From f83d38d7874d93e7e8f80395da2acc37bc07b078 Mon Sep 17 00:00:00 2001 From: Cole Medin Date: Mon, 6 Apr 2026 09:26:30 -0500 Subject: [PATCH] Claude Code Memory Compiler --- .claude/settings.json | 40 +++ .gitignore | 29 ++ AGENTS.md | 518 +++++++++++++++++++++++++++++++++ README.md | 52 ++++ hooks/pre-compact.py | 170 +++++++++++ hooks/session-end.py | 174 +++++++++++ hooks/session-start.py | 92 ++++++ pyproject.toml | 13 + scripts/compile.py | 224 +++++++++++++++ scripts/config.py | 33 +++ scripts/flush.py | 255 +++++++++++++++++ scripts/lint.py | 312 ++++++++++++++++++++ scripts/query.py | 138 +++++++++ scripts/utils.py | 133 +++++++++ uv.lock | 636 +++++++++++++++++++++++++++++++++++++++++ 15 files changed, 2819 insertions(+) create mode 100644 .claude/settings.json create mode 100644 .gitignore create mode 100644 AGENTS.md create mode 100644 README.md create mode 100644 hooks/pre-compact.py create mode 100644 hooks/session-end.py create mode 100644 hooks/session-start.py create mode 100644 pyproject.toml create mode 100644 scripts/compile.py create mode 100644 scripts/config.py create mode 100644 scripts/flush.py create mode 100644 scripts/lint.py create mode 100644 scripts/query.py create mode 100644 scripts/utils.py create mode 100644 uv.lock diff --git a/.claude/settings.json b/.claude/settings.json new file mode 100644 index 0000000..672746f --- /dev/null +++ b/.claude/settings.json @@ -0,0 +1,40 @@ +{ + "hooks": { + "SessionStart": [ + { + "matcher": "", + "hooks": [ + { + "type": "command", + "command": "uv run python hooks/session-start.py", + "timeout": 15 + } + ] + } + ], + "PreCompact": [ + { + "matcher": "", + "hooks": [ + { + "type": "command", + "command": "uv run python hooks/pre-compact.py", + "timeout": 10 + } + ] + } + ], + "SessionEnd": [ + { + "matcher": "", + "hooks": [ + { + "type": "command", + "command": "uv run python hooks/session-end.py", + "timeout": 10 + } + ] + } + ] + } +} diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..3ac1808 --- /dev/null +++ b/.gitignore @@ -0,0 +1,29 @@ +# Python +__pycache__/ +*.pyc +.venv/ + +# Runtime state (regenerated by scripts) +scripts/state.json +scripts/last-flush.json +scripts/flush.log +scripts/compile.log +scripts/session-flush-* +scripts/flush-context-* + +# Generated content (built up per-user by hooks and scripts) +daily/ +knowledge/ + +# Reports (regenerated by lint) +reports/ + +# uv lock (regenerated by uv sync) +scripts/uv.lock + +# Claude Code local settings +.claude/settings.local.json + +# OS files +.DS_Store +Thumbs.db diff --git a/AGENTS.md b/AGENTS.md new file mode 100644 index 0000000..1039e31 --- /dev/null +++ b/AGENTS.md @@ -0,0 +1,518 @@ +# AGENTS.md - Personal Knowledge Base Schema + +> Adapted from [Andrej Karpathy's LLM Knowledge Base](https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f) architecture. +> Instead of ingesting external articles, this system compiles knowledge from your own AI conversations. + +## The Compiler Analogy + +``` +daily/ = source code (your conversations - the raw material) +LLM = compiler (extracts and organizes knowledge) +knowledge/ = executable (structured, queryable knowledge base) +lint = test suite (health checks for consistency) +queries = runtime (using the knowledge) +``` + +You don't manually organize your knowledge. You have conversations, and the LLM handles the synthesis, cross-referencing, and maintenance. + +--- + +## Architecture + +### Layer 1: `daily/` - Conversation Logs (Immutable Source) + +Daily logs capture what happened in your AI coding sessions. These are the "raw sources" - append-only, never edited after the fact. + +``` +daily/ +├── 2026-04-01.md +├── 2026-04-02.md +├── ... +``` + +Each file follows this format: + +```markdown +# Daily Log: YYYY-MM-DD + +## Sessions + +### Session (HH:MM) - Brief Title + +**Context:** What the user was working on. + +**Key Exchanges:** +- User asked about X, assistant explained Y +- Decided to use Z approach because... +- Discovered that W doesn't work when... + +**Decisions Made:** +- Chose library X over Y because... +- Architecture: went with pattern Z + +**Lessons Learned:** +- Always do X before Y to avoid... +- The gotcha with Z is that... + +**Action Items:** +- [ ] Follow up on X +- [ ] Refactor Y when time permits +``` + +### Layer 2: `knowledge/` - Compiled Knowledge (LLM-Owned) + +The LLM owns this directory entirely. Humans read it but rarely edit it directly. + +``` +knowledge/ +├── index.md # Master catalog - every article with one-line summary +├── log.md # Append-only chronological build log +├── concepts/ # Atomic knowledge articles +├── connections/ # Cross-cutting insights linking 2+ concepts +└── qa/ # Filed query answers (compounding knowledge) +``` + +### Layer 3: This File (AGENTS.md) + +The schema that tells the LLM how to compile and maintain the knowledge base. This is the "compiler specification." + +--- + +## Structural Files + +### `knowledge/index.md` - Master Catalog + +A table listing every knowledge article. This is the primary retrieval mechanism - the LLM reads this FIRST when answering any query, then selects relevant articles to read in full. + +Format: + +```markdown +# Knowledge Base Index + +| Article | Summary | Compiled From | Updated | +|---------|---------|---------------|---------| +| [[concepts/supabase-auth]] | Row-level security patterns and JWT gotchas | daily/2026-04-02.md | 2026-04-02 | +| [[connections/auth-and-webhooks]] | Token verification patterns shared across Supabase auth and Stripe webhooks | daily/2026-04-02.md, daily/2026-04-04.md | 2026-04-04 | +``` + +### `knowledge/log.md` - Build Log + +Append-only chronological record of every compile, query, and lint operation. + +Format: + +```markdown +# Build Log + +## [2026-04-01T14:30:00] compile | Daily Log 2026-04-01 +- Source: daily/2026-04-01.md +- Articles created: [[concepts/nextjs-project-structure]], [[concepts/tailwind-setup]] +- Articles updated: (none) + +## [2026-04-02T09:00:00] query | "How do I handle auth redirects?" +- Consulted: [[concepts/supabase-auth]], [[concepts/nextjs-middleware]] +- Filed to: [[qa/auth-redirect-handling]] +``` + +--- + +## Article Formats + +### Concept Articles (`knowledge/concepts/`) + +One article per atomic piece of knowledge. These are facts, patterns, decisions, preferences, and lessons extracted from your conversations. + +```markdown +--- +title: "Concept Name" +aliases: [alternate-name, abbreviation] +tags: [domain, topic] +sources: + - "daily/2026-04-01.md" + - "daily/2026-04-03.md" +created: 2026-04-01 +updated: 2026-04-03 +--- + +# Concept Name + +[2-4 sentence core explanation] + +## Key Points + +- [Bullet points, each self-contained] + +## Details + +[Deeper explanation, encyclopedia-style paragraphs] + +## Related Concepts + +- [[concepts/related-concept]] - How it connects + +## Sources + +- [[daily/2026-04-01.md]] - Initial discovery during project setup +- [[daily/2026-04-03.md]] - Updated after debugging session +``` + +### Connection Articles (`knowledge/connections/`) + +Cross-cutting synthesis linking 2+ concepts. Created when a conversation reveals a non-obvious relationship. + +```markdown +--- +title: "Connection: X and Y" +connects: + - "concepts/concept-x" + - "concepts/concept-y" +sources: + - "daily/2026-04-04.md" +created: 2026-04-04 +updated: 2026-04-04 +--- + +# Connection: X and Y + +## The Connection + +[What links these concepts] + +## Key Insight + +[The non-obvious relationship discovered] + +## Evidence + +[Specific examples from conversations] + +## Related Concepts + +- [[concepts/concept-x]] +- [[concepts/concept-y]] +``` + +### Q&A Articles (`knowledge/qa/`) + +Filed answers from queries. Every complex question answered by the system can be permanently stored, making future queries smarter. + +```markdown +--- +title: "Q: Original Question" +question: "The exact question asked" +consulted: + - "concepts/article-1" + - "concepts/article-2" +filed: 2026-04-05 +--- + +# Q: Original Question + +## Answer + +[The synthesized answer with [[wikilinks]] to sources] + +## Sources Consulted + +- [[concepts/article-1]] - Relevant because... +- [[concepts/article-2]] - Provided context on... + +## Follow-Up Questions + +- What about edge case X? +- How does this change if Y? +``` + +--- + +## Core Operations + +### 1. Compile (daily/ -> knowledge/) + +When processing a daily log: + +1. Read the daily log file +2. Read `knowledge/index.md` to understand current knowledge state +3. Read existing articles that may need updating +4. For each piece of knowledge found in the log: + - If an existing concept article covers this topic: UPDATE it with new information, add the daily log as a source + - If it's a new topic: CREATE a new `concepts/` article +5. If the log reveals a non-obvious connection between 2+ existing concepts: CREATE a `connections/` article +6. UPDATE `knowledge/index.md` with new/modified entries +7. APPEND to `knowledge/log.md` + +**Important guidelines:** +- A single daily log may touch 3-10 knowledge articles +- Prefer updating existing articles over creating near-duplicates +- Use Obsidian-style `[[wikilinks]]` with full relative paths from knowledge/ +- Write in encyclopedia style - factual, concise, self-contained +- Every article must have YAML frontmatter +- Every article must link back to its source daily logs + +### 2. Query (Ask the Knowledge Base) + +1. Read `knowledge/index.md` (the master catalog) +2. Based on the question, identify 3-10 relevant articles from the index +3. Read those articles in full +4. Synthesize an answer with `[[wikilink]]` citations +5. If `--file-back` is specified: create a `knowledge/qa/` article and update index.md and log.md + +**Why this works without RAG:** At personal knowledge base scale (50-500 articles), the LLM reading a structured index outperforms cosine similarity. The LLM understands what the question is really asking and selects pages accordingly. Embeddings find similar words; the LLM finds relevant concepts. + +### 3. Lint (Health Checks) + +Seven checks, run periodically: + +1. **Broken links** - `[[wikilinks]]` pointing to non-existent articles +2. **Orphan pages** - Articles with zero inbound links from other articles +3. **Orphan sources** - Daily logs that haven't been compiled yet +4. **Stale articles** - Source daily log changed since article was last compiled +5. **Contradictions** - Conflicting claims across articles (requires LLM judgment) +6. **Missing backlinks** - A links to B but B doesn't link back to A +7. **Sparse articles** - Below 200 words, likely incomplete + +Output: a markdown report with severity levels (error, warning, suggestion). + +--- + +## Conventions + +- **Wikilinks:** Use Obsidian-style `[[path/to/article]]` without `.md` extension +- **Writing style:** Encyclopedia-style, factual, third-person where appropriate +- **Dates:** ISO 8601 (YYYY-MM-DD for dates, full ISO for timestamps in log.md) +- **File naming:** lowercase, hyphens for spaces (e.g., `supabase-row-level-security.md`) +- **Frontmatter:** Every article must have YAML frontmatter with at minimum: title, sources, created, updated +- **Sources:** Always link back to the daily log(s) that contributed to an article + +--- + +## Full Project Structure + +``` +llm-personal-kb/ +|-- .claude/ +| |-- settings.json # Hook configuration (auto-activates in Claude Code) +|-- .gitignore # Excludes runtime state, temp files, caches +|-- AGENTS.md # This file - schema + full technical reference +|-- README.md # Concise overview + quick start +|-- pyproject.toml # Dependencies (at root so hooks can find it) +|-- daily/ # "Source code" - conversation logs (immutable) +|-- knowledge/ # "Executable" - compiled knowledge (LLM-owned) +| |-- index.md # Master catalog - THE retrieval mechanism +| |-- log.md # Append-only build log +| |-- concepts/ # Atomic knowledge articles +| |-- connections/ # Cross-cutting insights linking 2+ concepts +| |-- qa/ # Filed query answers (compounding knowledge) +|-- scripts/ # CLI tools +| |-- compile.py # Compile daily logs -> knowledge articles +| |-- query.py # Ask questions (index-guided, no RAG) +| |-- lint.py # 7 health checks +| |-- flush.py # Extract memories from conversations (background) +| |-- config.py # Path constants +| |-- utils.py # Shared helpers +|-- hooks/ # Claude Code hooks +| |-- session-start.py # Injects knowledge into every session +| |-- session-end.py # Extracts conversation -> daily log +| |-- pre-compact.py # Safety net: captures context before compaction +|-- reports/ # Lint reports (gitignored) +``` + +--- + +## Hook System (Automatic Capture) + +Hooks are configured in `.claude/settings.json` and fire automatically when you use Claude Code in this project. + +### `.claude/settings.json` Format + +```json +{ + "hooks": { + "SessionStart": [{ "matcher": "", "hooks": [{ "type": "command", "command": "uv run python hooks/session-start.py", "timeout": 15 }] }], + "PreCompact": [{ "matcher": "", "hooks": [{ "type": "command", "command": "uv run python hooks/pre-compact.py", "timeout": 10 }] }], + "SessionEnd": [{ "matcher": "", "hooks": [{ "type": "command", "command": "uv run python hooks/session-end.py", "timeout": 10 }] }] + } +} +``` + +Commands use simple relative paths from the project root. Empty `matcher` catches all events. + +### Hook Details + +**`session-start.py`** (SessionStart) +- Pure local I/O, no API calls, runs in under 1 second +- Reads `knowledge/index.md` and the most recent daily log +- Outputs JSON to stdout: `{"hookSpecificOutput": {"hookEventName": "SessionStart", "additionalContext": "..."}}` +- Claude sees the knowledge base index at the start of every session +- Max context: 20,000 characters + +**`session-end.py`** (SessionEnd) +- Reads hook input from stdin (JSON with `session_id`, `transcript_path`, `cwd`) +- Copies the raw JSONL transcript to a temp file (no parsing in the hook - keeps it fast) +- Spawns `flush.py` as a fully detached background process +- Recursion guard: exits immediately if `CLAUDE_INVOKED_BY` env var is set + +**`pre-compact.py`** (PreCompact) +- Same architecture as session-end.py +- Fires before Claude Code auto-compacts the context window +- Guards against empty `transcript_path` (known Claude Code bug #13668) +- Critical for long sessions: captures context before summarization discards it + +**Why both PreCompact and SessionEnd?** Long-running sessions may trigger multiple auto-compactions before you close the session. Without PreCompact, intermediate context is lost to summarization before SessionEnd ever fires. + +### Background Flush Process (`flush.py`) + +Spawned by both hooks as a fully detached background process: +- **Windows:** `CREATE_NEW_PROCESS_GROUP | DETACHED_PROCESS` flags +- **Mac/Linux:** `start_new_session=True` + +This ensures flush.py survives after Claude Code's hook process exits. + +**What flush.py does:** +1. Sets `CLAUDE_INVOKED_BY=memory_flush` env var (prevents recursive hook firing) +2. Reads the pre-extracted conversation context from the temp `.md` file +3. Skips if context is empty or if same session was flushed within 60 seconds (deduplication) +4. Calls Claude Agent SDK (`query()` with `allowed_tools=[]`, `max_turns=2`) +5. Claude decides what's worth saving - returns structured bullet points or `FLUSH_OK` +6. Appends result to `daily/YYYY-MM-DD.md` +7. Cleans up temp context file +8. **End-of-day auto-compilation:** If it's past 6 PM local time (`COMPILE_AFTER_HOUR = 18`) and today's daily log has changed since its last compilation (hash comparison against `state.json`), spawns `compile.py` as another detached background process. This means compilation happens automatically once a day without needing a cron job or manual trigger. + +### JSONL Transcript Format + +Claude Code stores conversations as `.jsonl` files. Messages are nested under a `message` key: + +```python +entry = json.loads(line) +msg = entry.get("message", {}) +role = msg.get("role", "") # "user" or "assistant" +content = msg.get("content", "") # string or list of content blocks +``` + +Content can be a string or a list of blocks (`{"type": "text", "text": "..."