Use cases:
- **Impact analysis**: What calls this function I'm about to change?
- **Context expansion**: Show related functions
- **Entry point discovery**: Find functions with no callers

Call graph is indexed across all files - callers are found regardless of which file they're in.

## Notes

```bash
cqs notes list # List all project notes with sentiment
cqs notes add "text" --sentiment -0.5 --mentions file.rs # Add a note
cqs notes update "text" --new-text "updated" # Update a note
cqs notes remove "text" # Remove a note
```

## Discovery Tools

```bash
# Find functions similar to a given function (search by example)
cqs similar search_filtered # by name
cqs similar src/search.rs:search_filtered # by file:name

# Function card: signature, callers, callees, similar functions
cqs explain search_filtered
cqs explain src/search.rs:search_filtered --json

# Semantic diff between indexed snapshots
cqs diff old-version # project vs reference
cqs diff old-version new-ref # two references
cqs diff old-version --threshold 0.90 # stricter "modified" cutoff

# Drift detection — functions that changed most
cqs drift old-version # all drifted functions
cqs drift old-version --min-drift 0.1 # only significant changes
cqs drift old-version --lang rust --limit 20 # scoped + limited
```

## Planning & Orientation

```bash
# Task planning: classify task type, scout, generate checklist
cqs plan "add retry logic to search" # 11 task-type templates
cqs plan "fix timeout bug" --json # JSON output

# Implementation brief: scout + gather + impact + placement + notes in one call
cqs task "add rate limiting" # waterfall token budgeting
cqs task "refactor error handling" --tokens 4000

# Guided codebase tour: entry point, call chain, callers, key types, tests
cqs onboard "how search works"
cqs onboard "error handling" --tokens 3000

# Semantic git blame: who changed a function, when, and why
cqs blame search_filtered # last change + commit message
cqs blame search_filtered --callers # include affected callers
```

## Interactive & Batch Modes

```bash
# Interactive REPL with readline, history, tab completion
cqs chat

# Batch mode: stdin commands, JSONL output, pipeline syntax
cqs batch
echo 'search "error handling" | callers | test-map' | cqs batch
```

## Code Intelligence

```bash
# Diff review: structured risk analysis of changes
cqs review # review uncommitted changes
cqs review --base main # review changes since main
cqs review --json # JSON output for CI integration

# CI pipeline: review + dead code + gate (exit 3 on fail)
cqs ci # analyze uncommitted changes
cqs ci --base main # analyze changes since main
cqs ci --gate medium # fail on medium+ risk
cqs ci --gate off --json # report only, JSON output
echo "$diff" | cqs ci --stdin # pipe diff from CI system

# Follow a call chain between two functions (BFS shortest path)
cqs trace cmd_query search_filtered
cqs trace cmd_query search_filtered --max-depth 5

# Impact analysis: what breaks if I change this function?
cqs impact search_filtered # direct callers + affected tests
cqs impact search_filtered --depth 3 # transitive callers
cqs impact search_filtered --suggest-tests # suggest tests for untested callers
cqs impact search_filtered --type-impact # include type-level dependencies in impact

# Map functions to their tests
cqs test-map search_filtered
cqs test-map search_filtered --depth 3 --json

# Module overview: chunks, callers, callees, notes for a file
cqs context src/search.rs
cqs context src/search.rs --compact # signatures + caller/callee counts only
cqs context src/search.rs --summary # High-level summary only

# Co-occurrence analysis: what else to review when touching a function
cqs related search_filtered # shared callers, callees, types

# Placement suggestion: where to add new code
cqs where "rate limiting middleware" # best file, insertion point, local patterns

# Pre-investigation dashboard: plan before you code
cqs scout "add retry logic to search" # search + callers + tests + staleness + notes
```

## Maintenance

```bash
# Check index freshness
cqs stale # List files changed since last index
cqs stale --count-only # Just counts, no file list
cqs stale --json # JSON output

# Find dead code (functions never called by indexed code)
cqs dead # Conservative: excludes main, tests, trait impls
cqs dead --include-pub # Include public API functions
cqs dead --min-confidence high # Only high-confidence dead code
cqs dead --json # JSON output

# Garbage collection (remove stale index entries)
cqs gc # Prune deleted files, rebuild HNSW

# Codebase quality snapshot
cqs health # Codebase quality snapshot — dead code, staleness, hotspots, untested hotspots, notes
cqs suggest # Auto-suggest notes from patterns (dead clusters, untested hotspots, high-risk, stale mentions). `--apply` to add

# Cross-project search
cqs project register mylib /path/to/lib # Register a project
cqs project list # Show registered projects
cqs project search "retry logic" # Search across all projects
cqs project remove mylib # Unregister

# Smart context assembly (gather related code)
cqs gather "error handling" # Seed search + call graph expansion
cqs gather "auth flow" --expand 2 # Deeper expansion
cqs gather "config" --direction callers # Only callers, not callees
```

## Training Data Generation

Generate fine-tuning training data from git history:

```bash
cqs train-data --repos /path/to/repo --output triplets.jsonl
cqs train-data --repos /path/to/repo1 /path/to/repo2 --output data/triplets.jsonl
cqs train-data --repos . --output out.jsonl --max-commits 500 # Limit commit history
cqs train-data --repos . --output out.jsonl --resume # Resume from checkpoint
```

## Reranker Configuration

The cross-encoder reranker model can be overridden via environment variable:

```bash
export CQS_RERANKER_MODEL=cross-encoder/ms-marco-MiniLM-L-6-v2 # default
cqs "query" --rerank
```

