SkillOpt v0.1.0: initial release
- Skill optimization framework with training loop analogy - 11 benchmarks, 4 model backends (Azure OpenAI, Claude, Codex, Qwen) - WebUI for browser-based training control - Pluggable architecture for extending benchmarks and backends
This commit is contained in:
CharlesYang030
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"""ReflACT core Reflect engine -- minibatch trajectory analysis.
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Provides environment-agnostic minibatch trajectory analysis: instead of
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analyzing each trajectory independently, trajectories are grouped into
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minibatches of size M and analyzed together -- analogous to minibatch SGD
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vs per-sample SGD in neural network training.
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Two-level prompt priority system:
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1. **Custom prompt** (adapter returns non-None) -- used as-is.
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2. **Generic default prompt** (adapter returns None) -- built-in defaults
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that work for any environment without configuration.
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Public API
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----------
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- :func:`fmt_trajectory` -- format one conversation into text
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- :func:`fmt_minibatch_trajectories` -- format multiple trajectories for batch analysis
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- :func:`run_error_analyst_minibatch` -- one teacher call for a group of failures
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- :func:`run_success_analyst_minibatch` -- one teacher call for a group of successes
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- :func:`run_minibatch_reflect` -- full reflect stage dispatcher
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"""
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from __future__ import annotations
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import json
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import os
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import random
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import traceback
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from concurrent.futures import ThreadPoolExecutor, as_completed
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from skillopt.model import chat_teacher
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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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get_payload_items,
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is_full_rewrite_minibatch_mode,
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normalize_update_mode,
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payload_key,
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payload_label,
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truncate_payload,
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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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# ── Trajectory formatting ────────────────────────────────────────────────────
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_MAX_TRAJ_CHARS = 12_000
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def _clip_text(value, limit: int) -> str:
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"""Render optional trajectory fields safely before truncation."""
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if value is None:
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return ""
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return str(value)[:limit]
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def fmt_trajectory(
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conversation: list[dict],
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max_chars: int = _MAX_TRAJ_CHARS,
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) -> str:
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"""Format a conversation list into analyst-readable text.
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Accepts two common formats:
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1. Tool-call records: ``{"type": "tool_call", "cmd": ..., "obs": ...}``
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2. Step records: ``{"step": N, "action": ..., "env_feedback": ..., "reasoning": ...}``
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Any other dict is rendered via its ``"content"`` key.
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"""
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lines: list[str] = []
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for item in conversation:
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if not isinstance(item, dict):
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lines.append(f"[agent] {_clip_text(item, 500)}")
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continue
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if item.get("type") == "tool_call":
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cmd = _clip_text(item.get("cmd"), 500)
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obs = _clip_text(item.get("obs"), 800)
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lines.append(f"[action] {cmd}")
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lines.append(f"[obs] {obs}")
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elif "action" in item and "env_feedback" in item:
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step = item.get("step", "?")
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reasoning = _clip_text(item.get("reasoning"), 300)
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action = _clip_text(item.get("action"), 200)
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feedback = _clip_text(item.get("env_feedback"), 500)
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if reasoning:
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lines.append(f"[step {step} think] {reasoning}")
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lines.append(f"[step {step} action] {action}")
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lines.append(f"[step {step} obs] {feedback}")
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elif item.get("role") == "system":
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# Post-execution verification / enrichment info
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msg = _clip_text(item.get("content"), 2000)
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lines.append(f"[verification] {msg}")
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else:
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msg = _clip_text(item.get("content"), 500)
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role = item.get("role", "agent")
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lines.append(f"[{role}] {msg}")
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text = "\n".join(lines)
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if len(text) > max_chars:
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head = text[: max_chars // 2]
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tail = text[-max_chars // 2 :]
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text = head + "\n...[middle truncated]...\n" + tail
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return text
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# ── Minibatch trajectory formatting ──────────────────────────────────────────
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def fmt_minibatch_trajectories(
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items: list[dict],
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prediction_dir: str,
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) -> str:
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"""Format multiple trajectories for minibatch analyst consumption.
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Each item is a rollout result dict with ``"id"``, ``"task_description"``,
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``"task_type"``, ``"fail_reason"``, etc. Reads ``conversation.json``
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for each and formats them together with trajectory headers.
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If available, includes the spreadsheet preview and student system prompt
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so the analyst can see what the agent saw.
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Parameters
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----------
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items : list[dict]
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Rollout result dicts belonging to one minibatch.
