Simple_and_Complex_Agent_testing.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# This notebook compares a to-do list created via a LangChain Single-Shot Agent with a LangChain agent that has the executor step substituted with BabyAGI. This is to compare quality and the speed of a simple vs. complex AI agent. As you will see the complex agent that uses BabyAGI + LangChain+ Tools to create a to-do list (to prove aliens are real through multiple iterations) is the more relevant and detailed list."
      ],
      "metadata": {
        "id": "oI1vF9H8PQ9y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install langchain openai tiktoken faiss-cpu google-search-results  -q #Install and Import Required Modules\n"
      ],
      "metadata": {
        "id": "FTryDdLvP8YN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "\n",
        "os.environ[\"OPENAI_API_KEY\"] = \"\"  # https://platform.openai.com\n",
        "os.environ['SERPAPI_API_KEY'] = \"\" # https://serpapi.com/"
      ],
      "metadata": {
        "id": "hyvVXyyiQFYu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## LangChain Single Shot Agent\n"
      ],
      "metadata": {
        "id": "1drK7dFvRGKU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.agents import load_tools\n",
        "from langchain.agents import initialize_agent\n",
        "from langchain.agents import AgentType\n",
        "from langchain.llms import OpenAI"
      ],
      "metadata": {
        "id": "sy3TPUZC5edr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "llm = OpenAI(temperature=0) # First, let's load the language model we're going to use to control the agent."
      ],
      "metadata": {
        "id": "3i6TnV285e7V"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tools = load_tools([\"serpapi\"], llm=llm) #Next, let's load some tools to use."
      ],
      "metadata": {
        "id": "OVlsI8kE5fGD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True) #Finally, let's initialize an agent with the tools, the language model, and the type of agent we want to use."
      ],
      "metadata": {
        "id": "PgQ1ouVA5fOJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "898a210b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 375
        },
        "outputId": "4d80a02a-cb6e-4d36-a806-82948dfa5dee"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
            "\u001b[32;1m\u001b[1;3m I need to research and find evidence of aliens\n",
            "Action: Search\n",
            "Action Input: Evidence of aliens\u001b[0m\n",
            "Observation: \u001b[36;1m\u001b[1;3mA Pentagon spokesperson has denied the claims, while lawmakers are pushing for information on UFOs to be declassified.\u001b[0m\n",
            "Thought:\u001b[32;1m\u001b[1;3m I need to find first hand accounts of aliens\n",
            "Action: Search\n",
            "Action Input: First hand accounts of aliens\u001b[0m\n",
            "Observation: \u001b[36;1m\u001b[1;3mThree military veterans testified in Congress' highly anticipated hearing on UFOs Wednesday, including a former Air Force intelligence ...\u001b[0m\n",
            "Thought:\u001b[32;1m\u001b[1;3m I need to find research on extraterrestrial and/or intradimensional life\n",
            "Action: Search\n",
            "Action Input: Research on extraterrestrial and/or intradimensional life\u001b[0m\n",
            "Observation: \u001b[36;1m\u001b[1;3mBut that extraterrestrial presence on regular display is, of course, a fiction. No life beyond Earth has ever been found; there is no evidence that alien life ...\u001b[0m\n",
            "Thought:\u001b[32;1m\u001b[1;3m I now know the steps I need to take to prove aliens are real\n",
            "Final Answer: To prove aliens are real, I need to research and find evidence of aliens, find first hand accounts of aliens, and find research on extraterrestrial and/or intradimensional life.\u001b[0m\n",
            "\n",
            "\u001b[1m> Finished chain.\u001b[0m\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'To prove aliens are real, I need to research and find evidence of aliens, find first hand accounts of aliens, and find research on extraterrestrial and/or intradimensional life.'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "agent.run(\"You work for a non profit organization titled Aliens are Real that is trying to find UFOs and aliens among us and give them a warm welcome to Earth. Your goal is to prove Aliens are real through research, first hand accounts, and evidence of extraterrestrial and/or intradimensional life. Your first task in providing evidence that aliens exist is to make a todo list to map out your steps in order to prove aliens are real\") #Now let's test it out!\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# BabyAGI with Tools\n",