## Document Conversion

Convert PDF, HTML, CHM, web help sites, and Markdown documents to cleaned, indexed Markdown:

```bash
# Convert a single file
cqs convert doc.pdf --output converted/

# Batch-convert a directory
cqs convert samples/pdf/ --output samples/converted/

# Preview without writing (dry run)
cqs convert samples/ --dry-run

# Clean and rename an existing markdown file
cqs convert raw-notes.md --output cleaned/

# Control which cleaning rules run
cqs convert doc.pdf --clean-tags generic # skip vendor-specific rules
cqs convert doc.pdf --clean-tags aveva,generic # AVEVA + generic rules
```

**Supported formats:**

| Format | Engine | Requirements |
|--------|--------|-------------|
| PDF | Python pymupdf4llm | `pip install pymupdf4llm` |
| HTML/HTM | Rust fast_html2md | None |
| CHM | 7z + fast_html2md | `sudo apt install p7zip-full` |
| Web Help | fast_html2md (multi-page) | None |
| Markdown | Passthrough | None (cleaning + renaming only) |

Output files get kebab-case names derived from document titles, with collision-safe disambiguation.

## Reference Indexes (Multi-Index Search)

Search across your project and external codebases simultaneously:

```bash
cqs ref add tokio /path/to/tokio # Index an external codebase
cqs ref add stdlib /path/to/rust/library --weight 0.6 # Custom weight
cqs ref list # Show configured references
cqs ref update tokio # Re-index from source
cqs ref remove tokio # Remove reference and index files
```

Searches are project-only by default. Use `--include-refs` to also search references, or `--ref` to search a specific one:

```bash
cqs "spawn async task" # Searches project only (default)
cqs "spawn async task" --include-refs # Also searches configured references
cqs "spawn async task" --ref tokio # Searches only the tokio reference
cqs "spawn" --ref tokio --json # JSON output, ref-only search
```

Reference results are ranked with a weight multiplier (default 0.8) so project results naturally appear first at equal similarity.

References are configured in `.cqs.toml`:

```toml
[[reference]]
name = "tokio"
path = "/home/user/.local/share/cqs/refs/tokio"
source = "/home/user/code/tokio"
weight = 0.8
```

## Claude Code Integration

### Why use cqs?

Without cqs, Claude uses grep/glob to find code and reads entire files for context. With cqs:

- **Fewer tool calls**: `gather`, `impact`, `trace`, `context`, `explain` each replace 5-10 sequential file reads with a single call
- **Less context burn**: `cqs read --focus` returns a function + its type dependencies — not the whole file. Token budgeting (`--tokens N`) caps output across all commands.
- **Find code by concept**: "function that retries with backoff" finds retry logic even if it's named `doWithAttempts`. See the Retrieval Quality section for measured numbers.
- **Understand dependencies**: Call graphs, type dependencies, impact analysis, and risk scoring answer "what breaks if I change X?" without manual tracing
- **Navigate unfamiliar codebases**: Semantic search + `cqs scout` + `cqs where` provide instant orientation without knowing project structure

