Reuse completed Google embeddings and resume semantic staging

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# Completed-source reuse validation
2026-10-13. Windows VM, CPU-only inference (`SEARCHI_FORCE_CPU=1`), normal
hardware-detecting worker, pinned Google EmbeddingGemma 2 backbone/projector,
one inference thread. FFmpeg/ffprobe are the verified app-local helpers.
## Genuine Google integration
`cargo test -p searchi-service --test incremental_google_cpu --test google_cpu
--locked -- --ignored --nocapture --test-threads=1` executes actual models.
A counting adapter forwards every operation to `GemmaEmbedder`; no synthetic
vectors satisfy the integration assertions.
- Document and code initial encoding: two calls. Unchanged rebuild and folder
move: zero extra calls, bit-identical vectors, remapped current paths/locations.
A basename rename adds one call because the document title is in Google's
prompt. A single content change adds one call; unrelated code stays identical.
- Mixed document/code/image/audio/video fixture: five source groups. Unchanged
rebuild makes zero additional document/image/audio encoder calls and retains
exact vector bytes and citations. Replacing the BMP while preserving its
size and exact mtime adds one image call; the other four groups stay identical.
- Rejected activation retains the prior active generation. Reopening resumes
completed changed-document vectors with zero document calls; updated text
citations are returned after publication. Existing policy-change regression
covers lock admission, generation creation, repeated activation failures,
same-instance reader policy and resumed retry in both directions.
Synthetic colors/sine waves in media fixtures prove reuse, not media relevance.
Tests are correctness checks, not latency or throughput benchmarks.
## Storage and failure boundaries
Four unit regressions cover schema-6 legacy search without unsafe reuse,
completed versus partial restart state, revision-checked publication,
transactional completion failure and malformed stored text windows. Another
fault-only pipeline test covers primary inference error preservation when
partial-source cleanup also fails. Its provider returns no simulated vectors.
## Executed checks
- Windows workspace: 98 passed, 15 explicitly ignored; no failures.
- Native Ubuntu 24.04 headless workspace: 98 passed, 15 explicitly ignored.
- Windows native desktop unit suite: 17 passed.
- Strict Windows/Linux workspace and Windows native-desktop Clippy passed;
Windows workspace/desktop formatting passed.
- Final-source Windows real Google tests above: five passed. The two existing
lifecycle/policy tests took 25.31 s and the three incremental tests took
138.90 s. Concurrent compilation/resource contention was present; these
elapsed suite times are not application search benchmarks.
- Final-source native Linux actual Google integration: the same five tests
passed; existing lifecycle/policy suite 49.77 s, incremental suite 173.59 s.
Ubuntu 24.04 WSL x86-64 with two allocated CPUs, one inference thread,
isolated verified models/normal auto worker and pinned native FFmpeg helpers.
Cold Clippy compilation ran concurrently; these are not latency comparisons.
## Remaining gates
Actual process termination during a multi-batch source, simultaneous real-model
source mutation, wider policy changes, near-limit pruning, minimum-ABI/older-CPU
checks and first-query/foreground latency during indexing remain open. Full file
hash validation currently precedes ANN construction; direct file mutation during
that construction before watcher reconciliation needs a later validation callback.
See [INCREMENTAL-SEMANTIC.md](INCREMENTAL-SEMANTIC.md).
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# Incremental semantic indexing and recovery
Status: implementation design and acceptance gates; not implemented. Current
semantic rebuilds regenerate vectors and the ANN graph. Interrupted staging is
discarded by the next rebuild. The previous ready generation remains searchable.
Status: completed-source reuse and compatible staging recovery implemented in
schema 7. Unchanged vectors are copied with current citations; the ANN graph
is rebuilt. Incomplete source groups are discarded before retry. The previous
ready generation and its reader policy remain available on failures.
CPU tests use the normal hardware-detecting runtime forced to CPU.
Recorded integration evidence and remaining gates are below. This is not a
production readiness or media relevance claim.
## Reuse requires complete sources
@@ -17,7 +22,7 @@ encoder/preprocessing/sampling policy. Size and modification time alone are
insufficient. Existing media chunks are written progressively; their presence
does not prove that a file completed. A crash can leave an incomplete group.
Add a versioned completion manifest per source group: generation, task key,
The versioned completion manifest records per source group: generation, task key,
source type, encoder/policy key, source hash, title hash where applicable,
expected chunk count and completion state. Associate chunks with their group.
Only completed groups can supply reusable vectors. Historical generations
@@ -51,20 +56,25 @@ preserve both the previous active generation and the same reader's policy.
## Acceptance gates
- [ ] Migrate existing databases while preserving active-generation search.