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prediction_dir : str
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Path to ``predictions/`` directory containing per-task
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``<task_id>/conversation.json`` files.
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Returns
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-------
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str
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Formatted text with all trajectories separated by ``---``.
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"""
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parts: list[str] = []
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for idx, item in enumerate(items, 1):
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tid = str(item["id"])
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conv_path = os.path.join(prediction_dir, tid, "conversation.json")
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if not os.path.exists(conv_path):
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continue
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with open(conv_path) as f:
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conversation = json.load(f)
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if not conversation:
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continue
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traj_text = fmt_trajectory(conversation)
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header = (
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f"### Trajectory {idx} (id={tid})\n"
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f"Task: {item.get('task_description', item.get('instruction', ''))}\n"
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f"Task type: {item.get('task_type', item.get('instruction_type', ''))}\n"
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)
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fail_reason = item.get("fail_reason", "")
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if fail_reason:
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header += f"Failure reason: {fail_reason}\n"
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header += f"Steps: {item.get('n_turns', '?')}\n"
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reference_text = str(item.get("reference_text") or "").strip()
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if reference_text:
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header += (
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f"\n#### Hidden Reference\n"
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f"{reference_text[:4000]}\n"
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)
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# ── Append student context (what the agent saw) ──────────────
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student_prompt = item.get("student_system_prompt", "")
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if not student_prompt:
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prompt_path = os.path.join(prediction_dir, tid, "student_system_prompt.txt")
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if os.path.exists(prompt_path):
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with open(prompt_path) as f:
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student_prompt = f.read()
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if student_prompt:
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header += (
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f"\n#### Student System Prompt\n"
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f"{student_prompt[:3000]}\n"
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)
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user_prompt = item.get("student_user_prompt", "")
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if not user_prompt:
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user_prompt_path = os.path.join(prediction_dir, tid, "student_user_prompt.txt")
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if os.path.exists(user_prompt_path):
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with open(user_prompt_path) as f:
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user_prompt = f.read()
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if user_prompt:
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header += (
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f"\n#### Student User Prompt\n"
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f"{user_prompt[:3000]}\n"
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)
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if os.environ.get("REFLACT_CODEX_TRACE_TO_TEACHER", "0") == "1":
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codex_trace_summary = item.get("codex_trace_summary", "")
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if not codex_trace_summary:
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codex_trace_summary_path = os.path.join(prediction_dir, tid, "codex_trace_summary.txt")
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if os.path.exists(codex_trace_summary_path):
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with open(codex_trace_summary_path) as f:
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codex_trace_summary = f.read()
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if codex_trace_summary:
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header += (
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f"\n#### Codex Trace Summary\n"
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f"{codex_trace_summary}\n"
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)
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codex_probe_trace_steps = str(item.get("codex_probe_trace_steps") or "").strip()
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if codex_probe_trace_steps:
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header += (
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f"\n#### Codex Trace Steps\n"
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f"{codex_probe_trace_steps}\n"
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)
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preview = item.get("spreadsheet_preview", "")
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if not preview:
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preview_path = os.path.join(prediction_dir, tid, "spreadsheet_preview.txt")
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if os.path.exists(preview_path):
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with open(preview_path) as f:
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preview = f.read()
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if preview:
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header += (
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f"\n#### Spreadsheet Preview\n"
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f"{preview[:3000]}\n"
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)
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parts.append(header + "\n" + traj_text)
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return "\n\n---\n\n".join(parts)
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# ── Prompt resolution ───────────────────────────────────────────────────────
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def _resolve_prompt(custom: str | None, default_name: str, update_mode: str = "patch") -> str:
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"""Return *custom* if provided (non-None), otherwise load from file."""
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if custom is not None:
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return custom
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mode = normalize_update_mode(update_mode)
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actual_name = default_name
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if is_full_rewrite_minibatch_mode(mode):
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full_name = f"{default_name}_full_rewrite"
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try:
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return load_prompt(full_name)
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except FileNotFoundError:
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actual_name = default_name
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elif mode == "rewrite_from_suggestions":
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rewrite_name = f"{default_name}_rewrite"
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try:
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return load_prompt(rewrite_name)
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except FileNotFoundError:
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actual_name = default_name
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return load_prompt(actual_name)
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# ── Minibatch analysts ──────────────────────────────────────────────────────
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def run_error_analyst_minibatch(
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skill_content: str,
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items: list[dict],
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prediction_dir: str,
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edit_budget: int = 4,
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*,
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system_prompt: str | None = None,
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rejection_context: str = "",
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trajectory_memory_context: str = "",
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step_buffer_context: str = "",
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meta_skill_context: str = "",
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update_mode: str = "patch",
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) -> dict | None:
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"""Analyze a minibatch of failed trajectories in one teacher call.
