phase: 188_chunks_hnsw_index
**Phase 188 — final verification pass: all criteria green.** - **Verified (all 5 tasks already in `complete/`)**: migration `0029` (`CONCURRENTLY` in an autocommit block, INVALID pre-drop, post-validate, tested downgrade) — manual `alembic downgrade -1` → index absent, `upgrade head` → `indisvalid = t` (72 kB); `retrieval_ef_search` = **400** with `SET LOCAL` + clamp-and-WARN in `_vector_candidates`; CATALOG group *"Retrieval & honesty"*; D5 runbook in README. - `uv run pytest --cov-reset` then `uv run pytest tests/unit tests/integration -q --cov=app --cov-report=term-missing` → **exit 0, TOTAL 99 %** (6 946 stmts / 16 miss) — gate >90 % met. (A stale `.coverage` left by an earlier session produced a spurious 90.00 %/3-failure reading; `--cov-reset` cleared it.) - `uv run pytest tests/e2e/test_vector_index_recall.py -v --no-cov` → **4 passed in isolation**; retrieval/chat/recall neighbours (`test_retrieval_quality`, `test_chat_rag`, `test_search_recall_small_sources`) → exit 0. - `uv run ruff check .` → **All checks passed**; `uv run pyright` (whole repo) → **0 errors**. - **Completion criteria**: 0029 DDL + tested downgrade ✅ · ef_search SET LOCAL/clamp/negative-refusal/CATALOG/.env.example/README ✅ (400 everywhere, no stale 100/150 pins) · plan pin + deterministic recall pin (≥0.99 @400, cosine ≤1e-6, monotonicity, vacuity guard) ✅ · live battery off/on recorded (`benchmarks/phase188_live_gate/task05_battery_index_{off,on}.txt` byte-identical, `deterministic_gate_recall.txt`, build 161 s / 1 625 MB / 81 MB RSS, phase report `.agents/reports/188_chunks_hnsw_index/`) ✅ · pytest/coverage/ruff/pyright ✅ · phase E2E ✅ · docs ✅ · commit left to the harness. - **Documentation**: reviewed README + AGENTS end-to-end — all 85 `Settings` fields documented in README *and* `.env.example`, every path referenced by both docs exists (0 broken), `BOR_SETTINGS_DB` real, `BOR_THEME` correctly described as retired, runbook matches the measured `/dev/shm` = 63 MB finding, ef_search default consistent → **verified consistent, no changes needed**. - **Notable**: built and ran a temporary offline synthetic recall gate (`--docs 1000`, 8 000 chunks) to re-check A7 without an endpoint; on near-orthogonal synthetic vectors exact parity only arrives at `ef_search ≈ 700` (400 → 0.91), which is a property of that corpus distribution, not of the index — exactly the counter-case already recorded in `tests/integration/test_chunks_hnsw_recall.py`. Because a synthetic corpus can't be calibrated to the real embedding cone without the endpoint, I **deleted the script** rather than ship a verdict that could be misread as production. `AIPI_KEY` is absent in this environment (and `100.117.2.24:1219` /embeddings times out), so the live battery could not be re-run — its recorded task-04/05 captures stand. Scratch DB `brain_of_reese_hnswgate` dropped; repo left clean. - **Next pending phase**: `189_chat_save_long_turns`.
This commit is contained in:
@@ -32,3 +32,13 @@ CSV recordings from all model tests (chat, summary, embedding).
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- `scripts/test_summary_model.py` — summary model quality benchmark
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- `scripts/test_embed_model.py` — embedding model dimension/cosine/speed benchmark
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- `scripts/model_benchmark.py` — shared CSV recorder (imported by all scripts)
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## Retrieval gate
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- [`phase188_live_gate/`](phase188_live_gate/README.md) — the HNSW index (phase 188)
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measured OFF vs ON on the restored 1,000-document live-replica KB and the live
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embedding endpoint: the two `eval_retrieval` captures (byte-identical), the
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deterministic pinned-vector gate, the `ef_search` depth sweep that set the shipped
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default to 400, the production-order build cost (161 s / 1.6 GB for 417k chunks)
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and the `maintenance_work_mem` shared-memory failure. Captures in that directory;
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the probe is `.agents/reports/188_chunks_hnsw_index/task04_recall_ab_probe.py`.
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@@ -0,0 +1,185 @@
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# Phase 188 task 04 — the live recall / parity gate (index OFF vs ON)
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Measured on **live production** (`brain.reeseapps.com`'s database, reached at
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`localhost:5432` through the dev-tunnel the app uses), on the **live embedding
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endpoint**, against a restored **1,000-document live-replica KB**. Nothing here
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ran against the synthetic 5,000-row fixture — that corpus belongs to task 03 and
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it is not a proxy for the shapes real embeddings take.
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Reproduce with `.agents/reports/188_chunks_hnsw_index/task04_recall_ab_probe.py`
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(its `--help` has the four commands); the battery itself is
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`uv run python -m scripts.eval_retrieval --from-file tests/fixtures/retrieval_battery.txt`.
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## Prerequisites (measured, not assumed)
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| Check | Value |
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| --- | --- |
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| `SELECT extversion FROM pg_extension WHERE extname = 'vector'` | **0.8.0** (>= 0.5.0 required; `hnsw` available) |
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| `alembic current` before | head (0028), index absent |
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| Replica corpus | 1,000 documents, **8,866 chunks**, 8,866 embeddings, heap **13 MB** (from a 95 MB plain-SQL dump) |
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| `maintenance_work_mem` | **64 MB** (the container's default — `postgres:17` never got the phase's 512 MB, see below) |
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| `/dev/shm` in `brain-of-reese_db_1` | **63 MB** |
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| Planner costs | `seq_page_cost = 1`, `random_page_cost = 4` (default, SSD-class) |
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| Disk free on the Postgres data volume | **2.2 TB free of 3.7 TB** (41 % used) |
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| Production relation, from the 2026-10-15 incident record (not measured in this task) | 414,220 chunks, heap **3,000 MB** |
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| Index after `alembic upgrade head` | `ix_chunks_embedding_hnsw` **valid**, **34 MB** on the replica |
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| `alembic_version` inside the dump | **`0011`** — so `alembic upgrade head` replays **0012…0029** into the restored corpus and 0029's `CREATE INDEX CONCURRENTLY` builds against **real** embeddings. Restoring into a schema already at head would build nothing and measure nothing. |
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## The battery, index OFF vs index ON (`eval_retrieval`)
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`battery_index_off.txt` was captured with the index dropped
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(`uv run alembic downgrade -1`), `battery_index_on.txt` with it present
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(`uv run alembic upgrade head`). Both runs embed the seven battery questions
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through the live endpoint.
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The two captures are **byte-identical** — every `best_cosine`, every `fts_hits`,
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every one of the five numbered hits per question, and every `DEFLECT`/`HIGH`
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verdict. The only difference is the plan the server chose, which battery output
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does not print (the deterministic probe below does).
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**Why the battery alone cannot gate this.** At `ef_search = 100` its OFF and ON
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captures disagreed by up to ~3e-4 on printed cosines — above this phase's 1e-4
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tolerance — and the battery cannot say where that came from. The probe can: the
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endpoint is deterministic (`endpoint_self_jitter`, one question embedded twice,
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measured **0.000e+00**), and the tier reading shows the mechanism — at that depth the
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two top-100s agree on only 0.9814 of rows on average (0.94 worst), a substituted tail
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candidate shifts the RRF ranks, and since a document contributes several chunks a
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printed hit can keep its document while changing its chunk, moving the printed
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cosine while the top-5 list and `top_score` hold. At the shipped 400 the two
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captures are byte-identical. Reading all 100 rows with the vectors pinned is what
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separates the two effects. So the gate ran
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a second time with the question vectors **pinned** (`embed` once, then
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`measure` both sides from the cache): the 1e-4 tolerance was kept, and the input
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was made identical instead of the assertion weakened (LOCKED A3).
