Initial commit — memex
A compounding LLM-maintained knowledge wiki. Synthesis of Andrej Karpathy's persistent-wiki gist and milla-jovovich's mempalace, with an automation layer on top for conversation mining, URL harvesting, human-in-the-loop staging, staleness decay, and hygiene. Includes: - 11 pipeline scripts (extract, summarize, index, harvest, stage, hygiene, maintain, sync, + shared library) - Full docs: README, SETUP, ARCHITECTURE, DESIGN-RATIONALE, CUSTOMIZE - Example CLAUDE.md files (wiki schema + global instructions) tuned for the three-collection qmd setup - 171-test pytest suite (cross-platform, runs in ~1.3s) - MIT licensed
This commit is contained in:
Eric Turner
646
scripts/summarize-conversations.py
Executable file
646
scripts/summarize-conversations.py
Executable file
@@ -0,0 +1,646 @@
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#!/usr/bin/env python3
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"""Summarize extracted conversation transcripts via LLM.
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Phase B of the conversation mining pipeline. Sends transcripts to a local
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llama-server or Claude Code CLI for classification, summarization, and
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key exchange selection.
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Handles chunking and incremental summarization.
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Usage:
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python3 summarize-conversations.py # All unsummarized (local LLM)
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python3 summarize-conversations.py --claude # Use claude -p (haiku/sonnet)
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python3 summarize-conversations.py --claude --long 300 # Sonnet threshold: 300 msgs
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python3 summarize-conversations.py --project mc # One project only
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python3 summarize-conversations.py --file path.md # One file
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python3 summarize-conversations.py --dry-run # Show what would be done
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Claude mode uses Haiku for short conversations (<= threshold) and Sonnet
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for longer ones. Threshold default: 200 messages.
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"""
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from __future__ import annotations
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import argparse
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import json
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import os
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import re
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import subprocess
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import sys
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import time
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from pathlib import Path
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from typing import Any
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# Force unbuffered output for background/pipe usage
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sys.stdout.reconfigure(line_buffering=True)
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sys.stderr.reconfigure(line_buffering=True)
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# ---------------------------------------------------------------------------
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# Configuration
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# ---------------------------------------------------------------------------
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WIKI_DIR = Path(os.environ.get("WIKI_DIR", str(Path.home() / "projects" / "wiki")))
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CONVERSATIONS_DIR = WIKI_DIR / "conversations"
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MINE_STATE_FILE = WIKI_DIR / ".mine-state.json"
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# Prompt file lives next to this script, not in $WIKI_DIR
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MINE_PROMPT_FILE = Path(__file__).resolve().parent / "mine-prompt-v2.md"
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# Local LLM defaults (llama-server)
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AI_BASE_URL = "http://localhost:8080/v1"
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AI_MODEL = "Phi-4-14B-Q4_K_M"
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AI_TOKEN = "dummy"
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AI_TIMEOUT = 180
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AI_TEMPERATURE = 0.3
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# Claude CLI defaults
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CLAUDE_HAIKU_MODEL = "haiku"
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CLAUDE_SONNET_MODEL = "sonnet"
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CLAUDE_LONG_THRESHOLD = 200 # messages — above this, use Sonnet
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# Chunking parameters
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# Local LLM: 8K context → ~3000 tokens content per chunk
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MAX_CHUNK_CHARS_LOCAL = 12000
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MAX_ROLLING_CONTEXT_CHARS_LOCAL = 6000
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# Claude: 200K context → much larger chunks, fewer LLM calls
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MAX_CHUNK_CHARS_CLAUDE = 80000 # ~20K tokens
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MAX_ROLLING_CONTEXT_CHARS_CLAUDE = 20000
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def _update_config(base_url: str, model: str, timeout: int) -> None:
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global AI_BASE_URL, AI_MODEL, AI_TIMEOUT
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AI_BASE_URL = base_url
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AI_MODEL = model
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AI_TIMEOUT = timeout
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# ---------------------------------------------------------------------------
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# LLM interaction — local llama-server
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# ---------------------------------------------------------------------------
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def llm_call_local(system_prompt: str, user_message: str) -> str | None:
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"""Call the local LLM server and return the response content."""
