Vancetope — RAG (Retrieval-Augmented Generation)

How Vancetope places documents into a vector index, how the index is synchronized with the Project lifecycle, and how LLM engines query hits from this index. It builds upon the existing, low-level RAG subsystem (RagDocument/RagChunkDocument/RagBackend) and adds a canonical Project RAG layer on top.

See also: project-lifecycle.md auto-summary.md llm-resource-management.md knowledge-graph.md (standalone system, not RAG) planning/project-rag.md

1. Purpose

Project Documents should be addressable for RAG queries without explicit tool calls. Specifically:

A default RAG per Project named _documents, automatically created on the first bring().
Automatic indexing of all Documents under documents/** (path filter, MIME filter, user override).
Crash-/multi-Pod-safe incremental indexing using the same dirty-flag/scheduler pattern as the Auto-Summary.
Manual reindex/rebuild actions via REST and Web UI.
Recipe-driven AutoInject variant that patches RAG hits into the system prompt per Turn.

Manual RAGs (with non-_ prefix) remain unchanged — user tools (rag_create, rag_add_text, rag_query, rag_delete) continue to be an equally valid path alongside the default RAG.

2. Terminology

Term	Definition
RAG	A catalog unit (`RagDocument`) plus the set of its Chunks (`RagChunkDocument`). Unique per `(tenantId, projectId, name)`.
Project-Default-RAG	The RAG with the reserved name `_documents`. Idempotently created by `ProjectLifecycleService.bring()`, deleted on `close()`. Untouched on `suspend()`.
Reserved Name	RAG names with a `_` prefix are system-owned (`_documents`, potentially others later). `rag_create`/`rag_delete` reject them.
Eligibility	Filter that determines whether a Document lands in the Project-Default-RAG: path prefix `documents/` + textual MIME type + `ragEnabled` override.
`ragDirty`	Boolean on `DocumentDocument`. Set by DocumentService on Create/Update if the filter applies; cleared by the Indexer-Scheduler after successful write. Analogous to Auto-Summary `summaryDirty`.
`ragClaimedBy`/`ragClaimedAt`	Atomic claim for the Indexer (analogous to Summary `claimedBy`/`claimedAt`). TTL-based lock, recovery on Pod crashes.
Indexer-Tick	`ProjectRagIndexScheduler` runs periodically, claims a batch per Project, and calls `ProjectRagIndexer.reindexDocument` per Document.

3. Data Model

3.1 `ProjectDocument` (unchanged)

No new fields. The default RAG is a standalone Mongo Document (RagDocument) that references the Project Scope via tenantId+projectId+name=_documents.

3.2 `DocumentDocument` — RAG Fields

// Project-RAG inclusion override. null = auto (default per filter),
// true = always include, false = never include.
@Nullable Boolean ragEnabled;

// Dirty flag for the project-RAG indexer.
boolean ragDirty;

// Atomic claim — set by claimForRagIndex, cleared by markRagClean.
@Nullable String ragClaimedBy;
@Nullable Instant ragClaimedAt;

Compound index tenant_project_rag_claim_idx on (tenantId, projectId, ragDirty, ragClaimedBy) for efficient claim querying.

3.3 `RagDocument` (unchanged)

Pins embedding provider, model, and dimension on creation. Fields are immutable after Create — provider/model change is always “Drop + Re-Create”, no in-place re-embed (see §6.2).

3.4 `RagChunkDocument`

Fields unchanged. The existing sourceRef field is populated by the Project-RAG-Indexer as the Document ID, allowing ragService.removeBySource(ragId, docId) to remove a Document from the RAG. The metadata map on the Chunks includes two conventional keys:

kind: "content" for Body Chunks, "summary" for Summary Chunks (see §4.3).
path: Document path as plain text, primarily for debugging and UI display.