### Setup

Add to your project's `CLAUDE.md` so Claude Code uses cqs automatically:

```markdown
## Code Intelligence

Use `cqs` for semantic search, call graph analysis, and code intelligence instead of grep/glob:
- Find functions by concept ("retry with backoff", "parse config")
- Trace dependencies and impact ("what breaks if I change X?")
- Assemble context efficiently (one call instead of 5-10 file reads)

Key commands (`--json` works on all commands; `--format mermaid` also accepted on impact/trace):
- `cqs "query"` - semantic search (hybrid RRF by default, project-only)
- `cqs "query" --include-refs` - also search configured reference indexes
- `cqs "name" --name-only` - definition lookup (fast, no embedding)
- `cqs "query" --semantic-only` - pure vector similarity, no keyword RRF
- `cqs "query" --rerank` - cross-encoder re-ranking (slower, more accurate)
- `cqs "query" --splade` - sparse-dense hybrid search (requires SPLADE model)
- `cqs "query" --splade --splade-alpha 0.3` - tune fusion weight (0=pure sparse, 1=pure dense)
- `cqs read ` - file with context notes injected as comments
- `cqs read --focus ` - function + type dependencies only
- `cqs stats` - index stats, chunk counts, HNSW index status
- `cqs callers ` - find functions that call a given function
- `cqs callees ` - find functions called by a given function
- `cqs deps ` - type dependencies: who uses this type? `--reverse` for what types a function uses
- `cqs notes add/update/remove` - manage project memory notes
- `cqs audit-mode on/off` - toggle audit mode (exclude notes from search/read)
- `cqs similar ` - find functions similar to a given function
- `cqs explain ` - function card: signature, callers, callees, similar
- `cqs diff ` - semantic diff between indexed snapshots
- `cqs drift ` - semantic drift: functions that changed most between reference and project
- `cqs trace ` - follow call chain (BFS shortest path)
- `cqs impact ` - what breaks if you change X? Callers + affected tests
- `cqs impact-diff [--base REF]` - diff-aware impact: changed functions, callers, tests to re-run
- `cqs test-map ` - map functions to tests that exercise them
- `cqs context ` - module-level: chunks, callers, callees, notes
- `cqs context --compact` - signatures + caller/callee counts only
- `cqs gather "query"` - smart context assembly: seed search + call graph BFS
- `cqs related ` - co-occurrence: shared callers, callees, types
- `cqs where "description"` - suggest where to add new code
- `cqs scout "task"` - pre-investigation dashboard: search + callers + tests + staleness + notes
- `cqs plan "description"` - task planning: classify into 11 task-type templates + scout + checklist
- `cqs task "description"` - implementation brief: scout + gather + impact + placement + notes in one call
- `cqs onboard "concept"` - guided tour: entry point, call chain, callers, key types, tests
- `cqs review` - diff review: impact-diff + notes + risk scoring. `--base`, `--json`
- `cqs ci` - CI pipeline: review + dead code in diff + gate. `--base`, `--gate`, `--json`
- `cqs blame ` - semantic git blame: who changed a function, when, and why. `--callers` for affected callers
- `cqs chat` - interactive REPL with readline, history, tab completion. Same commands as batch
- `cqs batch` - batch mode: stdin commands, JSONL output. Pipeline syntax: `search "error" | callers | test-map`
- `cqs dead` - find functions/methods never called by indexed code
- `cqs health` - codebase quality snapshot: dead code, staleness, hotspots, untested functions
- `cqs suggest` - auto-suggest notes from code patterns. `--apply` to add them
- `cqs stale` - check index freshness (files changed since last index)
- `cqs gc` - report/clean stale index entries
- `cqs convert ` - convert PDF/HTML/CHM/Markdown to cleaned Markdown for indexing
- `cqs telemetry` - usage dashboard: command frequency, categories, sessions, top queries. `--reset`, `--all`, `--json`
- `cqs reconstruct ` - reassemble source file from indexed chunks (works without original file on disk)
- `cqs brief ` - one-line-per-function summary for a file
- `cqs neighbors ` - brute-force cosine nearest neighbors (exact top-K, unlike HNSW-based `similar`)
- `cqs affected` - diff-aware impact: changed functions, callers, tests, risk scores. `--base`, `--json`
- `cqs train-data` - generate fine-tuning training data from git history
- `cqs train-pairs` - extract (NL description, code) pairs from index as JSONL for embedding fine-tuning
- `cqs ref add/remove/list` - manage reference indexes for multi-index search
- `cqs project register/remove/list/search` - cross-project search registry
- `cqs export-model --repo ` - export a HuggingFace model to ONNX format for use with cqs
- `cqs cache stats/clear/prune/compact` - manage the project-scoped embeddings cache at `/.cqs/embeddings_cache.db`. `--per-model` on stats; `clear --model ` deletes all cached embeddings for one fingerprint; `prune ` or `prune --model `; `compact` runs VACUUM
- `cqs slot list/create/promote/remove/active` - named slots — side-by-side full indexes under `.cqs/slots//`. Promote is atomic; daemon restart picks up the new slot
- `cqs ping` - daemon healthcheck; reports daemon socket path and uptime if running
- `cqs eval ` - run a query fixture against the current index and emit R@K metrics. `--baseline ` to compare two reports
- `cqs model show/list/swap` - inspect the embedding model recorded in the index, list presets, or swap with restore-on-failure semantics
- `cqs serve [--bind ADDR]` - launch the read-only web UI (graph, hierarchy, cluster, chunk-detail). Per-launch auth token; banner prints the URL
- `cqs refresh` - invalidate daemon caches and re-open the Store. Alias `cqs invalidate`. No-op when no daemon is running
- `cqs doctor` - check model, index, hardware (execution provider, CAGRA availability)
- `cqs hook install/uninstall/status/fire` - manage `.git/hooks/post-{checkout,merge,rewrite}` for watch-mode reconciliation. Idempotent; respects third-party hooks via marker check (#1182)
- `cqs status --watch-fresh [--wait [--wait-secs N]]` - report watch-loop freshness; `--wait` blocks until `state == fresh` (default 30 s, capped at 600 s) (#1182)
- `cqs completions ` - generate shell completions (bash, zsh, fish, powershell, elvish)

Keep index fresh: run `cqs watch` in a background terminal, or `cqs index` after significant changes.
```

Supported Languages (54)

