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Uses SQLite + `sqlite-vec` for local embeddings and exposes\na stdio MCP server to Claude Code.\n\n## Quick start\n\n```bash\ncd plugin/ralph-knowledge\nnpm install\nnpm run build\nnpm run reindex                     # index default root (../../thoughts)\nnpm run reindex -- /path/to/roots   # CLI override, see \"Configuration\"\n```\n\nA SQLite file is written to `~/.ralph-hero/knowledge.db` by default.\n\n## Skills\n\n| Skill | Purpose |\n|-------|---------|\n| `/ralph-knowledge:setup` | First-time index build, roots configuration, optional dream-loop bootstrap. |\n| `/ralph-knowledge:capture` | In-flow memory capture — \"remember this\" distills the current insight/session into raw memories via `knowledge_remember`. |\n| `/ralph-knowledge:dream-loop` | On-demand memory consolidation: ingest raw activity (Gemma lab logs, git history, llm-cli transcripts, Claude Code sessions), synthesize reflections. |\n| `/ralph-knowledge:curate` | Human-gated promotion of recurring insights into the wiki tier. |\n| `/ralph-knowledge:setup-obsidian` | Configure `thoughts/` as an Obsidian vault for browsing. |\n\nThe memory path is: **capture/ingest (raw) → dream-loop (reflection) → curate (wiki)**.\n\n## Configuration\n\nralph-knowledge reads configuration from four sources. Each source can be\nmissing; later sources fill in only what earlier sources did not provide. The\nprecedence for **roots** (directories to index), from highest to lowest, is:\n\n1. CLI positional arguments (`npm run reindex -- /a /b /c`)\n2. `RALPH_KNOWLEDGE_DIRS` environment variable (comma-separated)\n3. `roots[]` in `~/.ralph/knowledge.config.json`\n4. Fallback: `../../thoughts` (relative to the current working directory)\n\n`dbPath` precedence is independent:\n\n1. CLI positional argument ending in `.db`\n2. `RALPH_KNOWLEDGE_DB` environment variable\n3. `dbPath` in `~/.ralph/knowledge.config.json`\n4. Default: `~/.ralph-hero/knowledge.db`\n\n### `~/.ralph/knowledge.config.json`\n\nCreate this file to persist multi-root setups and global ignore patterns.\nThe path can be overridden via the `RALPH_KNOWLEDGE_CONFIG` env var.\n\n```json\n{\n  \"roots\": [\n    \"~/projects/ralph-hero/thoughts\",\n    \"~/projects/acme-crawler/thoughts\",\n    \"~/notes\"\n  ],\n  \"ignorePatterns\": [\n    \"**/drafts/**\",\n    \"**/worktrees/**\",\n    \"*.bak\"\n  ],\n  \"dbPath\": \"~/.ralph-hero/knowledge.db\"\n}\n```\n\nAll fields are optional. Tilde (`~`) prefixes in `roots[]` and `dbPath` are\nexpanded to the user's home directory at load time. Malformed JSON, non-object\ntop levels, and non-string entries are ignored with a warning.\n\nOn startup, ralph-knowledge logs which source provided the roots, e.g.:\n\n```\nUsing roots from: config\n```\n\n## Ignoring files\n\nPer-root `.ralphignore` files use full gitignore syntax and are layered on\ntop of the config's `ignorePatterns` and the following default globals (always\napplied):\n\n- `.claude/`\n- `node_modules/`\n- `dist/`\n- `.git/`\n- `*.log`\n\nExample `.ralphignore` at the top of a root directory:\n\n```gitignore\n# Skip a whole subtree\n.claude/worktrees/**\n\n# Skip drafts but keep the index\ndrafts/**\n!drafts/INDEX.md\n\n# Skip anything ending in .bak\n*.bak\n```\n\nPatterns behave exactly like `.gitignore`:\n\n- `**/name/**` matches `name/` at any depth.\n- A leading `!` negates an earlier match, re-including a path.\n- A trailing `/` makes the pattern directory-only.\n\nDirectories whose names start with `.` or `_` are also always skipped, as a\nfast-path before any matcher is consulted.\n\n## Choosing between `knowledge_search` and `knowledge_recall`\n\nralph-knowledge exposes two retrieval MCP tools that wrap the same underlying\nhybrid search. They differ on **who decides the tier policy**:\n\n| Tool | When to use | Tier handling |\n|------|-------------|---------------|\n| `knowledge_search` | Power-user / explicit path. You know the tier and want full control over `rerank`, `lambda`, `return_diagnostics`, chunk metadata, etc. | You pass `memory_tier` explicitly (`doc`, `raw`, `reflection`, `wiki`, or `any` — default `any`). |\n| `knowledge_recall` | Default for agents and skills. You declare your role and the tool picks the right tiers. | A role-keyed policy fans out one rerank-enabled `hybrid.search()` per tier in the role's list, then merges and re-ranks. |\n| `knowledge_think` | You want a *synthesized, sourced answer* to a question, not a result list. Retrieves the top-K excerpts, then asks the local model for a **cited** answer + an explicit **gaps** report (\"what the bank does NOT know\"). Optional `role` biases the tier mix. | Fail-open: returns the retrieved sources even when the local model is offline. Borrowed from the `gbrain think` idiom (GH-1512). |\n\n### Role -> tier policy\n\n`knowledge_recall(query, role, ...)