mirror of
https://github.com/crewAIInc/crewAI.git
synced 2026-05-03 00:02:36 +00:00
cleaning up
This commit is contained in:
Brandon Hancock
@@ -1,3 +1,5 @@
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import json
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import re
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from typing import Any, Callable, Dict, List, Optional, Sequence, Union
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from crewai.agents.parser import (
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@@ -11,8 +13,17 @@ from crewai.llm import LLM
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from crewai.tools import BaseTool as CrewAITool
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from crewai.tools.base_tool import BaseTool
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from crewai.tools.structured_tool import CrewStructuredTool
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from crewai.utilities.i18n import I18N
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from crewai.utilities.printer import Printer
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from crewai.tools.tool_types import ToolResult
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from crewai.utilities import I18N, Printer
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from crewai.utilities.events import (
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ToolUsageErrorEvent,
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ToolUsageStartedEvent,
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crewai_event_bus,
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)
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from crewai.utilities.events.tool_usage_events import ToolUsageStartedEvent
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from crewai.utilities.exceptions.context_window_exceeding_exception import (
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LLMContextLengthExceededException,
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)
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def parse_tools(tools: List[BaseTool]) -> List[CrewStructuredTool]:
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@@ -169,3 +180,254 @@ def process_llm_response(
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answer = answer.split("Observation:")[0].strip()
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return format_answer(answer)
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def handle_agent_action_core(
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formatted_answer: AgentAction,
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tool_result: ToolResult,
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messages: Optional[List[Dict[str, str]]] = None,
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step_callback: Optional[Callable] = None,
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show_logs: Optional[Callable] = None,
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) -> Union[AgentAction, AgentFinish]:
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"""Core logic for handling agent actions and tool results.
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Args:
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formatted_answer: The agent's action
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tool_result: The result of executing the tool
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messages: Optional list of messages to append results to
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step_callback: Optional callback to execute after processing
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show_logs: Optional function to show logs
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Returns:
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Either an AgentAction or AgentFinish
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"""
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if step_callback:
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step_callback(tool_result)
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formatted_answer.text += f"\nObservation: {tool_result.result}"
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formatted_answer.result = tool_result.result
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if tool_result.result_as_answer:
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return AgentFinish(
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thought="",
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output=tool_result.result,
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text=formatted_answer.text,
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)
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if show_logs:
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show_logs(formatted_answer)
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if messages is not None:
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messages.append({"role": "assistant", "content": tool_result.result})
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return formatted_answer
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def handle_unknown_error(printer: Any, exception: Exception) -> None:
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"""Handle unknown errors by informing the user.
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Args:
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printer: Printer instance for output
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exception: The exception that occurred
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"""
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printer.print(
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content="An unknown error occurred. Please check the details below.",
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color="red",
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)
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printer.print(
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content=f"Error details: {exception}",
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color="red",
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)
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def handle_output_parser_exception(
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e: OutputParserException,
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messages: List[Dict[str, str]],
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iterations: int,
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log_error_after: int = 3,
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printer: Optional[Any] = None,
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) -> AgentAction:
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"""Handle OutputParserException by updating messages and formatted_answer.
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Args:
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e: The OutputParserException that occurred
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messages: List of messages to append to
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iterations: Current iteration count
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log_error_after: Number of iterations after which to log errors
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printer: Optional printer instance for logging
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Returns:
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AgentAction: A formatted answer with the error
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"""
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messages.append({"role": "user", "content": e.error})
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formatted_answer = AgentAction(
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text=e.error,
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tool="",
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tool_input="",
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thought="",
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)
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if iterations > log_error_after and printer:
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printer.print(
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content=f"Error parsing LLM output, agent will retry: {e.error}",
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color="red",
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)
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return formatted_answer
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def is_context_length_exceeded(exception: Exception) -> bool:
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"""Check if the exception is due to context length exceeding.
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Args:
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exception: The exception to check
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Returns:
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bool: True if the exception is due to context length exceeding
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"""
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return LLMContextLengthExceededException(str(exception))._is_context_limit_error(
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str(exception)
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)
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def handle_context_length(
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respect_context_window: bool,
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printer: Any,
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messages: List[Dict[str, str]],
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llm: Any,
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callbacks: List[Any],
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i18n: Any,
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) -> None:
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"""Handle context length exceeded by either summarizing or raising an error.
