mirror of
https://github.com/langchain-ai/langgraph.git
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update collab
This commit is contained in:
William Fu-Hinthorn
@@ -5,7 +5,7 @@
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"id": "a3e3ebc4-57af-4fe4-bdd3-36aff67bf276",
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"metadata": {},
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"source": [
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"## Example 2: Agent Team Supervisor\n",
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"## Multi-agent Example 2: Agent Team Supervisor\n",
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"\n",
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"The prevoius example routed messages automatically based on the output of the initial researcher agent.\n",
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"\n",
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@@ -30,6 +30,24 @@
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "f5b02e36-3b2e-485e-ac55-c3523c42ec58",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"env: LANGCHAIN_API_KEY=ls__4f8a0a0114d145a08a0c3c7f5631289c\n"
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]
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}
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],
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"source": [
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"%env LANGCHAIN_API_KEY=ls__4f8a0a0114d145a08a0c3c7f5631289c"
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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": 3,
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"id": "30c2f3de-c730-4aec-85a6-af2c2f058803",
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"metadata": {},
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"outputs": [],
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@@ -54,12 +72,12 @@
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"execution_count": 4,
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"id": "f04c6778-403b-4b49-9b93-678e910d5cec",
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"metadata": {},
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"outputs": [],
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"source": [
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"from typing import List, Tuple, Union\n",
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"from typing import Annotated, List, Tuple, Union\n",
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"\n",
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"import matplotlib.pyplot as plt\n",
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"from langchain_community.tools.tavily_search import TavilySearchResults\n",
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@@ -70,27 +88,21 @@
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"\n",
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"@tool\n",
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"def create_plot(\n",
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" data: Union[List[float], List[int]],\n",
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" labels: Union[List[str], None] = None,\n",
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" title: str = \"Plot\",\n",
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" xlabel: str = \"X\",\n",
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" ylabel: str = \"Y\",\n",
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" color: Union[str, List[str]] = \"blue\",\n",
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" plot_type: str = \"bar\",\n",
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") -> Tuple[plt.Figure, plt.Axes]:\n",
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" \"\"\"\n",
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" Generates a bar or line plot from the provided data and returns the figure and axis objects.\n",
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"\n",
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" :param data: A list of numerical values for the bar heights or line points.\n",
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" :param labels: A list of strings for the bar or point labels. Default is None.\n",
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" :param title: Title of the plot. Default is 'Plot'.\n",
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" :param xlabel: Label for the X-axis. Default is 'X'.\n",
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" :param ylabel: Label for the Y-axis. Default is 'Y'.\n",
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" :param color: Color of the bars or line. Can be a single color or a list of colors. Default is 'blue'.\n",
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" :param figsize: Size of the figure as a tuple (width, height). Default is (10, 6).\n",
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" :param plot_type: Type of plot ('bar' or 'line'). Default is 'bar'.\n",
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" :return: Tuple containing the figure and axes objects.\n",
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" \"\"\"\n",
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" data: Annotated[\n",
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" Union[List[float], List[int]],\n",
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" \"Numerical values for bar heights or line points.\",\n",
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" ],\n",
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" file_name: Annotated[str, \"File path to save the figure.\"],\n",
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" labels: Annotated[\n",
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" Union[List[str], None], \"Bar or point labels, defaults to None.\"\n",
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" ] = None,\n",
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" title: Annotated[str, \"Title of the plot.\"] = \"Plot\",\n",
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" xlabel: Annotated[str, \"Label for the X-axis.\"] = \"X\",\n",
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" ylabel: Annotated[str, \"Label for the Y-axis.\"] = \"Y\",\n",
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" color: Annotated[Union[str, List[str]], \"Color(s) for the bars or line.\"] = \"blue\",\n",
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" plot_type: Annotated[str, \"Type of plot ('bar' or 'line').\"] = \"bar\",\n",
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") -> Annotated[str, \"Path of the saved figure file.\"]:\n",
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" \"\"\"Create a line or bar chart.\"\"\"\n",
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" if plot_type not in [\"bar\", \"line\"]:\n",
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" raise ValueError(\"Invalid plot_type. Expected 'bar' or 'line'.\")\n",
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"\n",
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@@ -108,13 +120,14 @@
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" ax.set_title(title)\n",
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" ax.set_xlabel(xlabel)\n",