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import urllib.request
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import urllib.error
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payload = json.dumps({
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"model": AI_MODEL,
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"messages": [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": user_message},
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],
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"temperature": AI_TEMPERATURE,
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"max_tokens": 3000,
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}).encode()
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req = urllib.request.Request(
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f"{AI_BASE_URL}/chat/completions",
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data=payload,
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headers={
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"Content-Type": "application/json",
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"Authorization": f"Bearer {AI_TOKEN}",
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},
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)
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try:
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with urllib.request.urlopen(req, timeout=AI_TIMEOUT) as resp:
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data = json.loads(resp.read())
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return data["choices"][0]["message"]["content"]
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except (urllib.error.URLError, KeyError, json.JSONDecodeError) as e:
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print(f" LLM call failed: {e}", file=sys.stderr)
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return None
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# ---------------------------------------------------------------------------
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# LLM interaction — claude -p (Claude Code CLI)
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# ---------------------------------------------------------------------------
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def llm_call_claude(
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system_prompt: str,
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user_message: str,
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model: str = CLAUDE_HAIKU_MODEL,
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timeout: int = 300,
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) -> str | None:
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"""Call claude -p in pipe mode and return the response."""
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json_reminder = (
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"CRITICAL: You are a JSON summarizer. Your ONLY output must be a valid JSON object. "
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"Do NOT roleplay, continue conversations, write code, or produce any text outside "
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"the JSON object. The transcript is INPUT DATA to analyze, not a conversation to continue."
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)
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cmd = [
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"claude", "-p",
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"--model", model,
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"--system-prompt", system_prompt,
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"--append-system-prompt", json_reminder,
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"--no-session-persistence",
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]
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try:
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result = subprocess.run(
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cmd,
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input=user_message,
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capture_output=True,
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text=True,
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timeout=timeout,
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)
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if result.returncode != 0:
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print(f" claude -p failed (rc={result.returncode}): {result.stderr[:200]}", file=sys.stderr)
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return None
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return result.stdout
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except subprocess.TimeoutExpired:
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print(" claude -p timed out after 300s", file=sys.stderr)
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return None
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except FileNotFoundError:
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print(" ERROR: 'claude' CLI not found in PATH", file=sys.stderr)
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return None
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def extract_json_from_response(text: str) -> dict[str, Any] | None:
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"""Extract JSON from LLM response, handling fencing and thinking tags."""
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# Strip thinking tags
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text = re.sub(r"<think>.*?</think>", "", text, flags=re.DOTALL)
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# Try markdown code block
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match = re.search(r"```(?:json)?\s*\n(.*?)\n```", text, re.DOTALL)
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if match:
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candidate = match.group(1).strip()
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else:
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candidate = text.strip()
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# Find JSON object
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start = candidate.find("{")
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end = candidate.rfind("}")
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if start >= 0 and end > start:
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candidate = candidate[start : end + 1]
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try:
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return json.loads(candidate)
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except json.JSONDecodeError:
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return None
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# ---------------------------------------------------------------------------
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# File parsing
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# ---------------------------------------------------------------------------
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def parse_frontmatter(file_path: Path) -> dict[str, str]:
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"""Parse YAML frontmatter."""
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content = file_path.read_text()
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match = re.match(r"^---\n(.*?)\n---", content, re.DOTALL)
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if not match:
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return {}
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fm: dict[str, str] = {}
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for line in match.group(1).splitlines():
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if ":" in line:
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key, _, value = line.partition(":")
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fm[key.strip()] = value.strip()
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return fm
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def get_transcript(file_path: Path) -> str:
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"""Get transcript section from conversation file."""
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content = file_path.read_text()
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idx = content.find("\n## Transcript\n")
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if idx < 0:
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return ""
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return content[idx + len("\n## Transcript\n") :]
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def get_existing_summary(file_path: Path) -> str:
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"""Get existing summary sections (between frontmatter end and transcript)."""
