mirror of
https://github.com/shareAI-lab/analysis_claude_code.git
synced 2026-06-21 04:33:36 +08:00
470 lines
22 KiB
Python
470 lines
22 KiB
Python
#!/usr/bin/env python3
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"""
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s08_context_compact.py - Context Compact
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Four-layer compaction pipeline inserted before LLM calls:
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L1: snip_compact — trim middle messages when count > 50
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L2: micro_compact — replace old tool_results with placeholders
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L3: tool_result_budget — persist large results to disk
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L4: compact_history — LLM full summary (1 API call)
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Emergency: reactive_compact — when API still returns prompt_too_long
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┌─────────────────────────────────────────────────────────────┐
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│ messages[] │
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│ ↓ │
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│ L3 budget ─→ L1 snip ─→ L2 micro ─→ [token > threshold?] │
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│ ├─ No → LLM │
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│ └─ Yes → L4 summary │
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│ ↓ │
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│ LLM call │
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│ [prompt_too_long?] │
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│ └─ Yes → reactive │
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└─────────────────────────────────────────────────────────────┘
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Core principle: cheap first, expensive last.
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Execution order matches CC source: budget → snip → micro → auto.
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Builds on s07 (skill loading). Usage:
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python s08_context_compact/code.py
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Needs: pip install anthropic python-dotenv + ANTHROPIC_API_KEY in .env
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"""
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import os, subprocess, json, time
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from pathlib import Path
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try:
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import readline
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readline.parse_and_bind('set bind-tty-special-chars off')
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except ImportError:
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pass
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from anthropic import Anthropic
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from dotenv import load_dotenv
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load_dotenv(override=True)
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if os.getenv("ANTHROPIC_BASE_URL"): os.environ.pop("ANTHROPIC_AUTH_TOKEN", None)
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WORKDIR = Path.cwd()
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SKILLS_DIR = WORKDIR / "skills"
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TRANSCRIPT_DIR = WORKDIR / ".transcripts"
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TOOL_RESULTS_DIR = WORKDIR / ".task_outputs" / "tool-results"
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client = Anthropic(base_url=os.getenv("ANTHROPIC_BASE_URL"))
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MODEL = os.environ["MODEL_ID"]
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CURRENT_TODOS: list[dict] = []
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# s07: Skill catalog scan (inherited from s07)
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def _parse_frontmatter(text: str) -> tuple[dict, str]:
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if not text.startswith("---"):
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return {}, text
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parts = text.split("---", 2)
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if len(parts) < 3:
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return {}, text
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meta = {}
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for line in parts[1].strip().splitlines():
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if ":" in line:
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k, v = line.split(":", 1)
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meta[k.strip()] = v.strip().strip('"').strip("'")
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return meta, parts[2].strip()
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SKILL_REGISTRY: dict[str, dict] = {}
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def _scan_skills():
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if not SKILLS_DIR.exists():
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return
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for d in sorted(SKILLS_DIR.iterdir()):
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if not d.is_dir():
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continue
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manifest = d / "SKILL.md"
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if manifest.exists():
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raw = manifest.read_text()
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meta, body = _parse_frontmatter(raw)
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name = meta.get("name", d.name)
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desc = meta.get("description", raw.split("\n")[0].lstrip("#").strip())
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SKILL_REGISTRY[name] = {"name": name, "description": desc, "content": raw}
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_scan_skills()
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def list_skills() -> str:
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if not SKILL_REGISTRY:
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return "(no skills found)"
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return "\n".join(f"- **{s['name']}**: {s['description']}" for s in SKILL_REGISTRY.values())
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def load_skill(name: str) -> str:
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skill = SKILL_REGISTRY.get(name)
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if not skill:
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return f"Skill not found: {name}"
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return skill["content"]
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# s08: SYSTEM includes skill catalog (inherited from s07 build_system)
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def build_system() -> str:
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catalog = list_skills()
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return (
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f"You are a coding agent at {WORKDIR}. "
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f"Skills available:\n{catalog}\n"
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"Use load_skill to get full details when needed."
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)
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SYSTEM = build_system()
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# s08: subagent gets its own system prompt — no compact, no skill loading
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SUB_SYSTEM = (
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f"You are a coding agent at {WORKDIR}. "
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"Complete the task you were given, then return a concise summary. "
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"Do not delegate further."