- ASP.NET Web Forms (ASPX/ASCX/ASMX — C#/VB.NET code-behind in server script blocks and `<% %>` expressions, delegates to C#/VB.NET grammars)
- Bash (functions, command calls)
- C (functions, structs, enums, macros)
- C++ (classes, structs, namespaces, concepts, templates, out-of-class methods, preprocessor macros)
- C# (classes, structs, records, interfaces, enums, properties, delegates, events)
- CSS (rule sets, keyframes, media queries)
- CUDA (reuses C++ grammar — kernels, classes, structs, device/host functions)
- Dart (functions, classes, enums, mixins, extensions, methods, getters/setters)
- Elixir (functions, modules, protocols, implementations, macros, pipe calls)
- Elm (functions, type definitions, type aliases, ports, modules)
- Erlang (functions, modules, records, type aliases, behaviours, callbacks)
- F# (functions, records, discriminated unions, classes, interfaces, modules, members)
- Gleam (functions, type definitions, type aliases, constants)
- GLSL (reuses C grammar — vertex/fragment/compute shaders, structs, built-in function calls)
- Go (functions, structs, interfaces)
- GraphQL (types, interfaces, enums, unions, inputs, scalars, directives, operations, fragments)
- Haskell (functions, data types, newtypes, type synonyms, typeclasses, instances)
- HCL (resources, data sources, variables, outputs, modules, providers with qualified naming)
- HTML (headings, semantic landmarks, id'd elements; inline `` extracts JS/TS functions, `<style>` extracts CSS rules via multi-grammar injection)
- IEC 61131-3 Structured Text (function blocks, functions, programs, actions, methods, properties — also extracted from Rockwell L5X/L5K PLC exports)
- INI (sections, settings)
- Java (classes, interfaces, enums, methods)
- JavaScript (JSDoc `@param`/`@returns` tags improve search quality)
- JSON (top-level keys)
- Julia (functions, structs, abstract types, modules, macros)
- Kotlin (classes, interfaces, enum classes, objects, functions, properties, type aliases)
- LaTeX (sections, subsections, command definitions, environments)
- Lua (functions, local functions, method definitions, table constructors, call extraction)
- Make (rules/targets, variable assignments)
- Markdown (.md, .mdx — heading-based chunking with cross-reference extraction)
- Nix (function bindings, attribute sets, recursive sets, function application calls)
- OCaml (let bindings, type definitions, modules, function application)
- Objective-C (class interfaces, protocols, methods, properties, C functions)
- Perl (subroutines, packages, method/function calls)
- PHP (classes, interfaces, traits, enums, functions, methods, properties, constants, type references)
- PowerShell (functions, classes, methods, properties, enums, command calls)
- Protobuf (messages, services, RPCs, enums, type references)
- Python (functions, classes, methods)
- R (functions, S4 classes/generics/methods, R6 classes, formula assignments)
- Razor/CSHTML (ASP.NET — C# methods, properties, classes in @code blocks, HTML headings, JS/CSS injection from script/style elements)
- Ruby (classes, modules, methods, singleton methods)
- Rust (functions, structs, enums, traits, impls, macros)
- Scala (classes, objects, traits, enums, functions, val/var bindings, type aliases)
- Solidity (contracts, interfaces, libraries, structs, enums, functions, modifiers, events, state variables)
- SQL (T-SQL, PostgreSQL)
- Svelte (script/style extraction via multi-grammar injection, reuses JS/TS/CSS grammars)
- Swift (classes, structs, enums, actors, protocols, extensions, functions, type aliases)
- TOML (tables, arrays of tables, key-value pairs)
- TypeScript (functions, classes, interfaces, types)
- VB.NET (classes, modules, structures, interfaces, enums, methods, properties, events, delegates)
- Vue (script/style/template extraction via multi-grammar injection, reuses JS/TS/CSS grammars)
- XML (elements, processing instructions)
- YAML (mapping keys, sequences, documents)
- Zig (functions, structs, enums, unions, error sets, test declarations)

</details>

## Indexing

By default, `cqs index` respects `.gitignore` rules:

```bash
cqs index # Respects .gitignore
cqs index --no-ignore # Index everything
cqs index --force # Re-index all files
cqs index --dry-run # Show what would be indexed
cqs index --llm-summaries # Generate LLM summaries (requires ANTHROPIC_API_KEY)
cqs index --llm-summaries --improve-docs # Generate + write doc comments to source files
cqs index --llm-summaries --improve-all # Write doc comments to ALL functions (not just undocumented)
cqs index --llm-summaries --hyde-queries # Generate HyDE query predictions for better recall
cqs index --llm-summaries --max-docs 100 # Limit doc comment generation to N functions
cqs index --llm-summaries --max-hyde 200 # Limit HyDE query generation to N functions
```

## How It Works

**Parse → Describe → Embed → Enrich → Index → Search → Reason**

1. **Parse** — Tree-sitter extracts functions, classes, structs, enums, traits, interfaces, constants, tests, endpoints, modules, and 19 other chunk types across 54 languages (plus L5X/L5K PLC exports). Also extracts call graphs (who calls whom) and type dependencies (who uses which types).
2. **Describe** — Each code element gets a natural language description incorporating doc comments, parameter types, return types, and parent type context (e.g., methods include their struct/class name). Type-aware embeddings append full signatures for richer type discrimination. Optionally enriched with LLM-generated one-sentence summaries via `--llm-summaries`. This bridges the gap between how developers describe code and how it's written.
3. **Embed** — Configurable embedding model (`embeddinggemma-300m` default since v1.35.0; `bge-large`, `bge-large-ft`, `E5-base`, `v9-200k`, `nomic-coderank` presets, or custom ONNX) generates embeddings locally on CPU or GPU. See Retrieval Quality below for measured recall.
4. **Enrich** — Call-graph-enriched embeddings prepend caller/callee context. Optional LLM summaries (via Claude Batches API) add one-sentence function purpose. `--improve-docs` generates and writes doc comments back to source files. Both cached by content_hash.
5. **Index** — SQLite stores chunks, embeddings, call graph edges, and type dependency edges. HNSW provides fast approximate nearest-neighbor search. FTS5 enables keyword matching.
6. **Search** — Hybrid RRF (Reciprocal Rank Fusion) combines semantic similarity with keyword matching. Optional cross-encoder re-ranking for highest accuracy.
7. **Reason** — Call graph traversal, type dependency analysis, impact scoring, risk assessment, and smart context assembly build on the indexed data to answer questions like "what breaks if I change X?" in a single call.

Local-first ML, GPU-accelerated. Optional LLM enrichment via Claude API.

## HNSW Index Tuning

The HNSW (Hierarchical Navigable Small World) index provides fast approximate nearest neighbor search. Current parameters:

| Parameter | Value | Description |
|-----------|-------|-------------|
| M (connections) | 24 | Max edges per node. Higher = better recall, more memory |
| ef_construction | 200 | Search width during build. Higher = better index, slower build |
| max_layers | 16 | Graph layers. ~log(N) is typical |
| ef_search | 100 (adaptive) | Baseline search width; actual value scales with k and index size |

**Trade-offs:**
- **Recall vs speed**: Higher ef_search baseline improves recall but slows queries. ef_search adapts automatically based on k and index size
- **Index size**: ~4KB per vector with current settings
- **Build time**: O(N * M * ef_construction) complexity

For most codebases (<100k chunks), defaults work well. Large repos may benefit from tuning ef_search higher (200+) if recall matters more than latency.