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Parameters
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----------
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skill_content : str
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Current skill document text.
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items : list[dict]
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Rollout result dicts (all should have ``hard=0``).
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prediction_dir : str
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Path to ``predictions/`` directory.
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edit_budget : int
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Maximum number of edits (L).
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system_prompt : str | None
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Custom system prompt. ``None`` = use generic default.
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rejection_context : str
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*Deprecated* — use ``step_buffer_context``.
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trajectory_memory_context : str
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*Deprecated* — use ``step_buffer_context``.
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step_buffer_context : str
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Unified summary of previous steps (failure patterns + rejected edits).
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Returns
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-------
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dict | None
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Patch dict with ``source_type="failure"``, or ``None`` on error.
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"""
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mode = normalize_update_mode(update_mode)
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actual_system = _resolve_prompt(system_prompt, "analyst_error", mode)
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trajectories_text = fmt_minibatch_trajectories(items, prediction_dir)
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if not trajectories_text.strip():
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return None
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user = (
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f"## Current Skill\n{skill_content}\n\n"
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)
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if is_full_rewrite_minibatch_mode(mode):
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user += (
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f"## Update Format\n"
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f"Produce one complete replacement skill candidate for this minibatch. "
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f"Do not output edits, patches, or revise suggestions.\n\n"
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)
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else:
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user += (
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f"## {payload_label(mode, title=True)} Budget\n"
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f"Produce at most L={edit_budget} {payload_label(mode)}.\n\n"
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)
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# Unified step buffer context (preferred)
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ctx = step_buffer_context or rejection_context or ""
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if trajectory_memory_context:
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ctx = f"{ctx}\n{trajectory_memory_context}" if ctx else trajectory_memory_context
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if ctx.strip():
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user += f"## Previous Steps in This Epoch\n{ctx}\n\n"
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teacher_ctx = format_meta_skill_context(meta_skill_context)
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if teacher_ctx:
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user += teacher_ctx + "\n\n"
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user += f"## Failed Trajectories ({len(items)} total)\n{trajectories_text}"
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try:
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response, _ = chat_teacher(
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system=actual_system, user=user,
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max_completion_tokens=64000 if is_full_rewrite_minibatch_mode(mode) else 4096,
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retries=3,
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stage="analyst",
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)
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result = extract_json(response)
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if result and "patch" in result:
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result["source_type"] = "failure"
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if not is_full_rewrite_minibatch_mode(mode):
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truncate_payload(result["patch"], edit_budget, mode)
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return result
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except Exception: # noqa: BLE001
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traceback.print_exc()
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return None
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def run_success_analyst_minibatch(
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skill_content: str,
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items: list[dict],
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prediction_dir: str,
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edit_budget: int = 4,
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*,
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system_prompt: str | None = None,
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trajectory_memory_context: str = "",
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step_buffer_context: str = "",
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meta_skill_context: str = "",
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update_mode: str = "patch",
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) -> dict | None:
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"""Analyze a minibatch of successful trajectories in one teacher call.
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Parameters
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----------
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system_prompt : str | None
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Custom system prompt. ``None`` = use generic default.
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trajectory_memory_context : str
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*Deprecated* — use ``step_buffer_context``.
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step_buffer_context : str
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Unified summary of previous steps (failure patterns + rejected edits).
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Returns
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-------
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dict | None
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Patch dict with ``source_type="success"``, or ``None`` on error.
