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langgraph/examples/llm-compiler/LLMCompiler.ipynb
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William Fu-Hinthorn c26a8675c3 rename
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{
"cells": [
{
"cell_type": "markdown",
"id": "0c8b472b-f3fb-46c2-841f-930a4692697b",
"metadata": {},
"source": [
"# LLMCompiler\n",
"\n",
"This notebook shows how to implement [LLMCompiler, by Kim, et. al](https://arxiv.org/abs/2312.04511) in LangGraph.\n",
"\n",
"LLMCompiler is an agent architecture intented on speeding up the latency of agentic tasks via fast, parallel tool execution. It has 3 main components:\n",
"\n",
"1. Planner: generate a DAG of tasks.\n",
"2. Task Fetching Unit: schedules and executes the tasks\n",
"3. Joiner: Responds to the user or triggers a second plan\n",
"\n",
"![diagram](./img/diagram.png)\n",
"\n",
"This notebook walks through each component and shows how to wire them together using LangGraph. \n",
"\n",
"\n",
"**First,** install the dependencies, and set up LangSmith for tracing to more easily debug and observe the agent."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "16bd5497-35ad-44f2-94d9-19ff39a5ffed",
"metadata": {},
"outputs": [],
"source": [
"# %pip install -U --quiet langchain_openai langsmith langgraph langchain numexpr"
]
},
{
"cell_type": "code",
"execution_count": 41,
"id": "abbd6948-e9a3-47ca-89c7-7ac2fc5eca8b",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import getpass\n",
"\n",
"def _get_pass(var: str):\n",
" if var not in os.environ:\n",
" os.environ[var] = getpass.getpass(f\"{var}: \")\n",
"# Optional: Debug + trace calls using LangSmith\n",
"os.environ[\"LANGCHAIN_TRACING_V2\"] = \"True\"\n",
"os.environ[\"LANGCHAIN_PROJECT\"] = \"LLMCompiler\"\n",
"_get_pass(\"LANGCHAIN_API_KEY\")\n",
"_get_pass(\"OPENAI_API_KEY\")"
]
},
{
"cell_type": "markdown",
"id": "a61b48ee-8c6f-4863-913a-676f659287de",
"metadata": {},
"source": [
"## Part 1: Tools\n",
"\n",
"We'll first define the tools for the agent to use in our demo. We'll give it the class search engine + calculator combo.\n",
"\n",
"If you don't want to sign up for tavily, you can replace it with the free [DuckDuckGo](https://python.langchain.com/docs/integrations/tools/ddg)."
]
},
{
"cell_type": "code",
"execution_count": 42,
"id": "e7476bb2-1a51-42f6-b7ae-82a0300bbf84",
"metadata": {},
"outputs": [],
"source": [
"from langchain_openai import ChatOpenAI\n",
"from langchain_community.tools.tavily_search import TavilySearchResults\n",
"# Imported from the https://github.com/langchain-ai/langgraph/tree/main/examples/plan-and-execute repo\n",
"from math_tools import get_math_tool\n",
"\n",
"_get_pass(\"TAVILY_API_KEY\")\n",
"\n",
"calculate = get_math_tool(ChatOpenAI(model=\"gpt-4-turbo-preview\"))\n",
"search = TavilySearchResults(max_results=1, description='tavily_search_results_json(query=\"the search query\") - a search engine.')\n",
"\n",
"tools = [search, calculate]"
]
},
{
"cell_type": "code",
"execution_count": 43,
"id": "152eecf3-6bef-4718-af71-a0b3c5a3b009",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'37'"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"calculate.invoke({\"problem\": \"What's the temp of sf + 5?\", \"context\": [\"Thet empreature of sf is 32 degrees\"]})"
]
},
{
"cell_type": "markdown",
"id": "1abdedbd-d81b-4ee9-b46f-f29439ed1350",
"metadata": {},
"source": [
"# Part 2: Planner\n",
"\n",
"\n",
"Largely adapted from [the original source code](https://github.com/SqueezeAILab/LLMCompiler/blob/main/src/llm_compiler/output_parser.py), the planner accepts the input question and generates a task list to execute.\n",
"\n",
"If it is provided with a previous plan, it is instructed to re-plan, which is useful if, upon completion of the first batch of tasks, the agent must take more actions.\n",
"\n",
"The code below composes constructs the prompt template for the planner and composes it with LLM and output parser, defined in [output_parser.py](./output_parser.py). The output parser processes a task list in the following form:\n",
"\n",
"```plaintext\n",
"1. tool_1(arg1=\"arg1\", arg2=3.5, ...)\n",
"Thought: I then want to find out Y by using tool_2\n",
"2. tool_2(arg1=\"\", arg2=\"${1}\")'\n",
"3. join()<END_OF_PLAN>\"\n",
"```\n",
"\n",
"The \"Thought\" lines are optional. The `${#}` placeholders are variables. These are used to route tool (task) outputs to other tools."
