mirror of
https://github.com/crewAIInc/crewAI.git
synced 2026-01-09 08:08:32 +00:00
WIP: need tests but user inputted summarization strategy implemented - handling context window exceeding errors
This commit is contained in:
Lorenze Jay
@@ -0,0 +1,8 @@
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from prompt_toolkit.validation import Validator, ValidationError
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class YesNoValidator(Validator):
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def validate(self, document):
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text = document.text.lower()
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if text not in ["y", "n", "yes", "no"]:
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raise ValidationError(message="Please enter Y/N")
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@@ -1,6 +1,8 @@
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import threading
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import time
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from typing import Any, Dict, Iterator, List, Optional, Tuple, Union
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from typing import Any, Dict, Iterator, List, Literal, Optional, Tuple, Union
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from prompt_toolkit import prompt
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from langchain.agents import AgentExecutor
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from langchain.agents.agent import ExceptionTool
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@@ -10,17 +12,21 @@ from langchain_core.exceptions import OutputParserException
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from langchain_core.tools import BaseTool
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from langchain_core.utils.input import get_color_mapping
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from pydantic import InstanceOf
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import tiktoken
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from openai import BadRequestError
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from langchain.text_splitter import RecursiveCharacterTextSplitter
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from langchain.chains.summarize import load_summarize_chain
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from openai import BadRequestError
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from crewai.agents.agent_builder.base_agent_executor_mixin import CrewAgentExecutorMixin
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from crewai.agents.agent_builder.utilities.yes_no_cli_validator import YesNoValidator
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from crewai.agents.tools_handler import ToolsHandler
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from crewai.tools.tool_usage import ToolUsage, ToolUsageErrorException
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from crewai.utilities import I18N
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from crewai.utilities.constants import TRAINING_DATA_FILE
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from crewai.utilities.training_handler import CrewTrainingHandler
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from crewai.utilities.logger import Logger
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class CrewAgentExecutor(AgentExecutor, CrewAgentExecutorMixin):
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@@ -44,8 +50,8 @@ class CrewAgentExecutor(AgentExecutor, CrewAgentExecutorMixin):
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system_template: Optional[str] = None
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prompt_template: Optional[str] = None
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response_template: Optional[str] = None
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retry_summarize: bool = False
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retry_summarize_count: int = 2
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_logger: Logger = Logger()
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_fit_context_window_strategy: Optional[Literal["summarize"]] = "summarize"
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def _call(
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self,
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@@ -126,198 +132,187 @@ class CrewAgentExecutor(AgentExecutor, CrewAgentExecutorMixin):
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Override this to take control of how the agent makes and acts on choices.
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"""
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for attempt in range(self.retry_summarize_count):
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try:
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if self._should_force_answer():
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error = self._i18n.errors("force_final_answer")
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output = AgentAction("_Exception", error, error)
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self.have_forced_answer = True
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yield AgentStep(action=output, observation=error)
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return
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intermediate_steps = self._prepare_intermediate_steps(
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intermediate_steps
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)
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if self.retry_summarize:
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encoding = tiktoken.encoding_for_model(self.llm.model_name)
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original_token_count = len(
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encoding.encode(intermediate_steps[0][1])
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)
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if original_token_count > 8000:
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print(
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"BEFORE AGENT PLAN TOKEN LENGTH",
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original_token_count,
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)
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text = intermediate_steps[0][1]
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text_splitter = RecursiveCharacterTextSplitter(
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separators=["\n\n", "\n"],
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chunk_size=8000,
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chunk_overlap=500,
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)
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docs = text_splitter.create_documents([text])
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print("DOCS", docs)
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print("DOCS length", len(docs))
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breakpoint()
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# TODO: store to vector db - using memgpt like strategy
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summary_chain = load_summarize_chain(
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self.llm, chain_type="map_reduce", verbose=True
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)
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summary = summary_chain.run(docs)
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print("SUMMARY:", summary)
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intermediate_steps[0] = (intermediate_steps[0][0], summary)
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# Call the LLM to see what to do.
