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@@ -26,7 +26,10 @@
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"id": "0d30b6f7-3bec-4d9f-af50-43dfdc81ae6c",
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"metadata": {},
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"outputs": [],
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"source": ["%%capture --no-stderr\n%pip install -U langgraph langchain langchain_openai langchain_experimental langsmith pandas"]
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"source": [
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"%%capture --no-stderr\n",
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"%pip install -U langgraph langchain langchain_openai langchain_experimental langsmith pandas"
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]
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},
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{
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"cell_type": "code",
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@@ -34,7 +37,24 @@
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"id": "30c2f3de-c730-4aec-85a6-af2c2f058803",
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"metadata": {},
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"outputs": [],
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"source": ["import getpass\nimport os\n\n\ndef _set_if_undefined(var: str):\n if not os.environ.get(var):\n os.environ[var] = getpass.getpass(f\"Please provide your {var}\")\n\n\n_set_if_undefined(\"OPENAI_API_KEY\")\n_set_if_undefined(\"LANGCHAIN_API_KEY\")\n_set_if_undefined(\"TAVILY_API_KEY\")\n\n# Optional, add tracing in LangSmith\nos.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\nos.environ[\"LANGCHAIN_PROJECT\"] = \"Multi-agent Collaboration\""]
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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_if_undefined(var: str):\n",
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" if not os.environ.get(var):\n",
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" os.environ[var] = getpass.getpass(f\"Please provide your {var}\")\n",
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"\n",
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"\n",
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"_set_if_undefined(\"OPENAI_API_KEY\")\n",
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"_set_if_undefined(\"LANGCHAIN_API_KEY\")\n",
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"_set_if_undefined(\"TAVILY_API_KEY\")\n",
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"\n",
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"# Optional, add tracing in LangSmith\n",
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"os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
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"os.environ[\"LANGCHAIN_PROJECT\"] = \"Multi-agent Collaboration\""
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]
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},
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{
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"cell_type": "markdown",
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@@ -48,45 +68,51 @@
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": 1,
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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": ["from typing import Annotated\n\nfrom langchain_community.tools.tavily_search import TavilySearchResults\nfrom langchain_experimental.tools import PythonREPLTool\n\ntavily_tool = TavilySearchResults(max_results=5)\n\n# This executes code locally, which can be unsafe\npython_repl_tool = PythonREPLTool()"]
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"source": [
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"from typing import Annotated\n",
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"\n",
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"from langchain_community.tools.tavily_search import TavilySearchResults\n",
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"from langchain_experimental.tools import PythonREPLTool\n",
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"\n",
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"tavily_tool = TavilySearchResults(max_results=5)\n",
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"\n",
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"# This executes code locally, which can be unsafe\n",
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"python_repl_tool = PythonREPLTool()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d58d1e85-22d4-4c22-9062-72a346a0d709",
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"metadata": {},
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"source": [
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"## Helper Utilities\n",
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"\n",
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"Define a helper function below, which make it easier to add new agent worker nodes."