` follows this fixed policy map:\n\n| Role | Tiers (priority order) | Intent |\n|------|------------------------|--------|\n| `researcher` | `raw`, `reflection`, `doc` | Recovery of unfiltered observations + synthesized insights + curated research. Excludes wiki (we are looking for things the wiki does not already cover). |\n| `planner` | `reflection`, `wiki`, `doc` | Bias toward synthesized insights + canonical curated knowledge; excludes raw observations to keep the planning frame stable. |\n| `implementer` | `wiki`, `doc` | Only canonical references — never raw memory or speculative reflections. Keeps implementations grounded in agreed-upon truth. |\n| `reviewer` | `wiki`, `doc` | Same constraints as implementer — review against the canonical surface, not the raw or speculative tiers. |\n| `triager` | `doc`, `wiki` | Doc-first for issue context, wiki as fallback. |\n\n### Cost notes\n\n- `knowledge_recall` always runs the cross-encoder reranker (`rerank=true`)\n  because role-aware retrieval is the most context-sensitive call path in the\n  surface. Expect a one-time ~0.5-1 s cold-start (ONNX model load on the first\n  call after process boot) and ~25-45 ms per (query, doc) pair on warm calls.\n- Each tier sub-query targets <50 ms; a 3-tier fanout totals <150 ms before\n  reranking.\n- If a tier sub-query throws (e.g., transient DB lock), `knowledge_recall`\n  logs the error to stderr and continues with the remaining tiers — degraded\n  results rather than a hard failure.\n\n### Power-user override\n\nSkills can mix both tools. For example, `/ralph-hero:plan` calls\n`knowledge_recall(role=\"planner\", ...)` for the default tier-balanced context\ngather, and ALSO calls `knowledge_search(type=\"research\", ...)` for an explicit\nartifact lookup where it needs a precise type filter. Keeping both tools in a\nskill's allowlist is the recommended pattern.\n\n## `knowledge_expert` — domain-keyed memory bundles\n\n`knowledge_expert(domain, issue_number, ...)` returns a curated context bundle\nfor a named domain — wiki entries, recent reflections, and prior outcomes — so\nsub-agents become per-domain experts via memory rather than per-domain prompts.\nIt is the **domain-keyed** companion to `knowledge_recall`'s **role-keyed**\nretrieval: role decides which tiers to surface; domain decides which slice of\nthe corpus.\n\n### Signature\n\n```\nknowledge_expert(\n  domain: string,             // Tag to match (e.g. \"auth\", \"memory-tiers\", \"ralph-knowledge\")\n  issue_number: number,       // GitHub issue on whose behalf this call is made — required for telemetry\n  limit?: number,             // Max entries per bucket. Default 5.\n  recency_window_days?: number, // Reflection age cutoff in days. Default 30.\n  path_prefix?: string,       // Optional secondary filter: only docs whose path starts with this prefix.\n  session_id?: string,        // Team/hero session ID — passed through to the outcome event.\n)\n```\n\nDomain matching uses the `tags` table (frontmatter `tags:` arrays are the\nprimary signal). `path_prefix` is a secondary narrowing filter — not a\nreplacement for tags. Pass `\"thoughts/shared/\"` to restrict to the shared\ncorpus, for example.\n\n### Return shape\n\n```json\n{\n  \"query_id\": \"uuid-v4\",\n  \"domain\": \"auth\",\n  \"wiki\": [ ...DocumentRow ],\n  \"reflections\": [ ...DocumentRow ],\n  \"prior_outcomes\": [ ...OutcomeEventRow ],\n  \"warning\": null\n}\n```\n\n| Field | Description |\n|-------|-------------|\n| `query_id` | UUID generated per call. Save this and pass it to `knowledge_record_outcome` as `query_id` to correlate downstream outcomes back to this expert call. |\n| `domain` | Echo of the requested domain. |\n| `wiki` | Up to `limit` documents with `memory_tier = 'wiki'` tagged with `domain`, ordered by date descending. |\n| `reflections` | Up to `limit` documents with `memory_tier = 'reflection'` tagged with `domain` and dated within `recency_window_days`. |\n| `prior_outcomes` | Up to `limit` `outcome_events` rows whose `payload` JSON contains `domain` — pipeline history for this domain. |\n| `warning` | Non-null string when both `wiki` and `reflections` are empty, suggesting the caller tag existing docs or broaden the domain term. `null` on a successful hit. |\n\n### Telemetry\n\nEvery `knowledge_expert` call writes an `outcome_events` row with\n`event_type = 'expert_call'`. The `payload` JSON carries `query_id`, `domain`,\n`returned_doc_ids`, `limit`, `recency_window_days`, `path_prefix`, and\n`warning`. This makes per-domain hit rate queryable from day one:\n\n```\nknowledge_query_outcomes({ event_type: \"expert_call\", aggregate: true })\n```\n\nPass `query_id` to `knowledge_record_outcome` to tie subsequent phase/research\noutcomes back to the originating expert call:\n\n```\nknowledge_record_outcome({\n  event_type: \"research_completed\",\n  issue_number: 1306,\n  query_id: \"<query_id from knowledge_expert>\",\n  verdict: \"complete\"\n})\n```\n\n### Degradation\n\n`knowledge_expert` degrades the same way as the other knowledge tools — return\nan empty bundle with a `warning` when no matching documents exist; never throw\non a valid call. If the domain cannot be determined at call time, callers should\nskip the call rather than pass an empty string.\n\n## Environment variables\n\n| Variable | Purpose |\n|----------|---------|\n| `RALPH_KNOWLEDGE_CONFIG` | Override path to `knowledge.config.json` (tilde expanded). |\n| `RALPH_KNOWLEDGE_DIRS` | Comma-separated list of roots. Beats config, loses to CLI. |\n| `RALPH_KNOWLEDGE_DB` | Override SQLite path. Beats `config.dbPath`, loses to a CLI `.db` positional. |\n| `RALPH_KNOWLEDGE_HANDSHAKE_DEADLINE_SEC` | How long the launcher waits for a first-run bootstrap before answering the MCP handshake with a no-tools stub and finishing the install in the background (default `15`; `0` blocks until the install completes). |\n| `RALPH_KNOWLEDGE_BOOTSTRAP_WAIT_SEC` | How long to wait for *another* process's bootstrap before giving up (default `900`). Only reachable with the handshake deadline disabled. |\n\n### First run on a slow link\n\nThe first launch for a given runtime installs ~155MB and builds — about 4.5s on\na normal connection, comfortably inside Claude Code's 30s MCP startup deadline.\nOn a slow link the download can outrun that deadline, and the server would be\nmarked failed for the whole session *and* the install killed with it, so the\nnext session would start just as cold.\n\nPast `RALPH_KNOWLEDGE_HANDSHAKE_DEADLINE_SEC` the launcher instead answers the\nhandshake from a zero-dependency stub that reports **no tools**, and lets the\ninstall finish in the background. The session is honestly degraded — the model\nis told the toolset is unavailable and why — and a session started after the\ninstall completes gets the full server. A bootstrap that *fails* is not a slow\none: it aborts the launch with its error, as before.\n\nClosing that first session before the install finishes interrupts it, but npm's\ncache keeps whatever was already fetched, so the next launch resumes rather than\nrestarting the download.\n\n## Benchmarks\n\nStandalone benchmarks live under [`benchmark/`](./benchmark/) — see\n[`benchmark/README.md`](./benchmark/README.md) for the directory's conventions\n(scripts are not part of the published npm package and are not run by\n`vitest`).\n\n### Reranker benchmark (GH-901)\n\n[`benchmark/reranker-bench.ts`](./benchmark/reranker-bench.ts) compares two\nONNX cross-encoder rerankers loaded via the existing `@huggingface/transformers`\nv3 dependency:\n\n- `onnx-community/bge-reranker-v2-m3-ONNX` (int8 quantized) — primary candidate\n- `Xenova/ms-marco-MiniLM-L-6-v2` — speed baseline\n\nFor ~44 sample queries spanning the five query intent classes (prior-work\ntopic, plan-by-issue lookup, claim evidence, epic context, hero orientation),\nthe script fetches top-20 RRF candidates, reranks each candidate set with both\nmodels, and writes a TSV table with cold-start latency, p50/p95 per-pair\nlatency, batch-of-20 latency, RSS memory delta, and top-3 agreement vs\nRRF-only. Results land at `benchmark/results-YYYY-MM-DD.tsv`; the most recent\nrun is checked into the repo.\n\n```bash\nRALPH_KNOWLEDGE_DB=~/.ralph-hero/knowledge.db \\\n  npx tsx plugin/ralph-knowledge/benchmark/reranker-bench.ts\n```\n\nThe script does not modify `hybrid-search.ts` — production wiring of a\ndefault reranker is a separate followup gated on the benchmark's findings.\n","readmeFilename":"README.md"}