perf: audit EmbeddingGemma 2 and reuse measured query embeddings
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# EmbeddingGemma 2: efficiency and performance audit
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Research and Windows CPU measurements, 2026-10-12. **The required Google model
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is integrated correctly, but the surrounding pipeline still has substantial
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efficiency and media-quality work.** This audit keeps fresh inference, cached
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query vectors, indexing throughput and user-visible delays separate.
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## What is correct
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The pinned model is `google/embeddinggemma-2`, with Q8_0 backbone/projector
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artifacts verified by size and SHA-256. It is not the older text-only
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EmbeddingGemma or cloud Gemini Embedding 2. Our runtime uses its dedicated
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architecture, bidirectional encoding, mean pooling and L2-normalized 768d output.
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Text-only query workers retain the model; media projector initialization is lazy.
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No language-model generation or network round trip is involved in search.
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Our search-query and titled-document prefixes match Google's instructions.
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Overlapping 2,048-token document windows are a deliberate runtime policy below
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the model's 8,192-token limit; verified byte offsets retain the passage tail.
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Audio is mono 16 kHz. Native runtime parity was independently checked earlier;
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that does not establish broad retrieval quality.
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[Google's model card and guidance](https://ai.google.dev/gemma/docs/embeddinggemma/model_card_2),
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[local adapter](../crates/searchi-semantic/src/gemma.rs),
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[native worker](../crates/searchi-semantic/native/worker.cpp).
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## Actual CPU measurements
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Host: Windows x86-64 VM, four exposed Ryzen 7 7800X3D logical CPUs and about
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9.9 GiB RAM. No GPU inference, rendering or media decoding was used. Native
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worker is a Release build; MCP profile is identified separately below.
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OS file pages may already be warm. Tests used public/generated fixture content.
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### Fresh model inference
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Eight text queries, three repetitions each, with real Google vectors checked
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for finite values, 768 dimensions and unit length. Each request still runs the
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encoder; this table has no query cache.
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| CPU threads | Warm p50 | Warm p95 | First query after worker starts |
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| --- | ---: | ---: | ---: |
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| 1 | 74.90 ms | 86.59 ms | 113.29 ms |
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| 2 | 40.80 ms | 46.66 ms | 64.25 ms |
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| 4 | 34.16 ms | 49.86 ms | 49.98 ms |
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Includes Python NDJSON encode/decode and worker inference; excludes the Rust
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adapter, ANN, UI and media encoders. Thread configurations ran sequentially,
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not randomized. These 24 samples per setting are a diagnostic, not a production
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p95 guarantee. Two threads had the best observed tail; four improved the median
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and document throughput but did not improve this query tail.
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[Raw samples and worker counters](evidence/embeddinggemma2-native-cpu.json).
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Direct worker process startup took 653–816 ms; a separate verified backbone
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checksum took 170 ms in Python. These are not the whole desktop startup.
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Actual Release MCP startup through verified assets, shared service and real
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warmup took **4.88 seconds**. Its first unseen foreground search took **81.95 ms**.
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This exposes an additional startup gap to profile, without attributing it to an
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unmeasured stage or weakening artifact verification.
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[Release shared-service sample](evidence/embeddinggemma2-mcp-release.json).
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### Repeated searches and filter changes
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We added a 32-entry LRU of **actual query vectors**, scoped to the retained
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provider/fingerprint. Exact trimmed queries reuse their vector. Filters, source
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freshness, ANN retrieval and ranking run again; complete search results are not
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cached. The cache is bounded, in memory only, and never seeded with the warmup
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query to disguise first use. Debug traces distinguish hits from real inference.
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Same seven-file corpus, native CPU worker, one thread and Rust Debug executable:
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| Case | Before mean | After mean | Samples per run |
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| --- | ---: | ---: | ---: |
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| Repeat an already searched query | 71.19 ms | 1.20 ms | 8 |
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| Change filters for that query | 70.94 ms | 1.07 ms | 4 |
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All **20 compared result sets** retained identical filenames, source locations,
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snippets, scores and order, including seven different descriptive queries.
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The uncached first query still invoked Google: 82.99 ms before, 77.17 ms after;
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that small difference is not an optimization claim. The later Release run
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measured 0.75 ms mean for repeats and 0.70 ms for filter changes. There is no
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paired old-Release run, so do not call this a Release before/after speedup.