        "\n",
        "This notebook builds on top of [baby agi](baby_agi.ipynb), but shows how you can swap out the execution chain. ![Flowchart2.png](data:image/png;base64,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) Credit - https://python.langchain.com/en/latest/use_cases/agents/baby_agi_with_agent.html"
      ],
      "metadata": {
        "id": "ZAV6X0nFQEp6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from collections import deque\n",
        "from typing import Dict, List, Optional, Any\n",
        "\n",
        "from langchain import LLMChain, OpenAI, PromptTemplate\n",
        "from langchain.embeddings import OpenAIEmbeddings\n",
        "from langchain.llms import BaseLLM\n",
        "from langchain.vectorstores.base import VectorStore\n",
        "from pydantic import BaseModel, Field\n",
        "from langchain.chains.base import Chain"
      ],
      "metadata": {
        "id": "Rf8uCo01QSQ4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "09f70772"
      },
      "source": [
        "## Connect to the Vector Store\n",
        "\n",
        "Depending on what vectorstore you use, this step may look different."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "794045d4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7104561d-9b73-48e9-e2d7-33bacace1295"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: faiss-cpu in /usr/local/lib/python3.10/dist-packages (1.7.4)\n",
            "Requirement already satisfied: google-search-results in /usr/local/lib/python3.10/dist-packages (2.4.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from google-search-results) (2.31.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->google-search-results) (3.2.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->google-search-results) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->google-search-results) (1.26.16)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->google-search-results) (2023.7.22)\n"
          ]
        }
      ],
      "source": [
        "pip install faiss-cpu google-search-results\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.docstore import InMemoryDocstore\n",
        "from langchain.vectorstores.faiss import FAISS\n"
      ],
      "metadata": {
        "id": "U40MjY8Ue16_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6e0305eb"
      },
      "outputs": [],
      "source": [
        "# Define your embedding model\n",
        "embeddings_model = OpenAIEmbeddings()\n",
        "# Initialize the vectorstore as empty\n",
        "import faiss\n",
        "embedding_size = 1536\n",
        "index = faiss.IndexFlatL2(embedding_size)\n",
        "vectorstore = FAISS(embeddings_model.embed_query, index, InMemoryDocstore({}), {})"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0f3b72bf"
      },
      "source": [
        "## Define the Chains\n",
        "\n",
        "BabyAGI relies on three LLM chains:\n",
        "- Task creation chain to select new tasks to add to the list\n",
        "- Task prioritization chain to re-prioritize tasks\n",
        "- Execution Chain to execute the tasks\n",
        "\n",
        "\n",
        "NOTE: in this notebook, the Execution chain will now be an agent."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bf4bd5cd"
      },
      "outputs": [],
      "source": [
        "class TaskCreationChain(LLMChain):\n",
        "    \"\"\"Chain to generates tasks.\"\"\"\n",
        "\n",
        "    @classmethod\n",
        "    def from_llm(cls, llm: BaseLLM, verbose: bool = True) -> LLMChain:\n",
        "        \"\"\"Get the response parser.\"\"\"\n",
        "        task_creation_template = (\n",
        "            \"You are an task creation AI that uses the result of an execution agent\"\n",
        "            \" to create new tasks with the following objective: {objective},\"\n",
        "            \" The last completed task has the result: {result}.\"\n",
        "            \" This result was based on this task description: {task_description}.\"\n",
        "            \" These are incomplete tasks: {incomplete_tasks}.\"\n",
        "            \" Based on the result, create new tasks to be completed\"\n",
        "            \" by the AI system that do not overlap with incomplete tasks.\"\n",
        "            \" Return the tasks as an array.\"\n",
        "        )\n",
        "        prompt = PromptTemplate(\n",
