feat: integrate Google semantic search and branded desktop launcher
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@@ -102,3 +102,127 @@ is still not established. Query distribution and filters are not represented by
this four-case fixture; restrictive broad filters and sub-three-character searches
remain potentially slow, while correctness tests ensure late matching files are
not lost through arbitrary pre-filter caps.
## Actual first foreground inputs, schema 8
The schema-8 run persists trigram frequency statistics in background preparation
before allowing the measured inputs. Preparation took **17.21 seconds** and
loaded 1,268 frequency entries. That time is reported separately and is not
subtracted from a foreground query. The same 1M/100K fixture was reused.
The harness measures 20 different exact filenames, spread across the corpus,
with no preceding query warmups. It then measures 20 different filenames, each
on a newly opened reader without calling `prewarm` again, using persisted stats.
This second set includes reader-open time. Both sets validate exact-match priority
and record every raw query and duration. They are disjoint from each other and
from the four-case exact query for this full fixture.
| Distinct first-input set | p50 | p95 | Actual very first input | Samples ≥50 ms |
| --- | ---: | ---: | ---: | ---: |
| Prepared reader, 20 names | 13.96 ms | 18.97 ms | **98.49 ms** | 1/20 |
| Fresh readers, 20 other names | 16.78 ms | 22.73 ms | **103.57 ms** | 1/20 |
The p95 filename targets pass, but the very first zero-heavy filename in each
set fails the 50 ms target. The nearest-rank p95 excludes one maximum sample
when there are 20 samples, so p95 alone must not be used to claim that the actual
first input is always fast. Explicit first-sample flags and maximum durations
are included in the report, derived directly from the recorded raw samples.
| Existing case, first execution before its warmups | First input | First target | Repeated warm p95 |
| --- | ---: | --- | ---: |
| Exact filename at end of corpus | 16.31 ms | Passed <50 ms | 13.80 ms |
| Broad filename substring | 13.01 ms | Passed <50 ms | 0.47 ms |
| Unique content token | 14.07 ms | Passed <150 ms | 0.49 ms |
| Broad keyword | **264.53 ms** | **Failed <150 ms** | 145.19 ms |
These measurements expose remaining first-input work even after frequency
preparation. Zero-heavy names produce larger candidate sets, and broad content
retrieval has a first-use penalty. This report does not establish the requested
fast-first-input behavior as complete. It also does not represent a cold OS
page-cache test: background preparation and previous operations can warm disk
pages. Host workload isolation remains unverified. The retained report is
`windows-cpu-full-prepared-v8.json`; engine code was not changed during this run.
## Background representative search and restart cost, schema 9
Schema 9 refreshes frequency statistics only when filename/path membership is
dirty. It also runs representative searches in background. The report explicitly
records the actual five background inputs from this corpus: `invoice_0000000.txt`,
`invoice_0999999.txt`, `Supplier`, `termination`, and `cancellation`. Consequently
the later first foreground executions of overlapping inputs are prepared inputs,
not never-before-executed queries. This behavior is intentional application
preparation, rather than an undisclosed benchmark warmup.
Initial preparation took **17.723 seconds**. A second `prewarm()` on a newly
opened reader took **0.396 seconds**, reusing clean persisted statistics. That
second call occurred only after all foreground measurements, so it could not
improve their recorded first timings. Startup time is amortized when names have
not changed; refresh after mutations still has an explicit background cost.
| Twenty distinct foreground names | p50 | p95 | Very first name | ≥50 ms |
| --- | ---: | ---: | ---: | ---: |
| Prepared reader | 12.75 ms | 14.40 ms | **85.38 ms** | 1/20 |
| Newly reopened readers | 16.31 ms | 31.42 ms | **100.07 ms** | 1/20 |
The remaining zero-heavy filename outliers fail the first-input target even
after representative preparation. Repeating that representative query in
background does not eliminate its candidate-processing cost. The 20 distinct
query sets remain unchanged from schema 8, and every raw sample and explicit
first/max failure flag is retained.
| Existing case | First foreground | Repeated p95 | Target |
| --- | ---: | ---: | --- |
| Exact filename at corpus end | 13.48 ms | 13.22 ms | Passed <50 ms |
| Filename substring | 3.20 ms | 0.43 ms | Passed <50 ms |
| Unique content token | 2.57 ms | 0.53 ms | Passed <150 ms |
| Broad keyword | 99.10 ms | 106.20 ms | Passed <150 ms |
Background initialization fixes the observed broad-keyword first-use penalty,
but the full fast-first-input requirement remains incomplete because of the
filename outliers. Further algorithmic filename work is required; the report
does not infer that preparation makes every unseen query fast. This Windows
CPU-only run is stored in `windows-cpu-full-prepared-v9.json`.
## Positional candidate correction and finalist projection
Extending the rarest trigram through the entire suffix regressed retrieval:
the unchanged 20-name sets had p95 **136.60/129.17 ms**, and the first reopened
zero-heavy name took **554.32 ms**. That rejected experiment remains recorded in
`windows-cpu-full-suffix-anchor.json`. A long phrase can initialize dense suffix
postings even when its first term is rare.