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[Before](evidence/embeddinggemma2-mcp-before.json),
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[after](evidence/embeddinggemma2-mcp-after.json).
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The old CLI binary hash was not captured; its source checkpoint was `9870df7`.
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The new reports include executable hashes. No other inference benchmark was
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intentionally run concurrently, but compilation/background VM work can contend.
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### Indexing is a different workload
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Eight short documents in one native IPC request took 3.64 s / 1.87 s / 1.24 s
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with 1 / 2 / 4 threads. Eight separate requests took 3.84 s / 1.89 s / 1.24 s.
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The single/batch vector cosine minimum was 0.999999997. **Our eight-document
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request is serial inference, not genuine multi-sequence batching.** It mainly
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reduces IPC overhead. These are single comparisons, not throughput distributions.
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Earlier independent media indexing took 668 seconds for 20 assets/118 chunks on
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one CPU thread. Frame/recording costs and quality cannot be inferred from fast
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text queries. Precise spoken-topic localization, music confusion and no-answer
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handling remain failed gates; the earlier experimental media rating is still
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4/10. This cache change does not improve that score.
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[Actual media evidence](MEDIA-QUALITY.md).
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## Changes made from this audit
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- Added the bounded real-vector cache, with failure/eviction/fingerprint,
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filtering/source lifecycle and trace tests.
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- Reduced the intentional semantic UI delay from 350 to 120 ms; the separate
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60 ms lexical debounce and bounded request lanes remain. That removes 230 ms
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of configured waiting, not a measured 230 ms compositor improvement.
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- Kept an existing ready semantic generation searchable during rebuilds.
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Initial indexing still needs a generation; activation/warmup still withdraws
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backend readiness. Foreground/indexer CPU contention is not solved by this.
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- Updated capture validation to use an unseen query when recording actual
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worker-inference counters, rather than mislabeling a cache hit.
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## Highest-impact next work
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| Priority | Evidence / proposed work | Required acceptance evidence |
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| --- | --- | --- |
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| P0 | Bound the whole IPC exchange: synchronous writes currently precede the response timeout. | Nonreading-helper regression; large request deadline and recovery. |
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| P0 | Decide the x86 CPU baseline: current static build enables AVX2 without runtime CPU variants. | Declared minimum ISA or tested baseline/AVX2 dispatch; older-CPU launch. |
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| P1 | Reuse unchanged document/media vectors; replacement builds currently re-encode all sources. | One-file change embeds only affected chunks; atomic activation and stale/deletion tests. |
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| P1 | Pack independent short sequences by token budget; the native worker currently encodes serially. | Vector isolation/parity, length-bucketed CPU/GPU throughput and memory comparisons. |
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| P1 | Give interactive queries a shared resource budget/priority during indexing. | First-unseen and warm p95 with real background document/media jobs. |
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| P1 | Profile startup stages and compare a smaller query-only context. | Verification/load/graph-warmup/RSS measured separately; no fake first-query cache. |
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| P2 | Buffer bounded IPC reads, remove unnecessary stream tying, and make window offset work linear. | Protocol bounds, UTF-8 offsets/tails and real media/payload timing parity. |
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| P2 | Consider modality-selective projector loading; current lazy combined projector loads both encoders. | Real text/image/audio RSS and load measurements on supported backends. |
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[DigUp's pinned runtime](https://github.com/ARahim3/DigUp/blob/6faa29e4c315a3a591de1b41bd78751bdf39588d/Sources/LlamaRuntime/LlamaEmbedder.swift)
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demonstrates independent-sequence packing and a smaller text-query context.
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Those are useful patterns, not proof of an identical speedup on this VM.
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Google supports re-normalized MRL truncation to 512/256/128d. A 256d experiment
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could reduce vector storage threefold; it does **not** eliminate the model's
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forward pass. Keep 768d until independent retrieval tests pass. Higher vision
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budgets trade cost for detail, so retain content-specific quality experiments.
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[Google's truncation and modality guidance](https://ai.google.dev/gemma/docs/embeddinggemma/model_card_2).
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GPU execution remains untested here. Audit its precision against Google's
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BF16/FP32 guidance, and move the inference timing boundary past asynchronous
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embedding synchronization before interpreting GPU stage traces. CPU-only results
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do not validate accelerator throughput, quality or failure recovery.