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)
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# ═══════════════════════════════════════════════════════════
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# FROM s02-s07 (unchanged): Basic Tools
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# ═══════════════════════════════════════════════════════════
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def safe_path(p: str) -> Path:
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path = (WORKDIR / p).resolve()
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if not path.is_relative_to(WORKDIR): raise ValueError(f"Path escapes workspace: {p}")
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return path
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def run_bash(command: str) -> str:
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try:
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r = subprocess.run(command, shell=True, cwd=WORKDIR, capture_output=True, text=True, timeout=120)
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out = (r.stdout + r.stderr).strip()
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return out[:50000] if out else "(no output)"
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except subprocess.TimeoutExpired: return "Error: Timeout (120s)"
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def run_read(path: str, limit: int | None = None) -> str:
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try:
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lines = safe_path(path).read_text().splitlines()
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if limit and limit < len(lines): lines = lines[:limit] + [f"... ({len(lines) - limit} more lines)"]
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return "\n".join(lines)
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except Exception as e: return f"Error: {e}"
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def run_write(path: str, content: str) -> str:
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try:
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file_path = safe_path(path); file_path.parent.mkdir(parents=True, exist_ok=True)
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file_path.write_text(content); return f"Wrote {len(content)} bytes to {path}"
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except Exception as e: return f"Error: {e}"
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def run_edit(path: str, old_text: str, new_text: str) -> str:
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try:
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file_path = safe_path(path)
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text = file_path.read_text()
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if old_text not in text: return f"Error: text not found in {path}"
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file_path.write_text(text.replace(old_text, new_text, 1))
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return f"Edited {path}"
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except Exception as e: return f"Error: {e}"
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def run_glob(pattern: str) -> str:
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import glob as g
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try:
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results = []
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for match in g.glob(pattern, root_dir=WORKDIR):
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if (WORKDIR / match).resolve().is_relative_to(WORKDIR):
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results.append(match)
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return "\n".join(results) if results else "(no matches)"
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except Exception as e: return f"Error: {e}"
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def run_todo_write(todos: list) -> str:
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global CURRENT_TODOS
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for i, t in enumerate(todos):
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if "content" not in t or "status" not in t:
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return f"Error: todos[{i}] missing 'content' or 'status'"
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if t["status"] not in ("pending", "in_progress", "completed"):
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return f"Error: todos[{i}] has invalid status '{t['status']}'"
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CURRENT_TODOS = todos
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lines = ["\n\033[33m## Current Tasks\033[0m"]
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for t in CURRENT_TODOS:
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icon = {"pending": " ", "in_progress": "\033[36m▸\033[0m", "completed": "\033[32m✓\033[0m"}[t["status"]]
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lines.append(f" [{icon}] {t['content']}")
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print("\n".join(lines))
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return f"Updated {len(CURRENT_TODOS)} tasks"
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def extract_text(content) -> str:
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if not isinstance(content, list): return str(content)
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return "\n".join(getattr(b, "text", "") for b in content if getattr(b, "type", None) == "text")
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# ═══════════════════════════════════════════════════════════
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# FROM s06-s07 (unchanged): Subagent
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# ═══════════════════════════════════════════════════════════
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SUB_TOOLS = [
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{"name": "bash", "description": "Run a shell command.",
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"input_schema": {"type": "object", "properties": {"command": {"type": "string"}}, "required": ["command"]}},
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{"name": "read_file", "description": "Read file contents.",
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"input_schema": {"type": "object", "properties": {"path": {"type": "string"}}, "required": ["path"]}},
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{"name": "write_file", "description": "Write content to a file.",
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"input_schema": {"type": "object", "properties": {"path": {"type": "string"}, "content": {"type": "string"}}, "required": ["path", "content"]}},