}` dicts). + +--- + +## Script Details + +### compile.py - The Compiler + +Uses the Claude Agent SDK's async streaming `query()`: + +```python +async for message in query( + prompt=compile_prompt, + options=ClaudeAgentOptions( + cwd=str(ROOT_DIR), + system_prompt={"type": "preset", "preset": "claude_code"}, + allowed_tools=["Read", "Write", "Edit", "Glob", "Grep"], + permission_mode="acceptEdits", + max_turns=30, + ), +): +``` + +- Builds a prompt with: AGENTS.md schema, current index, all existing articles, and the daily log +- Claude reads the daily log, decides what concepts to extract, and writes files directly +- `permission_mode="acceptEdits"` auto-approves all file operations +- Incremental: tracks SHA-256 hashes of daily logs in `state.json`, skips unchanged files +- Cost: ~$0.45-0.65 per daily log (increases as KB grows) + +**CLI:** +```bash +uv run python scripts/compile.py # compile new/changed only +uv run python scripts/compile.py --all # force recompile everything +uv run python scripts/compile.py --file daily/2026-04-01.md +uv run python scripts/compile.py --dry-run +``` + +### query.py - Index-Guided Retrieval + +Loads the entire knowledge base into context (index + all articles). No RAG. + +At personal KB scale (50-500 articles), the LLM reading a structured index outperforms vector similarity. The LLM understands what you're really asking; cosine similarity just finds similar words. + +**CLI:** +```bash +uv run python scripts/query.py "What auth patterns do I use?" +uv run python scripts/query.py "What's my error handling strategy?" --file-back +``` + +With `--file-back`, creates a Q&A article in `knowledge/qa/` and updates the index and log. This is the compounding loop - every question makes the KB smarter. + +### lint.py - Health Checks + +Seven checks: + +| Check | Type | Catches | +|-------|------|---------| +| Broken links | Structural | `[[wikilinks]]` to non-existent articles | +| Orphan pages | Structural | Articles with zero inbound links | +| Orphan sources | Structural | Daily logs not yet compiled | +| Stale articles | Structural | Source logs changed since compilation | +| Missing backlinks | Structural | A links to B but B doesn't link back | +| Sparse articles | Structural | Under 200 words | +| Contradictions | LLM | Conflicting claims across articles | + +**CLI:** +```bash +uv run python scripts/lint.py # all checks +uv run python scripts/lint.py --structural-only # skip LLM check (free) +``` + +Reports saved to `reports/lint-YYYY-MM-DD.md`. + +--- + +## State Tracking + +`scripts/state.json` tracks: +- `ingested` - map of daily log filenames to SHA-256 hashes, compilation timestamps, and costs +- `query_count` - total queries run +- `last_lint` - timestamp of most recent lint +- `total_cost` - cumulative API cost + +`scripts/last-flush.json` tracks flush deduplication (session_id + timestamp). + +Both are gitignored and regenerated automatically. + +--- + +## Dependencies + +`pyproject.toml` (at project root): +- `claude-agent-sdk>=0.1.29` - Claude Agent SDK for LLM calls with tool use +- `python-dotenv>=1.0.0` - Environment variable management +- `tzdata>=2024.1` - Timezone data +- Python 3.12+, managed by [uv](https://docs.astral.sh/uv/) + +No API key needed - uses Claude Code's built-in credentials at `~/.claude/.credentials.json`. + +--- + +## Costs + +| Operation | Cost | +|-----------|------| +| Compile one daily log | $0.45-0.65 | +| Query (no file-back) | ~$0.15-0.25 | +| Query (with file-back) | ~$0.25-0.40 | +| Full lint (with contradictions) | ~$0.15-0.25 | +| Structural lint only | $0.00 | +| Memory flush (per session) | ~$0.02-0.05 | + +--- + +## Customization + +### Additional Article Types + +Add directories like `people/`, `projects/`, `tools/` to `knowledge/`. Define the article format in this file (AGENTS.md) and update `utils.py`'s `list_wiki_articles()` to include them. + +### Obsidian Integration + +The knowledge base is pure markdown with `[[wikilinks]]` - works natively in Obsidian. Point a vault at `knowledge/` for graph view, backlinks, and search. + +### Scaling Beyond Index-Guided Retrieval + +At ~2,000+ articles / ~2M+ tokens, the index becomes too large for the context window. At that point, add hybrid RAG (keyword + semantic search) as a retrieval layer before the LLM. See Karpathy's recommendation of `qmd` by Tobi Lutke for search at scale. diff --git a/README.md b/README.md new file mode 100644 index 0000000..055cf24 --- /dev/null +++ b/README.md @@ -0,0 +1,52 @@ +# LLM Personal Knowledge Base + +**Your AI conversations compile themselves into a searchable knowledge base.** + +Adapted from [Karpathy's LLM Knowledge Base](https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f) architecture, but instead of clipping web articles, the raw data is your own conversations with Claude Code. When a session ends (or auto-compacts mid-session), Claude Code hooks capture the conversation transcript and spawn a background process that uses the [Claude Agent SDK](https://github.com/anthropics/claude-agent-sdk) to extract the important stuff - decisions, lessons learned, patterns, gotchas - and appends it to a daily log. You then compile those daily logs into structured, cross-referenced knowledge articles organized by concept. Retrieval uses a simple index file instead of RAG - no vector database, no embeddings, just markdown. + +Anthropic has clarified that personal use of the Claude Agent SDK is covered under your existing Claude subscription (Max, Team, or Enterprise) - no separate API credits needed. Unlike OpenClaw, which requires API billing for its memory flush, this runs on your subscription. + +## Quick Start + +Tell your AI coding agent: + +> "Clone https://github.com/coleam00/llm-personal-kb into this project. Set up the Claude Code hooks so my conversations automatically get captured into daily logs, compiled into a knowledge base, and injected back into future sessions. Read the AGENTS.md for the full technical reference on how everything works." + +The agent will: +1. Clone the repo and run `uv sync` to install dependencies +2. Copy `.claude/settings.json` into your project (or merge the hooks into your existing settings) +3. The hooks activate automatically next time you open Claude Code + +From there, your conversations start accumulating. After 6 PM local time, the next session flush automatically triggers compilation of that day's logs into knowledge articles. You can also run `uv run python scripts/compile.py` manually at any time. + +## How It Works + +``` +Conversation -> SessionEnd/PreCompact hooks -> flush.py extracts knowledge + -> daily/YYYY-MM-DD.md -> compile.py -> knowledge/concepts/, connections/, qa/ + -> SessionStart hook injects index into next session -> cycle repeats +``` + +- **Hooks** capture conversations automatically (session end + pre-compaction safety net) +- **flush.py** calls the Claude Agent SDK to decide what's worth saving, and after 6 PM triggers end-of-day compilation automatically +- **compile.py** turns daily logs into organized concept articles with cross-references (triggered automatically or run manually) +- **query.py** answers questions using index-guided retrieval (no RAG needed at personal scale) +- **lint.py** runs 7 health checks (broken links, orphans, contradictions, staleness) + +## Key Commands + +```bash +uv run python scripts/compile.py # compile new daily logs +uv run python scripts/query.py "question" # ask the knowledge base +uv run python scripts/query.py "question" --file-back # ask + save answer back +uv run python scripts/lint.py # run health checks +uv run python scripts/lint.py --structural-only # free structural checks only +``` + +## Why No RAG? + +Karpathy's insight: at personal scale (50-500 articles), the LLM reading a structured `index.md` outperforms vector similarity. The LLM understands what you're