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## Reproduce
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The benchmark requires already verified model assets, a compiled CPU worker and
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an explicitly selected fixture index. It does not download or index user files.
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Read its scope fields and raw samples before comparing hardware or profiles.
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```powershell
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python tools/embeddinggemma2/benchmark.py --mode native --models .searchi/models/embeddinggemma2 --worker .searchi/runtime/cpu/Release/searchi-gemma-worker.exe --threads 1 2 4 --repeats 3 --output .searchi/native-performance.json
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python tools/embeddinggemma2/benchmark.py --mode mcp --models .searchi/models/embeddinggemma2 --worker .searchi/runtime/cpu/Release/searchi-gemma-worker.exe --cli target/release/searchi.exe --db .searchi/desktop-capture/index.db --repeats 8 --rust-profile release --output .searchi/mcp-performance.json
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```
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@@ -96,6 +96,16 @@ real Google inference remains required for product semantic search. Platform,
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large-corpus, concurrent-indexing and GPU performance gates remain independent.
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## Worker round-trip diagnostics
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The retained hybrid service now keeps a bounded 32-query vector LRU. Debug
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`semantic.query_embedding` spans report `query_cache_hit` and `query_cache_miss`.
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A hit reuses an actual model vector and performs no neural inference; it must
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not carry the previous worker's tokenization/inference counters. Filters, current
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source eligibility, ANN retrieval and fusion still execute. Warmup performs an
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actual neural probe and does not seed an unseen foreground-query result.
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Compare fresh queries, repeats and filter changes separately. The current
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[EmbeddingGemma 2 audit](EMBEDDINGGEMMA2-AUDIT.md) includes paired real-CPU
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measurements, startup boundaries and remaining indexing/media costs.
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Measured desktop failures return `{message, metrics}` and retain the trace for
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the failed attempt. Busy retries are separate attempts. If an unexpected worker
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task failure loses its collector, elapsed time is retained, the trace is null,
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@@ -80,6 +80,29 @@ Status: implementation started; no production release yet. Windows VM tests use
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- [ ] Native UI interaction and chosen-provider quality/latency checks before
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claiming production-ready answers or compatibility with every provider.
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## 7. EmbeddingGemma 2 efficiency
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Audit and measured priorities: [EMBEDDINGGEMMA2-AUDIT.md](EMBEDDINGGEMMA2-AUDIT.md).
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- [x] Audit actual required model, task prefixes, pooling/normalization, native
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sequence processing and CPU/runtime policies against primary sources.
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- [x] Record real CPU 1/2/4-thread query/first-use/document measurements and
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paired repeated-query/filter-change results with unchanged retrieval evidence.
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- [x] Bounded provider-scoped actual-query vector LRU with accurate cache traces;
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preserve filtering, source freshness, failures and actual warmup probes.
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- [x] Reduce semantic settle delay to 120 ms and retain old ready generation
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during rebuilding; test initial-generation and activation readiness boundaries.
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- [ ] Bound whole IPC write/read deadlines and recover from a nonreading helper.
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- [ ] Declare/test x86 CPU minimum or baseline/AVX2 runtime selection.
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- [ ] Incrementally reuse unchanged document/media vectors in atomic generations.
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- [ ] Benchmark genuine token-budgeted multi-sequence batching and isolation.
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- [ ] Implement and measure shared foreground/indexer resource budgets/priority.
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- [ ] Attribute startup cost; measure smaller query context and selective encoders.
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- [ ] Buffered bounded media IPC and linear UTF-8 source-window offset processing.
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- [ ] Evaluate 256d MRL and content-specific vision budgets on independent data.
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- [ ] Validate GPU precision, synchronization-aware timings, parity and fallback
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on actual accelerator hardware; CPU VM checks do not satisfy this gate.
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Completion requires recorded evidence; targets are not measured results.
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## Completed foundation details
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+15
-3
@@ -10,7 +10,7 @@ and `SEARCHI_FORCE_CPU=1`.
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- Native desktop: **13/13 tests passed**, including structured trace/basic responses
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and four optional-answer source/consent/revision tests.
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Post-generation cached query warmup is implemented but its interactive first-query gate remains open.
