Resolve conflict: use CTE approach from #455 with updated BM25
weights (1.5, 4.0, 1.0) from #462.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
PR #475 changed handelize() to preserve original case and dots,
but the tests still expected lowercase output. Update assertions
to match the new behavior.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The MCP query tool always ran LLM reranking, even for lex-only queries.
On CPU-only infrastructure (e.g. Railway), the reranker adds 60-120s
per query. The SDK and CLI already support skipping reranking, but the
MCP server did not expose this option.
Add a `rerank` boolean parameter (default: true) to the MCP query
tool's input schema, forwarded to store.search() as the existing
`rerank` option.
Fixes#477
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
The handelize() regex replaced all non-letter/non-number chars with
dashes, including dots in the filename stem. This mangled session
filenames like "topic-1773595309.753009.md" to "topic-1773595309-753009.md",
breaking memory_get path resolution (file not found on disk).
Fix: add dot to the preserved character class in the filename regex.
After deploying, run qmd-reindex.sh to rebuild indexes with correct paths.
The bm25() call only had 2 weights for 3 columns (filepath, title, body),
giving body an implicit weight of 0. Add proper weights: filepath=1.5,
title=4.0, body=1.0 so title matches are boosted and body content is scored.
sqlite-vec's vec0 virtual tables silently ignore the OR REPLACE conflict
clause. When a crash interrupts embedding mid-way, chunks that were
inserted into vectors_vec but not content_vectors get re-selected by
getHashesForEmbedding, causing a UNIQUE constraint error on re-embed.
Two changes:
1. Insert content_vectors first so getHashesForEmbedding won't re-select
the hash if a crash occurs between the two inserts.
2. Use DELETE + INSERT for vectors_vec instead of INSERT OR REPLACE.
Fixes#445
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
MODEL_CACHE_DIR was hardcoded to ~/.cache/qmd/models/, ignoring the
XDG_CACHE_HOME environment variable. This was inconsistent with the rest
of the codebase (store.ts, cli/qmd.ts) which already respects XDG paths.
Fixes#425
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
When searchFTS combines FTS5 MATCH with a collection filter (d.collection = ?)
in the same WHERE clause, SQLite's query planner abandons the FTS5 index and
falls back to a full scan. This turns an 8ms query into a 17+ second query on
large collections (16K+ documents).
The fix wraps the FTS5 query in a CTE so it runs first with proper index usage,
then filters by collection on the materialized results.
Benchmarks on a 16,258-document collection:
Before: qmd search "knowctl" -c <collection> → 19.8s
After: qmd search "knowctl" -c <collection> → 0.4s
The CTE fetches limit*10 candidates from the FTS index to ensure enough results
survive collection filtering. Without a collection filter, the query plan was
already optimal, so no CTE overhead is added in that case.
- Add missing subprocess import (NameError on any quantize path)
- Replace broken optimum-cli quantize calls with direct onnxruntime:
Q4 uses MatMulNBitsQuantizer, Q8 uses quantize_dynamic
- Add onnxconverter-common to deps for FP16 (was silently swallowed)
- Make FP16 fail loudly on missing dep instead of silently uploading FP32
- README and transformers_js_config now reflect actual quantize_type
instead of always hardcoding Q4
- Remove dead _convert_fp16_external function
- Use no_post_process=True for ONNX export to avoid protobuf serialize error
- Add --validate and --validate-only flags for inference verification
- Fix position_ids in validation feed (required by Qwen3 ONNX export)
- Use optimum-cli for quantization to handle external data format
- Fix optimum dependency to optimum[onnxruntime]
Tested: export + validation passes on CPU, KV cache present (56 tensors).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add convert_onnx.py that mirrors convert_gguf.py's structure:
- Loads base Qwen3 model, merges SFT + GRPO adapters
- Exports to ONNX via Optimum (text-generation-with-past task)
- Supports Q4 (MatMulNBits), Q8, FP16, and FP32 output
- Uploads to separate HF repo (e.g. tobil/qmd-query-expansion-1.7B-ONNX)
- Writes Transformers.js compatibility config
- Includes model card with usage example
Usage:
uv run convert_onnx.py --size 1.7B
uv run convert_onnx.py --size 1.7B --quantize q4 --no-upload
Also adds `just convert-onnx` and `just convert-gguf` tasks.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
bun.lock still resolved better-sqlite3 to 11.x after package.json was
bumped to ^12.4.5 in v2.0.0. This breaks sandboxed builds (e.g. Nix
with bun2nix) where network access is unavailable to resolve the
mismatch.