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" ax.set_ylabel(ylabel)\n",
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"\n",
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" return fig, ax"
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" fig.savefig(file_name)\n",
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" plt.close(fig)\n",
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" return f'Saved \"{title}\" plot to {file_name}'"
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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": 4,
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"execution_count": 5,
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"id": "6a430af7-8fce-4e66-ba9e-d940c1bc48e8",
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"metadata": {},
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"outputs": [],
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@@ -140,7 +153,7 @@
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"workflow = StateGraph(AgentState)\n",
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"\n",
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"\n",
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"def create_agent_node(name: str, llm: ChatOpenAI, tools: list, system_prompt: str):\n",
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"def create_worker_node(name: str, llm: ChatOpenAI, tools: list, system_prompt: str):\n",
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" prompt = ChatPromptTemplate.from_messages(\n",
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" [\n",
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" (\n",
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@@ -152,31 +165,21 @@
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" ]\n",
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" )\n",
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" agent = create_openai_functions_agent(llm, tools, prompt)\n",
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" executor = AgentExecutor(agent=agent, tools=tools, verbose=True)\n",
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"\n",
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" def _update_state(ai_message) -> dict:\n",
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" if isinstance(ai_message, FunctionMessage):\n",
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" result = ai_message\n",
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" else:\n",
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" message = ai_message.dict(exclude={\"type\"})\n",
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" message[\"name\"] = name\n",
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" result = HumanMessage(**message)\n",
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" return {\n",
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" \"messages\": [result],\n",
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" \"sender\": name,\n",
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" }\n",
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"\n",
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" chain = executor | _update_state\n",
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" executor = AgentExecutor(agent=agent, tools=tools)\n",
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" chain = executor | (\n",
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" lambda x: {\"messages\": [HumanMessage(content=x[\"output\"], name=name)]}\n",
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" )\n",
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" workflow.add_node(name, chain)\n",
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"\n",
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"\n",
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"llm = ChatOpenAI(model=\"gpt-4\")\n",
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"llm = ChatOpenAI(model=\"gpt-4-1106-preview\")\n",
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"\n",
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"create_agent_node(\"Researcher\", llm, [tavily_tool], \"You are a web researcher.\")\n",
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"create_agent_node(\"Chart Generator\", llm, [create_plot], \"You are a chart generator.\")\n",
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"# Note: these worker nodes don't _have_ to be agents. They can be any DAG, tool, or function\n",
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"create_worker_node(\"Researcher\", llm, [tavily_tool], \"You are a web researcher.\")\n",
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"create_worker_node(\"Chart Generator\", llm, [create_plot], \"You are a chart generator.\")\n",
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"# NOTE: THIS PERFORMS ARBITRARY CODE EXECUTION. PROCEED WITH CAUTION\n",
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"create_agent_node(\n",
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" \"Data Analyst\",\n",
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"create_worker_node(\n",
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" \"Coder\",\n",
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" llm,\n",
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" [PythonREPLTool()],\n",
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" \"You may generate safe python code to analyze data.\",\n",
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@@ -193,7 +196,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"execution_count": 6,
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"id": "17c108a0-6dc3-46fd-a5e6-a1fcfad5458a",
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"metadata": {},
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"outputs": [],
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@@ -202,7 +205,7 @@
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"from langchain.output_parsers.openai_functions import JsonOutputFunctionsParser\n",
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"\n",
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"\n",
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"def create_agent_supervisor(members: List[str], llm: ChatOpenAI, system_prompt: str):\n",
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"def create_supervisor(members: List[str], llm: ChatOpenAI, system_prompt: str):\n",
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" options = [\"FINISH\"] + members\n",
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" function_def = {\n",
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" \"name\": \"route\",\n",
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@@ -232,6 +235,8 @@
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" ),\n",
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" ]\n",
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" ).partial(options=str(options))\n",
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" if \"members\" in prompt.input_variables:\n",
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" prompt = prompt.partial(members=\", \".join(members))\n",
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" chain = (\n",
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" prompt\n",