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Frontend: **35/35 tests passed**, TypeScript/Vite production build and
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Frontend: **36/36 tests passed**, TypeScript/Vite production build and
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formatting passed. Preview policy tests cover fractional timestamp seeking,
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image labels, one active/latest pending request, stale replies and Escape.
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- Semantic adapter: **8 default tests passed**; the explicit 48 MP resource test
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@@ -43,8 +43,8 @@ and `SEARCHI_FORCE_CPU=1`.
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that run: [recorded benchmark](../crates/searchi-core/benchmarks/results/windows-cpu-full-candidate-first-final.json).
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These measurements do not establish every query or concurrent-indexing goal.
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The integrated CPU workspace checkpoint passed **72 default tests** (7 optional AI,
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8 assets, 8 CLI/MCP, 24 core, 4 media, 8 semantic, 13 service), with **10 explicit
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The integrated CPU workspace checkpoint passed **78 default tests** (7 optional AI,
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8 assets, 8 CLI/MCP, 24 core, 4 media, 8 semantic, 19 service), with **10 explicit
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opt-in tests ignored**. Workspace and native all-target Clippy passed with
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warnings denied. The later semantic CPU-restart counter change passed workspace
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Clippy and the actual CPU query diagnostics test. Earlier aggregate counts are historical.
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@@ -128,6 +128,18 @@ agent execution and release packaging remain open gates.
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## Historical evidence
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The [EmbeddingGemma 2 audit](EMBEDDINGGEMMA2-AUDIT.md) records new CPU diagnostics:
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24 uncached native queries per 1/2/4-thread setting, paired real MCP repeat/filter
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measurements, 20 unchanged result-set comparisons, actual Release startup/first
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query, and document single-versus-serial-batch parity. Six cache-control tests
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verify eviction, fingerprint/trim behavior, failures, unchanged filtering/source
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lifecycle and accurate hit traces. The frontend readiness regression retains an
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old committed generation during rebuilds and blocks initial/activation states.
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The four real-engine browser captures were regenerated and visually inspected;
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the performance capture deliberately uses an unseen query and proves actual
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worker inference rather than treating a cached vector as a new neural pass.
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Native visual/paint and production media relevance gates remain open.
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Initial engine/CLI checks covered retrieval, persistence, filesystem changes,
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exclusions/filters, migration, concurrent readers, PDF pages, DOCX paragraphs,
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corrupt replacement, encrypted PDF errors and bounded compressed input.
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@@ -0,0 +1,730 @@
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{
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"captured_at_utc": "2026-10-12T18:41:00Z",
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"os": "Windows-11-10.0.26300-SP0",
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"machine": "AMD64",
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"logical_cpus": 4,
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"cpu_only": true,
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"rust_profile": "debug",
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"label": "bounded-query-vector-cache",
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"worker_sha256": "1c3bd856e56f49a1ffc11ec3eeaef20297bda5f1539427ef772dc9045e7da8b3",
|
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"cli_sha256": "4a4c69c34f48162e2033fbfbc7b3500aa7496f6c1d9a03be6d611cf84bafe6e0",
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"results": {
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"startup_to_initialized_ms": 11040.071900002658,
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"stats": {
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"files": {
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"content_files": 5,
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"files": 7
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},
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"mode": "hybrid",
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"semantic_available": true,
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"worker": {
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"device": "cpu",
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"dimensions": 768,
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"media_supported": true,
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"model": "google/embeddinggemma-2",
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"ready": true,
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"runtime": "b11461-no-logits-v1",
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"threads": 1
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}
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},
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"samples": [