The replacement grows a positional anchor only through neighboring grams within
a bounded frequency factor, with a maximum of 32 Unicode characters. Full literal
verification and filter-before-limit completeness remain unchanged. Temporary
lane profiling separated cheap indexed equality (about 0.06 ms in the diagnostic
run) from first-use broad-prefix filename retrieval (about 67 ms). Generic
alphabetic tokens derived from representative names now initialize those common
postings in background. Unfiltered content searches rank compact IDs before
materializing finalist paths/snippets; filtered ranking is kept complete.
The final unchanged 1M/100K corpus run uses these disclosed background inputs:
first/last filenames, `invoice`, `txt`, and the first document words `Supplier`,
`termination`, and `cancellation`. Clean-statistics preparation took **0.709 s**;
a subsequent new-reader preparation took **0.264 s**. Earlier initial dirty
statistics refresh still took roughly 17 seconds; this run does not erase that
startup/refresh cost.
| Twenty distinct first inputs | p95 | Very first | Maximum | Samples ≥50 ms |
| --- | ---: | ---: | ---: | ---: |
| Prepared reader | 18.15 ms | 6.14 ms | 22.94 ms | 0/20 |
| Newly reopened readers, including open | 30.86 ms | 30.86 ms | 34.28 ms | 0/20 |
| Existing case | First foreground | Repeated p95 | Target |
| --- | ---: | ---: | --- |
| Exact filename at corpus end | 4.37 ms | 3.77 ms | Passed <50 ms |
| Filename substring | 14.18 ms | 1.23 ms | Passed <50 ms |
| Unique content token | 10.06 ms | 0.72 ms | Passed <150 ms |
| Broad keyword | 114.44 ms | 117.88 ms | Passed <150 ms |
All measured first-input samples and repeated-query p95 values pass in this
Windows CPU run. The retained final report is
`windows-cpu-full-candidate-first-final.json`, with every raw filename/sample.
This establishes the measured fixture result, not a guarantee for all queries,
filters, corpus distributions, cold OS caches, or other operating systems. The
intermediate density-only reports retain remaining failures and are not promoted
as final acceptance evidence.
@@ -0,0 +1,179 @@
{
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"reopened_background_prepare_seconds": 0.2639451,
"background_representative_queries": [
"invoice_0000000.txt",
"invoice_0999999.txt",
"invoice",
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"Supplier",
"termination",
"cancellation"
],
"prepared_frequency_count": 1268,
"first_foreground_distinct_names": {
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"includes_reader_open": false,
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"invoice_0299999.txt",
"invoice_0349999.txt",
"invoice_0399999.txt",
"invoice_0449999.txt",
"invoice_0499999.txt",
"invoice_0549998.txt",
"invoice_0599998.txt",
"invoice_0649998.txt",
"invoice_0699998.txt",
"invoice_0749998.txt",
"invoice_0799998.txt",
"invoice_0849998.txt",
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},
"reopened_reader_first_names": {
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"queries": [
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"invoice_0050000.txt",
"invoice_0100000.txt",
"invoice_0150000.txt",
"invoice_0200000.txt",
"invoice_0250000.txt",
"invoice_0300000.txt",
"invoice_0350000.txt",
"invoice_0400000.txt",
"invoice_0450000.txt",
"invoice_0500000.txt",
"invoice_0549999.txt",
"invoice_0599999.txt",
"invoice_0649999.txt",
"invoice_0699999.txt",
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"files": 1000000,
"documents": 100000,
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"database_path": "target/benchmarks/full.db",
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{
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"first_query_target_met": true,
"p95_ms": 1.228,
"target_p95_ms": 50.0,
"target_met": true
},
{
"query": "marker0099999",
"samples": 20,
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"first_query_target_met": true,
"p95_ms": 0.7228,
"target_p95_ms": 150.0,
"target_met": true
},
{
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"first_query_ms": 114.44120000000001,
"first_query_target_met": true,
"p95_ms": 117.8776,
"target_p95_ms": 150.0,
"target_met": true
}
]
}
@@ -0,0 +1,177 @@
{
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"reopened_background_prepare_seconds": 0.3083516,
"background_representative_queries": [
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"cancellation"
],
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"invoice_0299999.txt",
"invoice_0349999.txt",
"invoice_0399999.txt",
"invoice_0449999.txt",
"invoice_0499999.txt",
"invoice_0549998.txt",
"invoice_0599998.txt",
"invoice_0649998.txt",
"invoice_0699998.txt",
"invoice_0749998.txt",
"invoice_0799998.txt",
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@@ -0,0 +1,179 @@
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}
]
}
@@ -0,0 +1,169 @@
{
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"invoice_0249999.txt",
"invoice_0299999.txt",
"invoice_0349999.txt",
"invoice_0399999.txt",
"invoice_0449999.txt",
"invoice_0499999.txt",
"invoice_0549998.txt",
"invoice_0599998.txt",
"invoice_0649998.txt",
"invoice_0699998.txt",
"invoice_0749998.txt",
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