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"## Helper Utilities"
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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": "c4823dd9-26bd-4e1a-8117-b97b2860211a",
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"metadata": {},
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"outputs": [],
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"source": ["from langchain.agents import AgentExecutor, create_openai_tools_agent\nfrom langchain_core.messages import BaseMessage, HumanMessage\nfrom langchain_openai import ChatOpenAI\n\n\ndef create_agent(llm: ChatOpenAI, tools: list, system_prompt: str):\n # Each worker node will be given a name and some tools.\n prompt = ChatPromptTemplate.from_messages(\n [\n (\n \"system\",\n system_prompt,\n ),\n MessagesPlaceholder(variable_name=\"messages\"),\n MessagesPlaceholder(variable_name=\"agent_scratchpad\"),\n ]\n )\n agent = create_openai_tools_agent(llm, tools, prompt)\n executor = AgentExecutor(agent=agent, tools=tools)\n return executor"]
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},
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{
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"cell_type": "markdown",
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"id": "b7c302b0-cd57-4913-986f-5dc7d6d77386",
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"metadata": {},
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"source": [
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"We can also define a function that we will use to be the nodes in the graph - it takes care of converting the agent response to a human message. This is important because that is how we will add it the global state of the graph"
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"Define a helper function that we will use to create the nodes in the graph - it takes care of converting the agent response to a human message. This is important because that is how we will add it the global state of the graph"
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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": 2,
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"id": "80862241-a1a7-4726-bce5-f867b233832e",
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"metadata": {},
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"outputs": [],
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"source": ["def agent_node(state, agent, name):\n result = agent.invoke(state)\n return {\"messages\": [HumanMessage(content=result[\"output\"], name=name)]}"]
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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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"def agent_node(state, agent, name):\n",
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" result = agent.invoke(state)\n",
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" return {\"messages\": [HumanMessage(content=result[\"messages\"][-1].content, name=name)]}"
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]
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},
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{
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"cell_type": "markdown",
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@@ -100,11 +126,53 @@
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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": 13,
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"id": "311f0a58-b425-4496-adac-dc4cd8ffb912",
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"metadata": {},
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"outputs": [],
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"source": ["from langchain_core.output_parsers.openai_functions import JsonOutputFunctionsParser\nfrom langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n\nmembers = [\"Researcher\", \"Coder\"]\nsystem_prompt = (\n \"You are a supervisor tasked with managing a conversation between the\"\n \" following workers: {members}. Given the following user request,\"\n \" respond with the worker to act next. Each worker will perform a\"\n \" task and respond with their results and status. When finished,\"\n \" respond with FINISH.\"\n)\n# Our team supervisor is an LLM node. It just picks the next agent to process\n# and decides when the work is completed\noptions = [\"FINISH\"] + members\n# Using openai function calling can make output parsing easier for us\nfunction_def = {\n \"name\": \"route\",\n \"description\": \"Select the next role.\",\n \"parameters\": {\n \"title\": \"routeSchema\",\n \"type\": \"object\",\n \"properties\": {\n \"next\": {\n \"title\": \"Next\",\n \"anyOf\": [\n {\"enum\": options},\n ],\n }\n },\n \"required\": [\"next\"],\n },\n}\nprompt = ChatPromptTemplate.from_messages(\n [\n (\"system\", system_prompt),\n MessagesPlaceholder(variable_name=\"messages\"),\n (\n \"system\",\n \"Given the conversation above, who should act next?\"\n \" Or should we FINISH? Select one of: {options}\",\n ),\n ]\n).partial(options=str(options), members=\", \".join(members))\n\nllm = ChatOpenAI(model=\"gpt-4-1106-preview\")\n\nsupervisor_chain = (\n prompt\n | llm.bind_functions(functions=[function_def], function_call=\"route\")\n | JsonOutputFunctionsParser()\n)"]
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"source": [
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"from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
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"from langchain_openai import ChatOpenAI\n",
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"from pydantic import BaseModel\n",
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"from typing import Literal\n",
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"\n",
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"members = [\"Researcher\", \"Coder\"]\n",
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"system_prompt = (\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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"# Our team supervisor is an LLM node. It just picks the next agent to process\n",
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"# and decides when the work is completed\n",
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"options = [\"FINISH\"] + members\n",
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"\n",
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"class routeResponse(BaseModel):\n",
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" next: Literal[*options]\n",
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"\n",
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"prompt = ChatPromptTemplate.from_messages(\n",
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" [\n",
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" (\"system\", system_prompt),\n",