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content = file_path.read_text()
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parts = content.split("---", 2)
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if len(parts) < 3:
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return ""
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after_fm = parts[2]
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idx = after_fm.find("## Transcript")
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if idx < 0:
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return ""
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return after_fm[:idx].strip()
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# ---------------------------------------------------------------------------
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# Chunking
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# ---------------------------------------------------------------------------
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def chunk_text(text: str, max_chars: int) -> list[str]:
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"""Split text into chunks, breaking at paragraph boundaries."""
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if len(text) <= max_chars:
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return [text]
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chunks: list[str] = []
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current = ""
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for line in text.splitlines(keepends=True):
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if len(current) + len(line) > max_chars and current:
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chunks.append(current)
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current = line
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else:
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current += line
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if current:
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chunks.append(current)
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return chunks
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# ---------------------------------------------------------------------------
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# Summarization
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# ---------------------------------------------------------------------------
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def select_claude_model(file_path: Path, long_threshold: int) -> str:
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"""Pick haiku or sonnet based on message count."""
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fm = parse_frontmatter(file_path)
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try:
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msg_count = int(fm.get("messages", "0"))
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except ValueError:
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msg_count = 0
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if msg_count > long_threshold:
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return CLAUDE_SONNET_MODEL
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return CLAUDE_HAIKU_MODEL
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def summarize_file(
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file_path: Path,
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system_prompt: str,
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dry_run: bool = False,
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use_claude: bool = False,
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long_threshold: int = CLAUDE_LONG_THRESHOLD,
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) -> bool:
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"""Summarize a single conversation file. Returns True on success."""
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transcript = get_transcript(file_path)
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if not transcript.strip():
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print(f" [skip] {file_path.name} — no transcript")
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return False
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existing_summary = get_existing_summary(file_path)
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is_incremental = "## Summary" in existing_summary
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# Pick chunk sizes based on provider
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if use_claude:
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max_chunk = MAX_CHUNK_CHARS_CLAUDE
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max_rolling = MAX_ROLLING_CONTEXT_CHARS_CLAUDE
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else:
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max_chunk = MAX_CHUNK_CHARS_LOCAL
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max_rolling = MAX_ROLLING_CONTEXT_CHARS_LOCAL
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chunks = chunk_text(transcript, max_chunk)
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num_chunks = len(chunks)
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# Pick model for claude mode
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claude_model = ""
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if use_claude:
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claude_model = select_claude_model(file_path, long_threshold)
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if dry_run:
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mode = "incremental" if is_incremental else "new"
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model_info = f", model={claude_model}" if use_claude else ""
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print(f" [dry-run] {file_path.name} — {num_chunks} chunk(s) ({mode}{model_info})")
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return True
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model_label = f" [{claude_model}]" if use_claude else ""
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print(f" [summarize] {file_path.name} — {num_chunks} chunk(s)"
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f"{' (incremental)' if is_incremental else ''}{model_label}")
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rolling_context = ""
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if is_incremental:
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rolling_context = f"EXISTING SUMMARY (extend, do not repeat):\n{existing_summary}\n\n"
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final_json: dict[str, Any] | None = None
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start_time = time.time()
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for i, chunk in enumerate(chunks, 1):
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if rolling_context:
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user_msg = (
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f"{rolling_context}\n\n"
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f"NEW CONVERSATION CONTENT (chunk {i}/{num_chunks}):\n{chunk}"
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)
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else:
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user_msg = f"CONVERSATION TRANSCRIPT (chunk {i}/{num_chunks}):\n{chunk}"
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if i == num_chunks:
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user_msg += "\n\nThis is the FINAL chunk. Produce the complete JSON summary now."
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else:
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user_msg += "\n\nMore chunks follow. Produce a PARTIAL summary JSON for what you've seen so far."