CI and the publish workflow now use --frozen-lockfile so drift is caught
immediately. The release script also validates lockfile consistency
before tagging.
Closes#386
When a chunk exceeds the embedding model's context window (trainContextSize),
node-llama-cpp's getEmbeddingFor() triggers a native SIGABRT in GGML/Metal,
crashing the entire process.
Fix: Add truncateToContextSize() guard in embed() and embedBatch() that uses
the model's own tokenizer to check token count before calling getEmbeddingFor().
Oversized text is truncated to (trainContextSize - 4) tokens with a warning,
preserving partial embedding coverage instead of crashing.
Fixes#303
The bin/qmd wrapper checks for bun.lock to select the runtime, but since
bun.lock is committed to the repo, source builds using npm install are
incorrectly routed to Bun — causing native module ABI mismatches (#381)
and sqlite-vec crashes (#380).
Add package-lock.json as a higher-priority signal: if it exists, npm
installed the dependencies and Node should be used. Also fix
cleanupOrphanedVectors() to use the existing isSqliteVecAvailable()
guard instead of checking sqlite_master, which can report the virtual
table even when the vec0 module isn't loaded.
Fixes#381, fixes#380
Continuation of #362 (runtime detection false positives)
The caret range ^4.2.1 allows npm to resolve zod 4.3.x, which has
breaking type changes against @modelcontextprotocol/sdk. Source builds
fail with TypeScript errors. Pinning to exact 4.2.1 resolves this.
See: https://github.com/tobi/qmd/issues/379
On macOS, bun:sqlite uses Apple's system SQLite which is compiled with
SQLITE_OMIT_LOAD_EXTENSION, preventing sqlite-vec from loading. The v2.0
refactor also silently swallowed extension loading failures, losing the
actionable error messages that existed pre-2.0.
- Call Database.setCustomSQLite() on macOS to use Homebrew's SQLite
- Eagerly validate extension loading at init, not at first query
- Throw with platform-specific fix instructions in loadSqliteVec()
- Log warning in store.ts instead of silently catching
Fixes#363
On WSL, paths like /c/work/... are valid drvfs mount points, not Git
Bash drive-letter shortcuts. The existing code in isAbsolutePath() and
resolve() detected /c/ as a Windows C: path, converting drvfs paths to
C:/work/... which broke indexing entirely.
Fix: detect WSL via WSL_DISTRO_NAME or WSL_INTEROP environment variables
and skip the Git Bash /c/ -> C: branch on WSL. Native Linux path handling
continues as before.
Exposes the existing skipRerank option as a --no-rerank CLI flag for
qmd query. On CPU-only machines, reranking takes 120s+ for 20 chunks -
this flag lets users get RRF-fused results without the reranking penalty.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
When Bun is installed on the system but QMD was installed via npm,
$BUN_INSTALL is always set (typically to ~/.bun), causing the launcher
to incorrectly run QMD under Bun. This leads to ABI mismatches with
native modules (better-sqlite3, sqlite-vec) that were compiled for Node,
breaking vector operations with "no such module: vec0".
Only check for bun.lock/bun.lockb files, which reliably indicate that
QMD was actually installed with Bun.
Fixes#361
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add _ciMode flag to LlamaCpp that throws immediately on embedBatch,
generate, expandQuery, and rerank when CI=true — prevents silent 30s
timeouts. Skip MCP HTTP Transport tests in CI (they instantiate a real
LlamaCpp). Bump vitest/bun test timeouts to 60s for slower CI runners.