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"""
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mode = normalize_update_mode(update_mode)
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actual_system = _resolve_prompt(system_prompt, "analyst_success", mode)
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trajectories_text = fmt_minibatch_trajectories(items, prediction_dir)
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if not trajectories_text.strip():
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return None
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user = (
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f"## Current Skill\n{skill_content}\n\n"
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)
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if is_full_rewrite_minibatch_mode(mode):
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user += (
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f"## Update Format\n"
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f"Produce one complete replacement skill candidate for this minibatch. "
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f"Do not output edits, patches, or revise suggestions.\n\n"
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)
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else:
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user += (
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f"## {payload_label(mode, title=True)} Budget\n"
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f"Produce at most L={edit_budget} {payload_label(mode)}.\n\n"
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)
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ctx = step_buffer_context or trajectory_memory_context or ""
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if ctx.strip():
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user += f"## Previous Steps in This Epoch\n{ctx}\n\n"
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teacher_ctx = format_meta_skill_context(meta_skill_context)
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if teacher_ctx:
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user += teacher_ctx + "\n\n"
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user += f"## Successful Trajectories ({len(items)} total)\n{trajectories_text}"
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try:
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response, _ = chat_teacher(
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system=actual_system, user=user,
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max_completion_tokens=64000 if is_full_rewrite_minibatch_mode(mode) else 4096,
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retries=3,
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stage="analyst",
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)
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result = extract_json(response)
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if result and "patch" in result:
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result["source_type"] = "success"
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if not is_full_rewrite_minibatch_mode(mode):
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truncate_payload(result["patch"], edit_budget, mode)
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return result
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except Exception: # noqa: BLE001
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traceback.print_exc()
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return None
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# ── Minibatch reflect dispatcher ────────────────────────────────────────────
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def _split_minibatches(items: list, batch_size: int) -> list[list]:
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"""Split items into minibatches of at most *batch_size*."""
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return [items[i : i + batch_size] for i in range(0, len(items), batch_size)]
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def _shuffle_for_minibatch(items: list, seed: int | None) -> list:
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"""Return items in minibatch order.
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Uses a deterministic shuffle when a seed is provided so resume runs keep
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the same minibatch composition. Falls back to input order when no seed is
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||||
available.
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"""
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ordered = list(items)
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if seed is None:
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return ordered
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random.Random(seed).shuffle(ordered)
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return ordered
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def run_minibatch_reflect(
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results: list[dict],
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skill_content: str,
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prediction_dir: str,
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||||
patches_dir: str,
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||||
workers: int,
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||||
failure_only: bool,
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||||
minibatch_size: int = 8,
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||||
edit_budget: int = 4,
|
||||
random_seed: int | None = None,
|
||||
*,
|
||||
error_system: str | None = None,
|
||||
success_system: str | None = None,
|
||||
rejection_context: str = "",
|
||||
trajectory_memory_context: str = "",
|
||||
step_buffer_context: str = "",
|
||||
meta_skill_context: str = "",
|
||||
update_mode: str = "patch",
|
||||
) -> list[dict | None]:
|
||||
"""Full minibatch reflect stage: group → parallel teacher calls → patches.
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||||
|
||||
Separates failure and success trajectories, splits each into minibatches
|
||||
of size M, runs all minibatches in parallel, and saves patch files.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
results : list[dict]
|
||||
Rollout result dicts; see :class:`~skillopt.types.RolloutResult`.
|
||||
skill_content : str
|
||||
Current skill document.
|
||||
prediction_dir : str
|
||||
Path to ``predictions/`` with ``conversation.json`` files.
|
||||
patches_dir : str
|
||||
Path to save per-minibatch patch JSON files.
|
||||
workers : int
|
||||
Max parallel teacher calls.
|
||||
failure_only : bool
|
||||
If True, skip success trajectories.
|
||||
minibatch_size : int
|
||||
Trajectories per group (M).
|
||||
edit_budget : int
|
||||
Max edits per minibatch (L).
|
||||
random_seed : int | None
|
||||
Optional seed used to shuffle trajectories before minibatch splitting.
|
||||
error_system, success_system : str | None
|
||||
Optional custom prompts. ``None`` = use generic defaults.
|
||||
|
||||
Returns
|
||||
-------
|
||||
list[dict | None]
|
||||
Patch dicts (with ``source_type`` "failure" or "success").