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{
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"case": "first_unseen",
|
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"query": "cancel the supplier partnership",
|
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"extension": null,
|
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"request_ms": 79.04949999647215,
|
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"engine_ms": 77.1732,
|
||||
"matches": [
|
||||
{
|
||||
"filename": "Supplier Agreement.pdf",
|
||||
"source": {
|
||||
"kind": "page",
|
||||
"number": 2
|
||||
},
|
||||
"snippet": "Termination and cancellation The supplier partnership may be ended with sixty days written notice. Send the cancellation letter to the vendor. Return unused materials and settle outstanding invoices. \n",
|
||||
"score": 0.01639344262295082
|
||||
},
|
||||
{
|
||||
"filename": "Vendor Onboarding.md",
|
||||
"source": {
|
||||
"kind": "line",
|
||||
"number": 1
|
||||
},
|
||||
"snippet": "# Vendor onboarding\nCollect supplier tax details, banking information and insurance documents. The purchasing team completes compliance review before signing a new partnership.",
|
||||
"score": 0.016129032258064516
|
||||
},
|
||||
{
|
||||
"filename": "Q3 Procurement.md",
|
||||
"source": {
|
||||
"kind": "line",
|
||||
"number": 1
|
||||
},
|
||||
"snippet": "# Q3 procurement review\nWe will review supplier delivery times, packaging costs and the annual purchasing budget. Present the comparison of existing vendors at Thursday's planning meeting.",
|
||||
"score": 0.015873015873015872
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"case": "same_query",
|
||||
"query": "cancel the supplier partnership",
|
||||
"extension": null,
|
||||
"request_ms": 2.6412000006530434,
|
||||
"engine_ms": 1.4406,
|
||||
"matches": [
|
||||
{
|
||||
"filename": "Supplier Agreement.pdf",
|
||||
"source": {
|
||||
"kind": "page",
|
||||
"number": 2
|
||||
},
|
||||
"snippet": "Termination and cancellation The supplier partnership may be ended with sixty days written notice. Send the cancellation letter to the vendor. Return unused materials and settle outstanding invoices. \n",
|
||||
"score": 0.01639344262295082
|
||||
},
|
||||
{
|
||||
"filename": "Vendor Onboarding.md",
|
||||
"source": {
|
||||
"kind": "line",
|
||||
"number": 1
|
||||
},
|
||||
"snippet": "# Vendor onboarding\nCollect supplier tax details, banking information and insurance documents. The purchasing team completes compliance review before signing a new partnership.",
|
||||
"score": 0.016129032258064516
|
||||
},
|
||||
{
|
||||
"filename": "Q3 Procurement.md",
|
||||
"source": {
|
||||
"kind": "line",
|
||||
"number": 1
|
||||
},
|
||||
"snippet": "# Q3 procurement review\nWe will review supplier delivery times, packaging costs and the annual purchasing budget. Present the comparison of existing vendors at Thursday's planning meeting.",
|
||||
"score": 0.015873015873015872
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"case": "same_query",
|
||||
"query": "cancel the supplier partnership",
|
||||
"extension": null,
|
||||
"request_ms": 2.3130000045057386,
|
||||
"engine_ms": 1.1769,
|
||||
"matches": [
|
||||
{
|
||||
"filename": "Supplier Agreement.pdf",
|
||||
"source": {
|
||||
"kind": "page",
|
||||
"number": 2
|
||||
},
|
||||
"snippet": "Termination and cancellation The supplier partnership may be ended with sixty days written notice. Send the cancellation letter to the vendor. Return unused materials and settle outstanding invoices. \n",
|
||||
"score": 0.01639344262295082
|
||||
},
|
||||
{
|
||||
"filename": "Vendor Onboarding.md",
|
||||
"source": {
|
||||
"kind": "line",
|
||||
"number": 1
|
||||
},
|
||||
"snippet": "# Vendor onboarding\nCollect supplier tax details, banking information and insurance documents. The purchasing team completes compliance review before signing a new partnership.",
|
||||
"score": 0.016129032258064516
|
||||
},
|
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{
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},
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{
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},
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{
|
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{
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},
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{
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|
||||
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|
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|
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},
|
||||
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||||
}
|
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]
|
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},
|
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{
|
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"case": "distinct_query",
|
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|
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|
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@@ -0,0 +1,732 @@
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"captured_at_utc": "2026-10-12T18:35:22Z",
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},
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"score": 0.015873015873015872
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{
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"case": "same_query",
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"request_ms": 77.02430000063032,
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|
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"score": 0.01639344262295082
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||||
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{
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"case": "distinct_query",
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{
|
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},
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|
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},
|
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|
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{
|
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"case": "distinct_query",
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File diff suppressed because it is too large.