4. Indexing Pipeline

4.1 Filter

A Document lands in the _documents RAG if and only if all three conditions are met (checked by DocumentService.isRagEligible):

path.startsWith("documents/") — everything under _vance/, _vance/trash/, _chatbox/, _slart/, etc., is not indexed.
isTextual(mimeType) — text/* or one of the code MIME types from the existing CODE_MIME_WHITELIST.
ragEnabled != false — the user override trumps the default; if ragEnabled == true, it is included even against conditions 1+2.

Order in code: first ragEnabled override (if set), otherwise path prefix, then MIME.

4.2 Dirty-Flag Maintenance

DocumentService.create — after persisting, ragDirty=true is set if the filter applies.
DocumentService.update — if body content, path, or ragEnabled change, ragDirty=true is set. The Indexer re-checks the filter on pickup (race protection).
DocumentService.delete — synchronous cleanup via ragService.removeBySource(_documents, docId). No polling path needed: Delete is a Mongo Delete + Mongo Delete, no embed calls.
DocumentSummaryDriver.run — after successful Summary write, calls documentService.markRagDirty(docId) so that the Summary Chunk is written in the next tick.

4.3 Indexer Pipeline (per Document)

ProjectRagIndexer.reindexDocument(DocumentDocument):

1. Re-check filter §4.1.
2a. Filter says "no": ragService.removeBySource(_documents, docId) + markRagClean → done.
2b. Filter says "yes":
    a. ragService.removeBySource(_documents, docId)       # remove old Chunks
    b. ragService.addText(_documents, docId, content, {kind: "content", path})
    c. if rag.project.includeSummaries == true AND summary != null:
         ragService.addText(_documents, docId, summary, {kind: "summary", path})
    d. markRagClean(docId)

Failures from the embed call are caught by the scheduler tick → releaseRagClaim(docId) → TTL-based recovery on the next tick.

4.4 Scheduler (`ProjectRagIndexScheduler`)

Periodically (default 30s, configurable via vance.rag.indexer.intervalMs):

1. Determine Projects for this Pod: findRunningByHomeNode(selfNode) + findPodlessActive().
2. Per Project:
   a. Check rag.project.enabled cascade setting → skip if false.
   b. claimForRagIndex(tenant, project, podId, batchSize, claimTtl).
   c. Per claimed Doc: indexer.reindexDocument(doc).
      - Success: counter "outcome=success".
      - Failure: releaseRagClaim(docId) + counter "outcome=failed".

Settings:

Key	Default	Effect
`vance.rag.indexer.batchSize`	10	Max Documents per Project per tick
`vance.rag.indexer.intervalMs`	30000	Tick interval
`vance.rag.indexer.initialDelayMs`	60000	Delay after Pod boot
`vance.rag.indexer.claimTtlMinutes`	10	Claim TTL

Metrics (Micrometer counter): vance.rag.indexer.runs with tag outcome ∈ {success, failed, skipped}.

4.5 Cascade Settings

Key	Default	Effect
`rag.project.enabled`	`true`	Completely disables the `_documents` RAG for a Project. Indexer no-op, AutoInject no-op. Manual RAGs unaffected.
`rag.project.includeSummaries`	`true`	Whether `summary` Chunks are written in addition to `content` Chunks.
`rag.autoInject.enabled`	`null`	Tenant/Project default for Recipes without explicit RAG stance. Accepts `ON`/`OFF`/`AUTO` and legacy `true`/`false` (see §5). `null`/`AUTO` = no enforcement at this Scope. Does not override a Recipe that explicitly sets `ON`/`OFF` (innermost-wins).
`ai.embedding.provider`	`none`	Embedding backend for RAG. Values: `none` (RAG off for the Tenant — no indexing, no embed call, no WARN logs), `embedded` (in-process E5-small-v2, keyless, 384-dim, ~120 MB in JAR), `gemini`, `openai`. Pinned when new RAGs are created; existing RAGs continue to use their original backend unless the Tenant switches back to `none` (kill switch — see §6.1).
`ai.embedding.model`	provider-dependent (Gemini: `gemini-embedding-001`)	Model name. Ignored for `embedded`; for `openai` e.g. `text-embedding-3-small` or an Ollama model name (`nomic-embed-text`) behind `baseUrl`.
`ai.embedding.apiKey`	—	PASSWORD setting, separate from the Chat LLM credential namespace (`ai.provider.*.apiKey`). Ignored for `embedded`/`none`. Required for `gemini`/`openai` — even for keyless OpenAI-compatible endpoints (Ollama/TEI), a non-empty placeholder must be present.
`ai.embedding.baseUrl`	—	Read only by the `openai` provider. Empty = `https://api.openai.com`. Routes OpenAI-compatible endpoints: Ollama (`http://localhost:11434/v1`), TEI in cluster, vLLM, OpenRouter, Cortecs.