        "            template=task_creation_template,\n",
        "            input_variables=[\"result\", \"task_description\", \"incomplete_tasks\", \"objective\"],\n",
        "        )\n",
        "        return cls(prompt=prompt, llm=llm, verbose=verbose)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "b6488ffe"
      },
      "outputs": [],
      "source": [
        "class TaskPrioritizationChain(LLMChain):\n",
        "    \"\"\"Chain to prioritize tasks.\"\"\"\n",
        "\n",
        "    @classmethod\n",
        "    def from_llm(cls, llm: BaseLLM, verbose: bool = True) -> LLMChain:\n",
        "        \"\"\"Get the response parser.\"\"\"\n",
        "        task_prioritization_template = (\n",
        "            \"You are an task prioritization AI tasked with cleaning the formatting of and reprioritizing\"\n",
        "            \" the following tasks: {task_names}.\"\n",
        "            \" Consider the ultimate objective of your team: {objective}.\"\n",
        "            \" Do not remove any tasks. Return the result as a numbered list, like:\"\n",
        "            \" #. First task\"\n",
        "            \" #. Second task\"\n",
        "            \" Start the task list with number {next_task_id}.\"\n",
        "        )\n",
        "        prompt = PromptTemplate(\n",
        "            template=task_prioritization_template,\n",
        "            input_variables=[\"task_names\", \"next_task_id\", \"objective\"],\n",
        "        )\n",
        "        return cls(prompt=prompt, llm=llm, verbose=verbose)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "b43cd580"
      },
      "outputs": [],
      "source": [
        "from langchain.agents import ZeroShotAgent, Tool, AgentExecutor\n",
        "from langchain import OpenAI, SerpAPIWrapper, LLMChain\n",
        "todo_prompt = PromptTemplate.from_template(\"You are a planner who is an expert at coming up with a todo list for a given objective. Come up with a todo list for this objective: {objective}\")\n",
        "todo_chain = LLMChain(llm=OpenAI(temperature=0), prompt=todo_prompt)\n",
        "search = SerpAPIWrapper()\n",
        "tools = [\n",
        "    Tool(\n",
        "        name = \"Search\",\n",
        "        func=search.run,\n",
        "        description=\"useful for when you need to answer questions about current events\"\n",
        "    ),\n",
        "    Tool(\n",
        "        name = \"TODO\",\n",
        "        func=todo_chain.run,\n",
        "        description=\"useful for when you need to come up with todo lists. Input: an objective to create a todo list for. Output: a todo list for that objective. Please be very clear what the objective is!\"\n",
        "    )\n",
        "]\n",
        "\n",
        "\n",
        "prefix = \"\"\"You are an AI who performs one task based on the following objective: {objective}. Take into account these previously completed tasks: {context}.\"\"\"\n",
        "suffix = \"\"\"Question: {task}\n",
        "{agent_scratchpad}\"\"\"\n",
        "prompt = ZeroShotAgent.create_prompt(\n",
        "    tools,\n",
        "    prefix=prefix,\n",
        "    suffix=suffix,\n",
        "    input_variables=[\"objective\", \"task\", \"context\",\"agent_scratchpad\"]\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3ad996c5"
      },
      "source": [
        "### Define the BabyAGI Controller\n",
        "\n",
        "BabyAGI composes the chains defined above in a (potentially-)infinite loop."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0ada0636"
      },
      "outputs": [],
      "source": [
        "def get_next_task(task_creation_chain: LLMChain, result: Dict, task_description: str, task_list: List[str], objective: str) -> List[Dict]:\n",
        "    \"\"\"Get the next task.\"\"\"\n",
        "    incomplete_tasks = \", \".join(task_list)\n",
        "    response = task_creation_chain.run(result=result, task_description=task_description, incomplete_tasks=incomplete_tasks, objective=objective)\n",
        "    new_tasks = response.split('\\n')\n",
        "    return [{\"task_name\": task_name} for task_name in new_tasks if task_name.strip()]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "d35250ad"
      },
      "outputs": [],
      "source": [
        "def prioritize_tasks(task_prioritization_chain: LLMChain, this_task_id: int, task_list: List[Dict], objective: str) -> List[Dict]:\n",
        "    \"\"\"Prioritize tasks.\"\"\"\n",
        "    task_names = [t[\"task_name\"] for t in task_list]\n",
        "    next_task_id = int(this_task_id) + 1\n",