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" MessagesPlaceholder(variable_name=\"messages\"),\n",
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" (\n",
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" \"system\",\n",
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" \"Given the conversation above, who should act next?\"\n",
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" \" Or should we FINISH? Select one of: {options}\",\n",
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" ),\n",
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" ]\n",
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").partial(options=str(options), members=\", \".join(members))\n",
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"\n",
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"\n",
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"llm = ChatOpenAI(model=\"gpt-4o\")\n",
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"\n",
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"def supervisor_agent(state):\n",
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" supervisor_chain = (\n",
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" prompt\n",
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" | llm.with_structured_output(routeResponse)\n",
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" )\n",
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" return supervisor_chain.invoke(state)"
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]
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},
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{
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"cell_type": "markdown",
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@@ -118,11 +186,41 @@
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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": 14,
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"id": "6a430af7-8fce-4e66-ba9e-d940c1bc48e8",
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"metadata": {},
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"outputs": [],
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"source": ["import functools\nimport operator\nfrom typing import Sequence, TypedDict\n\nfrom langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n\nfrom langgraph.graph import END, StateGraph, START\n\n\n# The agent state is the input to each node in the graph\nclass AgentState(TypedDict):\n # The annotation tells the graph that new messages will always\n # be added to the current states\n messages: Annotated[Sequence[BaseMessage], operator.add]\n # The 'next' field indicates where to route to next\n next: str\n\n\nresearch_agent = create_agent(llm, [tavily_tool], \"You are a web researcher.\")\nresearch_node = functools.partial(agent_node, agent=research_agent, name=\"Researcher\")\n\n# NOTE: THIS PERFORMS ARBITRARY CODE EXECUTION. PROCEED WITH CAUTION\ncode_agent = create_agent(\n llm,\n [python_repl_tool],\n \"You may generate safe python code to analyze data and generate charts using matplotlib.\",\n)\ncode_node = functools.partial(agent_node, agent=code_agent, name=\"Coder\")\n\nworkflow = StateGraph(AgentState)\nworkflow.add_node(\"Researcher\", research_node)\nworkflow.add_node(\"Coder\", code_node)\nworkflow.add_node(\"supervisor\", supervisor_chain)"]
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"source": [
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"import functools\n",
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"import operator\n",
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"from typing import Sequence, TypedDict\n",
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"\n",
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"from langchain_core.messages import BaseMessage\n",
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"\n",
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"from langgraph.graph import END, StateGraph, START\n",
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"from langgraph.prebuilt import create_react_agent\n",
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"\n",
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"# The agent state is the input to each node in the graph\n",
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"class AgentState(TypedDict):\n",
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" # The annotation tells the graph that new messages will always\n",
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" # be added to the current states\n",
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" messages: Annotated[Sequence[BaseMessage], operator.add]\n",
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" # The 'next' field indicates where to route to next\n",
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" next: str\n",
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"\n",
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"\n",
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"research_agent = create_react_agent(llm, tools=[tavily_tool])\n",
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"research_node = functools.partial(agent_node, agent=research_agent, name=\"Researcher\")\n",
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"\n",
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"# NOTE: THIS PERFORMS ARBITRARY CODE EXECUTION. PROCEED WITH CAUTION\n",
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"code_agent = create_react_agent(llm, tools=[python_repl_tool])\n",
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"code_node = functools.partial(agent_node, agent=code_agent, name=\"Coder\")\n",
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"\n",
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"workflow = StateGraph(AgentState)\n",
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"workflow.add_node(\"Researcher\", research_node)\n",
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"workflow.add_node(\"Coder\", code_node)\n",
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"workflow.add_node(\"supervisor\", supervisor_chain)"
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]
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},
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{
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"cell_type": "markdown",
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@@ -134,11 +232,24 @@
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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": 15,
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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": ["for member in members:\n # We want our workers to ALWAYS \"report back\" to the supervisor when done\n workflow.add_edge(member, \"supervisor\")\n# The supervisor populates the \"next\" field in the graph state\n# which routes to a node or finishes\nconditional_map = {k: k for k in members}\nconditional_map[\"FINISH\"] = END\nworkflow.add_conditional_edges(\"supervisor\", lambda x: x[\"next\"], conditional_map)\n# Finally, add entrypoint\nworkflow.add_edge(START, \"supervisor\")\n\ngraph = workflow.compile()"]