5. AutoInject — RAG Hits in the System Prompt

Status: implemented for Arthur (Reactive-Chat-Engine). Other Engines (Ford, Marvin, Vogon, Eddie, Jeltz) are not yet connected — the RagAutoInjectService is generic and can be docked to any Engine that can add a dynamic system block via ObjectProvider injection.

Variant C — Pre-Turn Hybrid with Threshold:

1. Pre-Turn-Hook (in the Engine, after the Memory block) resolves
   enablement via innermost-wins (first decisive level wins):
     a. Recipe param rag.autoInject == ON/OFF → wins (Recipe identity).
     b. else Cascade Setting rag.autoInject.enabled (Tenant/Project),
        ON/OFF (also true/false) → wins.
     c. AUTO / not set anywhere → hard default OFF.
   Off → skip, no RAG block.
2. Extract the last User Message of the Turn from the Inbox.
   (For multiple UserChatInput entries: concatenation.)
3. ragService.query(_documents, userMessage, topK) — the RagService
   makes the embed call internally with the RAG's pinned model.
4. Filter out hits with score < rag.minScore.
5. If ≥1 hit: dynamic SystemMessage (Markdown block <rag-context>)
   is added below the Memory block:

     <rag-context>
     Top relevant excerpts from the project's documents. Cite the
     source path when you use these.

     - documents/notes/topic.md (score 0.81)
       Vancetope is a Think-Tool, not a productivity tool. …
     - documents/architecture.md [summary] (score 0.72)
       Multi-Pod cluster with Mongo-centric state. …

     </rag-context>

6. Tool Surface: rag_query remains unchanged in the Tool whitelist
   (no separate expose mechanism in v1 — the tool is Recipe-
   driven via the normal allowedTools schema).

Recipe Params (on process.engineParams):

rag:
  autoInject: AUTO     # ON | OFF | AUTO (default AUTO → hard Floor OFF)
  minScore: 0.65       # Threshold, model-dependent
  topK: 5

Three-value vocabulary (ON/OFF/AUTO) on Recipe param and setting rag.autoInject.enabled. Legacy booleans are mapped (true→ON, false→OFF) — the setting key remains .enabled, no new key. Caution YAML: bare ON/OFF are collapsed to booleans by SnakeYAML — for the enum value in the Recipe, quote ("OFF") or write AUTO/false.

Why three values instead of just rotating precedence on a boolean: a boolean cannot distinguish “Recipe explicitly says OFF” (→ override setting) from “Recipe says nothing” (→ let setting decide) — both would be false. Only the third value AUTO (= default) makes “defer” expressible.

Precedence innermost-wins (consistent with all other Vancetope cascades):

Recipe ON/OFF wins over the setting — the RAG stance is part of the Recipe’s identity. A discuss-like Recipe pins OFF and cannot be forcibly activated by any Project.
Recipe AUTO/absent → Cascade Setting rag.autoInject.enabled decides: ON/OFF wins, AUTO/absent defers further.
AUTO/absent at all levels → hard default OFF.

AUTO means “no enforcement at this Scope”: if set at a Scope, it overrides an outer ON/OFF back to the default. An absolute operator kill switch (setting overrides even Recipe-ON) deliberately does not exist — this would contradict innermost-wins; if ever needed, an explicit rag.autoInject.force would be added (YAGNI).