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{"name": "edit_file", "description": "Replace exact text in a file once.",
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"input_schema": {"type": "object", "properties": {"path": {"type": "string"}, "old_text": {"type": "string"}, "new_text": {"type": "string"}}, "required": ["path", "old_text", "new_text"]}},
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{"name": "glob", "description": "Find files matching a glob pattern.",
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"input_schema": {"type": "object", "properties": {"pattern": {"type": "string"}}, "required": ["pattern"]}},
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]
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SUB_HANDLERS = {"bash": run_bash, "read_file": run_read, "write_file": run_write,
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"edit_file": run_edit, "glob": run_glob}
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def spawn_subagent(task: str) -> str:
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print(f"\n\033[35m[Subagent spawned]\033[0m")
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messages = [{"role": "user", "content": task}]
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for _ in range(30):
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response = client.messages.create(model=MODEL, system=SUB_SYSTEM,
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messages=messages, tools=SUB_TOOLS, max_tokens=8000)
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messages.append({"role": "assistant", "content": response.content})
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if response.stop_reason != "tool_use":
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break
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results = []
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for block in response.content:
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if block.type == "tool_use":
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blocked = trigger_hooks("PreToolUse", block)
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if blocked:
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results.append({"type": "tool_result", "tool_use_id": block.id,
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"content": str(blocked)})
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continue
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handler = SUB_HANDLERS.get(block.name)
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output = handler(**block.input) if handler else f"Unknown: {block.name}"
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trigger_hooks("PostToolUse", block, output)
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print(f" \033[90m[sub] {block.name}: {str(output)[:100]}\033[0m")
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results.append({"type": "tool_result", "tool_use_id": block.id, "content": output})
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messages.append({"role": "user", "content": results})
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result = extract_text(messages[-1]["content"])
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if not result:
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for msg in reversed(messages):
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if msg["role"] == "assistant":
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result = extract_text(msg["content"])
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if result:
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break
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if not result:
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result = "Subagent stopped after 30 turns without final answer."
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print(f"\033[35m[Subagent done]\033[0m")
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return result
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# ═══════════════════════════════════════════════════════════
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# NEW in s08: Four-Layer Compaction Pipeline
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# ═══════════════════════════════════════════════════════════
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CONTEXT_LIMIT = 50000
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KEEP_RECENT = 3
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PERSIST_THRESHOLD = 30000
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def estimate_size(msgs): return len(str(msgs))
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# L1: snipCompact — trim middle messages
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def snip_compact(messages, max_messages=50):
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if len(messages) <= max_messages: return messages
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keep_head, keep_tail = 3, max_messages - 3
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snipped = len(messages) - keep_head - keep_tail
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return messages[:keep_head] + [{"role": "user", "content": f"[snipped {snipped} messages]"}] + messages[-keep_tail:]
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# L2: microCompact — old result placeholders
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def collect_tool_results(messages):
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blocks = []
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for mi, msg in enumerate(messages):
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if msg.get("role") != "user" or not isinstance(msg.get("content"), list): continue
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for bi, block in enumerate(msg["content"]):
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if isinstance(block, dict) and block.get("type") == "tool_result":
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blocks.append((mi, bi, block))
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return blocks
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def micro_compact(messages):
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tool_results = collect_tool_results(messages)
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if len(tool_results) <= KEEP_RECENT: return messages
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for _, _, block in tool_results[:-KEEP_RECENT]:
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if len(block.get("content", "")) > 120:
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block["content"] = "[Earlier tool result compacted. Re-run if needed.]"