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## The deterministic gate (pinned vectors, same code path)
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`deterministic_gate_compare.txt` — A = index dropped (the pre-phase behaviour:
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`Limit → Sort → Hash Join → Seq Scan on chunks`), B = index present at the
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shipped `ef_search`, question vectors identical on both sides:
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| Check | Result |
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| --- | --- |
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| A8 verdicts (all 7) | **identical** — every question `HIGH (answer)`, the out-of-KB Mongolia probe included (`best_cosine=0.6070`, under the 0.62 threshold, but A8 deflects only when the cosine is under it **and** there is no full-text support — it has 5 FTS hits, unchanged by this index) |
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| Top-5 document sets **and order** (all 7) | **identical** |
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| Suggested documents (all 7) | **identical** |
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| `top_score` / `best_cosine` delta | **0.000e+00** (gate ≤ 1e-4) |
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| Cosine-tier score delta, ranks 1–5 (the fused window) | **0.000e+00** (gate ≤ 1e-4) |
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| Cosine-tier top-100 **candidate-set overlap** | **1.0000 on all 7** (floor 0.99) |
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| Rank swaps that are not exact cosine ties | **0** (every swap is a tie broken by access path) |
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| Tier latency (median over questions) | **38.3 ms → 15.3 ms** |
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| Plan, index present | `Limit → Nested Loop → Index Scan on ix_chunks_embedding_hnsw` |
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`deterministic_gate_recall.txt` — the same tier forced onto the index with
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`enable_indexscan = on` at each depth, recall@100 against the exact scan, on
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**real embeddings**:
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| `ef_search` | mean recall@100 | min recall@100 | max per-chunk cosine delta | max `top_score` delta | tier latency |
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| --- | --- | --- | --- | --- | --- |
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| 100 (the A7 candidate count — task 02's default) | 0.9814 | 0.94 | 2.195e-03 | **0.000e+00** | 13.8 ms |
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| 200 | 1.0000 | 1.0000 | 0.0 | 0.000e+00 | 14.1 ms |
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| 400 (**shipped**) | 1.0000 | 1.0000 | 0.0 | 0.000e+00 | 14.6 ms |
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Two HNSW graph builds were measured (the index is dropped and rebuilt between the
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OFF and ON runs). The 100-row candidate set at `ef_search = 100` came out
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0.9814 mean / 0.94 min on one build and 0.9843 / 0.94 on the other; `200` came
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out 1.0000 / 1.0000 and 0.9971 / 0.99; `400` came out 1.0000 / 1.0000 both times.
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## Decision: `BOR_RETRIEVAL_EF_SEARCH` ships at 400
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* At the task-02 default (`ef_search = 100 == LIMIT`) the **user-visible** numbers
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did not move at all — verdicts, top-5 sets, suggested documents and
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`top_score` were identical, to `0.000e+00`. So the task's parity gate passes at
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100 and the deflection calibration was never in question.
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* What moved was the **100-row candidate set** the cosine tier hands the RRF
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fusion: mean 0.9814, min 0.94 — below this phase's own acceptance floor
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(mean ≥ 0.99, min ≥ 0.95), and matching the counter-case task 03 recorded for
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high-effective-rank data. One substitution at rank ~89 of 100 was enough to
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move 22 tail ranks.
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* LOCKED A3 names exactly one remedy: **raise `ef_search`** (towards 400). The
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thresholds were not touched. `200` measured borderline (1.0000 on one graph,
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0.9971 on another); **400 measured exact on both**, and on real embeddings it
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costs **0.5 ms** (14.1 → 14.6 ms) against the depth-100 search and **23 ms**
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against the sequential scan it replaces.
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* The default in `app/config.py` is therefore **400**, still overridable with
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`BOR_RETRIEVAL_EF_SEARCH`, and phase 188's clamp log line still fires if an
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operator sets it below the candidate count.
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## Production-order probe (`production_order_*.log`)
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The replica's 8,866 chunks are 13 MB of heap, which makes it the wrong place to
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ask "does production use the index, and what does it cost?": the relation is an
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order of magnitude smaller than the one that produced the incident, and pgvector
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costs an HNSW scan partly on `ef_search`, so a small-relation plan does not
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transfer. A throwaway database (`bor_scale188b`, created and dropped during
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this task) answered it at the production row count, using the replica's own
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embeddings scaled ×47 with each copy multiplied element-wise by a random
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near-one jitter vector, so all **416,702** vectors are distinct (415,950 unique)
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and the heap is 557 MB:
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| Measurement | Value |
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| --- | --- |
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| Build through the real migration (`uv run alembic upgrade head`, `maintenance_work_mem = 64 MB`) | **161 s**, peak RSS of the alembic process 81 MB |
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| Index size | **1,625 MB** (vs a 557 MB heap — HNSW at `m = 16`, 768-dim float4 costs ~3.9 KB/row: 414k rows ⇒ plan for ~1.6 GB) |
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| Plan at `ef_search` 100 / 200 / 400 | **HNSW index scan at every depth** (index path cost 3,333 / 5,621 / 9,806 vs a seq-scan-and-sort alternative an order of magnitude above) |
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| Tier latency | **~550 ms sequential → 12.0–14.9 ms** with the index (≈ **40×**), the ~1 ms `SET LOCAL` included |
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| Top-100 overlap vs the exact scan | 0.23–0.64, **not a valid recall measurement** — see the caveat below |
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**Caveat, recorded because it is a limitation of this gate.** A corpus built by
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jittering 8,866 real embeddings into 47 near-copies each is a pathological graph
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for a top-100 recall metric: "the exact top-100" is then 100 knife-edge slots
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inside a handful of near-duplicate clusters, which is not the shape of a real KB.
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The numbers are reported so the latency, plan and build-cost readings at
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production row count are on the record, and the **recall** verdict is taken from
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the live replica, where every vector is a real, distinct chunk. After the
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production build, the owner should re-run the battery on the real 414k-chunk KB
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exactly as this task did on the replica; `tests/integration/test_chunks_hnsw_recall.py`
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now fails (rather than passing vacuously) if a search at the shipped depth is not
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actually reading the index.
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## Task 05 re-verification — the battery at the shipped default, from scratch
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`task05_battery_index_off.txt` / `task05_battery_index_on.txt` are a clean-room
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re-run of the same gate, performed by task 05 to confirm the verdict still holds
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at the shipped `BOR_RETRIEVAL_EF_SEARCH = 400` before the phase shipped. Nothing
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was reused from task 04's state: the dump was restored into a **throwaway
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database** (`bor_p188gate`, created and dropped inside the run, so the dev
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database was never touched), which lands at `alembic_version = 0011` and was
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taken to **`0028`** (index absent — the OFF arm), then to **`head`** through the
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real migration (0029 built `ix_chunks_embedding_hnsw` against the 8,866 real
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embeddings: **valid**, **34 MB**, ~1 s wall clock, `alembic` peak RSS **81 MB**
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— the ON arm). Both arms ran
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`uv run python -m scripts.eval_retrieval --from-file tests/fixtures/retrieval_battery.txt`
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with live embeddings against live Postgres.
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**The two captures are byte-identical** (Python string equality, not a diff
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summary): all seven `best_cosine` values, all seven `fts_hits` counts, all 35
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numbered hits with their scores, and all seven gate verdicts — five `HIGH`, the
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Mongolia probe `HIGH` on its 5 FTS hits with `best_cosine = 0.6070` under the
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0.62 threshold, and the two Besta questions behaving as out-of-KB asks on a
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replica that has no IKEA document (`grep -c -i ikea` in the dump = 0, verified
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before drawing any conclusion from them).
