mirror of
https://github.com/microsoft/SkillOpt.git
synced 2026-07-03 14:02:58 +08:00
4a1b984d87
- Rename teacher -> optimizer, student -> target across all code, configs, docs, prompts - CLI: --teacher_model -> --optimizer_model, --student_model -> --target_model - Remove best_skill files, keep only initial skills - Fix slow update gate (force write into skill) - Fix SLOW_UPDATE marker stripping - Remove deep_reflect and meta_reflect mechanisms - Update .env.example with export prefix and azure_cli docs - Add endpoint empty validation in azure_openai.py Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
110 lines
3.9 KiB
Python
110 lines
3.9 KiB
Python
"""ReflACT gradient clipping — LLM-driven edit ranking and selection.
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Analogous to gradient clipping in neural network training: ranks candidate
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edits by importance and selects the top-L to apply, controlling the
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effective step size. Previously core/select.py.
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"""
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from __future__ import annotations
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from skillopt.model import chat_optimizer
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from skillopt.optimizer.meta_skill import format_meta_skill_context
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from skillopt.optimizer.update_modes import (
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describe_item,
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get_payload_items,
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is_rewrite_mode,
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normalize_update_mode,
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payload_key,
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payload_label,
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)
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from skillopt.prompts import load_prompt
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from skillopt.utils import extract_json
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# ── Public API ────────────────────────────────────────────────────────────────
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def rank_and_select(
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skill_content: str,
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patch: dict,
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max_edits: int,
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meta_skill_context: str = "",
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update_mode: str = "patch",
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) -> dict:
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"""Use a optimizer LLM to rank edits by importance, then keep top-L.
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If the edit pool is within budget, returns the patch unchanged.
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Otherwise, calls the optimizer to rank and select the most impactful edits.
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Parameters
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----------
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skill_content : str
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Current skill document.
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patch : dict
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Merged :class:`~skillopt.types.Patch` dict with ``edits`` list.
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max_edits : int
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Maximum number of edits to keep (the "edit budget").
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Returns
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-------
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dict
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:class:`~skillopt.types.Patch` dict with selected edits and
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optional ``ranking_details``.
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"""
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update_mode = normalize_update_mode(update_mode)
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edits = get_payload_items(patch, update_mode)
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if len(edits) <= max_edits:
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return patch
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# Build the edit pool description for the optimizer
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edits_desc = []
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for i, edit in enumerate(edits):
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edits_desc.append(f"[{i}] {describe_item(edit, update_mode, max_chars=500)}")
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user = (
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f"## Current Skill\n{skill_content}\n\n"
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f"## {payload_label(update_mode, title=True)} Pool ({len(edits)} {payload_label(update_mode)}, budget={max_edits})\n"
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+ "\n".join(edits_desc)
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+ f"\n\nSelect the {max_edits} most important {payload_label(update_mode)}. "
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f"Return their 0-based indices in priority order."
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)
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optimizer_ctx = format_meta_skill_context(meta_skill_context)
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if optimizer_ctx:
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user = f"{optimizer_ctx}\n\n{user}"
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prompt_name = "ranking_rewrite" if is_rewrite_mode(update_mode) else "ranking"
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try:
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response, _ = chat_optimizer(
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system=load_prompt(prompt_name), user=user,
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max_completion_tokens=2048, retries=3, stage="ranking",
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)
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result = extract_json(response)
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if result and "selected_indices" in result:
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indices = result["selected_indices"]
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selected = []
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seen: set[int] = set()
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for idx in indices:
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if (
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isinstance(idx, int)
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and 0 <= idx < len(edits)
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and idx not in seen
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):
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selected.append(edits[idx])
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seen.add(idx)
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if len(selected) >= max_edits:
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break
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if selected:
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return {
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"reasoning": patch.get("reasoning", "")
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+ f" [optimizer-ranked: selected {len(selected)}/{len(edits)} {payload_label(update_mode)}]",
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payload_key(update_mode): selected,
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"ranking_details": result,
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}
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except Exception: # noqa: BLE001
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pass
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# Fallback: simple truncation
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return {
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"reasoning": patch.get("reasoning", "")
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+ f" [fallback truncated {len(edits)}->{max_edits} {payload_label(update_mode)}]",
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payload_key(update_mode): edits[:max_edits],
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}
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