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"source": [
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"for member in members:\n",
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" # We want our workers to ALWAYS \"report back\" to the supervisor when done\n",
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" workflow.add_edge(member, \"supervisor\")\n",
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"# The supervisor populates the \"next\" field in the graph state\n",
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"# which routes to a node or finishes\n",
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"conditional_map = {k: k for k in members}\n",
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"conditional_map[\"FINISH\"] = END\n",
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"workflow.add_conditional_edges(\"supervisor\", lambda x: x[\"next\"], conditional_map)\n",
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"# Finally, add entrypoint\n",
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"workflow.add_edge(START, \"supervisor\")\n",
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"\n",
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"graph = workflow.compile()"
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]
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},
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{
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"cell_type": "markdown",
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@@ -152,7 +263,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"execution_count": 16,
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"id": "56ba78e9-d9c1-457c-a073-d606d5d3e013",
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"metadata": {},
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"outputs": [
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@@ -161,32 +272,30 @@
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"output_type": "stream",
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"text": [
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"{'supervisor': {'next': 'Coder'}}\n",
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"----\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Python REPL can execute arbitrary code. Use with caution.\n"
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]
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},
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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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"{'Coder': {'messages': [HumanMessage(content=\"The code `print('Hello, World!')` was executed, and the output is:\\n\\n```\\nHello, World!\\n```\", name='Coder')]}}\n",
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"----\n",
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"{'Coder': {'messages': [HumanMessage(content='The code to print \"Hello, World!\" to the terminal is:\\n\\n```python\\nprint(\\'Hello, World!\\')\\n```\\n\\nWhen executed, it prints:\\n```\\nHello, World!\\n```', name='Coder')]}}\n",
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"----\n",
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"{'supervisor': {'next': 'FINISH'}}\n",
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"----\n"
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]
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}
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],
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"source": ["for s in graph.stream(\n {\n \"messages\": [\n HumanMessage(content=\"Code hello world and print it to the terminal\")\n ]\n }\n):\n if \"__end__\" not in s:\n print(s)\n print(\"----\")"]
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"source": [
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"for s in graph.stream(\n",
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" {\n",
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" \"messages\": [\n",
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" HumanMessage(content=\"Code hello world and print it to the terminal\")\n",
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" ]\n",
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" }\n",
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"):\n",
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" if \"__end__\" not in s:\n",
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" print(s)\n",
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" print(\"----\")"
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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": 9,
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"execution_count": 7,
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"id": "45a92dfd-0e11-47f5-aad4-b68d24990e34",
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"metadata": {},
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"outputs": [
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@@ -196,22 +305,22 @@
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"text": [
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"{'supervisor': {'next': 'Researcher'}}\n",
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"----\n",
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"{'Researcher': {'messages': [HumanMessage(content='**Research Report on Pikas**\\n\\nPikas are small mammals related to rabbits, known for their distinctive chirping sounds. They inhabit some of the most challenging environments, particularly boulder fields at high elevations, such as those found along the treeless slopes of the Southern Rockies, where they can be found at altitudes of up to 14,000 feet. Pikas are well-adapted to cold climates and typically do not fare well in warmer temperatures.\\n\\nRecent studies have shown that pikas are being impacted by climate change. Research by Peter Billman, a Ph.D. student from the University of Connecticut, indicates that pikas have moved upslope by approximately 1,160 feet. This upslope retreat is a direct response to changing climatic conditions, as pikas seek cooler temperatures at higher elevations.\\n\\nPikas are also known to be industrious foragers, particularly during the summer months when they gather vegetation to create haypiles for winter sustenance. Their behavior is encapsulated in the saying, \"making hay while the sun shines,\" reflecting their proactive approach to survival in harsh conditions.