Failure Modes:

No _documents RAG present (e.g., embed provider not configured) → silent skip.
Embed/Query call fails → warn-log, silent skip (no RAG block, Turn continues normally).
Inbox contains no user text (e.g., pure wakeup Turn) → silent skip.

Render style is statically defined in RagAutoInjectService.composeBlock, not Pebble-templated. If a Recipe wants a different style, that is a subsequent refactor (separate rag.template param or Pebble var).

6. Lifecycle Integration

6.1 Project Lifecycle

Hook	Action	Tolerance
`ProjectLifecycleService.bring()` (after `workspaceService.init`)	`ProjectRagService.ensureDefaultRag(tenant, project)`	Tenant with `ai.embedding.provider=none` ⇒ silent skip (Optional empty, no WARN, no RAG Doc created). Otherwise best-effort: embedding provider misconfig does not throw, but warn-logs. Project reaches RUNNING in both cases.
`ProjectLifecycleService.suspend()`	no RAG action	—
`ProjectLifecycleService.close()` (after `workspaceService.dispose`)	`ProjectRagService.disposeDefaultRag(tenant, project)`	Best-effort: warn-log on error.

Tenant Kill Switch. ai.embedding.provider=none not only affects creation but also ongoing operations against existing data. ProjectRagIndexScheduler checks RagService.isEmbeddingEnabled(tenantId) per tick + per Tenant and completely skips the claim path (counter vance.rag.indexer.runs{outcome=tenant_disabled}). RagService.modelFor(rag) throws a clear exception if someone still makes a direct embed/query call against an old RAG — this prevents a Tenant from accidentally continuing embed calls after officially deactivating RAG.

suspend deliberately leaves the RAG in place — index storage is cheap in Mongo, resume uses it immediately.

6.2 Provider Change = Drop + Re-Create

RagDocument.embeddingProvider/-Model/-Dimension are immutable after Create. A change from e.g. gemini:text-embedding-004 to openai:text-embedding-3-small always proceeds as:

rag.reindex(rebuild=true) (REST/UI button or service call).
Server: disposeDefaultRag → ensureDefaultRag with current setting → markAllForReindex.
Scheduler re-indexes all eligible Docs into the fresh RAG.

Reason: Embeddings are not model-portable. Vector spaces of different models are geometrically incomparable; a mixed index would be garbage. Drop + Re-Create is the only clean migration.

7. REST Surface

Under /brain/{tenant}/projects/{project}/rag, JWT-protected, Permission WRITE on the Project Resource (same gate for status read and reindex):

Method	Path	Description
`POST`	`/reindex?rebuild={bool}`	`rebuild=false` queues all Docs for re-index. `rebuild=true` first drops and then re-creates with current settings. Returns `{rebuild, documentsQueued}`.
`GET`	`/status`	Read-only snapshot: `{exists, ragId, embeddingProvider, embeddingModel, chunkCount, createdAt}` for the Web UI.

Manual RAGs continue to be managed via the rag_* tools on the WebSocket Tool Surface — no REST Surface for this in v1.

8. Web UI

8.1 Documents Editor — `ragEnabled` Tristate

In the Document detail next to the autoSummary block, a radio tristate “Project RAG”:

Auto — follows filter §4.1 (default).
Always index — ragEnabled=true.
Never index — ragEnabled=false.

Wire format: DocumentUpdateRequest.ragEnabled: "auto" | "on" | "off". Server parses into Boolean null and calls documentService.setRagEnabledOverride(docId, value). Sets ragDirty=true so the Indexer adjusts the Chunks in the next tick.