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output = self.agent.plan( # type: ignore # Incompatible types in assignment (expression has type "AgentAction | AgentFinish | list[AgentAction]", variable has type "AgentAction")
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intermediate_steps,
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callbacks=run_manager.get_child() if run_manager else None,
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**inputs,
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)
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except OutputParserException as e:
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if isinstance(self.handle_parsing_errors, bool):
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raise_error = not self.handle_parsing_errors
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else:
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raise_error = False
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if raise_error:
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raise ValueError(
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"An output parsing error occurred. "
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"In order to pass this error back to the agent and have it try "
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"again, pass `handle_parsing_errors=True` to the AgentExecutor. "
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f"This is the error: {str(e)}"
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)
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str(e)
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if isinstance(self.handle_parsing_errors, bool):
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if e.send_to_llm:
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observation = f"\n{str(e.observation)}"
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str(e.llm_output)
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else:
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observation = ""
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elif isinstance(self.handle_parsing_errors, str):
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observation = f"\n{self.handle_parsing_errors}"
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elif callable(self.handle_parsing_errors):
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observation = f"\n{self.handle_parsing_errors(e)}"
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else:
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raise ValueError("Got unexpected type of `handle_parsing_errors`")
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output = AgentAction("_Exception", observation, "")
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if run_manager:
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run_manager.on_agent_action(output, color="green")
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tool_run_kwargs = self.agent.tool_run_logging_kwargs()
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observation = ExceptionTool().run(
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output.tool_input,
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verbose=False,
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color=None,
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callbacks=run_manager.get_child() if run_manager else None,
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**tool_run_kwargs,
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)
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if self._should_force_answer():
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error = self._i18n.errors("force_final_answer")
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output = AgentAction("_Exception", error, error)
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yield AgentStep(action=output, observation=error)
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return
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yield AgentStep(action=output, observation=observation)
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try:
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if self._should_force_answer():
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error = self._i18n.errors("force_final_answer")
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output = AgentAction("_Exception", error, error)
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self.have_forced_answer = True
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yield AgentStep(action=output, observation=error)
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return
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except BadRequestError as e:
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print("bad request string str(e)", str(e))
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if (
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"context_length_exceeded" in str(e)
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and attempt < self.retry_summarize_count - 1
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):
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print(
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f"Context length exceeded. Retrying with summarization (attempt {attempt + 1})..."
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)
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self.retry_summarize = True
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breakpoint()
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continue
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else:
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print("Error now raising occurred in _iter_next_step:", e)
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raise e
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intermediate_steps = self._prepare_intermediate_steps(intermediate_steps)
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except Exception as e:
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print("Error occurred in _iter_next_step:", e)
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# Call the LLM to see what to do.
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output = self.agent.plan( # type: ignore # Incompatible types in assignment (expression has type "AgentAction | AgentFinish | list[AgentAction]", variable has type "AgentAction")
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intermediate_steps,
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callbacks=run_manager.get_child() if run_manager else None,
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**inputs,
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)
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except OutputParserException as e:
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if isinstance(self.handle_parsing_errors, bool):
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raise_error = not self.handle_parsing_errors
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else:
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raise_error = False
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if raise_error:
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raise ValueError(
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"An output parsing error occurred. "
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"In order to pass this error back to the agent and have it try "
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"again, pass `handle_parsing_errors=True` to the AgentExecutor. "
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f"This is the error: {str(e)}"
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)
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str(e)
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if isinstance(self.handle_parsing_errors, bool):
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if e.send_to_llm:
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observation = f"\n{str(e.observation)}"
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str(e.llm_output)
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else:
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observation = ""
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elif isinstance(self.handle_parsing_errors, str):
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observation = f"\n{self.handle_parsing_errors}"
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elif callable(self.handle_parsing_errors):