really asking; cosine similarity just finds similar words. RAG becomes necessary at ~2,000+ articles when the index exceeds the context window. + +## Technical Reference + +See **[AGENTS.md](AGENTS.md)** for the complete technical reference: article formats, hook architecture, script internals, cross-platform details, costs, and customization options. AGENTS.md is designed to give an AI agent everything it needs to understand, modify, or rebuild the system. diff --git a/hooks/pre-compact.py b/hooks/pre-compact.py new file mode 100644 index 0000000..9b3b69d --- /dev/null +++ b/hooks/pre-compact.py @@ -0,0 +1,170 @@ +""" +PreCompact hook - captures conversation transcript before auto-compaction. + +When Claude Code's context window fills up, it auto-compacts (summarizes and +discards detail). This hook fires BEFORE that happens, extracting conversation +context and spawning flush.py to extract knowledge that would otherwise +be lost to summarization. + +The hook itself does NO API calls - only local file I/O for speed (<10s). +""" + +from __future__ import annotations + +import json +import logging +import os +import re +import subprocess +import sys +from datetime import datetime, timezone +from pathlib import Path + +# Recursion guard +if os.environ.get("CLAUDE_INVOKED_BY"): + sys.exit(0) + +ROOT = Path(__file__).resolve().parent.parent +SCRIPTS_DIR = ROOT / "scripts" +STATE_DIR = SCRIPTS_DIR + +logging.basicConfig( + filename=str(SCRIPTS_DIR / "flush.log"), + level=logging.INFO, + format="%(asctime)s %(levelname)s [pre-compact] %(message)s", + datefmt="%Y-%m-%d %H:%M:%S", +) + +MAX_TURNS = 30 +MAX_CONTEXT_CHARS = 15_000 +MIN_TURNS_TO_FLUSH = 5 + + +def extract_conversation_context(transcript_path: Path) -> tuple[str, int]: + """Read JSONL transcript and extract last ~N conversation turns as markdown.""" + turns: list[str] = [] + + with open(transcript_path, encoding="utf-8") as f: + for line in f: + line = line.strip() + if not line: + continue + try: + entry = json.loads(line) + except json.JSONDecodeError: + continue + + msg = entry.get("message", {}) + if isinstance(msg, dict): + role = msg.get("role", "") + content = msg.get("content", "") + else: + role = entry.get("role", "") + content = entry.get("content", "") + + if role not in ("user", "assistant"): + continue + + if isinstance(content, list): + text_parts = [] + for block in content: + if isinstance(block, dict) and block.get("type") == "text": + text_parts.append(block.get("text", "")) + elif isinstance(block, str): + text_parts.append(block) + content = "\n".join(text_parts) + + if isinstance(content, str) and content.strip(): + label = "User" if role == "user" else "Assistant" + turns.append(f"**{label}:** {content.strip()}\n") + + recent = turns[-MAX_TURNS:] + context = "\n".join(recent) + + if len(context) > MAX_CONTEXT_CHARS: + context = context[-MAX_CONTEXT_CHARS:] + boundary = context.find("\n**") + if boundary > 0: + context = context[boundary + 1 :] + + return context, len(recent) + + +def main() -> None: + # Read hook input from stdin + try: + raw_input = sys.stdin.read() + try: + hook_input: dict = json.loads(raw_input) + except json.JSONDecodeError: + fixed_input = re.sub(r'(? tuple[str, int]: + """Read JSONL transcript and extract last ~N conversation turns as markdown.""" + turns: list[str] = [] + + with open(transcript_path, encoding="utf-8") as f: + for line in f: + line = line.strip() + if not line: + continue + try: + entry = json.loads(line) + except json.JSONDecodeError: + continue + + msg = entry.get("message", {}) + if isinstance(msg, dict): + role = msg.get("role", "") + content = msg.get("content", "") + else: + role = entry.get("role", "") + content = entry.get("content", "") + + if role not in ("user", "assistant"): + continue + + if isinstance(content, list): + text_parts = [] + for block in content: + if isinstance(block, dict) and block.get("type") == "text": + text_parts.append(block.get("text", "")) + elif isinstance(block, str): + text_parts.append(block) + content = "\n".join(text_parts) + + if isinstance(content, str) and content.strip(): + label = "User" if role == "user" else "Assistant" + turns.append(f"**{label}:** {content.strip()}\n") + + recent = turns[-MAX_TURNS:] + context = "\n".join(recent) + + if len(context) > MAX_CONTEXT_CHARS: + context = context[-MAX_CONTEXT_CHARS:] + boundary = context.find("\n**") + if boundary > 0: + context = context[boundary + 1 :] + + return context, len(recent) + + +def main() -> None: + # Read hook input from stdin + # Claude Code on Windows may pass paths with unescaped backslashes + try: + raw_input = sys.stdin.read() + try: + hook_input: dict = json.loads(raw_input) + except json.JSONDecodeError: + fixed_input = re.sub(r'(? str: + """Read the most recent daily log (today or yesterday).""" + today = datetime.now(timezone.utc).astimezone() + + for offset in range(2): + date = today - timedelta(days=offset) + log_path = DAILY_DIR / f"{date.strftime('%Y-%m-%d')}.md" + if log_path.exists(): + lines = log_path.read_text(encoding="utf-8").splitlines() + # Return last N lines to keep context small + recent = lines[-MAX_LOG_LINES:] if len(lines) > MAX_LOG_LINES else lines + return "\n".join(recent) + + return "(no recent daily log)" + + +def build_context() -> str: + """Assemble the context to inject into the conversation.""" + parts = [] + + # Today's date + today = datetime.now(timezone.utc).astimezone() + parts.append(f"## Today\n{today.strftime('%A, %B %d, %Y')}") + + # Knowledge base index (the core retrieval mechanism) + if INDEX_FILE.exists(): + index_content = INDEX_FILE.read_text(encoding="utf-8") + parts.append(f"## Knowledge Base Index\n\n{index_content}") + else: + parts.append("## Knowledge Base Index\n\n(empty - no articles compiled yet)") + + # Recent daily log + recent_log = get_recent_log() + parts.append(f"## Recent Daily Log\n\n{recent_log}") + + context = "\n\n---\n\n".join(parts) + + # Truncate if too long + if len(context) > MAX_CONTEXT_CHARS: + context = context[:MAX_CONTEXT_CHARS] + "\n\n...(truncated)" + + return context + + +def main(): + context = build_context() + + output = { + "hookSpecificOutput": { + "hookEventName": "SessionStart", + "additionalContext": context, + } + } + + print(json.dumps(output)) + + +if __name__ == "__main__": + main() diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..1e701fb --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,13 @@ +[project] +name = "llm-personal-kb" +version = "0.1.0" +description = "Personal knowledge base compiled from AI conversations - inspired by Karpathy's LLM KB architecture" +requires-python = ">=3.12" +dependencies = [ + "claude-agent-sdk>=0.1.29", + "python-dotenv>=1.0.0", + "tzdata>=2024.1", +] + +[tool.ruff] +line-length = 100 diff --git a/scripts/compile.py b/scripts/compile.py new file mode 100644 index 0000000..43baad6 --- /dev/null +++ b/scripts/compile.py @@ -0,0 +1,224 @@ +""" +Compile daily conversation logs into structured knowledge articles. + +This is the "LLM compiler" - it reads daily logs (source code) and produces +organized knowledge articles (the executable). + +Usage: + uv run python compile.py # compile new/changed logs only + uv run python compile.py --all # force recompile everything + uv run python compile.py --file daily/2026-04-01.md # compile a specific log + uv run python compile.py --dry-run # show what would be compiled +""" + +from __future__ import annotations + +import argparse +import asyncio +import sys +from pathlib import Path + +from config import AGENTS_FILE, CONCEPTS_DIR, CONNECTIONS_DIR, DAILY_DIR, KNOWLEDGE_DIR, now_iso +from utils import ( + file_hash, + list_raw_files, + list_wiki_articles, + load_state, + read_wiki_index, + save_state, +) + +# ── Paths for the LLM to use ────────────────────────────────────────── +ROOT_DIR = Path(__file__).resolve().parent.parent + + +async def compile_daily_log(log_path: Path, state: dict) -> float: + """Compile a single daily log into knowledge articles. + + Returns the API