Load diff
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|
||||
"sha256": "a8347729d7995d0354fd45c1d0762d3fe33adbf0dfcf9ac3387073e522710b15",
|
||||
"sha256": "bd45041de13093f7a7e26285618f4c1afc5b081fb41e51ff06e1313216af3562",
|
||||
"query": "cancel the supplier partnership",
|
||||
"mode": "basic",
|
||||
"result_count": 7,
|
||||
@@ -374,7 +371,7 @@
|
||||
"first_source_kind": "page",
|
||||
"first_source_number": 2,
|
||||
"theme": "light",
|
||||
"summary": "7 RESULTS · KEYWORD + MEANING · SEARCH 85 MS · SHOWN 543 MS",
|
||||
"summary": "7 RESULTS · KEYWORD + MEANING · SEARCH 2.1 MS · SHOWN 233 MS",
|
||||
"state_assertions": {
|
||||
"semantic_ready": true,
|
||||
"hybrid_final": true,
|
||||
@@ -387,7 +384,7 @@
|
||||
},
|
||||
{
|
||||
"file": "search-dark.png",
|
||||
"sha256": "b8cf9505ada4afb439edd469d58868fff92dd99cf9b62b6d4a5127307c1e36a7",
|
||||
"sha256": "cd7e2aa3f7027cd9b40965171a232f6ee901e1e2b971f04628b4cfcc3b440263",
|
||||
"query": "cancel the supplier partnership",
|
||||
"mode": "basic",
|
||||
"result_count": 7,
|
||||
@@ -395,7 +392,7 @@
|
||||
"first_source_kind": "page",
|
||||
"first_source_number": 2,
|
||||
"theme": "dark",
|
||||
"summary": "7 RESULTS · KEYWORD + MEANING · SEARCH 85 MS · SHOWN 543 MS",
|
||||
"summary": "7 RESULTS · KEYWORD + MEANING · SEARCH 2.1 MS · SHOWN 233 MS",
|
||||
"state_assertions": {
|
||||
"semantic_ready": true,
|
||||
"hybrid_final": true,
|
||||
@@ -408,7 +405,7 @@
|
||||
},
|
||||
{
|
||||
"file": "media-search.png",
|
||||
"sha256": "db832104e3b2fc59bd07a4ea393d38d363c00385023e5f9e469726e076e9b9fe",
|
||||
"sha256": "4c278446963cd5dbad073bea93a3ca33fb9c10cb43a9544258d5fb6039014d81",
|
||||
"query": "mountains reflected in calm water",
|
||||
"mode": "basic",
|
||||
"result_count": 7,
|
||||
@@ -416,7 +413,7 @@
|
||||
"first_source_kind": "image",
|
||||
"first_source_number": 0,
|
||||
"theme": "light",
|
||||
"summary": "7 RESULTS · KEYWORD + MEANING · SEARCH 84 MS · SHOWN 519 MS",
|
||||
"summary": "7 RESULTS · KEYWORD + MEANING · SEARCH 1.6 MS · SHOWN 217 MS",
|
||||
"state_assertions": {
|
||||
"semantic_ready": true,
|
||||
"hybrid_final": true,
|
||||
@@ -427,23 +424,20 @@
|
||||
},
|
||||
{
|
||||
"file": "performance-details.png",
|
||||
"sha256": "149753ca90cda5d81254d43e15118b61325a61d7f0fb366b783d4e2dfac5d96d",
|
||||
"query": "cancel the supplier partnership",
|
||||
"sha256": "4f336d4c42d0c4a50ce33bca27595f02da7d896c8734d447350d50dab5c339df",
|
||||
"query": "how can we cancel the supplier partnership",
|
||||
"mode": "debug",
|
||||
"result_count": 7,
|
||||
"selected_filename": "Supplier Agreement.pdf",
|
||||
"first_source_kind": "page",
|
||||
"first_source_number": 2,
|
||||
"theme": "light",
|
||||
"summary": "7 RESULTS · KEYWORD + MEANING · SEARCH 82 MS · SHOWN 514 MS",
|
||||
"summary": "7 RESULTS · KEYWORD + MEANING · SEARCH 93 MS · SHOWN 311 MS",
|
||||
"state_assertions": {
|
||||
"semantic_ready": true,
|
||||
"hybrid_final": true,
|
||||
"image_visible": false,
|
||||
"actual_worker_inference": true,
|
||||
"literal_query_marks": true,
|
||||
"actual_page_explanation": true,
|
||||
"why_disclosure_collapsed_by_default": true
|
||||
"actual_worker_inference": true
|
||||
}
|
||||
}
|
||||
]
|
||||
|
||||
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Reference in new issue
Block a user