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observation = f"\n{self.handle_parsing_errors(e)}"
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else:
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raise ValueError("Got unexpected type of `handle_parsing_errors`")
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output = AgentAction("_Exception", observation, "")
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if run_manager:
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run_manager.on_agent_action(output, color="green")
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tool_run_kwargs = self.agent.tool_run_logging_kwargs()
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observation = ExceptionTool().run(
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output.tool_input,
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verbose=False,
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color=None,
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callbacks=run_manager.get_child() if run_manager else None,
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**tool_run_kwargs,
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)
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if self._should_force_answer():
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error = self._i18n.errors("force_final_answer")
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output = AgentAction("_Exception", error, error)
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yield AgentStep(action=output, observation=error)
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return
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yield AgentStep(action=output, observation=observation)
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return
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except BadRequestError as e:
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print("Bad Request Error", e)
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if "context_length_exceeded" in str(e):
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self._logger.log(
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"debug",
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"Context length exceeded. Asking user if they want to use summarize prompt to fit, this will reduce context length.",
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color="yellow",
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)
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user_choice = prompt(
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"Context length exceeded. Do you want to summarize the text to fit models context window? (Y/N): ",
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validator=YesNoValidator(),
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).lower()
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if user_choice in ["y", "yes"]:
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self._logger.log(
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"debug",
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"Context length exceeded. Using summarize prompt to fit, this will reduce context length.",
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color="bold_blue",
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)
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intermediate_steps = self._handle_context_length(intermediate_steps)
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output = self.agent.plan( # type: ignore # Incompatible types in assignment (expression has type "AgentAction | AgentFinish | list[AgentAction]", variable has type "AgentAction")
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intermediate_steps,
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callbacks=run_manager.get_child() if run_manager else None,
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**inputs,
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)
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if isinstance(output, AgentFinish):
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yield output
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else:
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yield AgentStep(action=output, observation=None)
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return
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else:
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self._logger.log(
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"info",
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"User chose not to use summarization. Try again with smaller text or use our various RAG tools from crewai_tools",
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color="red",
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)
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raise e
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else:
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raise e
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# If the tool chosen is the finishing tool, then we end and return.
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if isinstance(output, AgentFinish):
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if self.should_ask_for_human_input:
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human_feedback = self._ask_human_input(
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output.return_values["output"]
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)
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except Exception as e:
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yield AgentStep(
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action=AgentAction("_Exception", str(e), str(e)),
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observation=str(e),
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)
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return
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if self.crew and self.crew._train:
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self._handle_crew_training_output(output, human_feedback)
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# If the tool chosen is the finishing tool, then we end and return.
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if isinstance(output, AgentFinish):
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if self.should_ask_for_human_input:
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human_feedback = self._ask_human_input(output.return_values["output"])
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# Making sure we only ask for it once, so disabling for the next thought loop
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self.should_ask_for_human_input = False
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action = AgentAction(
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tool="Human Input", tool_input=human_feedback, log=output.log
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)
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if self.crew and self.crew._train:
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self._handle_crew_training_output(output, human_feedback)
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yield AgentStep(
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action=action,
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observation=self._i18n.slice("human_feedback").format(
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human_feedback=human_feedback
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),
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)
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return
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else:
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if self.crew and self.crew._train:
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self._handle_crew_training_output(output)
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yield output
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return
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self._create_short_term_memory(output)
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actions: List[AgentAction]
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actions = [output] if isinstance(output, AgentAction) else output
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yield from actions
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for agent_action in actions:
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if run_manager:
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run_manager.on_agent_action(agent_action, color="green")
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tool_usage = ToolUsage(
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tools_handler=self.tools_handler, # type: ignore # Argument "tools_handler" to "ToolUsage" has incompatible type "ToolsHandler | None"; expected "ToolsHandler"
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tools=self.tools, # type: ignore # Argument "tools" to "ToolUsage" has incompatible type "Sequence[BaseTool]"; expected "list[BaseTool]"
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original_tools=self.original_tools,
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tools_description=self.tools_description,
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tools_names=self.tools_names,
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function_calling_llm=self.function_calling_llm,
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task=self.task,
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agent=self.crew_agent,
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action=agent_action,
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# Making sure we only ask for it once, so disabling for the next thought loop
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self.should_ask_for_human_input = False
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action = AgentAction(
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tool="Human Input", tool_input=human_feedback, log=output.log
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)
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# print("tool_usage", tool_usage)
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tool_calling = tool_usage.parse(agent_action.log)
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# print("tool_calling", tool_calling)
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yield AgentStep(
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action=action,
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observation=self._i18n.slice("human_feedback").format(
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human_feedback=human_feedback
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),
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)
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return
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if isinstance(tool_calling, ToolUsageErrorException):
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observation = tool_calling.message
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else:
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if self.crew and self.crew._train:
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self._handle_crew_training_output(output)
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yield output
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return
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self._create_short_term_memory(output)
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actions: List[AgentAction]
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actions = [output] if isinstance(output, AgentAction) else output
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yield from actions
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for agent_action in actions:
|
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if run_manager:
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run_manager.on_agent_action(agent_action, color="green")
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tool_usage = ToolUsage(
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tools_handler=self.tools_handler, # type: ignore # Argument "tools_handler" to "ToolUsage" has incompatible type "ToolsHandler | None"; expected "ToolsHandler"
|
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tools=self.tools, # type: ignore # Argument "tools" to "ToolUsage" has incompatible type "Sequence[BaseTool]"; expected "list[BaseTool]"
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original_tools=self.original_tools,
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tools_description=self.tools_description,
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tools_names=self.tools_names,
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function_calling_llm=self.function_calling_llm,
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task=self.task,
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agent=self.crew_agent,
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action=agent_action,
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)
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|
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tool_calling = tool_usage.parse(agent_action.log)
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|
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if isinstance(tool_calling, ToolUsageErrorException):
|
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observation = tool_calling.message
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else:
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if tool_calling.tool_name.casefold().strip() in [
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name.casefold().strip() for name in name_to_tool_map
|
||||
] or tool_calling.tool_name.casefold().replace("_", " ") in [
|
||||
name.casefold().strip() for name in name_to_tool_map
|
||||
]:
|
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observation = tool_usage.use(tool_calling, agent_action.log)
|
||||
else:
|
||||
if tool_calling.tool_name.casefold().strip() in [
|
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name.casefold().strip() for name in name_to_tool_map
|
||||
] or tool_calling.tool_name.casefold().replace("_", " ") in [
|
||||
name.casefold().strip() for name in name_to_tool_map
|
||||
]:
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observation = tool_usage.use(tool_calling, agent_action.log)
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else:
|
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observation = self._i18n.errors("wrong_tool_name").format(
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tool=tool_calling.tool_name,
|
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tools=", ".join(
|
||||
[tool.name.casefold() for tool in self.tools]
|
||||
),
|
||||
)
|
||||
yield AgentStep(action=agent_action, observation=observation)
|
||||
observation = self._i18n.errors("wrong_tool_name").format(
|
||||
tool=tool_calling.tool_name,
|
||||
tools=", ".join([tool.name.casefold() for tool in self.tools]),
|
||||
)
|
||||
yield AgentStep(action=agent_action, observation=observation)
|
||||
|
||||
def _handle_crew_training_output(
|
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self, output: AgentFinish, human_feedback: str | None = None
|
||||
@@ -346,3 +341,44 @@ class CrewAgentExecutor(AgentExecutor, CrewAgentExecutorMixin):
|
||||
CrewTrainingHandler(TRAINING_DATA_FILE).append(
|
||||
self.crew._train_iteration, agent_id, training_data
|
||||
)
|
||||
|
||||
def _handle_context_length(
|
||||
self, intermediate_steps: List[Tuple[AgentAction, str]]
|
||||
) -> List[Tuple[AgentAction, str]]:
|
||||
text = intermediate_steps[0][1]
|
||||
original_action = intermediate_steps[0][0]
|
||||
|
||||
text_splitter = RecursiveCharacterTextSplitter(
|
||||
separators=["\n\n", "\n"],
|
||||
chunk_size=8000,
|
||||
chunk_overlap=500,
|
||||
)
|
||||
|
||||
if self._fit_context_window_strategy == "summarize":
|
||||
docs = text_splitter.create_documents([text])
|
||||
self._logger.log(
|
||||
"debug",
|
||||
"Summarizing Content, it is recommended to use a RAG tool",
|
||||
color="bold_blue",
|
||||
)
|
||||
summarize_chain = load_summarize_chain(
|
||||
self.llm, chain_type="map_reduce", verbose=True
|
||||
)
|
||||
summarized_docs = []
|
||||
for doc in docs:
|
||||
summary = summarize_chain.run([doc])
|
||||
summarized_docs.append(summary)
|
||||
|
||||
formatted_results = "\n\n".join(summarized_docs)
|
||||
summary_step = AgentStep(
|
||||
action=AgentAction(
|
||||
tool=original_action.tool,
|
||||
tool_input=original_action.tool_input,
|
||||
log=original_action.log,
|
||||
),
|
||||
observation=formatted_results,
|
||||
)
|
||||
summary_tuple = (summary_step.action, summary_step.observation)
|
||||
return [summary_tuple]
|
||||
|
||||
return intermediate_steps
|
||||
|
||||
Reference in New Issue
Block a user