cost of the compilation. + """ + from claude_agent_sdk import ( + AssistantMessage, + ClaudeAgentOptions, + ResultMessage, + TextBlock, + query, + ) + + log_content = log_path.read_text(encoding="utf-8") + schema = AGENTS_FILE.read_text(encoding="utf-8") + wiki_index = read_wiki_index() + + # Read existing articles for context + existing_articles_context = "" + existing = {} + for article_path in list_wiki_articles(): + rel = article_path.relative_to(KNOWLEDGE_DIR) + existing[str(rel)] = article_path.read_text(encoding="utf-8") + + if existing: + parts = [] + for rel_path, content in existing.items(): + parts.append(f"### {rel_path}\n```markdown\n{content}\n```") + existing_articles_context = "\n\n".join(parts) + + timestamp = now_iso() + + prompt = f"""You are a knowledge compiler. Your job is to read a daily conversation log +and extract knowledge into structured wiki articles. + +## Schema (AGENTS.md) + +{schema} + +## Current Wiki Index + +{wiki_index} + +## Existing Wiki Articles + +{existing_articles_context if existing_articles_context else "(No existing articles yet)"} + +## Daily Log to Compile + +**File:** {log_path.name} + +{log_content} + +## Your Task + +Read the daily log above and compile it into wiki articles following the schema exactly. + +### Rules: + +1. **Extract key concepts** - Identify 3-7 distinct concepts worth their own article +2. **Create concept articles** in `knowledge/concepts/` - One .md file per concept + - Use the exact article format from AGENTS.md (YAML frontmatter + sections) + - Include `sources:` in frontmatter pointing to the daily log file + - Use `[[concepts/slug]]` wikilinks to link to related concepts + - Write in encyclopedia style - neutral, comprehensive +3. **Create connection articles** in `knowledge/connections/` if this log reveals non-obvious + relationships between 2+ existing concepts +4. **Update existing articles** if this log adds new information to concepts already in the wiki + - Read the existing article, add the new information, add the source to frontmatter +5. **Update knowledge/index.md** - Add new entries to the table + - Each entry: `| [[path/slug]] | One-line summary | source-file | {timestamp[:10]} |` +6. **Append to knowledge/log.md** - Add a timestamped entry: + ``` + ## [{timestamp}] compile | {log_path.name} + - Source: daily/{log_path.name} + - Articles created: [[concepts/x]], [[concepts/y]] + - Articles updated: [[concepts/z]] (if any) + ``` + +### File paths: +- Write concept articles to: {CONCEPTS_DIR} +- Write connection articles to: {CONNECTIONS_DIR} +- Update index at: {KNOWLEDGE_DIR / 'index.md'} +- Append log at: {KNOWLEDGE_DIR / 'log.md'} + +### Quality standards: +- Every article must have complete YAML frontmatter +- Every article must link to at least 2 other articles via [[wikilinks]] +- Key Points section should have 3-5 bullet points +- Details section should have 2+ paragraphs +- Related Concepts section should have 2+ entries +- Sources section should cite the daily log with specific claims extracted +""" + + cost = 0.0 + + try: + async for message in query( + prompt=prompt, + options=ClaudeAgentOptions( + cwd=str(ROOT_DIR), + system_prompt={"type": "preset", "preset": "claude_code"}, + allowed_tools=["Read", "Write", "Edit", "Glob", "Grep"], + permission_mode="acceptEdits", + max_turns=30, + ), + ): + if isinstance(message, AssistantMessage): + for block in message.content: + if isinstance(block, TextBlock): + pass # compilation output - LLM writes files directly + elif isinstance(message, ResultMessage): + cost = message.total_cost_usd or 0.0 + print(f" Cost: ${cost:.4f}") + except Exception as e: + print(f" Error: {e}") + return 0.0 + + # Update state + rel_path = log_path.name + state.setdefault("ingested", {})[rel_path] = { + "hash": file_hash(log_path), + "compiled_at": now_iso(), + "cost_usd": cost, + } + state["total_cost"] = state.get("total_cost", 0.0) + cost + save_state(state) + + return cost + + +def main(): + parser = argparse.ArgumentParser(description="Compile daily logs into knowledge articles") + parser.add_argument("--all", action="store_true", help="Force recompile all logs") + parser.add_argument("--file", type=str, help="Compile a specific daily log file") + parser.add_argument("--dry-run", action="store_true", help="Show what would be compiled") + args = parser.parse_args() + + state = load_state() + + # Determine which files to compile + if args.file: + target = Path(args.file) + if not target.is_absolute(): + target = DAILY_DIR / target.name + if not target.exists(): + # Try resolving relative to project root + target = ROOT_DIR / args.file + if not target.exists(): + print(f"Error: {args.file} not found") + sys.exit(1) + to_compile = [target] + else: + all_logs = list_raw_files() + if args.all: + to_compile = all_logs + else: + to_compile = [] + for log_path in all_logs: + rel = log_path.name + prev = state.get("ingested", {}).get(rel, {}) + if not prev or prev.get("hash") != file_hash(log_path): + to_compile.append(log_path) + + if not to_compile: + print("Nothing to compile - all daily logs are up to date.") + return + + print(f"{'[DRY RUN] ' if args.dry_run else ''}Files to compile ({len(to_compile)}):") + for f in to_compile: + print(f" - {f.name}") + + if args.dry_run: + return + + # Compile each file sequentially + total_cost = 0.0 + for i, log_path in enumerate(to_compile, 1): + print(f"\n[{i}/{len(to_compile)}] Compiling {log_path.name}...") + cost = asyncio.run(compile_daily_log(log_path, state)) + total_cost += cost + print(f" Done.") + + articles = list_wiki_articles() + print(f"\nCompilation complete. Total cost: ${total_cost:.2f}") + print(f"Knowledge base: {len(articles)} articles") + + +if __name__ == "__main__": + main() diff --git a/scripts/config.py b/scripts/config.py new file mode 100644 index 0000000..aa54fea --- /dev/null +++ b/scripts/config.py @@ -0,0 +1,33 @@ +"""Path constants and configuration for the personal knowledge base.""" + +from pathlib import Path +from datetime import datetime, timezone + +# ── Paths ────────────────────────────────────────────────────────────── +ROOT_DIR = Path(__file__).resolve().parent.parent +DAILY_DIR = ROOT_DIR / "daily" +KNOWLEDGE_DIR = ROOT_DIR / "knowledge" +CONCEPTS_DIR = KNOWLEDGE_DIR / "concepts" +CONNECTIONS_DIR = KNOWLEDGE_DIR / "connections" +QA_DIR = KNOWLEDGE_DIR / "qa" +REPORTS_DIR = ROOT_DIR / "reports" +SCRIPTS_DIR = ROOT_DIR / "scripts" +HOOKS_DIR = ROOT_DIR / "hooks" +AGENTS_FILE = ROOT_DIR / "AGENTS.md" + +INDEX_FILE = KNOWLEDGE_DIR / "index.md" +LOG_FILE = KNOWLEDGE_DIR / "log.md" +STATE_FILE = SCRIPTS_DIR / "state.json" + +# ── Timezone ─────────────────────────────────────────────────────────── +TIMEZONE = "America/Chicago" + + +def now_iso() -> str: + """Current time in ISO 8601 format.""" + return datetime.now(timezone.utc).astimezone().isoformat(timespec="seconds") + + +def today_iso() -> str: + """Current date in ISO 8601 format.""" + return datetime.now(timezone.utc).astimezone().strftime("%Y-%m-%d") diff --git a/scripts/flush.py b/scripts/flush.py new file mode 100644 index 0000000..3cb25c4 --- /dev/null +++ b/scripts/flush.py @@ -0,0 +1,255 @@ +""" +Memory flush agent - extracts important knowledge from conversation context. + +Spawned by session-end.py or pre-compact.py as a background process. Reads +pre-extracted conversation context from a .md file, uses the Claude Agent SDK +to decide what's worth saving, and appends the result to today's daily log. + +Usage: + uv run python flush.py +""" + +from __future__ import annotations + +# Recursion prevention: set this BEFORE any imports that might trigger Claude +import os +os.environ["CLAUDE_INVOKED_BY"] = "memory_flush" + +import asyncio +import json +import logging +import sys +import time +from datetime import datetime, timezone +from pathlib import Path + +ROOT = Path(__file__).resolve().parent.parent +DAILY_DIR = ROOT / "daily" +SCRIPTS_DIR = ROOT / "scripts" +STATE_FILE = SCRIPTS_DIR / "last-flush.json" +LOG_FILE = SCRIPTS_DIR / "flush.log" + +# Set up file-based logging so we can verify the background process ran. +# The parent process sends stdout/stderr to DEVNULL (to avoid the inherited +# file handle bug on Windows), so this is our only observability channel. +logging.basicConfig( + filename=str(LOG_FILE), + level=logging.INFO, + format="%(asctime)s %(levelname)s %(message)s", + datefmt="%Y-%m-%d %H:%M:%S", +) + + +def load_flush_state() -> dict: + if STATE_FILE.exists(): + try: + return json.loads(STATE_FILE.read_text(encoding="utf-8")) + except (json.JSONDecodeError, OSError): + pass + return {} + + +def save_flush_state(state: dict) -> None: + STATE_FILE.write_text(json.dumps(state), encoding="utf-8") + + +def append_to_daily_log(content: str, section: str = "Session") -> None: + """Append content to today's daily log.""" + today = datetime.now(timezone.utc).astimezone() + log_path = DAILY_DIR / f"{today.strftime('%Y-%m-%d')}.md" + + if not log_path.exists(): + DAILY_DIR.mkdir(parents=True, exist_ok=True) + log_path.write_text( + f"# Daily Log: {today.strftime('%Y-%m-%d')}\n\n## Sessions\n\n## Memory Maintenance\n\n", + encoding="utf-8", + ) + + time_str = today.strftime("%H:%M") + entry = f"### {section} ({time_str})\n\n{content}\n\n" + + with open(log_path, "a", encoding="utf-8") as f: + f.write(entry) + + +async def run_flush(context: str) -> str: + """Use Claude Agent SDK to extract important knowledge from conversation context.""" + from claude_agent_sdk import ( + AssistantMessage, + ClaudeAgentOptions, + ResultMessage, + TextBlock, + query, + ) + + prompt = f"""Review the conversation context below and respond with a concise summary +of important items that should be preserved in the daily log. +Do NOT use any tools — just return plain text. + +Format your response as a structured daily log entry with these sections: + +**Context:** [One line about what the user was working on] + +**Key Exchanges:** +- [Important Q&A or discussions] + +**Decisions Made:** +- [Any decisions with rationale] + +**Lessons Learned:** +- [Gotchas, patterns, or insights discovered] + +**Action Items:** +- [Follow-ups or TODOs mentioned] + +Skip anything that is: +- Routine tool calls or file reads +- Content that's trivial or obvious +- Trivial back-and-forth or clarification exchanges + +Only include sections that have actual content. If nothing is worth saving, +respond with exactly: FLUSH_OK + +## Conversation Context + +{context}""" + + response = "" + + try: + async for message in query( + prompt=prompt, + options=ClaudeAgentOptions( + cwd=str(ROOT), + allowed_tools=[], + max_turns=2, + ), + ): + if isinstance(message, AssistantMessage): + for block in message.content: + if isinstance(block, TextBlock): + response += block.text + elif isinstance(message, ResultMessage): + pass + except Exception as e: + import traceback + logging.error("Agent SDK error: %s\n%s", e, traceback.format_exc()) + response = f"FLUSH_ERROR: {type(e).__name__}: {e}" + + return response + + +COMPILE_AFTER_HOUR = 18 # 6 PM local time + + +def maybe_trigger_compilation() -> None: + """If it's past the compile hour and today's log hasn't been compiled, run compile.py.""" + import subprocess as _sp + + now = datetime.now(timezone.utc).astimezone() + if now.hour < COMPILE_AFTER_HOUR: + return + + # Check if today's log has already been compiled + today_log = f"{now.strftime('%Y-%m-%d')}.md" + compile_state_file = SCRIPTS_DIR / "state.json" + if compile_state_file.exists(): + try: + compile_state = json.loads(compile_state_file.read_text(encoding="utf-8")) + ingested = compile_state.get("ingested", {}) + if today_log in ingested: + # Already compiled today - check if the log has changed since + from hashlib import sha256 + log_path = DAILY_DIR / today_log + if log_path.exists(): + current_hash = sha256(log_path.read_bytes()).hexdigest()[:16] + if ingested[today_log].get("hash") == current_hash: + return # log unchanged since last compile + except (json.JSONDecodeError, OSError): + pass + + compile_script = SCRIPTS_DIR / "compile.py" + if not compile_script.exists(): + return + + logging.info("End-of-day compilation triggered (after %d:00)", COMPILE_AFTER_HOUR) + + cmd = ["uv", "run", "--directory", str(ROOT), "python", str(compile_script)] + + kwargs: dict = {} + if sys.platform == "win32": + kwargs["creationflags"] = _sp.CREATE_NEW_PROCESS_GROUP | _sp.DETACHED_PROCESS + else: + kwargs["start_new_session"] = True + + try: + log_handle = open(str(SCRIPTS_DIR / "compile.log"), "a") + _sp.Popen(cmd, stdout=log_handle, stderr=_sp.STDOUT, cwd=str(ROOT), **kwargs) + except Exception as e: + logging.error("Failed to spawn compile.py: %s", e) + + +def main(): + if len(sys.argv) < 3: + logging.error("Usage: %s ", sys.argv[0]) + sys.exit(1) + + context_file = Path(sys.argv[1]) + session_id = sys.argv[2] + + logging.info("flush.py started for session %s, context: %s", session_id, context_file) + + if not context_file.exists(): + logging.error("Context file not found: %s", context_file) + return + + # Deduplication: skip if same session was flushed within 60 seconds + state = load_flush_state() + if ( + state.get("session_id") == session_id + and time.time() - state.get("timestamp", 0) < 60 + ): + logging.info("Skipping duplicate flush for session %s", session_id) + context_file.unlink(missing_ok=True) + return + + # Read pre-extracted context + context = context_file.read_text(encoding="utf-8").strip() + if not context: + logging.info("Context file is empty, skipping") + context_file.unlink(missing_ok=True) + return + + logging.info("Flushing session %s: %d chars", session_id, len(context)) + + # Run the LLM extraction + response = asyncio.run(run_flush(context)) + + # Append to daily log + if "FLUSH_OK" in response: + logging.info("Result: FLUSH_OK") + append_to_daily_log( + "FLUSH_OK - Nothing worth saving from this session", "Memory Flush" + ) + elif "FLUSH_ERROR" in response: + logging.error("Result: %s", response) + append_to_daily_log(response, "Memory Flush") + else: + logging.info("Result: saved to daily log (%d chars)", len(response)) + append_to_daily_log(response, "Session") + + # Update dedup state + save_flush_state({"session_id": session_id, "timestamp": time.time()}) + + # Clean up context file + context_file.unlink(missing_ok=True) + + # End-of-day auto-compilation: if it's past the compile hour and today's + # log hasn't been compiled yet, trigger compile.py in the background. + maybe_trigger_compilation() + + logging.info("Flush complete for session %s", session_id) + + +if __name__ == "__main__": + main() diff --git a/scripts/lint.py b/scripts/lint.py new file mode 100644 index 0000000..9079eb1 --- /dev/null +++ b/scripts/lint.py @@ -0,0 +1,312 @@ +""" +Lint the knowledge base for structural and semantic health. + +Runs 7 checks: broken links, orphan pages, orphan sources, stale articles, +contradictions (LLM), missing backlinks, and sparse articles. + +Usage: + uv run python lint.py # all checks + uv run python lint.py --structural-only # skip LLM checks (faster, cheaper) +""" + +from __future__ import annotations + +import argparse +import asyncio +from pathlib import Path + +from config import KNOWLEDGE_DIR, REPORTS_DIR, now_iso, today_iso +from utils import ( + count_inbound_links, + extract_wikilinks, + file_hash, + get_article_word_count, + list_raw_files, + list_wiki_articles, + load_state, + read_all_wiki_content, + save_state, + wiki_article_exists, +) + +ROOT_DIR = Path(__file__).resolve().parent.parent + + +def check_broken_links() -> list[dict]: + """Check for [[wikilinks]] that point to non-existent articles.""" + issues = [] + for article in list_wiki_articles(): + content = article.read_text(encoding="utf-8") + rel = article.relative_to(KNOWLEDGE_DIR) + for link in extract_wikilinks(content): + if link.startswith("daily/"): + continue # daily log references are valid + if not wiki_article_exists(link): + issues.append({ + "severity": "error", + "check": "broken_link", + "file": str(rel), + "detail": f"Broken