|
||||
"""
|
||||
os.makedirs(patches_dir, exist_ok=True)
|
||||
|
||||
# Separate failure / success
|
||||
failures = [r for r in results if not r.get("hard")]
|
||||
successes = [r for r in results if r.get("hard")] if not failure_only else []
|
||||
|
||||
failures = _shuffle_for_minibatch(failures, random_seed)
|
||||
successes = _shuffle_for_minibatch(successes, None if random_seed is None else random_seed + 1)
|
||||
|
||||
# Split into minibatches
|
||||
fail_batches = _split_minibatches(failures, minibatch_size)
|
||||
succ_batches = _split_minibatches(successes, minibatch_size)
|
||||
|
||||
n_fail_batches = len(fail_batches)
|
||||
n_succ_batches = len(succ_batches)
|
||||
print(
|
||||
f" [2/6 REFLECT minibatch] "
|
||||
f"failure={len(failures)}→{n_fail_batches} groups "
|
||||
f"success={len(successes)}→{n_succ_batches} groups "
|
||||
f"(M={minibatch_size}, L={edit_budget}, workers={workers})"
|
||||
)
|
||||
|
||||
raw_patches: list[dict | None] = []
|
||||
|
||||
# Resume support: check for already-done minibatch patches
|
||||
pending_fail: list[tuple[int, list[dict]]] = []
|
||||
for idx, batch in enumerate(fail_batches):
|
||||
path = os.path.join(patches_dir, f"minibatch_fail_{idx:03d}.json")
|
||||
if os.path.exists(path):
|
||||
with open(path) as f:
|
||||
raw_patches.append(json.load(f))
|
||||
else:
|
||||
pending_fail.append((idx, batch))
|
||||
|
||||
pending_succ: list[tuple[int, list[dict]]] = []
|
||||
for idx, batch in enumerate(succ_batches):
|
||||
path = os.path.join(patches_dir, f"minibatch_succ_{idx:03d}.json")
|
||||
if os.path.exists(path):
|
||||
with open(path) as f:
|
||||
raw_patches.append(json.load(f))
|
||||
else:
|
||||
pending_succ.append((idx, batch))
|
||||
|
||||
# ── Worker functions ──────────────────────────────────────────────────
|
||||
def _do_fail(idx: int, batch: list[dict]) -> tuple[str, dict | None]:
|
||||
patch = run_error_analyst_minibatch(
|
||||
skill_content, batch, prediction_dir,
|
||||
edit_budget=edit_budget,
|
||||
system_prompt=error_system,
|
||||
step_buffer_context=step_buffer_context,
|
||||
# backward compat fallback
|
||||
rejection_context=rejection_context,
|
||||
trajectory_memory_context=trajectory_memory_context,
|
||||
meta_skill_context=meta_skill_context,
|
||||
update_mode=update_mode,
|
||||
)
|
||||
return f"minibatch_fail_{idx:03d}", patch
|
||||
|
||||
def _do_succ(idx: int, batch: list[dict]) -> tuple[str, dict | None]:
|
||||
patch = run_success_analyst_minibatch(
|
||||
skill_content, batch, prediction_dir,
|
||||
edit_budget=edit_budget,
|
||||
system_prompt=success_system,
|
||||
step_buffer_context=step_buffer_context,
|
||||
trajectory_memory_context=trajectory_memory_context,
|
||||
meta_skill_context=meta_skill_context,
|
||||
update_mode=update_mode,
|
||||
)
|
||||
return f"minibatch_succ_{idx:03d}", patch
|
||||
|
||||
# Run all pending minibatches in parallel
|
||||
all_pending = (
|
||||
[("fail", idx, batch) for idx, batch in pending_fail]
|
||||
+ [("succ", idx, batch) for idx, batch in pending_succ]
|
||||
)
|
||||
|
||||
with ThreadPoolExecutor(max_workers=workers) as ex:
|
||||
futs = {}
|
||||
for kind, idx, batch in all_pending:
|
||||
if kind == "fail":
|
||||
futs[ex.submit(_do_fail, idx, batch)] = (kind, idx, len(batch))
|
||||
else:
|
||||
futs[ex.submit(_do_succ, idx, batch)] = (kind, idx, len(batch))
|
||||
|
||||
for i, fut in enumerate(as_completed(futs), 1):
|
||||
kind, idx, batch_len = futs[fut]
|
||||
tag, patch = fut.result()
|
||||
if patch:
|
||||
path = os.path.join(patches_dir, f"{tag}.json")
|
||||
with open(path, "w") as f:
|
||||
json.dump(patch, f, ensure_ascii=False, indent=2)
|
||||
raw_patches.append(patch)
|
||||
n_edits = len(get_payload_items(patch.get("patch", {}) if patch else {}, update_mode))
|
||||
print(
|
||||
f" [analyst] {i}/{len(all_pending)} {tag} "
|
||||
f"({batch_len} trajs) → {n_edits} {payload_label(update_mode)}"
|
||||
)
|
||||
|
||||
return raw_patches
|
||||
Reference in New Issue
Block a user