]
},
{
"cell_type": "code",
"execution_count": 44,
"id": "15dd9639-691f-4906-9012-83fd6e9ac126",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"================================\u001b[1m System Message \u001b[0m================================\n",
"\n",
"Given a user query, create a plan to solve it with the utmost parallelizability. Each plan should comprise an action from the following \u001b[33;1m\u001b[1;3m{num_tools}\u001b[0m types:\n",
"\u001b[33;1m\u001b[1;3m{tool_descriptions}\u001b[0m\n",
"\u001b[33;1m\u001b[1;3m{num_tools}\u001b[0m. join(): Collects and combines results from prior actions.\n",
"\n",
" - An LLM agent is called upon invoking join() to either finalize the user query or wait until the plans are executed.\n",
" - join should always be the last action in the plan, and will be called in two scenarios:\n",
" (a) if the answer can be determined by gathering the outputs from tasks to generate the final response.\n",
" (b) if the answer cannot be determined in the planning phase before you execute the plans. Guidelines:\n",
" - Each action described above contains input/output types and description.\n",
" - You must strictly adhere to the input and output types for each action.\n",
" - The action descriptions contain the guidelines. You MUST strictly follow those guidelines when you use the actions.\n",
" - Each action in the plan should strictly be one of the above types. Follow the Python conventions for each action.\n",
" - Each action MUST have a unique ID, which is strictly increasing.\n",
" - Inputs for actions can either be constants or outputs from preceding actions. In the latter case, use the format $id to denote the ID of the previous action whose output will be the input.\n",
" - Always call join as the last action in the plan. Say '<END_OF_PLAN>' after you call join\n",
" - Ensure the plan maximizes parallelizability.\n",
" - Only use the provided action types. If a query cannot be addressed using these, invoke the join action for the next steps.\n",
" - Never introduce new actions other than the ones provided.\n",
"\n",
"=============================\u001b[1m Messages Placeholder \u001b[0m=============================\n",
"\n",
"\u001b[33;1m\u001b[1;3m{messages}\u001b[0m\n",
"\n",
"================================\u001b[1m System Message \u001b[0m================================\n",
"\n",
"Remember, ONLY respond with the task list in the correct format! E.g.:\n",
"idx. tool(arg_name=args)\n",
"None\n"
]
}
],
"source": [
"from typing import Sequence\n",
"\n",
"from langchain_core.language_models import BaseChatModel\n",
"from langchain_core.prompts import ChatPromptTemplate\n",
"from langchain_core.runnables import RunnableBranch\n",
"from langchain_core.tools import BaseTool\n",
"from langchain_core.messages import BaseMessage, FunctionMessage, HumanMessage, SystemMessage\n",
"\n",
"from output_parser import LLMCompilerPlanParser, Task\n",
"from langchain import hub\n",
"from langchain_openai import ChatOpenAI\n",
"\n",
"\n",
"prompt = hub.pull(\"wfh/llm-compiler\")\n",
"print(prompt.pretty_print())"
]
},
{
"cell_type": "code",
"execution_count": 45,
"id": "45689d40-d8df-4316-a121-6ea9c87d2efe",
"metadata": {},
"outputs": [],
"source": [
"def create_planner(llm: BaseChatModel, tools: Sequence[BaseTool], base_prompt: ChatPromptTemplate):\n",
" tool_descriptions = \"\\n\".join(\n",
" f\"{i}. {tool.description}\\n\" for i, tool in enumerate(tools)\n",
" )\n",
" planner_prompt = base_prompt.partial(\n",
" replan=\"\",\n",
" num_tools=len(tools),\n",
" tool_descriptions=tool_descriptions,\n",
" )\n",
" replanner_prompt = base_prompt.partial(\n",
" replan=' - You are given \"Previous Plan\" which is the plan that the previous agent created along with the execution results '\n",
" \"(given as Observation) of each plan and a general thought (given as Thought) about the executed results.\"\n",
" 'You MUST use these information to create the next plan under \"Current Plan\".\\n'\n",
" ' - When starting the Current Plan, you should start with \"Thought\" that outlines the strategy for the next plan.\\n'\n",
" \" - In the Current Plan, you should NEVER repeat the actions that are already executed in the Previous Plan.\\n\"\n",
" \" - You must continue the task index from the end of the previous one. Do not repeat task indices.\",\n",
" num_tools=len(tools),\n",
" tool_descriptions=tool_descriptions,\n",
" )\n",
" \n",
" def should_replan(state: list):\n",
" # Context is passed as a system message\n",
" return isinstance(state[-1], SystemMessage)\n",
"\n",
" def wrap_messages(state: list):\n",
" return {\"messages\": state}\n",
"\n",
" def wrap_and_get_last_index(state: list):\n",
" next_task = 0\n",
" for message in state[::-1]:\n",
" if isinstance(message, FunctionMessage):\n",
" next_task = message.additional_kwargs[\"idx\"] + 1\n",
" break\n",
" state[-1].content = state[-1].content + f\" - Begin counting at : {next_task}\"\n",
