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• BabyAGI with LangChain and Faiss
• BabyAGI - LangChain Module

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

class BabyAGI[source]

BabyAGI(memory:Optional[BaseMemory]=None, callback_manager:BaseCallbackManager=None, verbose:bool=None, task_list:deque=None, task_creation_chain:TaskCreationChain, task_prioritization_chain:TaskPrioritizationChain, execution_chain:Chain, task_id_counter:int=1, vectorstore:VectorStore, max_iterations:Optional[int]=None) :: Chain

Controller model for the BabyAGI agent.

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With Tools

• BabyAGI with Tools - LangChain Module
• LangChain Utilities

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Executor

• LangChain ZeroShotAgent
  • Source Code
  • Modular Reasoning, Knowledge and Language system

class ZeroRoundAgent[source]

ZeroRoundAgent(llm_chain:LLMChain, output_parser:AgentOutputParser=None, allowed_tools:Optional[List[str]]=None) :: ZeroShotAgent

Agent for the MRKL chain.

LangChain MRKLOutputParser

class ChineseOutputParser[source]

ChineseOutputParser(default_action:Optional[str]=None, default_action_input:Optional[str]=None) :: AgentOutputParser

Class to parse the output of an LLM call.

Output parsers help structure language model responses.

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Running the BabyAGI

mock_baby_agi[source]

mock_baby_agi(objective)