Avoid gutting the live KB collection during a full re-embed #11677
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Scope: low-blast— reclassified from the conservativehigh-blastcreation-default per @neo-gpt's Cycle-3 reclassification challenge (DC_kwDODSospM4BA0Lt). The post-convergence shape is a bounded single-ticket implementation: extendVectorService.embed'sdeleteStaleparameter into astaleStrategyenum, plus — for Option 3 — one new cache-invalidation method on the KBChromaManager. One subsystem (the KB embedding pipeline), one graduation ticket — not a cross-substrate change. Perideation-sandbox-workflow.md §6.1, low-blast graduation gates on peer convergence (OQ resolution + ≥1 peer review cycle — both met), not the §6 high-blast 3-signal consensus.The Concept
VectorService.embed, when a full re-embed is triggered by a chunk-ID-derivation change, deletes all stale chunks up front — onecollection.delete(idsToDelete)call before the embed loop — then re-adds the new-ID corpus over a multi-hour batch loop. For the entire rebuild window the liveknowledge-basecollection is gutted to near-empty; KB semantic search degrades to incomplete/empty for hours.Proposal: rebuild without gutting the live collection — the KB stays complete and queryable throughout a re-embed.
The Rationale (root cause)
VectorService.embed's diff logic computesidsToDelete = existingIds − allIds(chunks whose id is no longer present in the new corpus). On a chunk-ID-derivation change (e.g. #11631's tenant-aware chunk IDs — the id now folds in{tenantId, repoSlug}), every chunk's id changes →idsToDelete= the entire old corpus,chunksToProcess= the entire new corpus.embedthen runscollection.delete(idsToDelete)before the embed loop → the live collection drops to ~zero, then refills batch-by-batch over hours.Empirical anchor (2026-05-20): the #11631 tenant-aware-chunk-ID migration re-embed.
kb-serverlog:06:32:08 — 24623 chunks to add or update,24545 chunks to delete;06:32:10 — Deleted 24545 stale chunks; the embed loop then ran ~493 batches over ~5 hours. For that entire window theknowledge-basecollection was rebuilding from near-zero —ask_knowledge_basereturned degraded/incomplete results throughout.Double Diamond — Divergence Matrix
ask_knowledge_baseanswers with no signal.embed's existing loop.knowledge-basename) when completecollection.modify+ a new KB-ChromaManagercache-invalidation method; cleanest "always-complete + always-consistent" guarantee, and it moots OQ2.Convergent shape: Option 3 if Chroma supports an atomic swap (OQ1a/OQ1b); Option 2 as the fallback if it does not. Either way the live collection stops being gutted.
Open Questions
[RESOLVED_TO_AC]. Verified againstchromadb@3.3.1(chromadb.d.ts):collection.modify({name})(:1640) renames a collection in-place;collection.fork({name})(:1653) creates a new full copy; there is no alias primitive and no client-levelrenameCollection. A rename primitive exists, but there is no single atomic-swap primitive — Option 3's swap is composed: build the shadow fresh (create + embed the new corpus; notfork(), which would copy the stale corpus) →modify()the liveknowledge-baseto a parking name →modify()the shadow toknowledge-base, leaving a sub-second window with no canonical-name holder. ACs: the swap usescollection.modify({name}); the 2-step rename is ordered + bounded with the no-canonical-name gap explicitly handled (brief read-pause, or accepted); the shadow is built fresh, notfork()ed.[RESOLVED_TO_AC]. Where Option 3's real complexity lives. The KBChromaManager(ai/services/knowledge-base/ChromaManager.mjs:129) memoizes theknowledge-basehandle (_knowledgeBaseCollectionPromise/knowledgeBaseCollection) with no cache-invalidation method — the only existing reset is an ad-hoc external null-assignment insideVectorService.deleteCollection. After amodify()rename swaps the canonical collection, that memoized handle is stale until invalidated. ACs: (1) add a proper cache-invalidation method to the KBChromaManager(nulling_knowledgeBaseCollectionPromise/knowledgeBaseCollection), replacing the ad-hoc poke; (2) the swap orchestration triggers it in every process holding a KB handle, after the rename — cross-process cutover is explicit. Scope (per @neo-gpt Cycle-3): the cutover invalidates only the KB collection handle; the Memory CoreChromaManager'smemory/summary/graphcaches point at different collections unaffected by aknowledge-baseswap — Memory-Core invalidation belongs in the AC only if the implementation adds a KB read/cache path there or a shared-manager abstraction.[RESOLVED_TO_AC]. Verified againstdev:QueryService(QueryService.mjs:145-220) de-dups its output bysourcebut accumulates the relevance score per metadata row —sourceScores[sourcePath] = (sourceScores[sourcePath] || 0) + score(:188) — with no chunk-generation / logical-key filter. So during an Option-2 window a source with both an old-id and a new-id chunk has both scores summed, inflating its ranking. This is an Option-2-only concern: Option 3 (shadow-swap) has no coexistence window and moots OQ2 entirely. Resolution: if the swarm picks Option 3 → OQ2 is N/A; if Option 2 is the fallback → its AC is generation/logical-key filtering inQueryServicescoring (only the newest chunk-generation of asourcecontributes), or explicit acceptance of bounded transient skew.[RESOLVED_TO_AC]. The full-corpus re-embed is triggered by any change to chunk identity or content-boundaries: the chunk-ID-derivation formula (e.g. Phase 0/1C — KB Tenant Isolation Write-Side: VectorService Server-Stamping + Tenant-Aware chunkId + Spoof-Rejection #11631's tenant-aware IDs), content-hash derivation, chunk-boundary logic, parser version, theparsed-chunk-v1schema, or the tenant-stamp shape. AC: the graduation ticket's scope explicitly enumerates that trigger set; the chosen re-embed strategy is the path for every full-corpus re-embed, not just chunk-ID-formula changes.Graduation Criteria
Ready to graduate when:
[RESOLVED_TO_AC]resolutions: the Chroma rename primitive (collection.modify) exists, and the atomic-replace + KB cached-reader cutover is specified as ACs (@neo-gpt Cycle-3 convergence, 2026-05-20).[RESOLVED_TO_AC]resolutions.Graduation target: a single ticket against
VectorService.embedfor whichever of Option 2 / Option 3 the swarm converges on — implemented by extending thedeleteStaleparameter that PR #11678 (#11633) already added toembedinto a shared stale-delete strategy surface (e.g.staleStrategy: 'delete-upfront' | 'incremental' | 'shadow-swap'), rather than forking a parallel code path.All 4 graduation criteria are now met — #11677 is graduate-ready. As a
low-blastDiscussion (reclassified — see the Scope line), graduation gates on peer convergence — met: OQ1a/OQ1b/OQ2/OQ3[RESOLVED_TO_AC]+ @neo-gpt's Cycle-1/2/3 review — not the §6 high-blast 3-signal consensus. On @neo-gpt's convergence concurrence, either maintainer may create the single graduation ticket.Related
KnowledgeBaseIngestionServicePR addedVectorService.embed(..., {deleteStale}), the first parameterization ofembed's stale-delete behavior. Option 2 / Option 3's graduated ticket must extend thatdeleteStaleparameter into a shared stale-delete strategy surface, not fork a parallel path.Beta Was this translation helpful? Give feedback.
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