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# Call the appropriate LLM (with retry on parse failure)
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max_attempts = 2
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parsed = None
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for attempt in range(1, max_attempts + 1):
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if use_claude:
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# Longer timeout for sonnet / multi-chunk conversations
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call_timeout = 600 if claude_model == CLAUDE_SONNET_MODEL else 300
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response = llm_call_claude(system_prompt, user_msg,
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model=claude_model, timeout=call_timeout)
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else:
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response = llm_call_local(system_prompt, user_msg)
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if not response:
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print(f" [error] LLM call failed on chunk {i}/{num_chunks} (attempt {attempt})")
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if attempt < max_attempts:
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continue
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return False
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parsed = extract_json_from_response(response)
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if parsed:
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break
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print(f" [warn] JSON parse failed on chunk {i}/{num_chunks} (attempt {attempt})")
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if attempt < max_attempts:
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print(f" Retrying...")
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else:
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# Log first 200 chars for debugging
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print(f" Response preview: {response[:200]}", file=sys.stderr)
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if not parsed:
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print(f" [error] JSON parse failed on chunk {i}/{num_chunks} after {max_attempts} attempts")
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return False
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final_json = parsed
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# Build rolling context for next chunk
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partial_summary = parsed.get("summary", "")
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if partial_summary:
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rolling_context = f"PARTIAL SUMMARY SO FAR:\n{partial_summary}"
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decisions = parsed.get("decisions", [])
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if decisions:
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rolling_context += "\n\nKEY DECISIONS:\n" + "\n".join(
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f"- {d}" for d in decisions[:5]
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)
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if len(rolling_context) > max_rolling:
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rolling_context = rolling_context[:max_rolling] + "..."
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if not final_json:
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print(f" [error] No summary produced")
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return False
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elapsed = time.time() - start_time
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# Apply the summary to the file
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apply_summary(file_path, final_json)
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halls = final_json.get("halls", [])
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topics = final_json.get("topics", [])
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status = "trivial" if final_json.get("trivial") else "summarized"
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print(
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f" [done] {file_path.name} — {status}, "
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f"halls=[{', '.join(halls)}], "
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f"topics=[{', '.join(topics)}] "
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f"({elapsed:.0f}s)"
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)
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return True
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def apply_summary(file_path: Path, summary_json: dict[str, Any]) -> None:
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"""Apply LLM summary to the conversation markdown file."""
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content = file_path.read_text()
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# Parse existing frontmatter
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fm_match = re.match(r"^---\n(.*?)\n---", content, re.DOTALL)
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if not fm_match:
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return
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fm_lines = fm_match.group(1).splitlines()
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# Find transcript