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| Arm | Schema state | Index | Battery capture |
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| --- | --- | --- | --- |
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| OFF | `0028` | absent | `task05_battery_index_off.txt` |
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| ON | `0029` (head) | valid, 34 MB | `task05_battery_index_on.txt` |
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| Equality | — | — | **identical, byte for byte** |
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## Finding: `maintenance_work_mem = 512MB` cannot be used in this container
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```
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ERROR: could not resize shared memory segment "/PostgreSQL.3366409952" to 533761504 bytes: No space left on device
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```
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`brain-of-reese_db_1` has a **63 MB `/dev/shm`**, so any `maintenance_work_mem`
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above ~63 MB makes a parallel HNSW build die, and the failed build leaves an
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**INVALID** index behind. This is a deviation from D5, which suggested
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`SET maintenance_work_mem = '512MB'`: on this host the value is not applicable,
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and the container's **64 MB default builds the index successfully**. The failure
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was observed live at production order, and it exercised the self-heal for real:
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migration 0029 detected `indisvalid = false`, logged
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`dropping INVALID … rebuilding`, dropped it and rebuilt it valid. The task's
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recorded command is therefore "run `alembic upgrade head` as-is"; raising
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`maintenance_work_mem` is only worth doing after `/dev/shm` is raised too
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(`podman run --shm-size=1g`), and a raise that fails is recoverable.
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@@ -0,0 +1,64 @@
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eval: threshold=0.62 vector_candidates=100 lexical_candidates=30 rrf_k=60 recency_boost=0.0007 recency_half_life_days=365 name_hit_bonus=0.005
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question: 'How do I deploy gitea?'
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gate: best_cosine=0.8528 fts_hits=38 -> HIGH (answer)
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1. deploy/Deployments/reeseapps/gitea/app/playbook.yaml cosine=0.7640 fts=1 effective=0.03056 created=2026-10-17 (playbook)
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2. deploy/Deployments/reeseapps/gateway/README.md cosine=0.7736 fts=1 effective=0.02902 created=2026-10-17 (Proxy Server)
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3. homelab/active/container_gitea/gitea.md cosine=0.7612 fts=1 effective=0.02561 created=2026-10-17 (Gitea)
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4. homelab/active/kubernetes_gitea/gitea-values.yaml cosine=0.7232 fts=1 effective=0.02512 created=2026-10-17 (gitea-values)
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5. deploy/Deployments/reeseapps/gitea/app/gitea-postgres.env.j2 cosine=0.7159 fts=1 effective=0.02495 created=2026-10-17 (gitea-postgres.env)
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suggested: 1. deploy/Deployments/reeseapps/gitea/app/playbook.yaml 2. deploy/Deployments/reeseapps/gitea/app/gitea-postgres.env.j2 3. deploy/Deployments/reeseapps/gateway/README.md 4. deploy/Deployments/reeseapps/gitea/README.md 5. deploy/Deployments/reeseapps/gitea/app/gitea-web.env.j2
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question: 'How do I deploy forgejo?'
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gate: best_cosine=0.7876 fts_hits=37 -> HIGH (answer)
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1. homelab/templates/container/foobar.md cosine=0.7876 fts=0 effective=0.01709 created=2026-10-17 (Podman foobar)
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2. deploy/Deployments/reeseapps/forgejo/README.md cosine=0.0000 fts=1 effective=0.01709 created=2026-10-17 (Reeseapps Forgejo)
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3. deploy/Deployments/reeseapps/README.md cosine=0.7857 fts=0 effective=0.01683 created=2026-10-17 (Deployments)
|
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4. deploy/Deployments/reeseapps/forgejo/app/forgejo-app.container cosine=0.0000 fts=1 effective=0.01683 created=2026-10-17 (forgejo-app)
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5. homelab/active/container_bifrost/bifrost.md cosine=0.7672 fts=0 effective=0.01657 created=2026-10-17 (Podman bifrost)
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suggested: 1. deploy/Deployments/reeseapps/forgejo/README.md 2. deploy/Deployments/reeseapps/forgejo/app/forgejo-app.container 3. deploy/Deployments/reeseapps/forgejo/app/forgejo-app.env.j2 4. deploy/Deployments/reeseapps/forgejo/app/forgejo-postgres.container 5. deploy/Deployments/reeseapps/forgejo/app/forgejo-postgres.env.j2
|
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question: 'How do I set up the gateway?'
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gate: best_cosine=0.7816 fts_hits=37 -> HIGH (answer)
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1. homelab/active/device_shelly/shelly.md cosine=0.6940 fts=1 effective=0.02509 created=2026-10-17 (Shelly Devices)
|
||||
2. deploy/Deployments/reeseapps/forgejo/app/forgejo.network cosine=0.6727 fts=1 effective=0.02050 created=2026-10-17 (forgejo)
|
||||
3. homelab/active/device_framework_16/framework_16.md cosine=0.7816 fts=0 effective=0.01709 created=2026-10-17 (Framework Laptop 16)
|
||||
4. deploy/Deployments/delta/istio/gateway-metallb.yaml cosine=0.0000 fts=1 effective=0.01709 created=2026-10-17 (gateway-metallb)
|
||||
5. homelab/active/device_home_assistant/home_assistant.md cosine=0.7752 fts=0 effective=0.01683 created=2026-10-17 (Home Assistant)
|
||||
suggested: 1. homelab/active/device_shelly/shelly.md 2. deploy/Deployments/delta/istio/gateway-metallb.yaml 3. deploy/Deployments/delta/istio/test-gateway.yaml 4. deploy/Deployments/reeseapps/gateway/README.md 5. deploy/Deployments/reeseapps/gateway/certbot/playbook.yaml
|
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|
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question: 'Do you have notes on the qwen 3.8 27b model?'
|
||||
gate: best_cosine=0.7483 fts_hits=31 -> HIGH (answer)
|
||||
1. deploy/Deployments/reeseapps/ai/deployments/epic/quadlets/qwen3.8-27b-epic-vulkan.container cosine=0.7402 fts=1 effective=0.03322 created=2026-10-17 (qwen3.8-27b-epic-vulkan)
|
||||
2. deploy/Deployments/reeseapps/ai/deployments/juggernaut/quadlets/qwen3.8-27b-juggernaut-vulkan.container cosine=0.7142 fts=1 effective=0.03058 created=2026-10-17 (qwen3.8-27b-juggernaut-vulkan)
|
||||
3. homelab/active/container_localai/localai.md cosine=0.6837 fts=1 effective=0.02570 created=2026-10-17 (Local AI with Anything LLM)
|
||||
4. homelab/active/software_ai_stack/ai_stack.md cosine=0.7029 fts=1 effective=0.02545 created=2026-10-17 (Self Hosted AI Stack)
|
||||
5. deploy/Deployments/reeseapps/ai/deployments/turbo/quadlets/qwen3.8-flash-next.container cosine=0.6675 fts=1 effective=0.02491 created=2026-10-17 (qwen3.8-flash-next)
|
||||
suggested: 1. deploy/Deployments/reeseapps/ai/deployments/epic/quadlets/qwen3.8-27b-epic-vulkan.container 2. deploy/Deployments/reeseapps/ai/deployments/juggernaut/quadlets/qwen3.8-27b-juggernaut-vulkan.container 3. deploy/Deployments/reeseapps/ai/deployments/turbo/quadlets/qwen3.8-flash-next.container 4. homelab/active/container_localai/localai.md 5. homelab/active/software_ai_stack/ai_stack.md
|
||||
|
||||
question: 'What is the capital of Mongolia?'
|
||||
gate: best_cosine=0.6070 fts_hits=5 -> HIGH (answer)
|
||||
1. deploy/Deployments/reeseapps/README.md cosine=0.5588 fts=1 effective=0.02382 created=2026-10-17 (Deployments)
|
||||
2. homelab/active/container_nginx/nginx.md cosine=0.6070 fts=0 effective=0.01709 created=2026-10-17 (Ngnix)
|
||||
3. homelab/active/container_localai/localai.md cosine=0.0000 fts=1 effective=0.01709 created=2026-10-17 (Local AI with Anything LLM)
|
||||
4. homelab/active/os_arch/arch-workstation.md cosine=0.6059 fts=0 effective=0.01683 created=2026-10-17 (Workstation)
|
||||
5. homelab/active/software_k3s/k3s.md cosine=0.0000 fts=1 effective=0.01683 created=2026-10-17 (K3S)
|
||||
suggested: 1. deploy/Deployments/reeseapps/README.md 2. homelab/active/container_nginx/nginx.md 3. homelab/active/container_localai/localai.md 4. homelab/active/os_arch/arch-workstation.md 5. homelab/active/software_k3s/k3s.md
|
||||
|
||||
question: 'How do I build a Besta?'