" return {\"messages\": state}\n",
" \n",
" return (\n",
" RunnableBranch(\n",
" (should_replan, wrap_and_get_last_index | replanner_prompt),\n",
" wrap_messages | planner_prompt,\n",
" )\n",
" | llm\n",
" | LLMCompilerPlanParser(tools=tools)\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 46,
"id": "bbdcb57b-5362-4b9e-88db-fb3fae443fb0",
"metadata": {},
"outputs": [],
"source": [
"llm = ChatOpenAI(model=\"gpt-4-turbo-preview\")\n",
"# This is the primary \"agent\" in our application\n",
"planner = create_planner(llm, tools, prompt)"
]
},
{
"cell_type": "code",
"execution_count": 47,
"id": "730490c6-6e3a-4173-82a1-9eb9d5eeff20",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"description='tavily_search_results_json(query=\"the search query\") - a search engine.' max_results=1 {'query': 'current temperature in San Francisco'}\n",
"---\n",
"name='math' description='math(problem: str, context: Optional[List[str]] = None, config: Optional[langchain_core.runnables.config.RunnableConfig] = None) - math(problem: str, context: Optional[list[str]]) -> float:\\n - Solves the provided math problem.\\n - `problem` can be either a simple math problem (e.g. \"1 + 3\") or a word problem (e.g. \"how many apples are there if there are 3 apples and 2 apples\").\\n - You cannot calculate multiple expressions in one call. For instance, `math(\\'1 + 3, 2 + 4\\')` does not work. If you need to calculate multiple expressions, you need to call them separately like `math(\\'1 + 3\\')` and then `math(\\'2 + 4\\')`\\n - Minimize the number of `math` actions as much as possible. For instance, instead of calling 2. math(\"what is the 10% of $1\") and then call 3. math(\"$1 + $2\"), you MUST call 2. math(\"what is the 110% of $1\") instead, which will reduce the number of math actions.\\n - You can optionally provide a list of strings as `context` to help the agent solve the problem. If there are multiple contexts you need to answer the question, you can provide them as a list of strings.\\n - `math` action will not see the output of the previous actions unless you provide it as `context`. You MUST provide the output of the previous actions as `context` if you need to do math on it.\\n - You MUST NEVER provide `search` type action\\'s outputs as a variable in the `problem` argument. This is because `search` returns a text blob that contains the information about the entity, not a number or value. Therefore, when you need to provide an output of `search` action, you MUST provide it as a `context` argument to `math` action. For example, 1. search(\"Barack Obama\") and then 2. math(\"age of $1\") is NEVER allowed. Use 2. math(\"age of Barack Obama\", context=[\"$1\"]) instead.\\n - When you ask a question about `context`, specify the units. For instance, \"what is xx in height?\" or \"what is xx in millions?\" instead of \"what is xx?\"' args_schema=<class 'pydantic.v1.main.mathSchema'> func=<function get_math_tool.<locals>.calculate_expression at 0x119a318a0> {'problem': 'pow($0, 3)', 'context': ['$0']}\n",
"---\n",
"join ()\n",
"---\n"
]
}
],
"source": [
"example_question = \"What's the temperature in SF raised to the 3rd power?\"\n",
"\n",
"for task in planner.stream([HumanMessage(content=example_question)]):\n",
" print(task['tool'], task['args'])\n",
" print('---')"
]
},
{
"cell_type": "markdown",
"id": "5d0e795f-61ff-4553-9823-23e7624ca180",
"metadata": {},
"source": [
"## 3. Task Fetching Unit\n",
"\n",
"This component schedules the tasks. It receives a stream of tools of the following format:\n",
"\n",
"```typescript\n",
"{\n",
" tool: BaseTool,\n",
" dependencies: number[],\n",
"}\n",
"```\n",
"\n",
"The basic idea is to begin executing tools as soon as their dependencies are met. This is done through multi-threading."
]
},
{
"cell_type": "code",
"execution_count": 48,
"id": "c1fbafdd-42d4-4575-8466-e5951cee71f4",
"metadata": {
"jp-MarkdownHeadingCollapsed": true
},
"outputs": [],
"source": [
"from typing import Any, Union, Iterable, List, Tuple, Dict\n",
"from typing_extensions import TypedDict\n",
"\n",
"from langchain_core.runnables import (\n",
" chain as as_runnable,\n",
")\n",
"\n",
"from concurrent.futures import ThreadPoolExecutor, wait\n",
"import time\n",
"\n",
"\n",
"def _get_observations(messages: List[BaseMessage]) -> Dict[int, Any]:\n",
" # Get all previous tool responses\n",
" results = {}\n",
" for message in messages[::-1]:\n",
" if isinstance(message, FunctionMessage):\n",
" results[int(message.additional_kwargs[\"idx\"])] = message.content\n",
" return results\n",
"\n",
"class SchedulerInput(TypedDict):\n",
" messages: List[BaseMessage]\n",
" tasks: Iterable[Task]\n",
"\n",
"\n",
"def _execute_task(task, observations, config):\n",
" tool_to_use = task[\"tool\"]\n",
" if isinstance(tool_to_use, str):\n",
" return tool_to_use\n",
" args = task[\"args\"]\n",
" try:\n",
" if isinstance(args, str):\n",