        "    response = task_prioritization_chain.run(task_names=task_names, next_task_id=next_task_id, objective=objective)\n",
        "    new_tasks = response.split('\\n')\n",
        "    prioritized_task_list = []\n",
        "    for task_string in new_tasks:\n",
        "        if not task_string.strip():\n",
        "            continue\n",
        "        task_parts = task_string.strip().split(\".\", 1)\n",
        "        if len(task_parts) == 2:\n",
        "            task_id = task_parts[0].strip()\n",
        "            task_name = task_parts[1].strip()\n",
        "            prioritized_task_list.append({\"task_id\": task_id, \"task_name\": task_name})\n",
        "    return prioritized_task_list"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "e3f1840c"
      },
      "outputs": [],
      "source": [
        "def _get_top_tasks(vectorstore, query: str, k: int) -> List[str]:\n",
        "    \"\"\"Get the top k tasks based on the query.\"\"\"\n",
        "    results = vectorstore.similarity_search_with_score(query, k=k)\n",
        "    if not results:\n",
        "        return []\n",
        "    sorted_results, _ = zip(*sorted(results, key=lambda x: x[1], reverse=True))\n",
        "    return [str(item.metadata['task']) for item in sorted_results]\n",
        "\n",
        "def execute_task(vectorstore, execution_chain: LLMChain, objective: str, task: str, k: int = 5) -> str:\n",
        "    \"\"\"Execute a task.\"\"\"\n",
        "    context = _get_top_tasks(vectorstore, query=objective, k=k)\n",
        "    return execution_chain.run(objective=objective, context=context, task=task)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1e978938"
      },
      "outputs": [],
      "source": [
        "\n",
        "class BabyAGI(Chain, BaseModel):\n",
        "    \"\"\"Controller model for the BabyAGI agent.\"\"\"\n",
        "\n",
        "    task_list: deque = Field(default_factory=deque)\n",
        "    task_creation_chain: TaskCreationChain = Field(...)\n",
        "    task_prioritization_chain: TaskPrioritizationChain = Field(...)\n",
        "    execution_chain: AgentExecutor = Field(...)\n",
        "    task_id_counter: int = Field(1)\n",
        "    vectorstore: VectorStore = Field(init=False)\n",
        "    max_iterations: Optional[int] = None\n",
        "\n",
        "    class Config:\n",
        "        \"\"\"Configuration for this pydantic object.\"\"\"\n",
        "        arbitrary_types_allowed = True\n",
        "\n",
        "    def add_task(self, task: Dict):\n",
        "        self.task_list.append(task)\n",
        "\n",
        "    def print_task_list(self):\n",
        "        print(\"\\033[95m\\033[1m\" + \"\\n*****TASK LIST*****\\n\" + \"\\033[0m\\033[0m\")\n",
        "        for t in self.task_list:\n",
        "            print(str(t[\"task_id\"]) + \": \" + t[\"task_name\"])\n",
        "\n",
        "    def print_next_task(self, task: Dict):\n",
        "        print(\"\\033[92m\\033[1m\" + \"\\n*****NEXT TASK*****\\n\" + \"\\033[0m\\033[0m\")\n",
        "        print(str(task[\"task_id\"]) + \": \" + task[\"task_name\"])\n",
        "\n",
        "    def print_task_result(self, result: str):\n",
        "        print(\"\\033[93m\\033[1m\" + \"\\n*****TASK RESULT*****\\n\" + \"\\033[0m\\033[0m\")\n",
        "        print(result)\n",
        "\n",
        "    @property\n",
        "    def input_keys(self) -> List[str]:\n",
        "        return [\"objective\"]\n",
        "\n",
        "    @property\n",
        "    def output_keys(self) -> List[str]:\n",
        "        return []\n",
        "\n",
        "    def _call(self, inputs: Dict[str, Any]) -> Dict[str, Any]:\n",
        "        \"\"\"Run the agent.\"\"\"\n",
        "        objective = inputs['objective']\n",
        "        first_task = inputs.get(\"first_task\", \"Make a todo list\")\n",
        "        self.add_task({\"task_id\": 1, \"task_name\": first_task})\n",
        "        num_iters = 0\n",
        "        while True:\n",
        "            if self.task_list:\n",
        "                self.print_task_list()\n",
        "\n",
        "                # Step 1: Pull the first task\n",
        "                task = self.task_list.popleft()\n",
        "                self.print_next_task(task)\n",
        "\n",
        "                # Step 2: Execute the task\n",
        "                result = execute_task(\n",
        "                    self.vectorstore, self.execution_chain, objective, task[\"task_name\"]\n",
        "                )\n",
        "                this_task_id = int(task[\"task_id\"])\n",
