Xi Zhang
65db3a4fcd
* Add status bar and compact summary widgets with context window resolution - Implemented a shared status bar for CLI and TUI frontends, including helpers for managing session metrics and context windows. - Created a `CompactSummaryWidget` for displaying manual summaries in a collapsible format. - Introduced a `CompactingWidget` to indicate ongoing compacting processes. - Added a base class `TimedStatusWidget` for widgets that require a timer. - Developed context window resolution helpers to retrieve context window sizes from various model attributes. - Enhanced tests for context window resolution and status bar functionalities, ensuring accurate behavior across different scenarios. - Updated existing tests to cover new features and maintain code quality. * refactor(Channel): simplify lambda function in _send_with_retry method * feat: enhance context editing logic and improve error handling in StreamState * refactor(Channel): streamline lambda function in _send_with_retry method * feat: rename auto-approve option to auto-mode for unattended execution; update checkpoint queries to filter by agent name; improve compatibility validation logic * feat: rename auto-approve option to auto-mode; update related logic and tests for improved unattended execution * fix: correct formatting of console message for MCP server configuration status * feat: add check for None summary_message in _apply_summarization_event to prevent errors * feat: enhance _load_checkpoint_messages to validate message format and apply summarization event
61 lines
1.9 KiB
Python
61 lines
1.9 KiB
Python
"""ContextEditingMiddleware configuration for EvoScientist.
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Wraps LangChain's built-in ``ContextEditingMiddleware`` with project-specific
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defaults: dynamic trigger based on model context window, ``keep=5`` for
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multi-step tool chains, and ``think_tool`` excluded from clearing.
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Usage::
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from EvoScientist.middleware import create_context_editing_middleware
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middleware = create_context_editing_middleware(model)
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"""
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from __future__ import annotations
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from langchain_core.language_models import BaseChatModel
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from ..llm.context_window import get_context_window
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def compute_context_editing_trigger(
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model: BaseChatModel,
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fraction: float = 0.50,
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fallback: int = 100_000,
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) -> int:
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"""Compute ClearToolUsesEdit trigger based on model context window.
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Uses 50% of the best available model context window when metadata is
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available, otherwise falls back to a fixed token count. This fires well
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before ``SummarizationMiddleware`` (~85% / 170k).
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"""
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context_window = get_context_window(model)
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if context_window is not None and context_window > 0:
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return max(1, int(context_window * fraction))
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return fallback
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def create_context_editing_middleware(model: BaseChatModel | None = None):
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"""Build a ContextEditingMiddleware with EvoScientist defaults.
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Args:
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model: Chat model used to determine context window size.
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If *None*, the default model is resolved via ``_ensure_chat_model()``.
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"""
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from langchain.agents.middleware import ClearToolUsesEdit, ContextEditingMiddleware
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if model is None:
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from EvoScientist.EvoScientist import _ensure_chat_model
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model = _ensure_chat_model()
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return ContextEditingMiddleware(
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edits=[
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ClearToolUsesEdit(
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trigger=compute_context_editing_trigger(model),
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keep=5,
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exclude_tools=["think_tool"],
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),
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],
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)
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