Source code for langchain_experimental.autonomous_agents.baby_agi.baby_agi

"""BabyAGI agent."""

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

from langchain.chains.base import Chain
from langchain_core.callbacks.manager import CallbackManagerForChainRun
from langchain_core.language_models import BaseLanguageModel
from langchain_core.vectorstores import VectorStore

from langchain_experimental.autonomous_agents.baby_agi.task_creation import (
    TaskCreationChain,
)
from langchain_experimental.autonomous_agents.baby_agi.task_execution import (
    TaskExecutionChain,
)
from langchain_experimental.autonomous_agents.baby_agi.task_prioritization import (
    TaskPrioritizationChain,
)
from langchain_experimental.pydantic_v1 import BaseModel, Field


# This class has a metaclass conflict: both `Chain` and `BaseModel` define a metaclass
# to use, and the two metaclasses attempt to define the same functions but
# in mutually-incompatible ways. It isn't clear how to resolve this,
# and this code predates mypy beginning to perform that check.
#
# Mypy errors:
# ```
# Definition of "__repr_str__" in base class "Representation" is
#   incompatible with definition in base class "BaseModel"  [misc]
# Definition of "__repr_name__" in base class "Representation" is
#   incompatible with definition in base class "BaseModel"  [misc]
# Definition of "__rich_repr__" in base class "Representation" is
#   incompatible with definition in base class "BaseModel"  [misc]
# Definition of "__pretty__" in base class "Representation" is
#   incompatible with definition in base class "BaseModel"  [misc]
# Metaclass conflict: the metaclass of a derived class must be
#   a (non-strict) subclass of the metaclasses of all its bases  [misc]
# ```
#
# TODO: look into refactoring this class in a way that avoids the mypy type errors
[docs]class BabyAGI(Chain, BaseModel): # type: ignore[misc] """Controller model for the BabyAGI agent.""" task_list: deque = Field(default_factory=deque) task_creation_chain: Chain = Field(...) task_prioritization_chain: Chain = Field(...) execution_chain: Chain = 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
[docs] def add_task(self, task: Dict) -> None: self.task_list.append(task)
[docs] def print_task_list(self) -> None: print("\033[95m\033[1m" + "\n*****TASK LIST*****\n" + "\033[0m\033[0m") # noqa: T201 for t in self.task_list: print(str(t["task_id"]) + ": " + t["task_name"]) # noqa: T201
[docs] def print_next_task(self, task: Dict) -> None: print("\033[92m\033[1m" + "\n*****NEXT TASK*****\n" + "\033[0m\033[0m") # noqa: T201 print(str(task["task_id"]) + ": " + task["task_name"]) # noqa: T201
[docs] def print_task_result(self, result: str) -> None: print("\033[93m\033[1m" + "\n*****TASK RESULT*****\n" + "\033[0m\033[0m") # noqa: T201 print(result) # noqa: T201
@property def input_keys(self) -> List[str]: return ["objective"] @property def output_keys(self) -> List[str]: return []
[docs] def get_next_task( self, result: str, task_description: str, objective: str, **kwargs: Any ) -> List[Dict]: """Get the next task.""" task_names = [t["task_name"] for t in self.task_list] incomplete_tasks = ", ".join(task_names) response = self.task_creation_chain.run( result=result, task_description=task_description, incomplete_tasks=incomplete_tasks, objective=objective, **kwargs, ) new_tasks = response.split("\n") return [ {"task_name": task_name} for task_name in new_tasks if task_name.strip() ]
[docs] def prioritize_tasks( self, this_task_id: int, objective: str, **kwargs: Any ) -> List[Dict]: """Prioritize tasks.""" task_names = [t["task_name"] for t in list(self.task_list)] next_task_id = int(this_task_id) + 1 response = self.task_prioritization_chain.run( task_names=", ".join(task_names), next_task_id=str(next_task_id), objective=objective, **kwargs, ) 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(self, query: str, k: int) -> List[str]: """Get the top k tasks based on the query.""" results = self.vectorstore.similarity_search(query, k=k) if not results: return [] return [str(item.metadata["task"]) for item in results]
[docs] def execute_task(self, objective: str, task: str, k: int = 5, **kwargs: Any) -> str: """Execute a task.""" context = self._get_top_tasks(query=objective, k=k) return self.execution_chain.run( objective=objective, context="\n".join(context), task=task, **kwargs )
def _call( self, inputs: Dict[str, Any], run_manager: Optional[CallbackManagerForChainRun] = None, ) -> Dict[str, Any]: """Run the agent.""" _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager() 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 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 = self.execute_task( objective, task["task_name"], callbacks=_run_manager.get_child() ) this_task_id = int(task["task_id"]) self.print_task_result(result) # Step 3: Store the result in Pinecone result_id = f"result_{task['task_id']}_{num_iters}" 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 = self.get_next_task( result, task["task_name"], objective, callbacks=_run_manager.get_child(), ) 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( self.prioritize_tasks( this_task_id, objective, callbacks=_run_manager.get_child() ) ) num_iters += 1 if self.max_iterations is not None and num_iters == self.max_iterations: print( # noqa: T201 "\033[91m\033[1m" + "\n*****TASK ENDING*****\n" + "\033[0m\033[0m" ) break return {}
[docs] @classmethod def from_llm( cls, llm: BaseLanguageModel, vectorstore: VectorStore, verbose: bool = False, task_execution_chain: Optional[Chain] = None, **kwargs: Any, ) -> "BabyAGI": """Initialize the BabyAGI Controller.""" task_creation_chain = TaskCreationChain.from_llm(llm, verbose=verbose) task_prioritization_chain = TaskPrioritizationChain.from_llm( llm, verbose=verbose ) if task_execution_chain is None: execution_chain: Chain = TaskExecutionChain.from_llm(llm, verbose=verbose) else: execution_chain = task_execution_chain return cls( task_creation_chain=task_creation_chain, task_prioritization_chain=task_prioritization_chain, execution_chain=execution_chain, vectorstore=vectorstore, **kwargs, )