" resolved_args = _resolve_arg(args, observations)\n",
" elif isinstance(args, dict):\n",
" resolved_args = {key: _resolve_arg(val, observations) for key, val in args.items()}\n",
" else:\n",
" # This will likely fail\n",
" resolved_args = args\n",
" except Exception as e:\n",
" return (\n",
" f\"ERROR(Failed to call {tool_to_use.name} with args {args}.)\"\n",
" f\" Args could not be resolved. Error: {repr(e)}\"\n",
" )\n",
" try:\n",
" return tool_to_use.invoke(resolved_args, config)\n",
" except Exception as e:\n",
" return (\n",
" f\"ERROR(Failed to call {tool_to_use.name} with args {args}.\"\n",
" + f\" Args resolved to {resolved_args}. Error: {repr(e)})\"\n",
" )\n",
"\n",
"\n",
"def _resolve_arg(arg: Union[str, Any], observations: Dict[int, Any]):\n",
" if isinstance(arg, str) and arg.startswith(\"$\"):\n",
" try:\n",
" stripped = arg[1:].replace(\".output\", \"\").strip(\"{}\")\n",
" idx = int(stripped)\n",
" except Exception:\n",
" return str(arg)\n",
" return str(observations[idx])\n",
" elif isinstance(arg, list):\n",
" return [_resolve_arg(a, observations) for a in arg]\n",
" else:\n",
" return str(arg)\n",
"\n",
"\n",
"@as_runnable\n",
"def schedule_task(task_inputs, config):\n",
" task: Task = task_inputs['task']\n",
" observations: Dict[int, Any] = task_inputs['observations']\n",
" try:\n",
" observation = _execute_task(task, observations, config)\n",
" except Exception:\n",
" import traceback\n",
" observation = traceback.format_exception() #repr(e) + \n",
" observations[task['idx']] = observation\n",
"\n",
"def schedule_pending_task(task: Task, observations: Dict[int, Any], retry_after: float = 0.2):\n",
" while True:\n",
" deps = task[\"dependencies\"]\n",
" if (\n",
" deps\n",
" and (\n",
" any([dep not in observations for dep in deps])\n",
" )\n",
" ):\n",
" # Dependencies not yet satisfied\n",
" time.sleep(retry_after)\n",
" continue\n",
" schedule_task.invoke({\"task\": task, \"observations\": observations})\n",
" break\n",
"\n",
"@as_runnable\n",
"def schedule_tasks(scheduler_input: SchedulerInput) -> List[FunctionMessage]:\n",
" \"\"\"Group the tasks into a DAG schedule.\"\"\"\n",
" # For streaming, we are making a few simplifying assumption:\n",
" # 1. The LLM does not create cyclic dependencies\n",
" # 2. That the LLM will not generate tasks with future deps\n",
" # If this ceases to be a good assumption, you can either\n",
" # adjust to do a proper topological sort (not-stream)\n",
" # or use a more complicated data structure\n",
" tasks = scheduler_input[\"tasks\"]\n",
" messages = scheduler_input[\"messages\"]\n",
" # If we are re-planning, we may have calls that depend on previous\n",
" # plans. Start with those.\n",
" observations = _get_observations(messages)\n",
" task_names = {}\n",
" originals = set(observations)\n",
" # ^^ We assume each task inserts a different key above to\n",
" # avoid race conditions...\n",
" futures = []\n",
" retry_after = 0.25 # Retry every quarter second\n",
" with ThreadPoolExecutor() as executor:\n",
" for task in tasks:\n",
" deps = task[\"dependencies\"]\n",
" task_names[task[\"idx\"]] = task[\"tool\"] if isinstance(task[\"tool\"], str) else task[\"tool\"].name\n",
" if (\n",
" # Depends on other tasks\n",
" deps\n",
" and (\n",
" any([dep not in observations for dep in deps])\n",
" )\n",
" ):\n",
" futures.append(executor.submit(schedule_pending_task, task, observations, retry_after))\n",
" else:\n",
" # No deps or all deps satisfied\n",
" # can schedule now\n",
" schedule_task.invoke(dict(task=task, observations=observations))\n",
" # futures.append(executor.submit(schedule_task.invoke dict(task=task, observations=observations)))\n",
"\n",
" # All tasks have been submitted or enqueued\n",
" # Wait for them to complete\n",
" wait(futures)\n",
" # Convert observations to new tool messages to add to the state\n",
" new_observations = {k: (task_names[k], observations[k]) for k in sorted(observations.keys() - originals)}\n",
" tool_messages = [\n",
" FunctionMessage(\n",
" name=name,\n",
" content=str(obs),\n",
" additional_kwargs={\"idx\": k}\n",
" ) for k, (name, obs) in new_observations.items()]\n",
" return tool_messages"
]
},
{
"cell_type": "code",
"execution_count": 49,
"id": "052f6b16-103a-40e9-94dd-8fcc37e77ba4",
"metadata": {},
"outputs": [],
"source": [
"import itertools\n",
"\n",
"@as_runnable\n",
"def plan_and_schedule(messages: List[BaseMessage], config):\n",
" tasks = planner.stream(messages, config)\n",
" # Begin executing the planner immediately\n",
" tasks = itertools.chain([next(tasks)], tasks)\n",
" scheduled_tasks = schedule_tasks.invoke({\n",
" \"messages\": messages,\n",
" \"tasks\": tasks,\n",
" }, config)\n",
" return scheduled_tasks"
]
},
{
"cell_type": "markdown",
"id": "9efa15ae-817a-48c6-86ed-16bc112fedc5",
"metadata": {},
"source": [
"#### Example Plan\n",
"\n",
"We still haven't introduced any cycles in our computation graph, so this is all easily expressed in LCEL."