## Retrieval Quality

**Live codebase eval** — 218 queries (109 test + 109 dev) over the cqs source tree, each with a dual-judge (Gemma-4 + Claude) consensus gold chunk. v3.v2 fixture. Categories: `identifier_lookup`, `behavioral`, `conceptual`, `structural`, `negation`, `type_filtered`, `multi_step`, `cross_language` — every category N ≥ 16. Hard mode; measures the full production pipeline.

**Per-preset (apples-to-apples 2026-05-02; all 5 slots reindexed --force --llm-summaries on cqs v1.35.0):**

| Preset | Params | Test R@1 | Test R@5 | Test R@20 | Dev R@1 | Dev R@5 | Dev R@20 | Agg R@1 | Agg R@5 | Agg R@20 |
|--------|--------|---------:|---------:|----------:|--------:|--------:|---------:|--------:|--------:|---------:|
| **embeddinggemma-300m** (default) | 308M | **48.6%** | 68.8% | 83.5% | 49.5% | **76.1%** | **89.0%** | **49.1%** | 72.5% | **86.2%** |
| bge-large-ft | 335M | 45.0% | **71.6%** | **85.3%** | 50.5% | 75.2% | 87.2% | 47.7% | **73.4%** | **86.2%** |
| BGE-large | 335M | 43.1% | 68.8% | 82.6% | **51.4%** | 75.2% | 86.2% | 47.2% | 72.0% | 84.4% |
| v9-200k | 110M | 44.0% | 67.9% | 79.8% | 45.9% | 69.7% | 81.7% | 45.0% | 68.8% | 80.7% |
| nomic-coderank | 137M | 43.1% | 67.0% | 78.0% | 46.8% | 68.8% | 79.8% | 45.0% | 67.9% | 78.9% |

Per-slot summary coverage at measurement: `default` 62.1%, `gemma` 99.0%, `bge-ft` 62.1%, `v9` 67.6%, `coderank` 65.5%. Variance is structural — only `chunk_type.is_code()` chunks are summary-eligible (markdown / json / ini are skipped at `src/llm/mod.rs:115`), and tokenizers produce different chunk-type distributions. Each slot has all *its* eligible chunks summarized.

Each split is ±2-3pp noisy on a single trial; quote both when comparing config changes.

**Default config:** EmbeddingGemma-300m dense + SPLADE sparse, RRF-fused with per-category α (set via offline sweep), centroid query classifier active by default for category routing. Gemma wins agg R@1 (+1.9pp over BGE) and ties bge-large-ft on agg R@20 at half the params. `bge-large-ft` (#1289 LoRA fine-tune of BGE-large on `cqs-code-search-200k`) wins agg R@5 by 0.9pp — opt-in via `CQS_EMBEDDING_MODEL=bge-large-ft` for R@5-sensitive flows. `nomic-coderank` and `v9-200k` are 137M / 110M alternatives for resource-constrained environments.

## Environment Variables

Quick index by domain (everything is searchable in the table below):

- **Trust / injection defence** — `CQS_TRUST_DELIMITERS`, `CQS_SUMMARY_VALIDATION`
- **Retrieval & search** — `CQS_RRF_K`, `CQS_TYPE_BOOST`, `CQS_SPLADE_ALPHA*`, `CQS_RERANK*`, `CQS_RERANKER_*`, `CQS_CENTROID_*`, `CQS_MMR_LAMBDA`, `CQS_FORCE_BASE_INDEX`, `CQS_DISABLE_BASE_INDEX`, `CQS_QUERY_CACHE_*`
- **Indexing & embedding** — `CQS_EMBEDDING_*`, `CQS_EMBED_*`, `CQS_ONNX_DIR`, `CQS_HNSW_*`, `CQS_CAGRA_*`, `CQS_TRT_ENGINE_CACHE`, `CQS_DISABLE_TENSORRT`, `CQS_SPARSE_CHUNKS_PER_TX`, `CQS_SPLADE_BATCH/MAX_*/MODEL/THRESHOLD/RESET_EVERY`, `CQS_PARSER_MAX_*`, `CQS_PARSE_CHANNEL_DEPTH`, `CQS_FILE_BATCH_SIZE`, `CQS_DEFERRED_FLUSH_INTERVAL`, `CQS_FTS_NORMALIZE_MAX`, `CQS_MAX_FILE_SIZE`, `CQS_MAX_QUERY_BYTES`, `CQS_MAX_SEQ_LENGTH`, `CQS_MAX_CONTRASTIVE_CHUNKS`, `CQS_MD_*`, `CQS_SKIP_ENRICHMENT`, `CQS_HYDE_MAX_TOKENS`, `CQS_RAYON_THREADS`
- **Daemon, watch, batch** — `CQS_NO_DAEMON`, `CQS_DAEMON_*`, `CQS_MAX_DAEMON_CLIENTS`, `CQS_BATCH_*IDLE_MINUTES`, `CQS_REFS_LRU_SIZE`, `CQS_WATCH_*`, `CQS_CHAT_HISTORY`
- **Graph & impact** — `CQS_CALL_GRAPH_MAX_EDGES`, `CQS_TYPE_GRAPH_MAX_EDGES`, `CQS_GATHER_MAX_NODES`, `CQS_IMPACT_MAX_*`, `CQS_TRACE_MAX_NODES`, `CQS_TEST_MAP_MAX_NODES`
- **SQLite storage** — `CQS_BUSY_TIMEOUT_MS`, `CQS_IDLE_TIMEOUT_SECS`, `CQS_MAX_CONNECTIONS`, `CQS_MMAP_SIZE`, `CQS_SQLITE_CACHE_SIZE`, `CQS_CACHE_MAX_SIZE`, `CQS_INTEGRITY_CHECK`, `CQS_SKIP_INTEGRITY_CHECK`, `CQS_MIGRATE_REQUIRE_BACKUP`
- **CLI I/O caps** — `CQS_MAX_DIFF_BYTES`, `CQS_MAX_DISPLAY_FILE_SIZE`, `CQS_READ_MAX_FILE_SIZE`
- **LLM & document conversion** — `CQS_LLM_*`, `CQS_API_BASE`, `CQS_LLM_ALLOW_INSECURE`, `CQS_PDF_SCRIPT`, `CQS_CONVERT_*`
- **Telemetry & eval** — `CQS_TELEMETRY`, `CQS_TELEMETRY_REDACT_QUERY`, `CQS_EVAL_OUTPUT`, `CQS_EVAL_TIMEOUT_SECS`
- **Training data extraction** — `CQS_TRAIN_GIT_DIFF_TREE_MAX_BYTES`, `CQS_TRAIN_GIT_SHOW_MAX_BYTES`

