[Part 3] 2시간 안에 업무 자동화 AI Agent 만들기: 구글 검색 기능

import os
os.environ["OPENAI_API_KEY"] = " "
os.environ["SERPAPI_API_KEY"] = " "

from collections import deque
from typing import Dict, List, Optional, Any

from langchain import LLMChain, OpenAI, PromptTemplate
from langchain.embeddings import OpenAIEmbeddings
from langchain.llms import BaseLLM
from langchain.vectorstores.base import VectorStore
from pydantic import BaseModel, Field
from langchain.chains.base import Chain

from langchain.vectorstores import FAISS
from langchain.docstore import InMemoryDocstore

from langchain.agents import ZeroShotAgent, Tool, AgentExecutor
from langchain import OpenAI, SerpAPIWrapper, LLMChain

class ExecutionChain(LLMChain):
    """Chain to execute tasks."""

    @classmethod
    def from_llm(cls, llm: BaseLLM, verbose: bool = True) -> LLMChain:
        """Get the response parser."""
        execution_template = (
            "You are an AI who performs one task based on the following objective: {objective}."
            " Take into account these previously completed tasks: {context}."
            " Your task: {task}."
            " Response:"
        )
        prompt = PromptTemplate(
            template=execution_template,
            input_variables=["objective", "context", "task"],
        )
        return cls(prompt=prompt, llm=llm, verbose=verbose)

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}"
)
todo_chain = LLMChain(llm=OpenAI(temperature=0), prompt=todo_prompt)
search = SerpAPIWrapper()
tools = [
    Tool(
        name="Search",
        func=search.run,
        description="useful for when you need to answer questions about current events",
    ),
    Tool(
        name="TODO",
        func=todo_chain.run,
        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!",
    ),
]

prefix = """You are an AI who performs one task based on the following objective: {objective}. Take into account these previously completed tasks: {context}."""
suffix = """Question: {task}
{agent_scratchpad}"""
prompt = ZeroShotAgent.create_prompt(
    tools,
    prefix=prefix,
    suffix=suffix,
    input_variables=["objective", "task", "context", "agent_scratchpad"],
)

@classmethod
    def from_llm(
        cls, llm: BaseLLM, vectorstore: VectorStore, verbose: bool = False, **kwargs
    ) -> "BabyAGI":
        """Initialize the BabyAGI Controller."""
        task_creation_chain = TaskCreationChain.from_llm(llm, verbose=verbose)
        task_prioritization_chain = TaskPrioritizationChain.from_llm(
            llm, verbose=verbose
        )
        execution_chain = ExecutionChain.from_llm(llm, verbose=verbose)
        return cls(
            task_creation_chain=task_creation_chain,
            task_prioritization_chain=task_prioritization_chain,
            execution_chain=execution_chain,
            vectorstore=vectorstore,
            **kwargs,
        )

@classmethod
    def from_llm(
        cls, llm: BaseLLM, vectorstore: VectorStore, verbose: bool = False, **kwargs
    ) -> "BabyAGI":
        """Initialize the BabyAGI Controller."""
        task_creation_chain = TaskCreationChain.from_llm(llm, verbose=verbose)
        task_prioritization_chain = TaskPrioritizationChain.from_llm(
            llm, verbose=verbose
        )
        llm_chain = LLMChain(llm=llm, prompt=prompt)
        tool_names = [tool.name for tool in tools]
        agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names)
        agent_executor = AgentExecutor.from_agent_and_tools(
            agent=agent, tools=tools, verbose=True
        )
        return cls(
            task_creation_chain=task_creation_chain,
            task_prioritization_chain=task_prioritization_chain,
            execution_chain=agent_executor,
            vectorstore=vectorstore,
            **kwargs,
        )

llm_chain = LLMChain(llm=llm, prompt=prompt)
tool_names = [tool.name for tool in tools]
agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names)
agent_executor = AgentExecutor.from_agent_and_tools(
    agent=agent, tools=tools, verbose=True
)

class BabyAGI(Chain, BaseModel):
    """Controller model for the BabyAGI agent."""

    task_list: deque = Field(default_factory=deque)
    task_creation_chain: TaskCreationChain = Field(...)
    task_prioritization_chain: TaskPrioritizationChain = Field(...)
    execution_chain: ExecutionChain = Field(...)
    task_id_counter: int = Field(1)
    vectorstore: VectorStore = Field(init=False)
    max_iterations: Optional[int] = None

class BabyAGI(Chain, BaseModel):
    """Controller model for the BabyAGI agent."""

    task_list: deque = Field(default_factory=deque)
    task_creation_chain: TaskCreationChain = Field(...)
    task_prioritization_chain: TaskPrioritizationChain = Field(...)
    execution_chain: AgentExecutor = Field(...)
    task_id_counter: int = Field(1)
    vectorstore: VectorStore = Field(init=False)
    max_iterations: Optional[int] = None