]
},
{
"cell_type": "code",
"execution_count": 50,
"id": "55142257-2674-4a47-988e-0d2810917329",
"metadata": {},
"outputs": [],
"source": [
"tool_messages = plan_and_schedule.invoke([HumanMessage(content=example_question)])"
]
},
{
"cell_type": "code",
"execution_count": 51,
"id": "a98e0525-2fcf-4fa1-baf6-79858bb8a6bd",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[FunctionMessage(content=\"[{'url': 'https://en.climate-data.org/north-america/united-states-of-america/california/san-francisco-385/t/september-9/', 'content': 'San Francisco Weather in September San Francisco weather in September San Francisco weather by month // weather averages 9.6 (49.2) 6.2 (43.2) 14 (57.3) Data: 1999 - 2019: avg. Sun hours San Francisco weather and climate for further months San Francisco weather in September // weather averages Airport close to San FranciscoJanuary February March April May June July August September October November December; Avg. Temperature °C (°F) 9.6 °C (49.2) °F. 10.5 °C (50.8) °F. 11.6 °C'}]\", additional_kwargs={'idx': 1}, name='tavily_search_results_json'),\n",
" FunctionMessage(content='1', additional_kwargs={'idx': 2}, name='math'),\n",
" FunctionMessage(content='join', additional_kwargs={'idx': 3}, name='join')]"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tool_messages"
]
},
{
"cell_type": "markdown",
"id": "563d5311-55f0-4ca1-afbd-01fd970cf3e3",
"metadata": {},
"source": [
"## 4. \"Joiner\" \n",
"\n",
"So now we have the planning and initial execution done. We need a component to process these outputs and either:\n",
"\n",
"1. Respond with the correct answer.\n",
"2. Loop with a new plan.\n",
"\n",
"The paper refers to this as the \"joiner\". It's another LLM call. We are using function calling to improve parsing reliability."
]
},
{
"cell_type": "code",
"execution_count": 52,
"id": "942dab42-ad42-4ba2-90d5-49edbe4fae68",
"metadata": {},
"outputs": [],
"source": [
"from langchain_core.pydantic_v1 import BaseModel, Field\n",
"from langchain.chains.openai_functions import create_structured_output_runnable\n",
"from langchain_core.messages import AIMessage\n",
"\n",
"class FinalResponse(BaseModel):\n",
" \"\"\"The final response/answer.\"\"\"\n",
" response: str\n",
"\n",
"class Replan(BaseModel):\n",
" feedback: str = Field(description=\"Analysis of the previous attempts and recommendations on what needs to be fixed.\")\n",
"\n",
"class JoinOutputs(BaseModel):\n",
" \"\"\"Decide whether to replan or whether you can return the final response.\"\"\"\n",
" thought: str = Field(description=\"The chain of thought reasoning for the selected action\")\n",
" action: Union[FinalResponse, Replan]\n",
"\n",
"\n",
"joiner_prompt = hub.pull(\"wfh/llm-compiler-joiner\").partial(examples=\"\") # You can optionally add examples\n",
"llm = ChatOpenAI(model=\"gpt-4-turbo-preview\")\n",
"\n",
"runnable = create_structured_output_runnable(JoinOutputs, llm, joiner_prompt)"
]
},
{
"cell_type": "markdown",
"id": "fb50c4cd-947c-4a5d-a9f7-f0d92a10600f",
"metadata": {},
"source": [
"We will select only the most recent messages in the state, and format the output to be more useful for\n",
"the planner, should the agent need to loop."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "951a33cf-2a05-4a33-899a-0ab1d97122fa",
"metadata": {},
"outputs": [],
"source": [
"def _parse_joiner_output(decision: JoinOutputs) -> List[BaseMessage]:\n",
" response = [AIMessage(content=f\"Thought: {decision.thought}\")]\n",
" if isinstance(decision.action, Replan):\n",
" return response + [SystemMessage(content=f\"Context from last attempt: {decision.action.feedback}\")]\n",
" else:\n",
" return response + [AIMessage(content=decision.action.response)]\n",
"\n",
"\n",
"def select_recent_messages(messages: list) -> dict:\n",
" selected = []\n",
" for msg in messages[::-1]:\n",
" selected.append(msg)\n",
" if isinstance(msg, HumanMessage):\n",
" break\n",
" return {\"messages\": selected[::-1]}\n",
"\n",
"joiner = (\n",
" select_recent_messages\n",
" | runnable\n",
" | _parse_joiner_output\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 53,
"id": "1e49d4b1-8266-4520-a566-1448b1c31c8f",
"metadata": {},
"outputs": [],
"source": [
"input_messages = [HumanMessage(content=example_question)] + tool_messages"
]
},
{
"cell_type": "code",
"execution_count": 54,
"id": "31854dfd-b82f-4c24-9b58-6bae66777909",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[AIMessage(content=\"Thought: The information provided gives an average temperature for San Francisco in different months, but it doesn't specify the current temperature or any specific temperature to be raised to the 3rd power. Without the current temperature or a specific temperature value, it's impossible to calculate its value raised to the 3rd power.\"),\n",
" SystemMessage(content='Context from last attempt: The information provided is not sufficient to answer the question as it lacks the current temperature of San Francisco or any specific temperature value to be raised to the 3rd power. Need to find the current or a specific temperature to perform the calculation.')]"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"joiner.invoke(input_messages)"
]
},
{
"cell_type": "markdown",
"id": "b099e5ee-2c23-47d9-9387-0f64e02627d3",
"metadata": {},
"source": [
"## 5. Compose using LangGraph\n",
"\n",
"We'll define the agent as a stateful graph, with the main nodes being:\n",
"\n",
"1. Plan and execute (the DAG from the first step above)\n",
"2. Join: determine if we should finish or replan\n",
"3. Recontextualize: update the graph state based on the output from the joiner"
]
},
{
"cell_type": "code",
"execution_count": 55,
"id": "768b5f11-e3d2-47be-8143-a7dcd8765243",
"metadata": {},
"outputs": [],
"source": [
"from langgraph.graph import MessageGraph, END\n",
"from typing import Dict\n",
"\n",
"graph_builder = MessageGraph()\n",
"\n",
"# 1. Define vertices\n",
"# We defined plan_and_schedule above already\n",
"# Assign each node to a state variable to update\n",
"graph_builder.add_node(\"plan_and_schedule\", plan_and_schedule)\n",
"graph_builder.add_node(\"join\", joiner)\n",
"\n",
"\n",
"## Define edges\n",
"graph_builder.add_edge(\"plan_and_schedule\", \"join\")\n",
"\n",
"### This condition determines looping logic\n",
"\n",
"\n",
"def should_continue(state: List[BaseMessage]):\n",
" if isinstance(state[-1], AIMessage):\n",
" return END\n",
" return \"plan_and_schedule\"\n",
"\n",
"graph_builder.add_conditional_edges(\n",
" start_key=\"join\",\n",
" # Next, we pass in the function that will determine which node is called next.\n",
" condition=should_continue,\n",
")\n",
"graph_builder.set_entry_point(\"plan_and_schedule\")\n",
"chain = graph_builder.compile()"
]
},
{
"cell_type": "markdown",
"id": "9f8c9849-8531-463d-a0ef-dcc3d9888b2d",
"metadata": {},
"source": [
"#### Simple question\n",
"\n",
"Let's ask a simple question of the agent."