| Variable | Default | Description |
|----------|---------|-------------|
| `CQS_API_BASE` | (none) | LLM API base URL (legacy alias for `CQS_LLM_API_BASE`) |
| `CQS_BATCH_DATA_IDLE_MINUTES` | `30` | Minutes of inactivity before `cqs batch` / `cqs chat` evicts heavy data caches (HNSW, SPLADE index, call graph, test chunks, file set, refs). Independent of the ONNX-session sweep above. `0` disables. |
| `CQS_BATCH_IDLE_MINUTES` | `5` | Minutes of inactivity before `cqs batch` / `cqs chat` clears ONNX sessions (`0` disables eviction). |
| `CQS_BUSY_TIMEOUT_MS` | `5000` | SQLite busy timeout in milliseconds |
| `CQS_CACHE_MAX_SIZE` | `1073741824` (1 GB) | Global embedding cache size limit |
| `CQS_CAGRA_GRAPH_DEGREE` | `64` | CAGRA output graph degree at build time (cuVS default 64; higher → better recall, longer build) |
| `CQS_CHAT_HISTORY` | `1` | Set to `0` to disable disk-persisted `cqs chat` REPL history. |
| `CQS_MAX_DAEMON_CLIENTS` | `16` | Max concurrent in-flight handlers in the daemon socket loop. ~2 MiB stack each → default budget ~32 MiB. Read once at daemon startup. |
| `CQS_QUERY_CACHE_MAX_SIZE` | `104857600` (100 MiB) | Disk-cap on the embedding query cache. Best-effort prune past the cap; default is 100 MiB. |
| `CQS_TELEMETRY_REDACT_QUERY` | `1` | Set to `0` to log raw query strings in telemetry. Default redacts so search queries containing secrets/snippets aren't persisted. |
| `CQS_CALL_GRAPH_MAX_EDGES` | `500000` | Max `function_calls` rows loaded into the in-memory call graph (`cqs impact`, `cqs trace`, `cqs related`). Bump for very large monorepos that exceed 500K edges. |
| `CQS_CAGRA_INTERMEDIATE_GRAPH_DEGREE` | `128` | CAGRA pruned-input graph degree at build time (cuVS default 128) |
| `CQS_CAGRA_ITOPK_MAX` | (log₂(n)·32 clamped 128-4096) | Upper clamp on CAGRA `itopk_size`. Default scales with corpus size (1k→320, 100k→532, 1M→640). Raise for better recall on large indexes at the cost of search latency. |
| `CQS_CAGRA_ITOPK_MIN` | `128` | Lower clamp on CAGRA `itopk_size`. `itopk_size = (k*2).clamp(min, max)`. |
| `CQS_CAGRA_MAX_BYTES` | (auto) | Max GPU memory for CAGRA index |
| `CQS_CAGRA_PERSIST` | `1` | Persist the CAGRA graph to `{cqs_dir}/index.cagra` after build and reload it on restart. Set to `0` to disable (daemon rebuilds from scratch every startup). |
| `CQS_CAGRA_STREAM_BATCH_SIZE` | `10000` | Embedding rows streamed per batch during CAGRA index construction. At dim=1024 this is ~40 MB/batch; raise/lower to fit a per-batch byte budget for non-default-dim models. (P3-15 / SHL-V1.33-9) |
| `CQS_CAGRA_THRESHOLD` | `50000` | Min chunks to trigger CAGRA over HNSW |
| `CQS_CENTROID_ALPHA_FLOOR` | `0.7` | Minimum α when the centroid classifier overrides the rule-based classifier. Caps downside of wrong-category alpha routing. |
| `CQS_CENTROID_CLASSIFIER` | `1` | Embedding-centroid query classifier — fills `Unknown` gaps from the rule-based classifier with embedding-space matching. Enabled by default; set to `0` to opt out. |
| `CQS_CAGRA_MAX_GPU_BYTES` | (unset) | Hard cap (bytes) on GPU memory the CAGRA index is allowed to allocate. When set, exceeding the cap aborts the build with a clear error rather than OOM-ing the GPU. P2.42. |
| `CQS_CENTROID_THRESHOLD` | `0.01` | Minimum cosine margin (top1 − top2) for the centroid classifier to commit to a category. Below this, falls back to the rule-based classifier. |
| `CQS_CONVERT_MAX_FILE_SIZE` | `104857600` (100 MiB) | Max bytes a single-file converter (HTML, Markdown passthrough) will read. Shared across `cqs convert <file.html>` and markdown passthrough. Bump for pathologically large single-file docs; the cap exists as a malicious-input guard, not a normal-case constraint. |
| `CQS_CONVERT_MAX_PAGES` | `1000` | Max HTML pages processed from a single CHM archive or web-help directory by `cqs convert`. Excess pages are dropped with a warn. Bump for multi-thousand-page vendor docs. |
| `CQS_CONVERT_MAX_WALK_DEPTH` | `50` | Max recursion depth for `cqs convert <dir>`'s walkdir. Entries deeper than this are silently dropped by walkdir; depth-cap-hit emits a warn so you can detect the truncation. |
