m3taversal
a292ab75c2
Reads event_type: proposal sources from archive, calls Sonnet for summary/significance/KB-connections, writes decision records with full verbatim proposal text + structured analysis on top. 224 proposal sources archived, 0 processed. This closes the gap. Pentagon-Agent: Epimetheus <3D35839A-7722-4740-B93D-51157F7D5E70>
369 lines
14 KiB
Python
369 lines
14 KiB
Python
#!/usr/bin/env python3
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"""Extract decision records from proposal sources.
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Reads event_type: proposal sources from archive, produces decision records
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in decisions/{domain}/ with full verbatim proposal text + LLM-generated
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summary, significance, and KB connections.
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Usage:
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python3 extract-decisions.py [--dry-run] [--limit N] [--source FILE]
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Pentagon-Agent: Epimetheus <3D35839A-7722-4740-B93D-51157F7D5E70>
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"""
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import argparse
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import csv
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import json
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import os
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import re
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import sys
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from datetime import date
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from pathlib import Path
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import requests
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import yaml
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# ─── Constants ──────────────────────────────────────────────────────────────
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OPENROUTER_URL = "https://openrouter.ai/api/v1/chat/completions"
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MODEL = "anthropic/claude-sonnet-4.5"
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USAGE_CSV = "/opt/teleo-eval/logs/openrouter-usage.csv"
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REPO_DIR = Path("/opt/teleo-eval/workspaces/main")
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ARCHIVE_DIR = REPO_DIR / "inbox" / "archive"
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DECISIONS_DIR = REPO_DIR / "decisions"
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# ─── LLM Call ───────────────────────────────────────────────────────────────
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def call_llm(prompt: str, max_tokens: int = 4096) -> str | None:
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"""Call OpenRouter API."""
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api_key = os.environ.get("OPENROUTER_API_KEY", "")
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if not api_key:
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# Try reading from file
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key_file = Path("/opt/teleo-eval/.openrouter-key")
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if key_file.exists():
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api_key = key_file.read_text().strip()
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if not api_key:
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print("ERROR: No OPENROUTER_API_KEY", file=sys.stderr)
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return None
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resp = requests.post(
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OPENROUTER_URL,
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headers={"Authorization": f"Bearer {api_key}"},
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json={
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"model": MODEL,
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"messages": [{"role": "user", "content": prompt}],
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"max_tokens": max_tokens,
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"temperature": 0.3,
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},
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timeout=120,
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)
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if resp.status_code != 200:
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print(f"ERROR: OpenRouter {resp.status_code}: {resp.text[:200]}", file=sys.stderr)
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return None
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data = resp.json()
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# Log usage
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usage = data.get("usage", {})
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try:
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with open(USAGE_CSV, "a") as f:
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writer = csv.writer(f)
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writer.writerow([
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date.today().isoformat(),
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"extract-decisions",
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MODEL,
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usage.get("prompt_tokens", 0),
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usage.get("completion_tokens", 0),
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"",
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])
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except Exception:
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pass
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return data["choices"][0]["message"]["content"]
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# ─── Frontmatter Parsing ───────────────────────────────────────────────────
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def parse_frontmatter(path: Path) -> tuple[dict | None, str]:
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"""Parse YAML frontmatter and body."""
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text = path.read_text(errors="replace")
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if not text.startswith("---"):
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return None, text
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end = text.find("\n---", 3)
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if end == -1:
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return None, text
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try:
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fm = yaml.safe_load(text[3:end])
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if not isinstance(fm, dict):
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return None, text
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body = text[end + 4:].strip()
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return fm, body
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except yaml.YAMLError:
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return None, text
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# ─── Find Unprocessed Proposal Sources ──────────────────────────────────────
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def find_proposal_sources() -> list[Path]:
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"""Find all unprocessed proposal sources in archive."""
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sources = []
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for md_file in sorted(ARCHIVE_DIR.rglob("*.md")):
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fm, _ = parse_frontmatter(md_file)
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if not fm:
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continue
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if fm.get("event_type") == "proposal" and fm.get("status") in ("unprocessed", None):
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sources.append(md_file)
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return sources
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# ─── Check if Decision Record Exists ────────────────────────────────────────
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def decision_exists(slug: str, domain: str = "internet-finance") -> bool:
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"""Check if a decision record already exists."""
