#!/usr/bin/env python # coding: utf-8 # # BabyAGI with Tools # # This notebook builds on top of [baby agi](baby_agi.ipynb), but shows how you can swap out the execution chain. The previous execution chain was just an LLM which made stuff up. By swapping it out with an agent that has access to tools, we can hopefully get re #al reliable information # ## Install and Import Required Modules import os 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 import faiss import openai # Connect to the Vector Store # Depending on what vectorstore you use, this step may look different. #get_ipython().run_line_magic('pip', 'install faiss-gpu > /dev/null') #get_ipython().run_line_magic('pip', 'install google-search-results > /dev/null') from langchain.vectorstores import FAISS from langchain.docstore import InMemoryDocstore # Define your embedding model openai.api_key = os.environ.get('openai_api_token') embeddings_model = OpenAIEmbeddings(openai_api_key=openai.api_key, model='text-embedding-ada-002') # Initialize the vectorstore as empty embedding_size = 1536 index = faiss.IndexFlatL2(embedding_size) vectorstore = FAISS(embeddings_model.embed_query, index, InMemoryDocstore({}), {}) # ## Define the Chains # # BabyAGI relies on three LLM chains: # - Task creation chain to select new tasks to add to the list # - Task prioritization chain to re-prioritize tasks # - Execution Chain to execute the tasks # class TaskCreationChain(LLMChain): """Chain to generates tasks.""" @classmethod def from_llm(cls, llm: BaseLLM, verbose: bool = True) -> LLMChain: """Get the response parser.""" task_creation_template = ( "You are an task creation AI that uses the result of an execution agent" " to create new tasks with the following objective: {objective}," " The last completed task has the result: {result}." " This result was based on this task description: {task_description}." " These are incomplete tasks: {incomplete_tasks}." " Based on the result, create new tasks to be completed" " by the AI system that do not overlap with incomplete tasks." " Return the tasks as an array." ) prompt = PromptTemplate( template=task_creation_template, input_variables=[ "result", "task_description", "incomplete_tasks", "objective", ], ) return cls(prompt=prompt, llm=llm, verbose=verbose) class TaskPrioritizationChain(LLMChain): """Chain to prioritize tasks.""" @classmethod def from_llm(cls, llm: BaseLLM, verbose: bool = True) -> LLMChain: """Get the response parser.""" task_prioritization_template = ( "You are an task prioritization AI tasked with cleaning the formatting of and reprioritizing" " the following tasks: {task_names}." " Consider the ultimate objective of your team: {objective}." " Do not remove any tasks. Return the result as a numbered list, like:" " #. First task" " #. Second task" " Start the task list with number {next_task_id}." ) prompt = PromptTemplate( template=task_prioritization_template, input_variables=["task_names", "next_task_id", "objective"], ) return cls(prompt=prompt, llm=llm, verbose=verbose) from langchain.agents import ZeroShotAgent, Tool, AgentExecutor from langchain import OpenAI, SerpAPIWrapper, LLMChain 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, openai_api_key=openai.api_key), prompt=todo_prompt) # search = SerpAPIWrapper(serpapi_api_key=os.environ.get('serp_api_token')) search = SerpAPIWrapper(serpapi_api_key='11f15a428a0386d7705e5a0f6ada5f5e74ee03b7cb51d2f2bfb3450e62db4273') 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"], ) # ### Define the BabyAGI Controller # BabyAGI composes the chains defined above in a (potentially-)infinite loop. def get_next_task( task_creation_chain: LLMChain, result: Dict, task_description: str, task_list: List[str], objective: str, ) -> List[Dict]: """Get the next task.""" incomplete_tasks = ", ".join(task_list) response = task_creation_chain.run( result=result, task_description=task_description, incomplete_tasks=incomplete_tasks, objective=objective, ) new_tasks = response.split("\n") return [{"task_name": task_name} for task_name in new_tasks if task_name.strip()] def prioritize_tasks( task_prioritization_chain: LLMChain, this_task_id: int, task_list: List[Dict], objective: str, ) -> List[Dict]: """Prioritize tasks.""" task_names = [t["task_name"] for t in task_list] next_task_id = int(this_task_id) + 1 response = task_prioritization_chain.run( task_names=task_names, next_task_id=next_task_id, objective=objective ) new_tasks = response.split("\n") prioritized_task_list = [] for task_string in new_tasks: if not task_string.strip(): continue task_parts = task_string.strip().split(".", 1) if len(task_parts) == 2: task_id = task_parts[0].strip() task_name = task_parts[1].strip() prioritized_task_list.append({"task_id": task_id, "task_name": task_name}) return prioritized_task_list def _get_top_tasks(vectorstore, query: str, k: int) -> List[str]: """Get the top k tasks based on the query.""" results = vectorstore.similarity_search_with_score(query, k=k) if not results: return [] sorted_results, _ = zip(*sorted(results, key=lambda x: x[1], reverse=True)) return [str(item.metadata["task"]) for item in sorted_results] def execute_task( vectorstore, execution_chain: LLMChain, objective: str, task: str, k: int = 5 ) -> str: """Execute a task.""" context = _get_top_tasks(vectorstore, query=objective, k=k) return execution_chain.run(objective=objective, context=context, task=task) 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 class Config: """Configuration for this pydantic object.""" arbitrary_types_allowed = True def add_task(self, task: Dict): self.task_list.append(task) def print_task_list(self): print("\033[95m\033[1m" + "\n*****TASK LIST*****\n" + "\033[0m\033[0m") for t in self.task_list: print(str(t["task_id"]) + ": " + t["task_name"]) def print_next_task(self, task: Dict): print("\033[92m\033[1m" + "\n*****NEXT TASK*****\n" + "\033[0m\033[0m") print(str(task["task_id"]) + ": " + task["task_name"]) def print_task_result(self, result: str): print("\033[93m\033[1m" + "\n*****TASK RESULT*****\n" + "\033[0m\033[0m") print(result) @property def input_keys(self) -> List[str]: return ["objective"] @property def output_keys(self) -> List[str]: return [] def _call(self, inputs: Dict[str, Any]) -> Dict[str, Any]: """Run the agent.""" objective = inputs["objective"] first_task = inputs.get("first_task", "Make a todo list") self.add_task({"task_id": 1, "task_name": first_task}) num_iters = 0 my_dict = {} while True: if self.task_list: self.print_task_list() # Step 1: Pull the first task task = self.task_list.popleft() self.print_next_task(task) # Step 2: Execute the task result = execute_task( self.vectorstore, self.execution_chain, objective, task["task_name"] ) this_task_id = int(task["task_id"]) self.print_task_result(result) my_dict.update({"output": result}) # Step 3: Store the result in Pinecone result_id = f"result_{task['task_id']}" self.vectorstore.add_texts( texts=[result], metadatas=[{"task": task["task_name"]}], ids=[result_id], ) # Step 4: Create new tasks and reprioritize task list new_tasks = get_next_task( self.task_creation_chain, result, task["task_name"], [t["task_name"] for t in self.task_list], objective, ) for new_task in new_tasks: self.task_id_counter += 1 new_task.update({"task_id": self.task_id_counter}) self.add_task(new_task) self.task_list = deque( prioritize_tasks( self.task_prioritization_chain, this_task_id, list(self.task_list), objective, ) ) num_iters += 1 if self.max_iterations is not None and num_iters == self.max_iterations: print( "\033[91m\033[1m" + "\n*****TASK ENDING*****\n" + "\033[0m\033[0m" ) break #return my_dict ## Need fix to return result as a dict but this does not seem to work for some reason???? return{} @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, )