link: [[{link}]] - target does not exist", + }) + return issues + + +def check_orphan_pages() -> list[dict]: + """Check for articles with zero inbound links.""" + issues = [] + for article in list_wiki_articles(): + rel = article.relative_to(KNOWLEDGE_DIR) + link_target = str(rel).replace(".md", "").replace("\\", "/") + inbound = count_inbound_links(link_target) + if inbound == 0: + issues.append({ + "severity": "warning", + "check": "orphan_page", + "file": str(rel), + "detail": f"Orphan page: no other articles link to [[{link_target}]]", + }) + return issues + + +def check_orphan_sources() -> list[dict]: + """Check for daily logs that haven't been compiled yet.""" + state = load_state() + ingested = state.get("ingested", {}) + issues = [] + for log_path in list_raw_files(): + if log_path.name not in ingested: + issues.append({ + "severity": "warning", + "check": "orphan_source", + "file": f"daily/{log_path.name}", + "detail": f"Uncompiled daily log: {log_path.name} has not been ingested", + }) + return issues + + +def check_stale_articles() -> list[dict]: + """Check if source daily logs have changed since compilation.""" + state = load_state() + ingested = state.get("ingested", {}) + issues = [] + for log_path in list_raw_files(): + rel = log_path.name + if rel in ingested: + stored_hash = ingested[rel].get("hash", "") + current_hash = file_hash(log_path) + if stored_hash != current_hash: + issues.append({ + "severity": "warning", + "check": "stale_article", + "file": f"daily/{rel}", + "detail": f"Stale: {rel} has changed since last compilation", + }) + return issues + + +def check_missing_backlinks() -> list[dict]: + """Check for asymmetric links: A links to B but B doesn't link to A.""" + issues = [] + for article in list_wiki_articles(): + content = article.read_text(encoding="utf-8") + rel = article.relative_to(KNOWLEDGE_DIR) + source_link = str(rel).replace(".md", "").replace("\\", "/") + + for link in extract_wikilinks(content): + if link.startswith("daily/"): + continue + target_path = KNOWLEDGE_DIR / f"{link}.md" + if target_path.exists(): + target_content = target_path.read_text(encoding="utf-8") + if f"[[{source_link}]]" not in target_content: + issues.append({ + "severity": "suggestion", + "check": "missing_backlink", + "file": str(rel), + "detail": f"[[{source_link}]] links to [[{link}]] but not vice versa", + "auto_fixable": True, + }) + return issues + + +def check_sparse_articles() -> list[dict]: + """Check for articles with fewer than 200 words.""" + issues = [] + for article in list_wiki_articles(): + word_count = get_article_word_count(article) + if word_count < 200: + rel = article.relative_to(KNOWLEDGE_DIR) + issues.append({ + "severity": "suggestion", + "check": "sparse_article", + "file": str(rel), + "detail": f"Sparse article: {word_count} words (minimum recommended: 200)", + }) + return issues + + +async def check_contradictions() -> list[dict]: + """Use LLM to detect contradictions across articles.""" + from claude_agent_sdk import ( + AssistantMessage, + ClaudeAgentOptions, + ResultMessage, + TextBlock, + query, + ) + + wiki_content = read_all_wiki_content() + + prompt = f"""Review this knowledge base for contradictions, inconsistencies, or +conflicting claims across articles. + +## Knowledge Base + +{wiki_content} + +## Instructions + +Look for: +- Direct contradictions (article A says X, article B says not-X) +- Inconsistent recommendations (different articles recommend conflicting approaches) +- Outdated information that conflicts with newer entries + +For each issue found, output EXACTLY one line in this format: +CONTRADICTION: [file1] vs [file2] - description of the conflict +INCONSISTENCY: [file] - description of the inconsistency + +If no issues found, output exactly: NO_ISSUES + +Do NOT output anything else - no preamble, no explanation, just the formatted lines.""" + + response = "" + try: + async for message in query( + prompt=prompt, + options=ClaudeAgentOptions( + cwd=str(ROOT_DIR), + allowed_tools=[], + max_turns=2, + ), + ): + if isinstance(message, AssistantMessage): + for block in message.content: + if isinstance(block, TextBlock): + response += block.text + except Exception as e: + return [{"severity": "error", "check": "contradiction", "file": "(system)", "detail": f"LLM check failed: {e}"}] + + issues = [] + if "NO_ISSUES" not in response: + for line in response.strip().split("\n"): + line = line.strip() + if line.startswith("CONTRADICTION:") or line.startswith("INCONSISTENCY:"): + issues.append({ + "severity": "warning", + "check": "contradiction", + "file": "(cross-article)", + "detail": line, + }) + + return issues + + +def generate_report(all_issues: list[dict]) -> str: + """Generate a markdown lint report.""" + errors = [i for i in all_issues if i["severity"] == "error"] + warnings = [i for i in all_issues if i["severity"] == "warning"] + suggestions = [i for i in all_issues if i["severity"] == "suggestion"] + + lines = [ + f"# Lint Report - {today_iso()}", + "", + f"**Total issues:** {len(all_issues)}", + f"- Errors: {len(errors)}", + f"- Warnings: {len(warnings)}", + f"- Suggestions: {len(suggestions)}", + "", + ] + + for severity, issues, marker in [ + ("Errors", errors, "x"), + ("Warnings", warnings, "!"), + ("Suggestions", suggestions, "?"), + ]: + if issues: + lines.append(f"## {severity}") + lines.append("") + for issue in issues: + fixable = " (auto-fixable)" if issue.get("auto_fixable") else "" + lines.append(f"- **[{marker}]** `{issue['file']}` - {issue['detail']}{fixable}") + lines.append("") + + if not all_issues: + lines.append("All checks passed. Knowledge base is healthy.") + lines.append("") + + return "\n".join(lines) + + +def main(): + parser = argparse.ArgumentParser(description="Lint the knowledge base") + parser.add_argument( + "--structural-only", + action="store_true", + help="Skip LLM-based checks (contradictions) - faster and free", + ) + args = parser.parse_args() + + print("Running knowledge base lint checks...") + all_issues: list[dict] = [] + + # Structural checks (free, instant) + checks = [ + ("Broken links", check_broken_links), + ("Orphan pages", check_orphan_pages), + ("Orphan sources", check_orphan_sources), + ("Stale articles", check_stale_articles), + ("Missing backlinks", check_missing_backlinks), + ("Sparse articles", check_sparse_articles), + ] + + for name, check_fn in checks: + print(f" Checking: {name}...") + issues = check_fn() + all_issues.extend(issues) + print(f" Found {len(issues)} issue(s)") + + # LLM check (costs money) + if not args.structural_only: + print(" Checking: Contradictions (LLM)...") + issues = asyncio.run(check_contradictions()) + all_issues.extend(issues) + print(f" Found {len(issues)} issue(s)") + else: + print(" Skipping: Contradictions (--structural-only)") + + # Generate and save report + report = generate_report(all_issues) + REPORTS_DIR.mkdir(parents=True, exist_ok=True) + report_path = REPORTS_DIR / f"lint-{today_iso()}.md" + report_path.write_text(report, encoding="utf-8") + print(f"\nReport saved to: {report_path}") + + # Update state + state = load_state() + state["last_lint"] = now_iso() + save_state(state) + + # Summary + errors = sum(1 for i in all_issues if i["severity"] == "error") + warnings = sum(1 for i in all_issues if i["severity"] == "warning") + suggestions = sum(1 for i in all_issues if i["severity"] == "suggestion") + print(f"\nResults: {errors} errors, {warnings} warnings, {suggestions} suggestions") + + if errors > 0: + print("\nErrors found - knowledge base needs attention!") + return 1 + return 0 + + +if __name__ == "__main__": + exit(main()) diff --git a/scripts/query.py b/scripts/query.py new file mode 100644 index 0000000..fe82ab6 --- /dev/null +++ b/scripts/query.py @@ -0,0 +1,138 @@ +""" +Query the knowledge base using index-guided retrieval (no RAG). + +The LLM reads the index, picks relevant articles, and synthesizes an answer. +No vector database, no embeddings, no chunking - just structured markdown +and an index the LLM