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target_dir = DECISIONS_DIR / domain
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if not target_dir.exists():
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return False
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# Check exact slug match
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if (target_dir / f"{slug}.md").exists():
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return True
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# Check partial match (slug might be truncated)
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for f in target_dir.iterdir():
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if slug[:40] in f.name:
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return True
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return False
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def slugify(text: str) -> str:
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"""Convert text to filename slug."""
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text = text.lower()
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text = re.sub(r'[^a-z0-9\s-]', '', text)
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text = re.sub(r'[\s]+', '-', text.strip())
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text = re.sub(r'-+', '-', text)
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return text[:80]
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# ─── Build Decision Record ──────────────────────────────────────────────────
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ANALYSIS_PROMPT = """You are analyzing a futarchy/governance proposal to create a structured decision record for a knowledge base.
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Given this proposal source, produce a JSON object with these fields:
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- "name": The full proposal name (e.g., "MetaDAO: Hire Robin Hanson as Advisor")
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- "status": "passed" or "failed" or "active" (from the source data)
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- "proposer": Who proposed it (name or handle)
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- "proposal_date": ISO date when created
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- "resolution_date": ISO date when resolved (null if active)
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- "category": One of: treasury, hiring, product, governance, fundraise, incentives, migration, other
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- "summary": 1-2 sentence summary of what this proposal does and why it matters. Be specific — include dollar amounts, key parameters, and outcomes.
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- "significance": 2-3 paragraphs analyzing why this proposal matters for the futarchy ecosystem. What does it prove or test? What precedent does it set? How does it relate to broader governance patterns?
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- "related_claims": List of 2-5 wiki-link titles from the Teleo knowledge base that this proposal is evidence for or against. Use full prose-as-title format like "futarchy-governed DAOs converge on traditional corporate governance scaffolding for treasury operations because market mechanisms alone cannot provide operational security and legal compliance"
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IMPORTANT: Only output valid JSON. No markdown, no commentary.
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Here is the proposal source:
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{source_text}
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"""
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def build_decision_record(source_path: Path, dry_run: bool = False) -> Path | None:
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"""Build a decision record from a proposal source."""
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fm, body = parse_frontmatter(source_path)
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if not fm:
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print(f" SKIP: No frontmatter in {source_path.name}")
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return None
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title = fm.get("title", "")
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domain = fm.get("domain", "internet-finance")
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url = fm.get("url", "")
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source_date = fm.get("date", "")
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tags = fm.get("tags", []) or []
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# Extract project name from body
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project_match = re.search(r'Project:\s*(.+)', body)
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project = project_match.group(1).strip() if project_match else "Unknown"
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# Build slug from title
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slug = slugify(title.replace("Futardio: ", "").replace("futardio: ", ""))
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if not slug:
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slug = slugify(source_path.stem)
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# Check if already exists
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if decision_exists(slug, domain):
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print(f" SKIP: Decision record already exists for {slug}")
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return None
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# Full source text for LLM (truncate at 8K to fit in context)
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source_text = f"Title: {title}\nURL: {url}\nDate: {source_date}\n\n{body}"
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if len(source_text) > 8000:
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source_text = source_text[:8000] + "\n\n[... truncated for analysis ...]"