| `CQS_CONVERT_PAGE_BYTES` | `10485760` (10 MiB) | Max bytes read per page from CHM and web-help archives. A pathological archive with one huge HTML page can't OOM the process. A file that hits the cap is truncated with a warn; bump for vendor docs with unusually large single pages. |
| `CQS_CONVERT_WEBHELP_BYTES` | `52428800` (50 MiB) | Max merged-output bytes for `cqs convert <webhelp-dir>`. Concatenation past this bound truncates with a warn; this guards against runaway concatenation, not a normal-case workload. |
| `CQS_DAEMON_MAX_RESPONSE_BYTES` | `16777216` (16 MiB) | Max response bytes the CLI accepts from the daemon socket before falling back to direct execution. Larger `gather`/`task` outputs need this lifted. |
| `CQS_DAEMON_PERIODIC_GC` | `1` | Set to `0` to disable the daemon's idle-time periodic GC (#1024). When on, every 30 min of idle the daemon prunes a bounded batch of missing-file and gitignored chunks so the index stays close to a fresh `cqs index --force` over long sessions. |
| `CQS_DAEMON_PERIODIC_GC_CAP` | `1000` | Max distinct origins examined per periodic-GC tick. Lower = shorter write transactions; higher = faster convergence on a polluted index. |
| `CQS_DAEMON_PERIODIC_GC_IDLE_SECS` | `60` | Minimum idle gap (seconds) between the last file event and a periodic-GC tick. Prevents GC from running mid-burst during long edit sequences. |
| `CQS_DAEMON_PERIODIC_GC_INTERVAL_SECS` | `1800` (30 min) | Idle-time periodic GC interval (seconds). A tick fires only once this many seconds have passed since the previous sweep; combined with `CQS_DAEMON_PERIODIC_GC_IDLE_SECS`, keeps GC off the hot path. |
| `CQS_DAEMON_STARTUP_GC` | `1` | Set to `0` to skip the daemon's startup GC pass (#1024). The startup pass drops chunks for files no longer on disk and chunks whose path is now matched by `.gitignore`. Synchronous, runs once when `cqs watch --serve` starts. |
| `CQS_DAEMON_TIMEOUT_MS` | `2000` | Daemon client connect/read timeout in milliseconds (CLI → daemon) |
| `CQS_DAEMON_WORKER_THREADS` | `min(num_cpus, 4)` | Worker threads for the daemon's shared tokio runtime (replaces three per-struct runtimes). Bump on large hosts where the default cap leaves cores idle under heavy concurrent client load. |
| `CQS_DEFERRED_FLUSH_INTERVAL` | `50` | Chunks between deferred flushes during indexing |
| `CQS_DIFF_EMBEDDING_BATCH_SIZE` | `64` | Batch size for embedding `cqs review --diff` / `cqs impact --diff` chunks. Default scales to ~12 MB at 1024-dim; override for larger models or tight memory budgets. |
| `CQS_DISABLE_BASE_INDEX` | (none) | Set to `1` to force queries through the enriched HNSW only, skipping the base (non-enriched) HNSW. Used to A/B the dual-index router during config testing. |
| `CQS_DISABLE_TENSORRT` | (none) | Set to `1` to skip the TensorRT execution-provider probe in `detect_provider`, falling through to CUDA. Useful when a model's ONNX graph uses ops TensorRT can't compile — e.g. EmbeddingGemma's bidirectional-attention head emits a plugin op TRT 10 doesn't recognise, and `create_session` fails at engine build time. CUDA's op coverage is broader (it falls back to ORT's own kernel for unknown ops) at the cost of TRT's perf wins. |
| `CQS_EMBED_BATCH_SIZE` | `64` | ONNX inference batch size (reduce if GPU OOM) |
| `CQS_EMBED_CHANNEL_DEPTH` | `64` | Embedding pipeline channel depth (bounds memory) |
| `CQS_EMBEDDING_DIM` | (auto) | Override embedding dimension for custom ONNX models |
| `CQS_EMBEDDING_MODEL` | `embeddinggemma-300m` | Embedding model preset (`embeddinggemma-300m`, `bge-large`, `bge-large-ft`, `v9-200k`, `e5-base`, `nomic-coderank`) or custom HF repo. See `src/embedder/models.rs` for the full preset list and per-preset trade-offs. |
| `CQS_EVAL_OUTPUT` | (none) | Path to write per-query eval diagnostics JSON (used by eval harness) |
| `CQS_EVAL_REQUIRE_FRESH` | `1` | Set to `0`/`false`/`no`/`off` to disable the freshness gate that `cqs eval` applies before running (#1182). When on, the eval harness blocks until the running `cqs watch --serve` daemon reports `state == fresh`, or errors out if the daemon isn't reachable — prevents silent stale-index runs that look like 5-25pp R@K regressions. Pass `--no-require-fresh` for the same effect on a single invocation. |