\\n\\nThe effects of climate change on pikas are not limited to the Southern Rockies. Studies published in Global Change Biology suggest that climate change is influencing pikas even in areas where they were previously thought to be less vulnerable, such as the Northern Rockies. These findings point to a broader trend of pikas moving to higher elevations, a behavior that may indicate a search for cooler, more suitable habitats.\\n\\nMoreover, researchers are exploring the possibility that pikas at lower elevations may have developed warm adaptations that could be beneficial for their future survival, given the ongoing climatic shifts. This line of research could help conservationists understand how pikas might cope with a warming world.\\n\\nIn conclusion, pikas are a species that not only fascinate with their unique behaviors and adaptations but also serve as indicators of environmental changes. Their upslope migration in response to climate change highlights the urgency for understanding and mitigating the effects of global warming on mountain ecosystems and the species that inhabit them.\\n\\n**Sources:**\\n- [Colorado Sun](https://coloradosun.com/2023/08/27/colorado-pika-population-climate-change/)\\n- [Wildlife.org](https://wildlife.org/climate-change-affects-pikas-even-in-unlikely-areas/)', name='Researcher')]}}\n",
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"{'Researcher': {'messages': [HumanMessage(content='# Research Report on Pikas\\n\\nPikas, belonging to the genus Ochotona, are small, short-legged, and virtually tailless mammals that are often found in the mountains of western North America and across much of Asia. Despite their rodent-like appearance, pikas are not rodents but rather are part of the order Lagomorpha, which also includes rabbits and hares.\\n\\n## Behavior and Ecology\\nPikas are known for their unique behavior of not hibernating and remaining active throughout the winter. They navigate through tunnels under rocks and snow and rely on dried plants, which they have stored during warmer months in caches known as \"haypiles.\" This foraging strategy, termed \"haying,\" is crucial for their survival during the harsh winter months.\\n\\nPikas have a preference for cooler temperatures, typically foraging in temperatures below 25°C (77°F). They tend to avoid direct sunlight and stay in shaded regions when it gets warmer. A study has shown that for every 1°C (1.8°F) increase in ambient temperature, pikas can lose 3% of their foraging time, making them sensitive to climate change.\\n\\n## Distribution and Habitat\\nThe American pika (Ochotona princeps) and its relative, the collared pika (O. collaris), are found throughout the high mountainous regions of western North America. These species prefer cooler climates and have been observed to retreat to higher elevations as a response to increasing temperatures. Their current distribution is believed to be a result of a retreat from much larger ranges they occupied in the past, which included Western Europe and Eastern North America.\\n\\n## Conservation Status\\nThe International Union for Conservation of Nature and Natural Resources (IUCN) lists the American pika as a species of Least Concern but notes that populations are declining and unlikely to rebound due to habitat loss from extreme temperatures. The sensitivity of pikas to summer heat makes them an indicator species for the potential effects of climate change. Studies have shown that some populations are in decline, and there have been cases of local extirpation, particularly in the Great Basin.\\n\\n## Human Impact\\nHuman activity has impacted the ecosystems where pikas live, with recorded interactions dating back to the 1970s. Such interactions have been linked to pikas having reduced foraging time, limiting the amount of food they can stockpile for winter. Additionally, pikas have been considered pests in regions like the Tibetan plateau, where high densities of burrowing pikas are thought to reduce forage for domestic livestock and damage grasslands.\\n\\n## Conclusion\\nPikas are fascinating creatures with distinct adaptations that allow them to thrive in alpine environments. However, their future is uncertain due to the looming threats of climate change and habitat alteration. Conservation efforts, research, and monitoring are vital to ensure the survival of these unique mammals in a changing world.\\n\\n---\\n\\n**Sources:**\\n- [Wikipedia - Pika](https://en.wikipedia.org/wiki/Pika)\\n- [Treehugger - American Pika](https://www.treehugger.com/surprising-facts-about-american-pika-4864528)\\n- [National Park Service - Pikas at Rocky Mountain National Park](https://www.nps.gov/romo/learn/nature/pikas.htm)\\n- [Wikipedia - American Pika](https://en.wikipedia.org/wiki/American_pika)\\n- [Britannica - Pika](https://www.britannica.com/animal/pika)', name='Researcher')]}}\n",
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"----\n",
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"{'supervisor': {'next': 'FINISH'}}\n",
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"----\n"
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]
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}
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],
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"source": ["for s in graph.stream(\n {\"messages\": [HumanMessage(content=\"Write a brief research report on pikas.\")]},\n {\"recursion_limit\": 100},\n):\n if \"__end__\" not in s:\n print(s)\n print(\"----\")"]
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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": "1d363d2c-e0da-4cce-ba47-ad2aa9df0fef",
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"metadata": {},
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"outputs": [],
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"source": [""]
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"source": [
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"for s in graph.stream(\n",
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" {\"messages\": [HumanMessage(content=\"Write a brief research report on pikas.\")]},\n",
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" {\"recursion_limit\": 100},\n",
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"):\n",
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" if \"__end__\" not in s:\n",
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" print(s)\n",
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" print(\"----\")"
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]
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}
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],
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"metadata": {
|
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@@ -230,7 +339,7 @@
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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": "3.11.1"
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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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Isaac Francisco