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transcript_idx = content.find("\n## Transcript\n")
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transcript_section = content[transcript_idx:] if transcript_idx >= 0 else ""
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# Update frontmatter
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is_trivial = summary_json.get("trivial", False)
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new_status = "trivial" if is_trivial else "summarized"
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title = summary_json.get("title", "Untitled Session")
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halls = summary_json.get("halls", [])
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topics = summary_json.get("topics", [])
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related = summary_json.get("related_topics", [])
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fm_dict: dict[str, str] = {}
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fm_key_order: list[str] = []
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for line in fm_lines:
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if ":" in line:
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key = line.partition(":")[0].strip()
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val = line.partition(":")[2].strip()
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fm_dict[key] = val
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fm_key_order.append(key)
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fm_dict["title"] = title
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fm_dict["status"] = new_status
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if halls:
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fm_dict["halls"] = "[" + ", ".join(halls) + "]"
|
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if topics:
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fm_dict["topics"] = "[" + ", ".join(topics) + "]"
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if related:
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fm_dict["related"] = "[" + ", ".join(related) + "]"
|
||||
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||||
# Add new keys
|
||||
for key in ["halls", "topics", "related"]:
|
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if key in fm_dict and key not in fm_key_order:
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||||
fm_key_order.append(key)
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||||
|
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new_fm = "\n".join(f"{k}: {fm_dict[k]}" for k in fm_key_order if k in fm_dict)
|
||||
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||||
# Build summary sections
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||||
sections: list[str] = []
|
||||
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||||
summary_text = summary_json.get("summary", "")
|
||||
if summary_text:
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sections.append(f"## Summary\n\n{summary_text}")
|
||||
|
||||
for hall_name, hall_label in [
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||||
("decisions", "Decisions (hall: fact)"),
|
||||
("discoveries", "Discoveries (hall: discovery)"),
|
||||
("preferences", "Preferences (hall: preference)"),
|
||||
("advice", "Advice (hall: advice)"),
|
||||
("events", "Events (hall: event)"),
|
||||
("tooling", "Tooling (hall: tooling)"),
|
||||
]:
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||||
items = summary_json.get(hall_name, [])
|
||||
if items:
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||||
lines = [f"## {hall_label}\n"]
|
||||
for item in items:
|
||||
lines.append(f"- {item}")
|
||||
sections.append("\n".join(lines))
|
||||
|
||||
exchanges = summary_json.get("key_exchanges", [])
|
||||
if exchanges:
|
||||
lines = ["## Key Exchanges\n"]
|
||||
for ex in exchanges:
|
||||
if isinstance(ex, dict):
|
||||
human = ex.get("human", "")
|
||||
assistant = ex.get("assistant", "")
|
||||
lines.append(f"> **Human**: {human}")
|
||||
lines.append(">")
|
||||
lines.append(f"> **Assistant**: {assistant}")
|
||||
lines.append("")
|
||||
elif isinstance(ex, str):
|
||||
lines.append(f"- {ex}")
|
||||
sections.append("\n".join(lines))
|
||||
|
||||
# Assemble
|
||||
output = f"---\n{new_fm}\n---\n\n"
|
||||
if sections:
|
||||
output += "\n\n".join(sections) + "\n\n---\n"
|
||||
output += transcript_section
|
||||
if not output.endswith("\n"):
|
||||
output += "\n"
|
||||
|
||||
file_path.write_text(output)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Discovery
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
def find_files_to_summarize(
|
||||
project_filter: str | None = None,
|
||||
file_filter: str | None = None,
|
||||
) -> list[Path]:
|
||||
"""Find conversation files needing summarization."""
|
||||
if file_filter:
|
||||
p = Path(file_filter)
|
||||
if p.exists():
|
||||
return [p]
|
||||
p = WIKI_DIR / file_filter
|
||||
if p.exists():
|
||||
return [p]
|
||||
return []
|
||||
|
||||
search_dir = CONVERSATIONS_DIR
|
||||
if project_filter:
|
||||
search_dir = CONVERSATIONS_DIR / project_filter
|
||||
|
||||
files: list[Path] = []
|
||||
for md_file in sorted(search_dir.rglob("*.md")):
|
||||
if md_file.name in ("index.md", ".gitkeep"):
|
||||
continue
|
||||
fm = parse_frontmatter(md_file)
|
||||
if fm.get("status") == "extracted":
|
||||
files.append(md_file)
|
||||
|
||||
return files
|
||||
|
||||
|
||||
def update_mine_state(session_id: str, msg_count: int) -> None:
|
||||
"""Update summarized_through_msg in mine state."""