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return messages
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# L3: toolResultBudget — persist large results to disk
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def persist_large_output(tool_use_id, output):
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if len(output) <= PERSIST_THRESHOLD: return output
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TOOL_RESULTS_DIR.mkdir(parents=True, exist_ok=True)
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path = TOOL_RESULTS_DIR / f"{tool_use_id}.txt"
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if not path.exists(): path.write_text(output)
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return f"<persisted-output>\nFull output: {path}\nPreview:\n{output[:2000]}\n</persisted-output>"
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def tool_result_budget(messages, max_bytes=200_000):
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last = messages[-1] if messages else None
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if not last or last.get("role") != "user" or not isinstance(last.get("content"), list): return messages
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blocks = [(i, b) for i, b in enumerate(last["content"]) if isinstance(b, dict) and b.get("type") == "tool_result"]
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total = sum(len(str(b.get("content", ""))) for _, b in blocks)
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if total <= max_bytes: return messages
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ranked = sorted(blocks, key=lambda p: len(str(p[1].get("content", ""))), reverse=True)
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for _, block in ranked:
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if total <= max_bytes: break
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content = str(block.get("content", ""))
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if len(content) <= PERSIST_THRESHOLD: continue
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tid = block.get("tool_use_id", "unknown")
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block["content"] = persist_large_output(tid, content)
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total = sum(len(str(b.get("content", ""))) for _, b in blocks)
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return messages
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# L4: autoCompact — LLM full summary
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def write_transcript(messages):
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TRANSCRIPT_DIR.mkdir(parents=True, exist_ok=True)
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path = TRANSCRIPT_DIR / f"transcript_{int(time.time())}.jsonl"
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with path.open("w") as f:
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for msg in messages: f.write(json.dumps(msg, default=str) + "\n")
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return path
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def summarize_history(messages):
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conversation = json.dumps(messages, default=str)[:80000]
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prompt = ("Summarize this coding-agent conversation so work can continue.\n"
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"Preserve: 1. current goal, 2. key findings/decisions, 3. files read/changed, "
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"4. remaining work, 5. user constraints.\nBe compact but concrete.\n\n" + conversation)
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response = client.messages.create(model=MODEL, messages=[{"role": "user", "content": prompt}], max_tokens=2000)
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return "\n".join(
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getattr(block, "text", "")
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for block in response.content
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if getattr(block, "type", None) == "text").strip() or "(empty summary)"
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def compact_history(messages):
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transcript_path = write_transcript(messages)
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print(f"[transcript saved: {transcript_path}]")
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summary = summarize_history(messages)
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return [{"role": "user", "content": f"[Compacted]\n\n{summary}"}]
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# Emergency: reactiveCompact — on API error
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def reactive_compact(messages):
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transcript = write_transcript(messages)
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summary = summarize_history(messages)
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return [{"role": "user", "content": f"[Reactive compact]\n\n{summary}"}, *messages[-5:]]
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# ═══════════════════════════════════════════════════════════
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# FROM s07: Tool Definitions
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# ═══════════════════════════════════════════════════════════
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TOOLS = [
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{"name": "bash", "description": "Run a shell command.",
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"input_schema": {"type": "object", "properties": {"command": {"type": "string"}}, "required": ["command"]}},
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{"name": "read_file", "description": "Read file contents.",
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"input_schema": {"type": "object", "properties": {"path": {"type": "string"}, "limit": {"type": "integer"}}, "required": ["path"]}},
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{"name": "write_file", "description": "Write content to a file.",
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"input_schema": {"type": "object", "properties": {"path": {"type": "string"}, "content": {"type": "string"}}, "required": ["path", "content"]}},
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{"name": "edit_file", "description": "Replace exact text in a file once.",
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"input_schema": {"type": "object", "properties": {"path": {"type": "string"}, "old_text": {"type": "string"}, "new_text": {"type": "string"}}, "required": ["path", "old_text", "new_text"]}},
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{"name": "glob", "description": "Find files matching a glob pattern.",
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"input_schema": {"type": "object", "properties": {"pattern": {"type": "string"}}, "required": ["pattern"]}},
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{"name": "todo_write", "description": "Create and manage a task list for your current coding session.",