|
||||
gate: best_cosine=0.7078 fts_hits=33 -> HIGH (answer)
|
||||
1. homelab/active/container_bifrost/bifrost.md cosine=0.7078 fts=0 effective=0.01709 created=2026-10-17 (Podman bifrost)
|
||||
2. homelab/active/software_distoolbox/arch-build.sh cosine=0.0000 fts=1 effective=0.01709 created=2026-10-17 (arch-build)
|
||||
3. vibe-bot/README.md cosine=0.6962 fts=0 effective=0.01683 created=2026-10-17 (Vibe Discord Bot with RAG Chat History)
|
||||
4. homepage/build.sh cosine=0.0000 fts=1 effective=0.01683 created=2026-10-17 (build)
|
||||
5. vibe-bot/build.sh cosine=0.0000 fts=1 effective=0.01657 created=2026-10-17 (build)
|
||||
suggested: 1. homelab/active/software_distoolbox/arch-build.sh 2. homepage/build.sh 3. vibe-bot/build.sh 4. homelab/active/container_bifrost/bifrost.md 5. vibe-bot/README.md
|
||||
|
||||
question: 'How do I build the BESTÅ?'
|
||||
gate: best_cosine=0.6969 fts_hits=33 -> HIGH (answer)
|
||||
1. vibe-bot/build.sh cosine=0.6267 fts=1 effective=0.02519 created=2026-10-17 (build)
|
||||
2. homelab/active/container_bifrost/bifrost.md cosine=0.6969 fts=0 effective=0.01709 created=2026-10-17 (Podman bifrost)
|
||||
3. homelab/active/software_distoolbox/arch-build.sh cosine=0.0000 fts=1 effective=0.01709 created=2026-10-17 (arch-build)
|
||||
4. vibe-bot/README.md cosine=0.6887 fts=0 effective=0.01683 created=2026-10-17 (Vibe Discord Bot with RAG Chat History)
|
||||
5. homepage/build.sh cosine=0.0000 fts=1 effective=0.01683 created=2026-10-17 (build)
|
||||
suggested: 1. vibe-bot/build.sh 2. homelab/active/software_distoolbox/arch-build.sh 3. homepage/build.sh 4. homelab/active/container_bifrost/bifrost.md 5. vibe-bot/README.md
|
||||
@@ -0,0 +1,64 @@
|
||||
eval: threshold=0.62 vector_candidates=100 lexical_candidates=30 rrf_k=60 recency_boost=0.0007 recency_half_life_days=365 name_hit_bonus=0.005
|
||||
|
||||
question: 'How do I deploy gitea?'
|
||||
gate: best_cosine=0.8528 fts_hits=38 -> HIGH (answer)
|
||||
1. deploy/Deployments/reeseapps/gitea/app/playbook.yaml cosine=0.7640 fts=1 effective=0.03056 created=2026-10-17 (playbook)
|
||||
2. deploy/Deployments/reeseapps/gateway/README.md cosine=0.7736 fts=1 effective=0.02902 created=2026-10-17 (Proxy Server)
|
||||
3. homelab/active/container_gitea/gitea.md cosine=0.7612 fts=1 effective=0.02561 created=2026-10-17 (Gitea)
|
||||
4. homelab/active/kubernetes_gitea/gitea-values.yaml cosine=0.7232 fts=1 effective=0.02512 created=2026-10-17 (gitea-values)
|
||||
5. deploy/Deployments/reeseapps/gitea/app/gitea-postgres.env.j2 cosine=0.7159 fts=1 effective=0.02495 created=2026-10-17 (gitea-postgres.env)
|
||||
suggested: 1. deploy/Deployments/reeseapps/gitea/app/playbook.yaml 2. deploy/Deployments/reeseapps/gitea/app/gitea-postgres.env.j2 3. deploy/Deployments/reeseapps/gateway/README.md 4. deploy/Deployments/reeseapps/gitea/README.md 5. deploy/Deployments/reeseapps/gitea/app/gitea-web.env.j2
|
||||
|
||||
question: 'How do I deploy forgejo?'
|
||||
gate: best_cosine=0.7876 fts_hits=37 -> HIGH (answer)
|
||||
1. homelab/templates/container/foobar.md cosine=0.7876 fts=0 effective=0.01709 created=2026-10-17 (Podman foobar)
|
||||
2. deploy/Deployments/reeseapps/forgejo/README.md cosine=0.0000 fts=1 effective=0.01709 created=2026-10-17 (Reeseapps Forgejo)
|
||||
3. deploy/Deployments/reeseapps/README.md cosine=0.7857 fts=0 effective=0.01683 created=2026-10-17 (Deployments)
|
||||
4. deploy/Deployments/reeseapps/forgejo/app/forgejo-app.container cosine=0.0000 fts=1 effective=0.01683 created=2026-10-17 (forgejo-app)
|
||||
5. homelab/active/container_bifrost/bifrost.md cosine=0.7672 fts=0 effective=0.01657 created=2026-10-17 (Podman bifrost)
|
||||
suggested: 1. deploy/Deployments/reeseapps/forgejo/README.md 2. deploy/Deployments/reeseapps/forgejo/app/forgejo-app.container 3. deploy/Deployments/reeseapps/forgejo/app/forgejo-app.env.j2 4. deploy/Deployments/reeseapps/forgejo/app/forgejo-postgres.container 5. deploy/Deployments/reeseapps/forgejo/app/forgejo-postgres.env.j2
|
||||
|
||||
question: 'How do I set up the gateway?'
|
||||
gate: best_cosine=0.7816 fts_hits=37 -> HIGH (answer)
|
||||
1. homelab/active/device_shelly/shelly.md cosine=0.6940 fts=1 effective=0.02509 created=2026-10-17 (Shelly Devices)
|
||||
2. deploy/Deployments/reeseapps/forgejo/app/forgejo.network cosine=0.6727 fts=1 effective=0.02050 created=2026-10-17 (forgejo)
|
||||
3. homelab/active/device_framework_16/framework_16.md cosine=0.7816 fts=0 effective=0.01709 created=2026-10-17 (Framework Laptop 16)
|
||||
4. deploy/Deployments/delta/istio/gateway-metallb.yaml cosine=0.0000 fts=1 effective=0.01709 created=2026-10-17 (gateway-metallb)
|
||||
5. homelab/active/device_home_assistant/home_assistant.md cosine=0.7752 fts=0 effective=0.01683 created=2026-10-17 (Home Assistant)
|
||||
suggested: 1. homelab/active/device_shelly/shelly.md 2. deploy/Deployments/delta/istio/gateway-metallb.yaml 3. deploy/Deployments/delta/istio/test-gateway.yaml 4. deploy/Deployments/reeseapps/gateway/README.md 5. deploy/Deployments/reeseapps/gateway/certbot/playbook.yaml
|
||||
|
||||
question: 'Do you have notes on the qwen 3.8 27b model?'