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" | llm.bind_functions(functions=[function_def], function_call=\"route\")\n",
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@@ -243,24 +248,24 @@
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"\n",
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" for member in members:\n",
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" workflow.add_edge(member, \"supervisor\")\n",
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" workflow.add_conditional_edges(\n",
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" \"supervisor\", lambda x: x[\"next\"], conditional_map\n",
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" )"
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" workflow.add_conditional_edges(\"supervisor\", lambda x: x[\"next\"], conditional_map)"
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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": 6,
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"execution_count": 7,
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"id": "14778e86-077b-4e6a-893c-400e59b0cdbf",
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"metadata": {},
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"outputs": [],
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"source": [
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"create_agent_supervisor(\n",
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" [\"Researcher\", \"Chart Generator\", \"Data Analyst\"],\n",
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"create_supervisor(\n",
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" [\"Researcher\", \"Chart Generator\", \"Coder\"],\n",
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" llm,\n",
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" \"You are an agent supervisor tasked with managing work order.\"\n",
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" \" Respond with only the role will optimally help us accomplish the user's task or question.\"\n",
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" \" When finished, respond with FINISH.\",\n",
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" \"You are a supervisor tasked with managing a conversation between the\"\n",
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" \" following workers: {members}. Given the following user request,\"\n",
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" \" respond with the worker to act next. Each worker will perform a\"\n",
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" \" task and respond with their results and status. When finished,\"\n",
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" \" respond with FINISH.\",\n",
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")\n",
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"\n",
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"# Finally, add entrypoint\n",
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@@ -276,32 +281,66 @@
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"execution_count": 8,
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"id": "56ba78e9-d9c1-457c-a073-d606d5d3e013",
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"metadata": {},
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"outputs": [
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{
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"ename": "InvalidUpdateError",
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"evalue": "",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mInvalidUpdateError\u001b[0m Traceback (most recent call last)",
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"Cell \u001b[0;32mIn[7], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mgraph\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mCode hello world and print it to the terminal\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
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"File \u001b[0;32m~/code/lc/langchain/libs/core/langchain_core/runnables/base.py:1780\u001b[0m, in \u001b[0;36mRunnableSequence.invoke\u001b[0;34m(self, input, config)\u001b[0m\n\u001b[1;32m 1778\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1779\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, step \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msteps):\n\u001b[0;32m-> 1780\u001b[0m \u001b[38;5;28minput\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[43mstep\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1781\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1782\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# mark each step as a child run\u001b[39;49;00m\n\u001b[1;32m 1783\u001b[0m \u001b[43m \u001b[49m\u001b[43mpatch_config\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1784\u001b[0m \u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrun_manager\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_child\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mseq:step:\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mi\u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1785\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1786\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1787\u001b[0m \u001b[38;5;66;03m# finish the root run\u001b[39;00m\n\u001b[1;32m 1788\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n",
|
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"File \u001b[0;32m~/code/lc/langgraph/langgraph/pregel/__init__.py:521\u001b[0m, in \u001b[0;36mPregel.invoke\u001b[0;34m(self, input, config, output_keys, input_keys, **kwargs)\u001b[0m\n\u001b[1;32m 511\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minvoke\u001b[39m(\n\u001b[1;32m 512\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 513\u001b[0m \u001b[38;5;28minput\u001b[39m: Union[\u001b[38;5;28mdict\u001b[39m[\u001b[38;5;28mstr\u001b[39m, Any], Any],\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 518\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any,\n\u001b[1;32m 519\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Union[\u001b[38;5;28mdict\u001b[39m[\u001b[38;5;28mstr\u001b[39m, Any], Any]:\n\u001b[1;32m 520\u001b[0m latest: Union[\u001b[38;5;28mdict\u001b[39m[\u001b[38;5;28mstr\u001b[39m, Any], Any] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m--> 521\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mchunk\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstream\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 522\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 523\u001b[0m \u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 524\u001b[0m \u001b[43m \u001b[49m\u001b[43moutput_keys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_keys\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43moutput_keys\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moutput\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 525\u001b[0m \u001b[43m \u001b[49m\u001b[43minput_keys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minput_keys\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 526\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 527\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[1;32m 528\u001b[0m \u001b[43m \u001b[49m\u001b[43mlatest\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mchunk\u001b[49m\n\u001b[1;32m 529\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m latest\n",