can reason over. + +Usage: + uv run python query.py "How should I handle auth redirects?" + uv run python query.py "What patterns do I use for API design?" --file-back +""" + +from __future__ import annotations + +import argparse +import asyncio +from pathlib import Path + +from config import KNOWLEDGE_DIR, QA_DIR, now_iso +from utils import load_state, read_all_wiki_content, save_state + +ROOT_DIR = Path(__file__).resolve().parent.parent + + +async def run_query(question: str, file_back: bool = False) -> str: + """Query the knowledge base and optionally file the answer back.""" + from claude_agent_sdk import ( + AssistantMessage, + ClaudeAgentOptions, + ResultMessage, + TextBlock, + query, + ) + + wiki_content = read_all_wiki_content() + + tools = ["Read", "Glob", "Grep"] + if file_back: + tools.extend(["Write", "Edit"]) + + file_back_instructions = "" + if file_back: + timestamp = now_iso() + file_back_instructions = f""" + +## File Back Instructions + +After answering, do the following: +1. Create a Q&A article at {QA_DIR}/ with the filename being a slugified version + of the question (e.g., knowledge/qa/how-to-handle-auth-redirects.md) +2. Use the Q&A article format from the schema (frontmatter with title, question, + consulted articles, filed date) +3. Update {KNOWLEDGE_DIR / 'index.md'} with a new row for this Q&A article +4. Append to {KNOWLEDGE_DIR / 'log.md'}: + ## [{timestamp}] query (filed) | question summary + - Question: {question} + - Consulted: [[list of articles read]] + - Filed to: [[qa/article-name]] +""" + + prompt = f"""You are a knowledge base query engine. Answer the user's question by +consulting the knowledge base below. + +## How to Answer + +1. Read the INDEX section first - it lists every article with a one-line summary +2. Identify 3-10 articles that are relevant to the question +3. Read those articles carefully (they're included below) +4. Synthesize a clear, thorough answer +5. Cite your sources using [[wikilinks]] (e.g., [[concepts/supabase-auth]]) +6. If the knowledge base doesn't contain relevant information, say so honestly + +## Knowledge Base + +{wiki_content} + +## Question + +{question} +{file_back_instructions}""" + + answer = "" + cost = 0.0 + + try: + async for message in query( + prompt=prompt, + options=ClaudeAgentOptions( + cwd=str(ROOT_DIR), + system_prompt={"type": "preset", "preset": "claude_code"}, + allowed_tools=tools, + permission_mode="acceptEdits", + max_turns=15, + ), + ): + if isinstance(message, AssistantMessage): + for block in message.content: + if isinstance(block, TextBlock): + answer += block.text + elif isinstance(message, ResultMessage): + cost = message.total_cost_usd or 0.0 + except Exception as e: + answer = f"Error querying knowledge base: {e}" + + # Update state + state = load_state() + state["query_count"] = state.get("query_count", 0) + 1 + state["total_cost"] = state.get("total_cost", 0.0) + cost + save_state(state) + + return answer + + +def main(): + parser = argparse.ArgumentParser(description="Query the personal knowledge base") + parser.add_argument("question", help="The question to ask") + parser.add_argument( + "--file-back", + action="store_true", + help="File the answer back into the knowledge base as a Q&A article", + ) + args = parser.parse_args() + + print(f"Question: {args.question}") + print(f"File back: {'yes' if args.file_back else 'no'}") + print("-" * 60) + + answer = asyncio.run(run_query(args.question, file_back=args.file_back)) + print(answer) + + if args.file_back: + print("\n" + "-" * 60) + qa_count = len(list(QA_DIR.glob("*.md"))) if QA_DIR.exists() else 0 + print(f"Answer filed to knowledge/qa/ ({qa_count} Q&A articles total)") + + +if __name__ == "__main__": + main() diff --git a/scripts/utils.py b/scripts/utils.py new file mode 100644 index 0000000..67f8664 --- /dev/null +++ b/scripts/utils.py @@ -0,0 +1,133 @@ +"""Shared utilities for the personal knowledge base.""" + +import hashlib +import json +import re +from pathlib import Path + +from config import ( + CONCEPTS_DIR, + CONNECTIONS_DIR, + DAILY_DIR, + INDEX_FILE, + KNOWLEDGE_DIR, + LOG_FILE, + QA_DIR, + STATE_FILE, +) + + +# ── State management ────────────────────────────────────────────────── + +def load_state() -> dict: + """Load persistent state from state.json.""" + if STATE_FILE.exists(): + return json.loads(STATE_FILE.read_text(encoding="utf-8")) + return {"ingested": {}, "query_count": 0, "last_lint": None, "total_cost": 0.0} + + +def save_state(state: dict) -> None: + """Save state to state.json.""" + STATE_FILE.write_text(json.dumps(state, indent=2), encoding="utf-8") + + +# ── File hashing ────────────────────────────────────────────────────── + +def file_hash(path: Path) -> str: + """SHA-256 hash of a file (first 16 hex chars).""" + return hashlib.sha256(path.read_bytes()).hexdigest()[:16] + + +# ── Slug / naming ───────────────────────────────────────────────────── + +def slugify(text: str) -> str: + """Convert text to a filename-safe slug.""" + text = text.lower().strip() + text = re.sub(r"[^\w\s-]", "", text) + text = re.sub(r"[\s_]+", "-", text) + text = re.sub(r"-+", "-", text) + return text.strip("-") + + +# ── Wikilink helpers ────────────────────────────────────────────────── + +def extract_wikilinks(content: str) -> list[str]: + """Extract all [[wikilinks]] from markdown content.""" + return re.findall(r"\[\[([^\]]+)\]\]", content) + + +def wiki_article_exists(link: str) -> bool: + """Check if a wikilinked article exists on disk.""" + path = KNOWLEDGE_DIR / f"{link}.md" + return path.exists() + + +# ── Wiki content helpers ────────────────────────────────────────────── + +def read_wiki_index() -> str: + """Read the knowledge base index file.""" + if INDEX_FILE.exists(): + return INDEX_FILE.read_text(encoding="utf-8") + return "# Knowledge Base Index\n\n| Article | Summary | Compiled From | Updated |\n|---------|---------|---------------|---------|" + + +def read_all_wiki_content() -> str: + """Read index + all wiki articles into a single string for context.""" + parts = [f"## INDEX\n\n{read_wiki_index()}"] + + for subdir in [CONCEPTS_DIR, CONNECTIONS_DIR, QA_DIR]: + if not subdir.exists(): + continue + for md_file in sorted(subdir.glob("*.md")): + rel = md_file.relative_to(KNOWLEDGE_DIR) + content = md_file.read_text(encoding="utf-8") + parts.append(f"## {rel}\n\n{content}") + + return "\n\n---\n\n".join(parts) + + +def list_wiki_articles() -> list[Path]: + """List all wiki article files.""" + articles = [] + for subdir in [CONCEPTS_DIR, CONNECTIONS_DIR, QA_DIR]: + if subdir.exists(): + articles.extend(sorted(subdir.glob("*.md"))) + return articles + + +def list_raw_files() -> list[Path]: + """List all daily log files.""" + if not DAILY_DIR.exists(): + return [] + return sorted(DAILY_DIR.glob("*.md")) + + +# ── Index helpers ───────────────────────────────────────────────────── + +def count_inbound_links(target: str, exclude_file: Path | None = None) -> int: + """Count how many wiki articles link to a given target.""" + count = 0 + for article in list_wiki_articles(): + if article == exclude_file: + continue + content = article.read_text(encoding="utf-8") + if f"[[{target}]]" in content: + count += 1 + return count + + +def get_article_word_count(path: Path) -> int: + """Count words in an article, excluding YAML frontmatter.""" + content = path.read_text(encoding="utf-8") + # Strip frontmatter + if content.startswith("---"): + end = content.find("---", 3) + if end != -1: + content = content[end + 3:] + return len(content.split()) + + +def build_index_entry(rel_path: str, summary: str, sources: str, updated: str) -> str: + """Build a single index table row.""" + link = rel_path.replace(".md", "") + return f"| [[{link}]] | {summary} | {sources} | {updated} |" diff --git a/uv.lock b/uv.lock new file mode 100644 index 0000000..d158ff8 --- /dev/null +++ b/uv.lock @@ -0,0 +1,636 @@ +version = 1 +revision = 1 +requires-python = ">=3.12" + +[[package]] +name 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