|
||||
gate: best_cosine=0.7483 fts_hits=31 -> HIGH (answer)
|
||||
1. deploy/Deployments/reeseapps/ai/deployments/epic/quadlets/qwen3.8-27b-epic-vulkan.container cosine=0.7402 fts=1 effective=0.03322 created=2026-10-17 (qwen3.8-27b-epic-vulkan)
|
||||
2. deploy/Deployments/reeseapps/ai/deployments/juggernaut/quadlets/qwen3.8-27b-juggernaut-vulkan.container cosine=0.7142 fts=1 effective=0.03058 created=2026-10-17 (qwen3.8-27b-juggernaut-vulkan)
|
||||
3. homelab/active/container_localai/localai.md cosine=0.6837 fts=1 effective=0.02570 created=2026-10-17 (Local AI with Anything LLM)
|
||||
4. homelab/active/software_ai_stack/ai_stack.md cosine=0.7029 fts=1 effective=0.02545 created=2026-10-17 (Self Hosted AI Stack)
|
||||
5. deploy/Deployments/reeseapps/ai/deployments/turbo/quadlets/qwen3.8-flash-next.container cosine=0.6675 fts=1 effective=0.02491 created=2026-10-17 (qwen3.8-flash-next)
|
||||
suggested: 1. deploy/Deployments/reeseapps/ai/deployments/epic/quadlets/qwen3.8-27b-epic-vulkan.container 2. deploy/Deployments/reeseapps/ai/deployments/juggernaut/quadlets/qwen3.8-27b-juggernaut-vulkan.container 3. deploy/Deployments/reeseapps/ai/deployments/turbo/quadlets/qwen3.8-flash-next.container 4. homelab/active/container_localai/localai.md 5. homelab/active/software_ai_stack/ai_stack.md
|
||||
|
||||
question: 'What is the capital of Mongolia?'
|
||||
gate: best_cosine=0.6070 fts_hits=5 -> HIGH (answer)
|
||||
1. deploy/Deployments/reeseapps/README.md cosine=0.5588 fts=1 effective=0.02382 created=2026-10-17 (Deployments)
|
||||
2. homelab/active/container_nginx/nginx.md cosine=0.6070 fts=0 effective=0.01709 created=2026-10-17 (Ngnix)
|
||||
3. homelab/active/container_localai/localai.md cosine=0.0000 fts=1 effective=0.01709 created=2026-10-17 (Local AI with Anything LLM)
|
||||
4. homelab/active/os_arch/arch-workstation.md cosine=0.6059 fts=0 effective=0.01683 created=2026-10-17 (Workstation)
|
||||
5. homelab/active/software_k3s/k3s.md cosine=0.0000 fts=1 effective=0.01683 created=2026-10-17 (K3S)
|
||||
suggested: 1. deploy/Deployments/reeseapps/README.md 2. homelab/active/container_nginx/nginx.md 3. homelab/active/container_localai/localai.md 4. homelab/active/os_arch/arch-workstation.md 5. homelab/active/software_k3s/k3s.md
|
||||
|
||||
question: 'How do I build a Besta?'
|
||||
gate: best_cosine=0.7078 fts_hits=33 -> HIGH (answer)
|
||||
1. homelab/active/container_bifrost/bifrost.md cosine=0.7078 fts=0 effective=0.01709 created=2026-10-17 (Podman bifrost)
|
||||
2. homelab/active/software_distoolbox/arch-build.sh cosine=0.0000 fts=1 effective=0.01709 created=2026-10-17 (arch-build)
|
||||
3. vibe-bot/README.md cosine=0.6962 fts=0 effective=0.01683 created=2026-10-17 (Vibe Discord Bot with RAG Chat History)
|
||||
4. homepage/build.sh cosine=0.0000 fts=1 effective=0.01683 created=2026-10-17 (build)
|
||||
5. vibe-bot/build.sh cosine=0.0000 fts=1 effective=0.01657 created=2026-10-17 (build)
|
||||
suggested: 1. homelab/active/software_distoolbox/arch-build.sh 2. homepage/build.sh 3. vibe-bot/build.sh 4. homelab/active/container_bifrost/bifrost.md 5. vibe-bot/README.md
|
||||
|
||||
question: 'How do I build the BESTÅ?'
|
||||
gate: best_cosine=0.6969 fts_hits=33 -> HIGH (answer)
|
||||
1. vibe-bot/build.sh cosine=0.6267 fts=1 effective=0.02519 created=2026-10-17 (build)
|
||||
2. homelab/active/container_bifrost/bifrost.md cosine=0.6969 fts=0 effective=0.01709 created=2026-10-17 (Podman bifrost)
|
||||
3. homelab/active/software_distoolbox/arch-build.sh cosine=0.0000 fts=1 effective=0.01709 created=2026-10-17 (arch-build)
|
||||
4. vibe-bot/README.md cosine=0.6887 fts=0 effective=0.01683 created=2026-10-17 (Vibe Discord Bot with RAG Chat History)
|
||||
5. homepage/build.sh cosine=0.0000 fts=1 effective=0.01683 created=2026-10-17 (build)
|
||||
suggested: 1. vibe-bot/build.sh 2. homelab/active/software_distoolbox/arch-build.sh 3. homepage/build.sh 4. homelab/active/container_bifrost/bifrost.md 5. vibe-bot/README.md
|
||||
@@ -0,0 +1,19 @@
|
||||
A=/tmp/final_off.json index_present=0 ef=400
|
||||
B=/tmp/final_on.json index_present=1 ef=400
|
||||
'How do I deploy gitea?': verdict HIGH (answer) / HIGH (answer) | top5 SAME | suggested SAME | candidate-set overlap 1.0000 | best_cosine delta 0.000e+00 | rank swaps 2 (all exact ties)
|
||||
'How do I deploy forgejo?': verdict HIGH (answer) / HIGH (answer) | top5 SAME | suggested SAME | candidate-set overlap 1.0000 | best_cosine delta 0.000e+00 | rank swaps 10 (all exact ties)
|
||||
'How do I set up the gateway?': verdict HIGH (answer) / HIGH (answer) | top5 SAME | suggested SAME | candidate-set overlap 1.0000 | best_cosine delta 0.000e+00 | rank swaps 10 (all exact ties)
|
||||
'Do you have notes on the qwen 3.8 27b model?': verdict HIGH (answer) / HIGH (answer) | top5 SAME | suggested SAME | candidate-set overlap 1.0000 | best_cosine delta 0.000e+00 | rank swaps 10 (all exact ties)
|
||||
'What is the capital of Mongolia?': verdict HIGH (answer) / HIGH (answer) | top5 SAME | suggested SAME | candidate-set overlap 1.0000 | best_cosine delta 0.000e+00 | rank swaps 20 (all exact ties)
|
||||
'How do I build a Besta?': verdict HIGH (answer) / HIGH (answer) | top5 SAME | suggested SAME | candidate-set overlap 1.0000 | best_cosine delta 0.000e+00 | rank swaps 9 (all exact ties)
|
||||
'How do I build the BESTÅ?': verdict HIGH (answer) / HIGH (answer) | top5 SAME | suggested SAME | candidate-set overlap 1.0000 | best_cosine delta 0.000e+00 | rank swaps 17 (all exact ties)
|
||||
|
||||
max top_score (best_cosine) delta = 0.000e+00 (gate <= 1e-4)
|
||||
max in-window (rank <= 5) tier cosine delta = 0.000e+00 (gate <= 1e-4)
|
||||
max tier cosine delta over all 100 ranks = 0.000e+00 (reported only)
|
||||
tail note: ranks below the fused window are gated as the candidate-SET overlap (>= 0.99), not as a score delta: a graph search may hand back a different tail chunk at a near-identical score without moving anything the turn reads.