- [ ] Rebuild mixed text/code/image/audio/video fixtures unchanged with zero
- [x] Schema-6 storage migration preserves active-generation search; legacy
chunks without completion evidence cannot supply reusable vectors.
- [x] Rebuild mixed text/code/image/audio/video fixtures unchanged with zero
document/media inference calls and identical vectors and source locations.
- [ ] Change one document and one media file; re-encode only affected groups.
- [ ] Re-encode text after a basename rename; reuse a folder-only move with
current citations. Verify media bytes before reusing a renamed media file.
- [x] Change one document and one image; re-encode only affected groups.
Image regression preserves file size and exact mtime to exercise SHA identity.
- [x] Re-encode text after a basename rename; reuse a folder-only move with
current citations.
- [ ] Verify media bytes and current citations after a media-file rename.
- [ ] Regenerate affected media when sampling policy changes; retain eligible
text groups when component encoder-key reuse is implemented.
- [ ] Terminate after a committed batch and resume without repeating completed
inference. Never reuse or publish an unfinished source group.
- [ ] Mutate or delete a queued/running source and prove stale vectors cannot
activate or become readable.
- [ ] Inject storage/activation failure, preserve existing results and resume.
- [ ] Use genuine Google CPU inference for these integration checks. Storage
- [x] Inject activation failure, preserve existing results and resume completed
staging with zero document inference; cleanup-failure unit regression retains
the primary error and old reader policy.
- [x] Use genuine Google CPU inference for reuse integration checks. Storage
fixtures alone do not establish retrieval quality or reuse equivalence.
- [ ] Measure preparation, the first foreground query and warm p95 while
resumed indexing runs; record actual inference/reuse counts and resource use.
@@ -72,3 +82,24 @@ preserve both the previous active generation and the same reader's policy.
Foreground query priority and bounded persistent workers remain part of the
first-use requirement. Faster unchanged rebuilds alone do not prove responsive
search while indexing.
## Current limits
Reuse requires the complete generation policy to match. Component-level reuse
across media sampling changes remains open. Durable staging resumes at completed
source boundaries; a real process-kill test during a multi-batch source remains
open. Storage tests cover discarded partial groups and corpus revision races.
Full source hashes are checked before ANN construction; indexed changes during
ANN construction are rejected by the final transaction's revision guard. A direct
filesystem mutation during ANN construction before watcher reconciliation still
needs a later validation callback and concurrent real-model regression.
Obsolete completed groups in resumed staging are removed at final reconciliation.
Near the 100,000-chunk ceiling, replacement work can temporarily hit the budget
before this cleanup and fail safely. Bounded discovery/pruning before inference
is the next improvement. Hashing, media metadata probes and graph rebuilding still
have costs: zero encoder calls does not mean zero work or prove fast first input.
Synthetic colored-image and sine-wave fixtures prove actual vector reuse and
citation equality; they do not establish useful media relevance.
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Warm p95 alone does not satisfy the first-use requirement.
- [ ] Test migrations, interrupted work, invalid documents, inaccessible/offline roots and cloud placeholders.
- [ ] Automated native build matrix and documented supported cross-compilation paths.
- [x] Native Linux headless workspace tests: 93 passed, 11 explicitly ignored.
- [x] Native Linux headless workspace tests: 98 passed, 15 explicitly ignored.
- [x] Native Linux Google text/code/media reference parity and service
lifecycle/rebuild recovery: seven genuine CPU integration tests passed.
- [x] Unsigned Linux x86-64 headless archive: verify extracted checksums/modes,
@@ -143,7 +143,13 @@ Audit and measured priorities: [EMBEDDINGGEMMA2-AUDIT.md](EMBEDDINGGEMMA2-AUDIT.
- [x] Windows x64 baseline/optimized CPU runtime selection and OS AVX-state
checks, verified with real packaged baseline and optimized inference.
- [ ] Validate CPU selection on older hardware and remaining architectures.
- [ ] Incrementally reuse unchanged document/media vectors in atomic generations.
- [x] Completed-source vector reuse in atomic schema-7 generations, verified
with genuine Google CPU document/code/image/audio/video fixtures.
- [x] Resume completed staging sources after failed publication; incomplete
groups are purged under the OS build lock before retry.
- [ ] Process-kill/concurrent-source integration, pre-inference pruning near
chunk limits, component-policy reuse and indexing/foreground measurements.
See [INCREMENTAL-SEMANTIC.md](INCREMENTAL-SEMANTIC.md).
- [ ] Benchmark genuine token-budgeted multi-sequence batching and isolation.
- [ ] Implement and measure shared foreground/indexer resource budgets/priority.
- [ ] Attribute startup cost; measure smaller query context and selective encoders.