", "metadata": {}, "outputs": [], - "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)"] + "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\n", + "app = workflow.compile(checkpointer=memory)" + ] + }, + { + "cell_type": "markdown", + "id": "7654ebcc-2179-41b4-92d1-6666f6f8634f", + "metadata": {}, + "source": [ + "
\n", + "

Note

\n", + "

\n", + " If you're using LangGraph Cloud, you don't need to pass checkpointer when compiling the graph, since it's done automatically.\n", + "

\n", + "
" + ] }, { "cell_type": "code", @@ -293,7 +409,15 @@ "output_type": "display_data" } ], - "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"] + "source": [ + "from IPython.display import Image, display\n", + "\n", + "try:\n", + " display(Image(app.get_graph().draw_mermaid_png()))\n", + "except Exception:\n", + " # This requires some extra dependencies and is optional\n", + " pass" + ] }, { "cell_type": "markdown", @@ -324,7 +448,14 @@ ] } ], - "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()"] + "source": [ + "from langchain_core.messages import HumanMessage\n", + "\n", + "config = {\"configurable\": {\"thread_id\": \"2\"}}\n", + "input_message = HumanMessage(content=\"hi! I'm bob\")\n", + "for event in app.stream({\"messages\": [input_message]}, config, stream_mode=\"values\"):\n", + " event[\"messages\"][-1].pretty_print()" + ] }, { "cell_type": "code", @@ -345,7 +476,11 @@ ] } ], - "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()"] + "source": [ + "input_message = HumanMessage(content=\"what is my name?\")\n", + "for event in app.stream({\"messages\": [input_message]}, config, stream_mode=\"values\"):\n", + " event[\"messages\"][-1].pretty_print()" + ] }, { "cell_type": "markdown", @@ -374,7 +509,15 @@ ] } ], - "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()"] + "source": [ + "input_message = HumanMessage(content=\"what is my name?\")\n", + "for event in app.stream(\n", + " {\"messages\": [input_message]},\n", + " {\"configurable\": {\"thread_id\": \"3\"}},\n", + " stream_mode=\"values\",\n", + "):\n", + " event[\"messages\"][-1].pretty_print()" + ] }, { "cell_type": "markdown", @@ -403,7 +546,15 @@ ] } ], - "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()"] + "source": [ + "input_message = HumanMessage(content=\"You forgot??\")\n", + "for event in app.stream(\n", + " {\"messages\": [input_message]},\n", + " {\"configurable\": {\"thread_id\": \"2\"}},\n", + " stream_mode=\"values\",\n", + "):\n", + " event[\"messages\"][-1].pretty_print()" + ] }, { "cell_type": "code", @@ -411,7 +562,7 @@ "id": "eb20430f", "metadata": {}, "outputs": [], - "source": [""] + "source": [] } ], "metadata": { @@ -430,7 +581,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.2" + "version": "3.11.9" } }, "nbformat": 4,