]
},
{
"cell_type": "code",
"execution_count": 56,
"id": "5bc4584a-e31c-4065-805e-76a6db30676a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'plan_and_schedule': [FunctionMessage(content='[{\\'url\\': \\'https://www.statista.com/statistics/188087/gdp-of-the-us-federal-state-of-new-york-since-1997/\\', \\'content\\': \"Strategy and business building for the data-driven economy: U.S. real GDP of New York 2000-2022 Real gross domestic product of New York in the United States from 2000 to 2022 (in billion U.S. dollars) Economy U.S. New York metro area GDP 2001-2022 You only have access to basic statistics. U.S. state and local government outstanding debt 2021, by state Demographics Resident population in New York 1960-2022In 2022, the real gross domestic product (GDP) of New York was about 1.56 trillion U.S. dollars. This is an increase from the previous year, when the state\\'s GDP stood at 1.51 trillion...\"}]', additional_kwargs={'idx': 0}, name='tavily_search_results_json')]}\n",
"---\n",
"{'join': [AIMessage(content='Thought: The search result provides the information that in 2022, the real gross domestic product (GDP) of New York was about 1.56 trillion U.S. dollars.'), AIMessage(content='The GDP of New York in 2022 was about 1.56 trillion U.S. dollars.')]}\n",
"---\n",
"{'__end__': [HumanMessage(content=\"What's the GDP of New York?\"), FunctionMessage(content='[{\\'url\\': \\'https://www.statista.com/statistics/188087/gdp-of-the-us-federal-state-of-new-york-since-1997/\\', \\'content\\': \"Strategy and business building for the data-driven economy: U.S. real GDP of New York 2000-2022 Real gross domestic product of New York in the United States from 2000 to 2022 (in billion U.S. dollars) Economy U.S. New York metro area GDP 2001-2022 You only have access to basic statistics. U.S. state and local government outstanding debt 2021, by state Demographics Resident population in New York 1960-2022In 2022, the real gross domestic product (GDP) of New York was about 1.56 trillion U.S. dollars. This is an increase from the previous year, when the state\\'s GDP stood at 1.51 trillion...\"}]', additional_kwargs={'idx': 0}, name='tavily_search_results_json'), AIMessage(content='Thought: The search result provides the information that in 2022, the real gross domestic product (GDP) of New York was about 1.56 trillion U.S. dollars.'), AIMessage(content='The GDP of New York in 2022 was about 1.56 trillion U.S. dollars.')]}\n",
"---\n"
]
}
],
"source": [
"for step in chain.stream([HumanMessage(content=\"What's the GDP of New York?\")]):\n",
" print(step)\n",
" print('---')\n"
]
},
{
"cell_type": "code",
"execution_count": 57,
"id": "b96efd08-5314-44f0-a694-3073b638adad",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The GDP of New York in 2022 was about 1.56 trillion U.S. dollars.\n"
]
}
],
"source": [
"# Final answer\n",
"print(step[END][-1].content)"
]
},
{
"cell_type": "markdown",
"id": "33c65ef5-b4b2-4ab2-8c78-a551da7819b9",
"metadata": {},
"source": [
"#### Multi-hop question\n",
"\n",
"This question requires that the agent perform multiple searches."