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Args:
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respect_context_window: Whether to respect context window
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printer: Printer instance for output
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messages: List of messages to summarize
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llm: LLM instance for summarization
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callbacks: List of callbacks for LLM
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i18n: I18N instance for messages
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"""
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if respect_context_window:
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printer.print(
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content="Context length exceeded. Summarizing content to fit the model context window.",
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color="yellow",
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)
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summarize_messages(messages, llm, callbacks, i18n)
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else:
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printer.print(
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content="Context length exceeded. Consider using smaller text or RAG tools from crewai_tools.",
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color="red",
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)
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raise SystemExit(
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"Context length exceeded and user opted not to summarize. Consider using smaller text or RAG tools from crewai_tools."
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)
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def summarize_messages(
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messages: List[Dict[str, str]],
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llm: Any,
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callbacks: List[Any],
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i18n: Any,
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) -> None:
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"""Summarize messages to fit within context window.
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Args:
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messages: List of messages to summarize
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llm: LLM instance for summarization
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callbacks: List of callbacks for LLM
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i18n: I18N instance for messages
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"""
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messages_groups = []
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for message in messages:
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content = message["content"]
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cut_size = llm.get_context_window_size()
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for i in range(0, len(content), cut_size):
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messages_groups.append({"content": content[i : i + cut_size]})
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summarized_contents = []
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for group in messages_groups:
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summary = llm.call(
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[
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format_message_for_llm(
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i18n.slice("summarizer_system_message"), role="system"
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),
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format_message_for_llm(
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i18n.slice("summarize_instruction").format(group=group["content"]),
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),
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],
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callbacks=callbacks,
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)
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summarized_contents.append({"content": str(summary)})
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merged_summary = " ".join(content["content"] for content in summarized_contents)
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messages.clear()
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messages.append(
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format_message_for_llm(
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i18n.slice("summary").format(merged_summary=merged_summary)
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)
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)
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def show_agent_logs(
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printer: Printer,
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agent_role: str,
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formatted_answer: Optional[Union[AgentAction, AgentFinish]] = None,
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task_description: Optional[str] = None,
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verbose: bool = False,
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) -> None:
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"""Show agent logs for both start and execution states.
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Args:
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printer: Printer instance for output
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agent_role: Role of the agent
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formatted_answer: Optional AgentAction or AgentFinish for execution logs
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task_description: Optional task description for start logs
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verbose: Whether to show verbose output
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"""
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if not verbose:
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return
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agent_role = agent_role.split("\n")[0]
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if formatted_answer is None:
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# Start logs
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printer.print(
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content=f"\033[1m\033[95m# Agent:\033[00m \033[1m\033[92m{agent_role}\033[00m"
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)
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if task_description:
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printer.print(
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content=f"\033[95m## Task:\033[00m \033[92m{task_description}\033[00m"
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)
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else:
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# Execution logs
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printer.print(
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content=f"\n\n\033[1m\033[95m# Agent:\033[00m \033[1m\033[92m{agent_role}\033[00m"
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)
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if isinstance(formatted_answer, AgentAction):
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thought = re.sub(r"\n+", "\n", formatted_answer.thought)
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formatted_json = json.dumps(
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formatted_answer.tool_input,
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indent=2,
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ensure_ascii=False,
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)
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if thought and thought != "":
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printer.print(
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content=f"\033[95m## Thought:\033[00m \033[92m{thought}\033[00m"
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)
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printer.print(
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content=f"\033[95m## Using tool:\033[00m \033[92m{formatted_answer.tool}\033[00m"
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)
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printer.print(
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content=f"\033[95m## Tool Input:\033[00m \033[92m\n{formatted_json}\033[00m"
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)
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printer.print(
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content=f"\033[95m## Tool Output:\033[00m \033[92m\n{formatted_answer.result}\033[00m"
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)
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elif isinstance(formatted_answer, AgentFinish):
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printer.print(
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content=f"\033[95m## Final Answer:\033[00m \033[92m\n{formatted_answer.output}\033[00m\n\n"
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)
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