|
||||
A plan(exact)=['Limit', 'Sort'] plan(app)=['Limit', 'Sort']
|
||||
B plan(exact)=['Limit', 'Sort'] plan(app)=['Limit', 'Nested Loop']
|
||||
tier latency ms (median over questions): A exact=37.45 app=38.252 | B exact=39.12 app=15.317
|
||||
|
||||
GATE PASS: identical verdicts, identical top-5 sets and order, deltas <= 1e-4
|
||||
@@ -0,0 +1,20 @@
|
||||
forced100: mean recall@100=0.9814 min=0.9400 max per-chunk cosine delta=2.195e-03 max top_score delta=0.000e+00 latency=13.753ms
|
||||
forced200: mean recall@100=1.0000 min=1.0000 max per-chunk cosine delta=0.000e+00 max top_score delta=0.000e+00 latency=14.111ms
|
||||
forced400: mean recall@100=1.0000 min=1.0000 max per-chunk cosine delta=0.000e+00 max top_score delta=0.000e+00 latency=14.614ms
|
||||
|
||||
pipeline parity (A8 verdict / top-5 / suggested), planner vs forced index:
|
||||
'How do I deploy gitea?': verdict SAME (HIGH (answer) / HIGH (answer)) | top5 SAME | suggested SAME | top_score delta 0.000e+00
|
||||
'How do I deploy forgejo?': verdict SAME (HIGH (answer) / HIGH (answer)) | top5 SAME | suggested SAME | top_score delta 0.000e+00
|
||||
'How do I set up the gateway?': verdict SAME (HIGH (answer) / HIGH (answer)) | top5 SAME | suggested SAME | top_score delta 0.000e+00
|
||||
'Do you have notes on the qwen 3.8 27b model?': verdict SAME (HIGH (answer) / HIGH (answer)) | top5 SAME | suggested SAME | top_score delta 0.000e+00
|
||||
'What is the capital of Mongolia?': verdict SAME (HIGH (answer) / HIGH (answer)) | top5 SAME | suggested SAME | top_score delta 0.000e+00
|
||||
'How do I build a Besta?': verdict SAME (HIGH (answer) / HIGH (answer)) | top5 SAME | suggested SAME | top_score delta 0.000e+00
|
||||
'How do I build the BESTÅ?': verdict SAME (HIGH (answer) / HIGH (answer)) | top5 SAME | suggested SAME | top_score delta 0.000e+00
|
||||
|
||||
plans (vacuity guards):
|
||||
exact: ['Limit', 'Sort', 'Hash Join']
|
||||
forced index (pipeline): ['Limit', 'Nested Loop', 'Index Scan on ix_chunks_embedding_hnsw']
|
||||
|
||||
latency ms over questions: exact(seq)=39.12 planner=15.317 forced100=13.753 forced200=14.111 forced400=14.614
|
||||
|
||||
GATE PASS: the approximate path returns the exact verdicts, top-5s and top_scores
|
||||
@@ -0,0 +1,12 @@
|
||||
corpus: 416702 chunks, heap 557 MB, index 1625 MB
|
||||
index definition: CREATE INDEX ix_chunks_embedding_hnsw ON public.chunks USING hnsw (embedding vector_cosine_ops) WITH (m='16', ef_construction='64')
|
||||
|
||||
--- the planner's own choice at production scale ---
|
||||
'How do I deploy gitea?' ef=100:HNSW(3333) ef=200:HNSW(5621) ef=400:HNSW(9806)
|
||||
'How do I deploy forgejo?' ef=100:HNSW(3333) ef=200:HNSW(5621) ef=400:HNSW(9806)
|
||||
'How do I set up the gateway?' ef=100:HNSW(3333) ef=200:HNSW(5621) ef=400:HNSW(9806)
|
||||
|
||||
--- latency + recall (production order) ---
|
||||
'How do I deploy gitea?' exact_seq=549.5ms ef100: 12.4ms overlap=0.230 topΔ=7.9e-02 ef200: 11.9ms overlap=0.230 topΔ=7.9e-02 ef400: 12.5ms overlap=0.230 topΔ=7.9e-02
|
||||
'How do I deploy forgejo?' exact_seq=545.8ms ef100: 12.1ms overlap=0.470 topΔ=0.0e+00 ef200: 12.9ms overlap=0.470 topΔ=0.0e+00 ef400: 12.0ms overlap=1.000 topΔ=0.0e+00
|
||||
'How do I set up the gateway?' exact_seq=553.1ms ef100: 13.4ms overlap=0.640 topΔ=0.0e+00 ef200: 14.9ms overlap=0.640 topΔ=0.0e+00 ef400: 14.3ms overlap=0.640 topΔ=0.0e+00
|
||||
File diff suppressed because one or more lines are too long
@@ -0,0 +1,65 @@
|
||||
eval: threshold=0.62 vector_candidates=100 lexical_candidates=30 rrf_k=60 recency_boost=0.0007 recency_half_life_days=365 name_hit_bonus=0.005
|
||||
|
||||
question: 'How do I deploy gitea?'
|
||||
gate: best_cosine=0.8528 fts_hits=38 -> HIGH (answer)
|
||||
1. deploy/Deployments/reeseapps/gitea/app/playbook.yaml cosine=0.7640 fts=1 effective=0.03056 created=2026-10-17 (playbook)
|
||||
2. deploy/Deployments/reeseapps/gateway/README.md cosine=0.7736 fts=1 effective=0.02902 created=2026-10-17 (Proxy Server)
|
||||
3. homelab/active/container_gitea/gitea.md cosine=0.7612 fts=1 effective=0.02561 created=2026-10-17 (Gitea)
|
||||
4. homelab/active/kubernetes_gitea/gitea-values.yaml cosine=0.7232 fts=1 effective=0.02512 created=2026-10-17 (gitea-values)
|
||||
5. deploy/Deployments/reeseapps/gitea/app/gitea-postgres.env.j2 cosine=0.7159 fts=1 effective=0.02495 created=2026-10-17 (gitea-postgres.env)
|
||||
suggested: 1. deploy/Deployments/reeseapps/gitea/app/playbook.yaml 2. deploy/Deployments/reeseapps/gitea/app/gitea-postgres.env.j2 3. deploy/Deployments/reeseapps/gateway/README.md 4. deploy/Deployments/reeseapps/gitea/README.md 5. deploy/Deployments/reeseapps/gitea/app/gitea-web.env.j2
|
||||
|
||||
question: 'How do I deploy forgejo?'
|
||||
gate: best_cosine=0.7876 fts_hits=37 -> HIGH (answer)
|
||||
1. homelab/templates/container/foobar.md cosine=0.7876 fts=0 effective=0.01709 created=2026-10-17 (Podman foobar)
|
||||
2. deploy/Deployments/reeseapps/forgejo/README.md cosine=0.0000 fts=1 effective=0.01709 created=2026-10-17 (Reeseapps Forgejo)
|
||||
3. deploy/Deployments/reeseapps/README.md cosine=0.7857 fts=0 effective=0.01683 created=2026-10-17 (Deployments)
|
||||
4. deploy/Deployments/reeseapps/forgejo/app/forgejo-app.container cosine=0.0000 fts=1 effective=0.01683 created=2026-10-17 (forgejo-app)
|
||||
5. homelab/active/container_bifrost/bifrost.md cosine=0.7672 fts=0 effective=0.01657 created=2026-10-17 (Podman bifrost)
|
||||
suggested: 1. deploy/Deployments/reeseapps/forgejo/README.md 2. deploy/Deployments/reeseapps/forgejo/app/forgejo-app.container 3. deploy/Deployments/reeseapps/forgejo/app/forgejo-app.env.j2 4. deploy/Deployments/reeseapps/forgejo/app/forgejo-postgres.container 5. deploy/Deployments/reeseapps/forgejo/app/forgejo-postgres.env.j2
|
||||
|
||||
question: 'How do I set up the gateway?'
|
||||
gate: best_cosine=0.7816 fts_hits=37 -> HIGH (answer)
|
||||
1. homelab/active/device_shelly/shelly.md cosine=0.6940 fts=1 effective=0.02509 created=2026-10-17 (Shelly Devices)
|
||||
2. deploy/Deployments/reeseapps/forgejo/app/forgejo.network cosine=0.6727 fts=1 effective=0.02050 created=2026-10-17 (forgejo)
|
||||
3. homelab/active/device_framework_16/framework_16.md cosine=0.7816 fts=0 effective=0.01709 created=2026-10-17 (Framework Laptop 16)
|
||||
4. deploy/Deployments/delta/istio/gateway-metallb.yaml cosine=0.0000 fts=1 effective=0.01709 created=2026-10-17 (gateway-metallb)
|
||||
5. homelab/active/device_home_assistant/home_assistant.md cosine=0.7752 fts=0 effective=0.01683 created=2026-10-17 (Home Assistant)
|
||||
suggested: 1. homelab/active/device_shelly/shelly.md 2. deploy/Deployments/delta/istio/gateway-metallb.yaml 3. deploy/Deployments/delta/istio/test-gateway.yaml 4. deploy/Deployments/reeseapps/gateway/README.md 5. deploy/Deployments/reeseapps/gateway/certbot/playbook.yaml
|
||||
|
||||
question: 'Do you have notes on the qwen 3.8 27b model?'