        "                self.print_task_result(result)\n",
        "\n",
        "                # Step 3: Store the result in Pinecone\n",
        "                result_id = f\"result_{task['task_id']}\"\n",
        "                self.vectorstore.add_texts(\n",
        "                    texts=[result],\n",
        "                    metadatas=[{\"task\": task[\"task_name\"]}],\n",
        "                    ids=[result_id],\n",
        "                )\n",
        "\n",
        "                # Step 4: Create new tasks and reprioritize task list\n",
        "                new_tasks = get_next_task(\n",
        "                    self.task_creation_chain, result, task[\"task_name\"], [t[\"task_name\"] for t in self.task_list], objective\n",
        "                )\n",
        "                for new_task in new_tasks:\n",
        "                    self.task_id_counter += 1\n",
        "                    new_task.update({\"task_id\": self.task_id_counter})\n",
        "                    self.add_task(new_task)\n",
        "                self.task_list = deque(\n",
        "                    prioritize_tasks(\n",
        "                        self.task_prioritization_chain, this_task_id, list(self.task_list), objective\n",
        "                    )\n",
        "                )\n",
        "            num_iters += 1\n",
        "            if self.max_iterations is not None and num_iters == self.max_iterations:\n",
        "                print(\"\\033[91m\\033[1m\" + \"\\n*****TASK ENDING*****\\n\" + \"\\033[0m\\033[0m\")\n",
        "                break\n",
        "        return {}\n",
        "\n",
        "    @classmethod\n",
        "    def from_llm(\n",
        "        cls,\n",
        "        llm: BaseLLM,\n",
        "        vectorstore: VectorStore,\n",
        "        verbose: bool = False,\n",
        "        **kwargs\n",
        "    ) -> \"BabyAGI\":\n",
        "        \"\"\"Initialize the BabyAGI Controller.\"\"\"\n",
        "        task_creation_chain = TaskCreationChain.from_llm(\n",
        "            llm, verbose=verbose\n",
        "        )\n",
        "        task_prioritization_chain = TaskPrioritizationChain.from_llm(\n",
        "            llm, verbose=verbose\n",
        "        )\n",
        "        llm_chain = LLMChain(llm=llm, prompt=prompt)\n",
        "        tool_names = [tool.name for tool in tools]\n",
        "        agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names)\n",
        "        agent_executor = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True)\n",
        "        return cls(\n",
        "            task_creation_chain=task_creation_chain,\n",
        "            task_prioritization_chain=task_prioritization_chain,\n",
        "            execution_chain=agent_executor,\n",
        "            vectorstore=vectorstore,\n",
        "            **kwargs\n",
        "        )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "05ba762e"
      },
      "source": [
        "### Run the BabyAGI\n",
        "\n",
        "Now it's time to create the BabyAGI controller and watch it try to accomplish your objective."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3d220b69"
      },
      "outputs": [],
      "source": [
        "OBJECTIVE = \"You work for a non profit organization titled Aliens are Real that is trying to find UFOs and aliens among us and give them a warm welcome to Earth. Your goal is to prove Aliens are real through research, first hand accounts, and evidence of extraterrestrial and/or intradimensional life. Your first task in providing evidence that aliens exist is to make a todo list to map out your steps in order to prove aliens are real.\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8a8e5543"
      },
      "outputs": [],
      "source": [
        "llm = OpenAI(temperature=0) #less halluciations and more deterministic"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3d69899b"
      },
      "outputs": [],
      "source": [
        "# Logging of LLMChains\n",
        "verbose=False\n",
        "# If None, will keep on going forever\n",
        "max_iterations: Optional[int] = 2\n",
        "baby_agi = BabyAGI.from_llm(\n",
        "    llm=llm,\n",
        "    vectorstore=vectorstore,\n",
        "    verbose=verbose,\n",
        "    max_iterations=max_iterations\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": false,
        "id": "f7957b51"
      },
      "outputs": [],
      "source": [
        "baby_agi({\"objective\": OBJECTIVE})"
      ]
    }
  ]
}