|
||||
"File \u001b[0;32m~/code/lc/langgraph/langgraph/pregel/__init__.py:557\u001b[0m, in \u001b[0;36mPregel.transform\u001b[0;34m(self, input, config, output_keys, input_keys, **kwargs)\u001b[0m\n\u001b[1;32m 548\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mtransform\u001b[39m(\n\u001b[1;32m 549\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 550\u001b[0m \u001b[38;5;28minput\u001b[39m: Iterator[Union[\u001b[38;5;28mdict\u001b[39m[\u001b[38;5;28mstr\u001b[39m, Any], Any]],\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 555\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any,\n\u001b[1;32m 556\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Iterator[Union[\u001b[38;5;28mdict\u001b[39m[\u001b[38;5;28mstr\u001b[39m, Any], Any]]:\n\u001b[0;32m--> 557\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mchunk\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_transform_stream_with_config\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 558\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 559\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_transform\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 560\u001b[0m \u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 561\u001b[0m \u001b[43m \u001b[49m\u001b[43moutput_keys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_keys\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 562\u001b[0m \u001b[43m \u001b[49m\u001b[43minput_keys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minput_keys\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 563\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 564\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[1;32m 565\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43;01myield\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mchunk\u001b[49m\n",
|
||||
"File \u001b[0;32m~/code/lc/langchain/libs/core/langchain_core/runnables/base.py:1232\u001b[0m, in \u001b[0;36mRunnable._transform_stream_with_config\u001b[0;34m(self, input, transformer, config, run_type, **kwargs)\u001b[0m\n\u001b[1;32m 1230\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1231\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[0;32m-> 1232\u001b[0m chunk: Output \u001b[38;5;241m=\u001b[39m \u001b[43mcontext\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43miterator\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# type: ignore\u001b[39;00m\n\u001b[1;32m 1233\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m chunk\n\u001b[1;32m 1234\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m final_output_supported:\n",
|
||||
"File \u001b[0;32m~/code/lc/langgraph/langgraph/pregel/__init__.py:335\u001b[0m, in \u001b[0;36mPregel._transform\u001b[0;34m(self, input, run_manager, config, input_keys, output_keys)\u001b[0m\n\u001b[1;32m 332\u001b[0m _interrupt_or_proceed(done, inflight, step)\n\u001b[1;32m 334\u001b[0m \u001b[38;5;66;03m# apply writes to channels\u001b[39;00m\n\u001b[0;32m--> 335\u001b[0m \u001b[43m_apply_writes\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcheckpoint\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mchannels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpending_writes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstep\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 337\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdebug:\n\u001b[1;32m 338\u001b[0m print_checkpoint(step, channels)\n",
|
||||
"File \u001b[0;32m~/code/lc/langgraph/langgraph/pregel/__init__.py:687\u001b[0m, in \u001b[0;36m_apply_writes\u001b[0;34m(checkpoint, channels, pending_writes, config, for_step)\u001b[0m\n\u001b[1;32m 685\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m chan, vals \u001b[38;5;129;01min\u001b[39;00m pending_writes_by_channel\u001b[38;5;241m.\u001b[39mitems():\n\u001b[1;32m 686\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chan \u001b[38;5;129;01min\u001b[39;00m channels:\n\u001b[0;32m--> 687\u001b[0m \u001b[43mchannels\u001b[49m\u001b[43m[\u001b[49m\u001b[43mchan\u001b[49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mupdate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvals\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 688\u001b[0m checkpoint[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mchannel_versions\u001b[39m\u001b[38;5;124m\"\u001b[39m][chan] \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m 689\u001b[0m updated_channels\u001b[38;5;241m.\u001b[39madd(chan)\n",
|
||||
"File \u001b[0;32m~/code/lc/langgraph/langgraph/channels/last_value.py:47\u001b[0m, in \u001b[0;36mLastValue.update\u001b[0;34m(self, values)\u001b[0m\n\u001b[1;32m 45\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[1;32m 46\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(values) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m---> 47\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m InvalidUpdateError()\n\u001b[1;32m 49\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalue \u001b[38;5;241m=\u001b[39m values[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\n",
|
||||
"\u001b[0;31mInvalidUpdateError\u001b[0m: "
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Rate limit exceeded for https://api.smith.langchain.com/runs/e906b9ec-0dc1-4f78-b5d1-d6da08bac093. HTTPError('429 Client Error: Too Many Requests for url: https://api.smith.langchain.com/runs/e906b9ec-0dc1-4f78-b5d1-d6da08bac093', '{\"detail\":\"Hourly usage limit exceeded\"}')\n",
|
||||
"Rate limit exceeded for https://api.smith.langchain.com/runs. HTTPError('429 Client Error: Too Many Requests for url: https://api.smith.langchain.com/runs', '{\"detail\":\"Hourly usage limit exceeded\"}')\n",
|
||||
"Python REPL can execute arbitrary code. Use with caution.\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"================================\u001b[1m Human Message \u001b[0m=================================\n",
|
||||
"\n",
|
||||
"The code `print('Hello, World!')` has been executed, and it printed `Hello, World!` to the terminal.\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"graph.invoke(\"Code hello world and print it to the terminal\")"
|
||||
"results = graph.invoke(\"Code hello world and print it to the terminal\")\n",
|
||||
"results[\"messages\"][-1].pretty_print()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"id": "45a92dfd-0e11-47f5-aad4-b68d24990e34",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"================================\u001b[1m Human Message \u001b[0m=================================\n",
|
||||
"\n",
|
||||
"The chart summarizing the data on the 2023 California wildfires has been created successfully. You can see the representation of the statistics mentioned in the summary including the total number of fires, total acres burned, comparison with the five-year average, the size of the largest wildfire, and the number of fatalities.\n",
|
||||
"\n",
|
||||
"\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"results = graph.invoke(\n",
|
||||
" \"Write a research summary of CA wildfires in 2023. Include a chart.\"\n",
|
||||
")\n",
|
||||
"results[\"messages\"][-1].pretty_print()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "1d363d2c-e0da-4cce-ba47-ad2aa9df0fef",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
|
||||
Reference in New Issue
Block a user