|
||||
gate: best_cosine=0.7483 fts_hits=31 -> HIGH (answer)
|
||||
1. deploy/Deployments/reeseapps/ai/deployments/epic/quadlets/qwen3.8-27b-epic-vulkan.container cosine=0.7402 fts=1 effective=0.03322 created=2026-10-17 (qwen3.8-27b-epic-vulkan)
|
||||
2. deploy/Deployments/reeseapps/ai/deployments/juggernaut/quadlets/qwen3.8-27b-juggernaut-vulkan.container cosine=0.7142 fts=1 effective=0.03058 created=2026-10-17 (qwen3.8-27b-juggernaut-vulkan)
|
||||
3. homelab/active/container_localai/localai.md cosine=0.6837 fts=1 effective=0.02570 created=2026-10-17 (Local AI with Anything LLM)
|
||||
4. homelab/active/software_ai_stack/ai_stack.md cosine=0.7029 fts=1 effective=0.02545 created=2026-10-17 (Self Hosted AI Stack)
|
||||
5. deploy/Deployments/reeseapps/ai/deployments/turbo/quadlets/qwen3.8-flash-next.container cosine=0.6675 fts=1 effective=0.02491 created=2026-10-17 (qwen3.8-flash-next)
|
||||
suggested: 1. deploy/Deployments/reeseapps/ai/deployments/epic/quadlets/qwen3.8-27b-epic-vulkan.container 2. deploy/Deployments/reeseapps/ai/deployments/juggernaut/quadlets/qwen3.8-27b-juggernaut-vulkan.container 3. deploy/Deployments/reeseapps/ai/deployments/turbo/quadlets/qwen3.8-flash-next.container 4. homelab/active/container_localai/localai.md 5. homelab/active/software_ai_stack/ai_stack.md
|
||||
|
||||
question: 'What is the capital of Mongolia?'
|
||||
gate: best_cosine=0.6070 fts_hits=5 -> HIGH (answer)
|
||||
1. deploy/Deployments/reeseapps/README.md cosine=0.5588 fts=1 effective=0.02382 created=2026-10-17 (Deployments)
|
||||
2. homelab/active/container_nginx/nginx.md cosine=0.6070 fts=0 effective=0.01709 created=2026-10-17 (Ngnix)
|
||||
3. homelab/active/container_localai/localai.md cosine=0.0000 fts=1 effective=0.01709 created=2026-10-17 (Local AI with Anything LLM)
|
||||
4. homelab/active/os_arch/arch-workstation.md cosine=0.6059 fts=0 effective=0.01683 created=2026-10-17 (Workstation)
|
||||
5. homelab/active/software_k3s/k3s.md cosine=0.0000 fts=1 effective=0.01683 created=2026-10-17 (K3S)
|
||||
suggested: 1. deploy/Deployments/reeseapps/README.md 2. homelab/active/container_nginx/nginx.md 3. homelab/active/container_localai/localai.md 4. homelab/active/os_arch/arch-workstation.md 5. homelab/active/software_k3s/k3s.md
|
||||
|
||||
question: 'How do I build a Besta?'
|
||||
gate: best_cosine=0.7078 fts_hits=33 -> HIGH (answer)
|
||||
1. homelab/active/container_bifrost/bifrost.md cosine=0.7078 fts=0 effective=0.01709 created=2026-10-17 (Podman bifrost)
|
||||
2. homelab/active/software_distoolbox/arch-build.sh cosine=0.0000 fts=1 effective=0.01709 created=2026-10-17 (arch-build)
|
||||
3. vibe-bot/README.md cosine=0.6962 fts=0 effective=0.01683 created=2026-10-17 (Vibe Discord Bot with RAG Chat History)
|
||||
4. homepage/build.sh cosine=0.0000 fts=1 effective=0.01683 created=2026-10-17 (build)
|
||||
5. vibe-bot/build.sh cosine=0.0000 fts=1 effective=0.01657 created=2026-10-17 (build)
|
||||
suggested: 1. homelab/active/software_distoolbox/arch-build.sh 2. homepage/build.sh 3. vibe-bot/build.sh 4. homelab/active/container_bifrost/bifrost.md 5. vibe-bot/README.md
|
||||
|
||||
question: 'How do I build the BESTÅ?'
|
||||
gate: best_cosine=0.6969 fts_hits=33 -> HIGH (answer)
|
||||
1. vibe-bot/build.sh cosine=0.6267 fts=1 effective=0.02519 created=2026-10-17 (build)
|
||||
2. homelab/active/container_bifrost/bifrost.md cosine=0.6969 fts=0 effective=0.01709 created=2026-10-17 (Podman bifrost)
|
||||
3. homelab/active/software_distoolbox/arch-build.sh cosine=0.0000 fts=1 effective=0.01709 created=2026-10-17 (arch-build)
|
||||
4. vibe-bot/README.md cosine=0.6887 fts=0 effective=0.01683 created=2026-10-17 (Vibe Discord Bot with RAG Chat History)
|
||||
5. homepage/build.sh cosine=0.0000 fts=1 effective=0.01683 created=2026-10-17 (build)
|
||||
suggested: 1. vibe-bot/build.sh 2. homelab/active/software_distoolbox/arch-build.sh 3. homepage/build.sh 4. homelab/active/container_bifrost/bifrost.md 5. vibe-bot/README.md
|
||||
EXIT=0
|
||||
@@ -0,0 +1,65 @@
|
||||
eval: threshold=0.62 vector_candidates=100 lexical_candidates=30 rrf_k=60 recency_boost=0.0007 recency_half_life_days=365 name_hit_bonus=0.005
|
||||
|
||||
question: 'How do I deploy gitea?'
|
||||
gate: best_cosine=0.8528 fts_hits=38 -> HIGH (answer)
|
||||
1. deploy/Deployments/reeseapps/gitea/app/playbook.yaml cosine=0.7640 fts=1 effective=0.03056 created=2026-10-17 (playbook)
|
||||
2. deploy/Deployments/reeseapps/gateway/README.md cosine=0.7736 fts=1 effective=0.02902 created=2026-10-17 (Proxy Server)
|
||||
3. homelab/active/container_gitea/gitea.md cosine=0.7612 fts=1 effective=0.02561 created=2026-10-17 (Gitea)
|
||||
4. homelab/active/kubernetes_gitea/gitea-values.yaml cosine=0.7232 fts=1 effective=0.02512 created=2026-10-17 (gitea-values)
|
||||
5. deploy/Deployments/reeseapps/gitea/app/gitea-postgres.env.j2 cosine=0.7159 fts=1 effective=0.02495 created=2026-10-17 (gitea-postgres.env)
|
||||
suggested: 1. deploy/Deployments/reeseapps/gitea/app/playbook.yaml 2. deploy/Deployments/reeseapps/gitea/app/gitea-postgres.env.j2 3. deploy/Deployments/reeseapps/gateway/README.md 4. deploy/Deployments/reeseapps/gitea/README.md 5. deploy/Deployments/reeseapps/gitea/app/gitea-web.env.j2
|
||||
|
||||
question: 'How do I deploy forgejo?'
