7_BabyAGI.ipynb

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  {
   "cell_type": "markdown",
   "id": "132e452d-dbfd-4a47-b762-14facd7dd054",
   "metadata": {},
   "source": [
    "# 🦜🔗 LangChain agent BabyAGI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "36cb5a09-69ce-4577-9fd0-92e92a4d2dcd",
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install -r requirements.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7d53d5dd-bca0-4cfe-921e-08b7c37249e9",
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   "source": [
    "import os\n",
    "import faiss\n",
    "from collections import deque\n",
    "from typing import Dict, List, Optional, Any\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\n",
    "from langchain_experimental.autonomous_agents import BabyAGI\n",
    "from langchain.vectorstores import FAISS\n",
    "from langchain.docstore import InMemoryDocstore\n",
    "from langchain.agents import ZeroShotAgent, Tool, AgentExecutor\n",
    "from langchain import SerpAPIWrapper\n",
    "\n",
    "from dotenv import load_dotenv\n",
    "\n",
    "load_dotenv()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d14141a-86a1-4720-a488-0d59561e66ff",
   "metadata": {},
   "source": [
    "Embeddings because we need to remember."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4754a795-a858-4bd1-a7a3-d09075beba62",
   "metadata": {},
   "outputs": [],
   "source": [
    "embeddings = OpenAIEmbeddings()\n",
    "\n",
    "embedding_size = 1536\n",
    "index = faiss.IndexFlatL2(embedding_size)\n",
    "vectorstore = FAISS(embeddings.embed_query, index, InMemoryDocstore({}), {})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76fa4e73-2f53-4597-aafe-2aeba7253072",
   "metadata": {},
   "source": [
    "A todo chain to generate proper todos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9919af6a-2778-4366-871e-739115804010",
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   "outputs": [],
   "source": [
    "todo_prompt = PromptTemplate.from_template(\n",
    "    \"You are a planner who is an expert at coming up with a todo list for a given objective. You have a clear focus, so you write precise todos, but not more than 4. Come up with a todo list for this objective: {objective}\"\n",
    ")\n",
    "todo_chain = LLMChain(llm=OpenAI(temperature=0), prompt=todo_prompt)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9e77774-964a-4203-ab34-1030f789ff57",
   "metadata": {},
   "source": [
    "We need tools!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "285d86b0-cc3b-4bf3-bfb5-9774c09b66c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "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",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fb45e5f-f35a-456f-ab67-8a9d326e66aa",
   "metadata": {},
   "source": [
    "Setup the prompt for the agent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "101d9bbe-c27e-49ce-a031-2ef13f52f34a",
   "metadata": {},
   "outputs": [],
   "source": [
    "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",
   "id": "f45b3dea-a561-4a4c-a352-451e9df307e5",
   "metadata": {},
   "source": [
    "Start the agent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cb1f0a57-1951-4fcf-b7e8-1df2958cd4a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "llm = OpenAI(temperature=0)\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(\n",
    "    agent=agent, tools=tools, verbose=True\n",
    ")\n",
    "max_iterations: Optional[int] = 5\n",
    "baby_agi = BabyAGI.from_llm(\n",
    "    llm=llm,\n",
    "    vectorstore=vectorstore,\n",
    "    task_execution_chain=agent_executor,\n",
    "    verbose=False,\n",
    "    max_iterations=max_iterations,\n",
    ")\n",
    "\n",
    "baby_agi({\"objective\": \"Write an euphoric blog article about the latest new features of langchain.\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75af698e-8de9-440f-80b2-291b50ee7b60",
   "metadata": {},
   "source": [
    "Feel free to send it on another mission :-)"
   ]
  }
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