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if dry_run:
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print(f" DRY RUN: Would create {slug}.md from {source_path.name}")
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return None
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# Call LLM for analysis
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prompt = ANALYSIS_PROMPT.format(source_text=source_text)
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response = call_llm(prompt)
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if not response:
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print(f" ERROR: LLM call failed for {source_path.name}")
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return None
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# Parse LLM response
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try:
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# Strip markdown code fences if present
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cleaned = re.sub(r'^```json\s*', '', response.strip())
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cleaned = re.sub(r'\s*```$', '', cleaned)
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analysis = json.loads(cleaned)
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except json.JSONDecodeError as e:
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print(f" ERROR: Invalid JSON from LLM for {source_path.name}: {e}")
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print(f" Response: {response[:200]}")
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return None
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# Extract market data from body if present
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market_lines = []
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for line in body.split("\n"):
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line_stripped = line.strip()
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if any(kw in line_stripped.lower() for kw in
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["status:", "total volume", "pass", "fail", "spot", "outcome",
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"autocrat", "proposal account", "dao account", "proposer:"]):
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if line_stripped.startswith("- ") or line_stripped.startswith("**"):
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market_lines.append(line_stripped)
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# Build frontmatter
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record_fm = {
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"type": "decision",
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"entity_type": "decision_market",
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"name": analysis.get("name", title),
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"domain": domain,
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"status": analysis.get("status", "unknown"),
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"tracked_by": "rio",
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"created": str(date.today()),
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"last_updated": str(date.today()),
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"parent_entity": f"[[{project.lower()}]]" if project != "Unknown" else "",
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"platform": "metadao",
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"proposer": analysis.get("proposer", ""),
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"proposal_url": url,
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"proposal_date": analysis.get("proposal_date", str(source_date)),
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"resolution_date": analysis.get("resolution_date", ""),
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"category": analysis.get("category", "other"),
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"summary": analysis.get("summary", ""),
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"tags": tags + [project.lower()] if project != "Unknown" else tags,
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}
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# Build body
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name = analysis.get("name", title)
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summary = analysis.get("summary", "")
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significance = analysis.get("significance", "")
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related = analysis.get("related_claims", [])
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body_parts = [f"# {name}\n"]
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body_parts.append(f"## Summary\n\n{summary}\n")
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if market_lines:
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body_parts.append("## Market Data\n")
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for ml in market_lines:
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body_parts.append(ml)
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body_parts.append("")
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body_parts.append(f"## Significance\n\n{significance}\n")
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# Full proposal text — verbatim
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body_parts.append("## Full Proposal Text\n")
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body_parts.append(body)
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body_parts.append("")
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# KB relationships
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if related:
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body_parts.append("## Relationship to KB\n")
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for claim_title in related:
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slug_link = claim_title.replace(" ", "-").lower()
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body_parts.append(f"- [[{slug_link}]]")
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body_parts.append("")
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body_parts.append("---\n")
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body_parts.append("Relevant Entities:")
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if project != "Unknown":
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body_parts.append(f"- [[{project.lower()}]] — parent organization")
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body_parts.append(f"\nTopics:\n- [[internet finance and decision markets]]")
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# Write file
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target_dir = DECISIONS_DIR / domain
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target_dir.mkdir(parents=True, exist_ok=True)
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target_path = target_dir / f"{slug}.md"
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# Serialize frontmatter
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fm_str = yaml.dump(record_fm, default_flow_style=False, allow_unicode=True, sort_keys=False)
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content = f"---\n{fm_str}---\n\n" + "\n".join(body_parts)
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target_path.write_text(content)
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print(f" CREATED: {target_path.name} ({len(content)} chars)")
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# Mark source as processed
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source_text_full = source_path.read_text()
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updated = source_text_full.replace("status: unprocessed", "status: processed")
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source_path.write_text(updated)
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return target_path
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# ─── Main ───────────────────────────────────────────────────────────────────
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def main():
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parser = argparse.ArgumentParser(description="Extract decision records from proposal sources")
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parser.add_argument("--dry-run", action="store_true", help="Show what would be created without writing")
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parser.add_argument("--limit", type=int, default=0, help="Max proposals to process (0 = all)")
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parser.add_argument("--source", type=str, help="Process a single source file")
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parser.add_argument("--skip-existing", action="store_true", default=True,
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help="Skip sources that already have decision records")
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args = parser.parse_args()
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if args.source:
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source_path = Path(args.source)
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if not source_path.exists():
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print(f"ERROR: Source not found: {source_path}")
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sys.exit(1)
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result = build_decision_record(source_path, dry_run=args.dry_run)
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if result:
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print(f"Done: {result}")
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return
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# Find all unprocessed proposals
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sources = find_proposal_sources()
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print(f"Found {len(sources)} unprocessed proposal sources")
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if args.dry_run:
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for s in sources[:args.limit or len(sources)]:
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fm, _ = parse_frontmatter(s)
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title = fm.get("title", s.stem) if fm else s.stem
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print(f" {title}")
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return
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processed = 0
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created = 0
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skipped = 0
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errors = 0
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limit = args.limit or len(sources)
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for source_path in sources[:limit]:
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fm, _ = parse_frontmatter(source_path)
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title = fm.get("title", source_path.stem) if fm else source_path.stem
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print(f"\nProcessing: {title}")
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try:
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result = build_decision_record(source_path, dry_run=False)
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if result:
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created += 1
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else:
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skipped += 1
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except Exception as e:
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print(f" ERROR: {e}")
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errors += 1
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processed += 1
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print(f"\nDone: {processed} processed, {created} created, {skipped} skipped, {errors} errors")
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if __name__ == "__main__":
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main()
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