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"input_schema": {"type": "object", "properties": {"todos": {"type": "array", "items": {"type": "object", "properties": {"content": {"type": "string"}, "status": {"type": "string", "enum": ["pending", "in_progress", "completed"]}}, "required": ["content", "status"]}}}, "required": ["todos"]}},
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{"name": "task", "description": "Launch a subagent to handle a complex subtask. Returns only the final conclusion.",
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"input_schema": {"type": "object", "properties": {"description": {"type": "string"}}, "required": ["description"]}},
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{"name": "load_skill", "description": "Load the full content of a skill by name.",
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"input_schema": {"type": "object", "properties": {"name": {"type": "string"}}, "required": ["name"]}},
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# s08 change: new compact tool — triggers compact_history, not a no-op
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{"name": "compact", "description": "Summarize earlier conversation to free context space.",
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"input_schema": {"type": "object", "properties": {"focus": {"type": "string"}}}},
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]
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TOOL_HANDLERS = {
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"bash": run_bash, "read_file": run_read, "write_file": run_write,
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"edit_file": run_edit, "glob": run_glob, "todo_write": run_todo_write,
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"task": spawn_subagent, "load_skill": load_skill,
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}
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# FROM s04 (unchanged): Hooks
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HOOKS = {"PreToolUse": [], "PostToolUse": []}
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def trigger_hooks(event, *args):
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for cb in HOOKS[event]:
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r = cb(*args)
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if r is not None: return r
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return None
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DENY_LIST = ["rm -rf /", "sudo", "shutdown"]
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def permission_hook(block):
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if block.name == "bash":
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for p in DENY_LIST:
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if p in block.input.get("command", ""): return "Permission denied"
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return None
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def log_hook(block):
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print(f"\033[90m[HOOK] {block.name}\033[0m")
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return None
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HOOKS["PreToolUse"].append(permission_hook)
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HOOKS["PreToolUse"].append(log_hook)
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# ═══════════════════════════════════════════════════════════
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# agent_loop — s08 core: run compaction pipeline before LLM
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# ═══════════════════════════════════════════════════════════
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MAX_REACTIVE_RETRIES = 1 # retry limit for reactive compact
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def agent_loop(messages: list):
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reactive_retries = 0
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while True:
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# s08 change: three preprocessors (0 API calls, cheap first)
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# Order matches CC source: budget → snip → micro
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messages[:] = tool_result_budget(messages) # L3: persist large results first
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messages[:] = snip_compact(messages) # L1: trim middle
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messages[:] = micro_compact(messages) # L2: old result placeholders
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# s08 change: tokens still over threshold → LLM summary (1 API call)
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if estimate_size(messages) > CONTEXT_LIMIT:
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print("[auto compact]")
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messages[:] = compact_history(messages)
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try:
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response = client.messages.create(model=MODEL, system=SYSTEM, messages=messages, tools=TOOLS, max_tokens=8000)
|
|
reactive_retries = 0 # reset on successful API call
|
|
except Exception as e:
|
|
if ("prompt_too_long" in str(e).lower() or "too many tokens" in str(e).lower()) and reactive_retries < MAX_REACTIVE_RETRIES:
|
|
print("[reactive compact]")
|
|
messages[:] = reactive_compact(messages)
|
|
reactive_retries += 1
|
|
continue
|
|
raise
|
|
|
|
messages.append({"role": "assistant", "content": response.content})
|
|
if response.stop_reason != "tool_use": return
|
|
|
|
results = []
|
|
for block in response.content:
|
|
if block.type != "tool_use": continue
|
|
print(f"\033[36m> {block.name}\033[0m")
|
|
|
|
# s08: compact tool triggers compact_history, not a no-op string
|
|
if block.name == "compact":
|
|
messages[:] = compact_history(messages)
|
|
results.append({"type": "tool_result", "tool_use_id": block.id,
|
|
"content": "[Compacted. Conversation history has been summarized.]"})
|
|
messages.append({"role": "user", "content": results})
|
|
break # end current turn, start fresh with compacted context
|
|
|
|
blocked = trigger_hooks("PreToolUse", block)
|
|
if blocked:
|
|
results.append({"type": "tool_result", "tool_use_id": block.id, "content": str(blocked)})
|
|
continue
|
|
handler = TOOL_HANDLERS.get(block.name)
|
|
output = handler(**block.input) if handler else f"Unknown: {block.name}"
|
|
trigger_hooks("PostToolUse", block, output)
|
|
print(str(output)[:200])
|
|
results.append({"type": "tool_result", "tool_use_id": block.id, "content": str(output)})
|
|
else:
|
|
# normal path: no compact was called
|
|
messages.append({"role": "user", "content": results})
|
|
continue
|
|
# compact was called: results already appended above
|
|
continue
|
|
|
|
|
|
if __name__ == "__main__":
|
|
print("s08: Context Compact — four-layer compaction pipeline")
|
|
print("输入问题，回车发送。输入 q 退出。\n")
|
|
history = []
|
|
while True:
|
|
try: query = input("\033[36ms08 >> \033[0m")
|
|
except (EOFError, KeyboardInterrupt): break
|
|
if query.strip().lower() in ("q", "exit", ""): break
|
|
history.append({"role": "user", "content": query})
|
|
agent_loop(history)
|
|
for block in history[-1]["content"]:
|
|
if getattr(block, "type", None) == "text": print(block.text)
|
|
print()
|