|
||||
gate: best_cosine=0.7876 fts_hits=37 -> HIGH (answer)
|
||||
1. homelab/templates/container/foobar.md cosine=0.7876 fts=0 effective=0.01709 created=2026-10-17 (Podman foobar)
|
||||
2. deploy/Deployments/reeseapps/forgejo/README.md cosine=0.0000 fts=1 effective=0.01709 created=2026-10-17 (Reeseapps Forgejo)
|
||||
3. deploy/Deployments/reeseapps/README.md cosine=0.7857 fts=0 effective=0.01683 created=2026-10-17 (Deployments)
|
||||
4. deploy/Deployments/reeseapps/forgejo/app/forgejo-app.container cosine=0.0000 fts=1 effective=0.01683 created=2026-10-17 (forgejo-app)
|
||||
5. homelab/active/container_bifrost/bifrost.md cosine=0.7672 fts=0 effective=0.01657 created=2026-10-17 (Podman bifrost)
|
||||
suggested: 1. deploy/Deployments/reeseapps/forgejo/README.md 2. deploy/Deployments/reeseapps/forgejo/app/forgejo-app.container 3. deploy/Deployments/reeseapps/forgejo/app/forgejo-app.env.j2 4. deploy/Deployments/reeseapps/forgejo/app/forgejo-postgres.container 5. deploy/Deployments/reeseapps/forgejo/app/forgejo-postgres.env.j2
|
||||
|
||||
question: 'How do I set up the gateway?'
|
||||
gate: best_cosine=0.7816 fts_hits=37 -> HIGH (answer)
|
||||
1. homelab/active/device_shelly/shelly.md cosine=0.6940 fts=1 effective=0.02509 created=2026-10-17 (Shelly Devices)
|
||||
2. deploy/Deployments/reeseapps/forgejo/app/forgejo.network cosine=0.6727 fts=1 effective=0.02050 created=2026-10-17 (forgejo)
|
||||
3. homelab/active/device_framework_16/framework_16.md cosine=0.7816 fts=0 effective=0.01709 created=2026-10-17 (Framework Laptop 16)
|
||||
4. deploy/Deployments/delta/istio/gateway-metallb.yaml cosine=0.0000 fts=1 effective=0.01709 created=2026-10-17 (gateway-metallb)
|
||||
5. homelab/active/device_home_assistant/home_assistant.md cosine=0.7752 fts=0 effective=0.01683 created=2026-10-17 (Home Assistant)
|
||||
suggested: 1. homelab/active/device_shelly/shelly.md 2. deploy/Deployments/delta/istio/gateway-metallb.yaml 3. deploy/Deployments/delta/istio/test-gateway.yaml 4. deploy/Deployments/reeseapps/gateway/README.md 5. deploy/Deployments/reeseapps/gateway/certbot/playbook.yaml
|
||||
|
||||
question: 'Do you have notes on the qwen 3.8 27b model?'
|
||||
gate: best_cosine=0.7483 fts_hits=31 -> HIGH (answer)
|
||||
1. deploy/Deployments/reeseapps/ai/deployments/epic/quadlets/qwen3.8-27b-epic-vulkan.container cosine=0.7402 fts=1 effective=0.03322 created=2026-10-17 (qwen3.8-27b-epic-vulkan)
|
||||
2. deploy/Deployments/reeseapps/ai/deployments/juggernaut/quadlets/qwen3.8-27b-juggernaut-vulkan.container cosine=0.7142 fts=1 effective=0.03058 created=2026-10-17 (qwen3.8-27b-juggernaut-vulkan)
|
||||
3. homelab/active/container_localai/localai.md cosine=0.6837 fts=1 effective=0.02570 created=2026-10-17 (Local AI with Anything LLM)
|
||||
4. homelab/active/software_ai_stack/ai_stack.md cosine=0.7029 fts=1 effective=0.02545 created=2026-10-17 (Self Hosted AI Stack)
|
||||
5. deploy/Deployments/reeseapps/ai/deployments/turbo/quadlets/qwen3.8-flash-next.container cosine=0.6675 fts=1 effective=0.02491 created=2026-10-17 (qwen3.8-flash-next)
|
||||
suggested: 1. deploy/Deployments/reeseapps/ai/deployments/epic/quadlets/qwen3.8-27b-epic-vulkan.container 2. deploy/Deployments/reeseapps/ai/deployments/juggernaut/quadlets/qwen3.8-27b-juggernaut-vulkan.container 3. deploy/Deployments/reeseapps/ai/deployments/turbo/quadlets/qwen3.8-flash-next.container 4. homelab/active/container_localai/localai.md 5. homelab/active/software_ai_stack/ai_stack.md
|
||||
|
||||
question: 'What is the capital of Mongolia?'
|
||||
gate: best_cosine=0.6070 fts_hits=5 -> HIGH (answer)
|
||||
1. deploy/Deployments/reeseapps/README.md cosine=0.5588 fts=1 effective=0.02382 created=2026-10-17 (Deployments)
|
||||
2. homelab/active/container_nginx/nginx.md cosine=0.6070 fts=0 effective=0.01709 created=2026-10-17 (Ngnix)
|
||||
3. homelab/active/container_localai/localai.md cosine=0.0000 fts=1 effective=0.01709 created=2026-10-17 (Local AI with Anything LLM)
|
||||
4. homelab/active/os_arch/arch-workstation.md cosine=0.6059 fts=0 effective=0.01683 created=2026-10-17 (Workstation)
|
||||
5. homelab/active/software_k3s/k3s.md cosine=0.0000 fts=1 effective=0.01683 created=2026-10-17 (K3S)
|
||||
suggested: 1. deploy/Deployments/reeseapps/README.md 2. homelab/active/container_nginx/nginx.md 3. homelab/active/container_localai/localai.md 4. homelab/active/os_arch/arch-workstation.md 5. homelab/active/software_k3s/k3s.md
|
||||
|
||||
question: 'How do I build a Besta?'
|
||||
gate: best_cosine=0.7078 fts_hits=33 -> HIGH (answer)
|
||||
1. homelab/active/container_bifrost/bifrost.md cosine=0.7078 fts=0 effective=0.01709 created=2026-10-17 (Podman bifrost)
|
||||
2. homelab/active/software_distoolbox/arch-build.sh cosine=0.0000 fts=1 effective=0.01709 created=2026-10-17 (arch-build)
|
||||
3. vibe-bot/README.md cosine=0.6962 fts=0 effective=0.01683 created=2026-10-17 (Vibe Discord Bot with RAG Chat History)
|
||||
4. homepage/build.sh cosine=0.0000 fts=1 effective=0.01683 created=2026-10-17 (build)
|
||||
5. vibe-bot/build.sh cosine=0.0000 fts=1 effective=0.01657 created=2026-10-17 (build)
|
||||
suggested: 1. homelab/active/software_distoolbox/arch-build.sh 2. homepage/build.sh 3. vibe-bot/build.sh 4. homelab/active/container_bifrost/bifrost.md 5. vibe-bot/README.md
|
||||
|
||||
question: 'How do I build the BESTÅ?'
|
||||
gate: best_cosine=0.6969 fts_hits=33 -> HIGH (answer)
|
||||
1. vibe-bot/build.sh cosine=0.6267 fts=1 effective=0.02519 created=2026-10-17 (build)
|
||||
2. homelab/active/container_bifrost/bifrost.md cosine=0.6969 fts=0 effective=0.01709 created=2026-10-17 (Podman bifrost)
|
||||
3. homelab/active/software_distoolbox/arch-build.sh cosine=0.0000 fts=1 effective=0.01709 created=2026-10-17 (arch-build)
|
||||
4. vibe-bot/README.md cosine=0.6887 fts=0 effective=0.01683 created=2026-10-17 (Vibe Discord Bot with RAG Chat History)
|
||||
5. homepage/build.sh cosine=0.0000 fts=1 effective=0.01683 created=2026-10-17 (build)
|
||||
suggested: 1. vibe-bot/build.sh 2. homelab/active/software_distoolbox/arch-build.sh 3. homepage/build.sh 4. homelab/active/container_bifrost/bifrost.md 5. vibe-bot/README.md
|
||||
EXIT=0
|
||||
Reference in New Issue
Block a user