]
},
{
"cell_type": "code",
"execution_count": 58,
"id": "0b3a0916-d8ca-4092-b91c-d9e2b05259d8",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'plan_and_schedule': [FunctionMessage(content='[{\\'url\\': \\'https://savetheeaglesinternational.org/old-parrot/\\', \\'content\\': \"What Is The World\\'s Oldest Parrot? Living Long and Healthy Lives: A Look at the World’s Oldest Parrots certain parrot species are considered older: your parrot enters its senior years?One remarkable parrot that defied the odds and lived a long life is Cookie, a cockatoo who reached the impressive age of 83. Cookie spent his entire life at the Brookfield Zoo, serving as a testament to the exceptional care and environment provided by the zookeepers.\"}]', additional_kwargs={'idx': 1}, name='tavily_search_results_json'), FunctionMessage(content=\"[{'url': 'https://www.animalwised.com/how-long-does-a-parrot-live-3974.html', 'content': 'How Long Does a Parrot Live? How long does a parrot live in captivity? How long does a parrot live in the wild? Why do parrots live so long?Below is the average life expectancy of parrots in captivity, based on their species. Lovebirds. Lovebirds are members of the genus Agapornis, a small group of parrots in the parrot family Psittaculidae. The average life expectancy of a lovebird is between 12 and 15 years. Depending on care and circumstances, the bird can live up to 20 years ...'}]\", additional_kwargs={'idx': 2}, name='tavily_search_results_json'), FunctionMessage(content='join', additional_kwargs={'idx': 3}, name='join')]}\n",
"---\n",
"{'join': [AIMessage(content=\"Thought: The oldest parrot ever recorded is Cookie, a cockatoo, who lived to be 83 years old. However, the average lifespan provided is specifically for lovebirds, which is between 12 and 15 years. This information doesn't accurately reflect the average lifespan of all parrot species, which would be necessary to compare with Cookie's age accurately. Since parrots encompass a wide variety of species with different lifespans, the information on lovebirds' lifespan alone is insufficient for a comprehensive comparison.\"), SystemMessage(content=\"Context from last attempt: We need information on the average lifespan of parrots in general, not just lovebirds, to accurately compare with Cookie's age.\")]}\n",
"---\n",
"{'plan_and_schedule': [FunctionMessage(content='[{\\'url\\': \\'https://www.petmd.com/bird/how-long-do-parrots-live\\', \\'content\\': \"Average Parrot Lifespan and Aging How Long Do Parrots Live? How to Improve Your Parrot\\'s Lifespan Mcleod DVM, Lianne. The Spruce Pets. How Long do Pet Parrots and Other Birds Live?. 2023.Some pets, such as tortoises and parrots, may live for over 50 years. Because they are a lifelong commitment, lawyers often urge pet parents to provide documented plans for their pet parrots in their wills. Average Parrot Lifespan and Aging. Parrots are an incredibly diverse group of birds known by their scientific name: psittacines.\"}]', additional_kwargs={'idx': 4}, name='tavily_search_results_json')]}\n",
"---\n",
"{'join': [AIMessage(content=\"Thought: The information provided does not give a specific average lifespan for parrots in general, which is necessary for accurately comparing Cookie's age to the average lifespan of parrots. The search result mentions that parrots can live over 50 years but does not provide a detailed average lifespan applicable to all or most parrot species.\"), SystemMessage(content=\"Context from last attempt: We need information on the average lifespan of parrots in general to accurately compare with Cookie's age of 83 years. The provided information doesn't specify an average lifespan for parrots as a whole.\")]}\n",
"---\n",
"{'plan_and_schedule': [FunctionMessage(content='join', additional_kwargs={'idx': 5}, name='join')]}\n",
"---\n",
"{'join': [AIMessage(content=\"Thought: Despite multiple attempts, the specific average lifespan of parrots as a whole has not been provided. The information obtained mentions that parrots can live over 50 years, but a more precise average is necessary for a detailed comparison with Cookie's age of 83 years. However, it's clear that Cookie lived significantly longer than the average lifespan of many parrot species, including lovebirds which have an average lifespan of 12 to 15 years.\"), AIMessage(content=\"The oldest parrot on record is Cookie, a cockatoo, who lived to be 83 years old. While specific average lifespan information for all parrot species has not been provided, it's mentioned that some parrots can live over 50 years. This suggests that Cookie lived significantly longer than the average lifespan for many parrot species. For instance, lovebirds, a type of parrot, have an average lifespan of 12 to 15 years, indicating that Cookie's lifespan was exceptional among parrots.\")]}\n",
"---\n",
"{'__end__': [HumanMessage(content=\"What's the oldest parrot alive, and how much longer is that than the average?\"), FunctionMessage(content='[{\\'url\\': \\'https://savetheeaglesinternational.org/old-parrot/\\', \\'content\\': \"What Is The World\\'s Oldest Parrot? Living Long and Healthy Lives: A Look at the World’s Oldest Parrots certain parrot species are considered older: your parrot enters its senior years?One remarkable parrot that defied the odds and lived a long life is Cookie, a cockatoo who reached the impressive age of 83. Cookie spent his entire life at the Brookfield Zoo, serving as a testament to the exceptional care and environment provided by the zookeepers.\"}]', additional_kwargs={'idx': 1}, name='tavily_search_results_json'), FunctionMessage(content=\"[{'url': 'https://www.animalwised.com/how-long-does-a-parrot-live-3974.html', 'content': 'How Long Does a Parrot Live? How long does a parrot live in captivity? How long does a parrot live in the wild? Why do parrots live so long?Below is the average life expectancy of parrots in captivity, based on their species. Lovebirds. Lovebirds are members of the genus Agapornis, a small group of parrots in the parrot family Psittaculidae. The average life expectancy of a lovebird is between 12 and 15 years. Depending on care and circumstances, the bird can live up to 20 years ...'