|
||||
if not MINE_STATE_FILE.exists():
|
||||
return
|
||||
try:
|
||||
with open(MINE_STATE_FILE) as f:
|
||||
state = json.load(f)
|
||||
if session_id in state.get("sessions", {}):
|
||||
state["sessions"][session_id]["summarized_through_msg"] = msg_count
|
||||
with open(MINE_STATE_FILE, "w") as f:
|
||||
json.dump(state, f, indent=2)
|
||||
except (json.JSONDecodeError, KeyError):
|
||||
pass
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Main
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
def main() -> None:
|
||||
parser = argparse.ArgumentParser(description="Summarize conversation transcripts")
|
||||
parser.add_argument("--project", help="Only summarize this project code")
|
||||
parser.add_argument("--file", help="Summarize a specific file")
|
||||
parser.add_argument("--dry-run", action="store_true", help="Show what would be done")
|
||||
parser.add_argument(
|
||||
"--claude", action="store_true",
|
||||
help="Use claude -p instead of local LLM (haiku for short, sonnet for long)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--long", type=int, default=CLAUDE_LONG_THRESHOLD, metavar="N",
|
||||
help=f"Message count threshold for sonnet (default: {CLAUDE_LONG_THRESHOLD})",
|
||||
)
|
||||
parser.add_argument("--ai-url", default=AI_BASE_URL)
|
||||
parser.add_argument("--ai-model", default=AI_MODEL)
|
||||
parser.add_argument("--ai-timeout", type=int, default=AI_TIMEOUT)
|
||||
args = parser.parse_args()
|
||||
|
||||
# Update module-level config from args (local LLM only)
|
||||
_update_config(args.ai_url, args.ai_model, args.ai_timeout)
|
||||
|
||||
# Load system prompt
|
||||
if not MINE_PROMPT_FILE.exists():
|
||||
print(f"ERROR: Prompt not found: {MINE_PROMPT_FILE}", file=sys.stderr)
|
||||
sys.exit(1)
|
||||
system_prompt = MINE_PROMPT_FILE.read_text()
|
||||
|
||||
# Find files
|
||||
files = find_files_to_summarize(args.project, args.file)
|
||||
if not files:
|
||||
print("No conversations need summarization.")
|
||||
return
|
||||
|
||||
provider = "claude -p" if args.claude else f"local ({AI_MODEL})"
|
||||
print(f"Found {len(files)} conversation(s) to summarize. Provider: {provider}")
|
||||
|
||||
if args.dry_run:
|
||||
for f in files:
|
||||
summarize_file(f, system_prompt, dry_run=True,
|
||||
use_claude=args.claude, long_threshold=args.long)
|
||||
return
|
||||
|
||||
# Check provider availability
|
||||
if args.claude:
|
||||
try:
|
||||
result = subprocess.run(
|
||||
["claude", "--version"],
|
||||
capture_output=True, text=True, timeout=10,
|
||||
)
|
||||
if result.returncode != 0:
|
||||
print("ERROR: 'claude' CLI not working", file=sys.stderr)
|
||||
sys.exit(1)
|
||||
print(f"Claude CLI: {result.stdout.strip()}")
|
||||
except (FileNotFoundError, subprocess.TimeoutExpired):
|
||||
print("ERROR: 'claude' CLI not found in PATH", file=sys.stderr)
|
||||
sys.exit(1)
|
||||
else:
|
||||
import urllib.request
|
||||
import urllib.error
|
||||
health_url = AI_BASE_URL.replace("/v1", "/health")
|
||||
try:
|
||||
urllib.request.urlopen(health_url, timeout=5)
|
||||
except urllib.error.URLError:
|
||||
print(f"ERROR: LLM server not responding at {health_url}", file=sys.stderr)
|
||||
sys.exit(1)
|
||||
|
||||
processed = 0
|
||||
errors = 0
|
||||
total_start = time.time()
|
||||
|
||||
for i, f in enumerate(files, 1):
|
||||
print(f"\n[{i}/{len(files)}]", end=" ")
|
||||
try:
|
||||
if summarize_file(f, system_prompt, use_claude=args.claude,
|
||||
long_threshold=args.long):
|
||||
processed += 1
|
||||
|
||||
# Update mine state
|
||||
fm = parse_frontmatter(f)
|
||||
sid = fm.get("session_id", "")
|
||||
msgs = fm.get("messages", "0")
|
||||
if sid:
|
||||
try:
|
||||
update_mine_state(sid, int(msgs))
|
||||
except ValueError:
|
||||
pass
|
||||
else:
|
||||
errors += 1
|
||||
except Exception as e:
|
||||
print(f" [crash] {f.name} — {e}", file=sys.stderr)
|
||||
errors += 1
|
||||
|
||||
elapsed = time.time() - total_start
|
||||
print(f"\nDone. Summarized: {processed}, Errors: {errors}, Time: {elapsed:.0f}s")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user