8.2 Insights Editor — RAG Tab

Dedicated top tab “RAG” in the Insights Editor (next to Sessions, Recipes, …). Content:

Status Card: _documents RAG present? Provider/Model, Chunk count, Created-At.
Action Card with three buttons:
- Reindex — POST .../rag/reindex?rebuild=false, incremental rebuild with pinned model.
- Rebuild with current embedding model — POST .../rag/reindex?rebuild=true, confirmation dialog. Drop + Re-Create + Bulk-Dirty-Set.
- Refresh — new GET status call.

Manual RAGs are not managed in the UI v1 — they only appear as a side effect of Tool calls and are visible via tooling.

9. Manual RAGs (User Tools)

Still available, unchanged in v1:

rag_create(name, title?, description?, chunkSize?, chunkOverlap?) — rejects reserved names with _ prefix.
rag_add_text(ragId, sourceRef?, text, metadata?)
rag_add_workspace_file(ragId, sourceRef?, path)
rag_add_document(ragId, documentId) and rag_add_path(ragId, pathPrefix) — user-triggered bulk import of individual Docs.
rag_query(ragId, query, topK) — manual querying.
rag_delete(ragId) — rejects reserved names.
rag_list(projectId) — lists all RAGs (default + manual) for a Project.

User tools may read the _documents RAG (rag_query, rag_list), but not delete or rename it. Writing (rag_add_*) to the default RAG is allowed, but the next Indexer tick will overwrite manually added Chunks for the same sourceRef — useful only as a quick test, not a production pattern.

10. Failure Modes

Scenario	Behavior
Embedding provider not configured	`ensureDefaultRag` warn-logs, Lifecycle still reaches RUNNING. `findDefaultRag` returns empty until corrected. Indexer tick implicitly skips Projects without RAG (Indexer calls `ensureDefaultRag` itself and fails there).
Embed call fails (network, rate limit)	Indexer warn-logs, `releaseRagClaim`, TTL expiration → next tick retries. Self-healing.
Bulk import via Kit / Restore	Sets `ragDirty=true` on all eligible Docs. Indexer processes the queue with `batchSize` — no embed storm.
Pod crash between Claim and Write	Claim TTL expires, another Pod (or the same after reboot) re-claims.
`_documents` RAG manually deleted (Mongo direct access)	Re-created on next `bring()` or reindex, empty. Existing Docs are `ragDirty=false` and are only picked up on the next touch — therefore: after such a reset, call `rag.reindex(rebuild=false)` once (bulk dirty-set across all ACTIVE Docs).
Race “Doc update + Claim in parallel”	Update sets `ragDirty=true` again. Claim holds the TTL lock. When Indexer finishes, it sees the second dirty mark and the Doc is re-collected in the next tick. Duplicate work, but consistent.

11. What this Spec DOES NOT Cover

Federated Query across multiple RAGs in one call. Those who want this call rag_query per RAG.
Token-based Chunking. Character-based as today. Token chunking when LLM context boundaries become tight.
Background Reindex for very large Projects. v1 makes rag.reindex synchronous (markAllForReindex), the Web UI button blocks briefly. For >1000 Documents, a follow-up PR with GET /rag/reindex/status might be needed.
JSON export to the _vance/... Document layer. Mongo is the source of truth, no double storage.
In-place Re-Embed on provider change. Always “Drop + Re-Create” (§6.2).
Cross-Project Search. A RAG query is within the Project Scope; tenant-wide search is not v1.
AutoInject Variant A or B (Pre-Turn without Threshold / Tool-only). The plan is Variant C (Hybrid with Threshold).

12. Relation to Other Specs

project-lifecycle.md §5.1, §5.3 — bring()/close() as RAG lifecycle anchors.
auto-summary.md (readme) — identical dirty-flag/claim pattern; Summary Driver bumps ragDirty so Summary Chunks land.
llm-resource-management.md §3a — Cascade resolution for ai.embedding.* settings.
knowledge-graph.md — standalone system (Relations + Insights), not RAG. Both can exist in parallel per Project.
recipes.md — params.rag.* as Recipe Surface for AutoInject (Phase 5).
planning/project-rag.md — Implementation plan + phases + trade-offs.