}]\", additional_kwargs={'idx': 2}, name='tavily_search_results_json'), FunctionMessage(content='join', additional_kwargs={'idx': 3}, name='join'), AIMessage(content=\"Thought: The oldest parrot ever recorded is Cookie, a cockatoo, who lived to be 83 years old. However, the average lifespan provided is specifically for lovebirds, which is between 12 and 15 years. This information doesn't accurately reflect the average lifespan of all parrot species, which would be necessary to compare with Cookie's age accurately. Since parrots encompass a wide variety of species with different lifespans, the information on lovebirds' lifespan alone is insufficient for a comprehensive comparison.\"), SystemMessage(content=\"Context from last attempt: We need information on the average lifespan of parrots in general, not just lovebirds, to accurately compare with Cookie's age. - Begin counting at : 4\"), FunctionMessage(content='[{\\'url\\': \\'https://www.petmd.com/bird/how-long-do-parrots-live\\', \\'content\\': \"Average Parrot Lifespan and Aging How Long Do Parrots Live? How to Improve Your Parrot\\'s Lifespan Mcleod DVM, Lianne. The Spruce Pets. How Long do Pet Parrots and Other Birds Live?. 2023.Some pets, such as tortoises and parrots, may live for over 50 years. Because they are a lifelong commitment, lawyers often urge pet parents to provide documented plans for their pet parrots in their wills. Average Parrot Lifespan and Aging. Parrots are an incredibly diverse group of birds known by their scientific name: psittacines.\"}]', additional_kwargs={'idx': 4}, name='tavily_search_results_json'), AIMessage(content=\"Thought: The information provided does not give a specific average lifespan for parrots in general, which is necessary for accurately comparing Cookie's age to the average lifespan of parrots. The search result mentions that parrots can live over 50 years but does not provide a detailed average lifespan applicable to all or most parrot species.\"), SystemMessage(content=\"Context from last attempt: We need information on the average lifespan of parrots in general to accurately compare with Cookie's age of 83 years. The provided information doesn't specify an average lifespan for parrots as a whole. - Begin counting at : 5\"), FunctionMessage(content='join', additional_kwargs={'idx': 5}, name='join'), AIMessage(content=\"Thought: Despite multiple attempts, the specific average lifespan of parrots as a whole has not been provided. The information obtained mentions that parrots can live over 50 years, but a more precise average is necessary for a detailed comparison with Cookie's age of 83 years. However, it's clear that Cookie lived significantly longer than the average lifespan of many parrot species, including lovebirds which have an average lifespan of 12 to 15 years.\"), AIMessage(content=\"The oldest parrot on record is Cookie, a cockatoo, who lived to be 83 years old. While specific average lifespan information for all parrot species has not been provided, it's mentioned that some parrots can live over 50 years. This suggests that Cookie lived significantly longer than the average lifespan for many parrot species. For instance, lovebirds, a type of parrot, have an average lifespan of 12 to 15 years, indicating that Cookie's lifespan was exceptional among parrots.\")]}\n",
"---\n"
]
}
],
"source": [
"steps = chain.stream(\n",
" [HumanMessage(content=\"What's the oldest parrot alive, and how much longer is that than the average?\")],\n",
" {\n",
" \"recursion_limit\": 100,\n",
" },\n",
")\n",
"for step in steps:\n",
" print(step)\n",
" print('---')"
]
},
{
"cell_type": "code",
"execution_count": 59,
"id": "6c65c414-7668-4fdf-ba97-f42f659b1317",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The oldest parrot on record is Cookie, a cockatoo, who lived to be 83 years old. While specific average lifespan information for all parrot species has not been provided, it's mentioned that some parrots can live over 50 years. This suggests that Cookie lived significantly longer than the average lifespan for many parrot species. For instance, lovebirds, a type of parrot, have an average lifespan of 12 to 15 years, indicating that Cookie's lifespan was exceptional among parrots.\n"
]
}
],
"source": [
"# Final answer\n",
"print(step[END][-1].content)"
]
},
{
"cell_type": "markdown",
"id": "1b859bc7-1a85-4d35-b57b-f67c87282403",
"metadata": {},
"source": [
"#### Multi-step math"
]
},
{
"cell_type": "code",
"execution_count": 60,
"id": "38d3ea91-59ba-4267-8060-ed75bbc840c6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'plan_and_schedule': [FunctionMessage(content='3307.0', additional_kwargs={'idx': 0}, name='math'), FunctionMessage(content='7.565011820330969', additional_kwargs={'idx': 1}, name='math'), FunctionMessage(content='join', additional_kwargs={'idx': 2}, name='join')]}\n",
"{'join': [AIMessage(content='Thought: The first calculation resulted in 3307.0, and the second calculation gave 7.565011820330969. To find the sum of these two values, I will simply add them together.'), AIMessage(content='The sum of ((3*(4+5)/0.5)+3245) + 8 and 32/4.23 is approximately 3314.565.')]}\n",
"{'__end__': [HumanMessage(content=\"What's ((3*(4+5)/0.5)+3245) + 8? What's 32/4.23? What's the sum of those two values?\"), FunctionMessage(content='3307.0', additional_kwargs={'idx': 0}, name='math'), FunctionMessage(content='7.565011820330969', additional_kwargs={'idx': 1}, name='math'), FunctionMessage(content='join', additional_kwargs={'idx': 2}, name='join'), AIMessage(content='Thought: The first calculation resulted in 3307.0, and the second calculation gave 7.565011820330969. To find the sum of these two values, I will simply add them together.'), AIMessage(content='The sum of ((3*(4+5)/0.5)+3245) + 8 and 32/4.23 is approximately 3314.565.')]}\n"
]
}
],
"source": [
"for step in chain.stream([HumanMessage(content=\"What's ((3*(4+5)/0.5)+3245) + 8? What's 32/4.23? What's the sum of those two values?\")]):\n",
" print(step)"
]
},
{
"cell_type": "code",
"execution_count": 61,
"id": "a6cf5fe0-f178-4197-950f-257711bff8d2",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The sum of ((3*(4+5)/0.5)+3245) + 8 and 32/4.23 is approximately 3314.565.\n"
]
}
],
"source": [
"# Final answer\n",
"print(step[END][-1].content)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.2"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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