| `CQS_EVAL_TIMEOUT_SECS` | `300` | Per-query timeout in seconds inside `evals/run_ablation.py` |
| `CQS_FILE_BATCH_SIZE` | `5000` | Files per parse batch in pipeline |
| `CQS_FORCE_BASE_INDEX` | (none) | Set to `1` to force search via the base (non-enriched) HNSW index |
| `CQS_FRESHNESS_POLL_MS` | `100` | Initial poll interval (ms) for `wait_for_fresh` exponential backoff before the eval freshness gate fires. Clamped to `[25, 5000]`. Bump on slow filesystems (WSL `/mnt/c/`) where the daemon's first snapshot is rarely under 100 ms. |
| `CQS_FTS_NORMALIZE_MAX` | `16384` | Max bytes of `normalize_for_fts` output per chunk. Truncation is emitted at warn level; bump if FTS recall on long chunks (large generated tables, monolithic functions) is degraded. |
| `CQS_GATHER_MAX_NODES` | `200` | Max BFS nodes in `gather` context assembly |
| `CQS_HNSW_EF_CONSTRUCTION` | `200` | HNSW construction-time search width |
| `CQS_HNSW_EF_SEARCH` | `100` | HNSW query-time search width |
| `CQS_HNSW_BATCH_SIZE` | `10000` | Vectors per HNSW build batch |
| `CQS_HNSW_M` | `24` | HNSW connections per node |
| `CQS_HNSW_MAX_DATA_BYTES` | `1073741824` (1 GB) | Max HNSW data file size |
| `CQS_HNSW_MAX_GRAPH_BYTES` | `524288000` (500 MB) | Max HNSW graph file size |
| `CQS_HNSW_MAX_ID_MAP_BYTES` | `524288000` (500 MB) | Max HNSW ID map file size |
| `CQS_HEALTH_HOTSPOT_COUNT` | auto (log₂(n) clamped `[5, 50]`) | Number of top hotspots `cqs health` reports. Default scales with corpus size (1k→10, 100k→17, 1M→20). SHL-V1.29-7. |
| `CQS_HOTSPOT_MIN_CALLERS` | auto (log₂(n)·0.7 clamped `[5, 50]`) | Minimum caller count for "untested hotspot" / "high risk" detectors. Default scales with corpus size (1k→5, 100k→11, 1M→14). SHL-V1.29-7. |
| `CQS_DEAD_CLUSTER_MIN_SIZE` | auto (log₂(n)·0.7 clamped `[5, 50]`) | Minimum dead functions in a single file to flag as a "dead code cluster" in `cqs suggest`. Scales with corpus size. SHL-V1.29-7. |
| `CQS_SUGGEST_HOTSPOT_POOL` | auto (4× hotspot count, clamped `[20, 200]`) | Pool size `cqs suggest` evaluates for risk patterns. SHL-V1.29-7. |
| `CQS_SUMMARY_VALIDATION` | `loose` | LLM summary validation strictness. `strict`: drop summaries matching injection patterns; `loose`: log + keep matches; `off`: skip. Length cap (1500 chars) is always enforced via deterministic truncation. (#1170) |
| `CQS_RISK_HIGH` | `5.0` | Risk score threshold above which a function is "High" risk. Drives `cqs review` CI gating; override on monorepos where the default classifies too aggressively. SHL-V1.29-8. |
| `CQS_RISK_MEDIUM` | `2.0` | Risk score threshold above which a function is "Medium" risk. SHL-V1.29-8. |
| `CQS_BLAST_LOW_MAX` | `2` | Inclusive upper bound on caller count for "Low" blast radius (callers `0..=N`). SHL-V1.29-8. |
| `CQS_BLAST_HIGH_MIN` | `11` | Inclusive lower bound on caller count for "High" blast radius (callers `N..`). Medium sits between `CQS_BLAST_LOW_MAX` and this. SHL-V1.29-8. |
| `CQS_HYDE_MAX_TOKENS` | (config) | Max tokens for HyDE query prediction |
| `CQS_IDLE_TIMEOUT_SECS` | `30` | SQLite connection idle timeout in seconds |
| `CQS_INTEGRITY_CHECK` | `0` | Set to `1` to enable PRAGMA quick_check on write-mode store opens |
| `CQS_IMPACT_MAX_CHANGED_FUNCTIONS` | `500` | Cap on changed functions processed by `impact --diff` / `review --diff`. Excess is dropped and surfaced as `summary.truncated_functions` in JSON. |
| `CQS_IMPACT_MAX_NODES` | `10000` | Max BFS nodes in impact analysis |
| `CQS_LLM_ALLOW_INSECURE` | `0` | Set to `1` to permit `CQS_LLM_API_BASE` to use cleartext `http://`. Without it, any `http://` base is rejected so the API key isn't sent in the clear. Localhost-testing escape hatch only. |
| `CQS_LLM_API_BASE` | `https://api.anthropic.com/v1` | LLM API base URL. Required when `CQS_LLM_PROVIDER=local`; set to e.g. `http://localhost:8080/v1`. |
| `CQS_LLM_API_KEY` | (none) | Optional bearer token for `CQS_LLM_PROVIDER=local`. Sent as `Authorization: Bearer $CQS_LLM_API_KEY`. Ignored by the anthropic provider (which uses `ANTHROPIC_API_KEY`). |
| `CQS_LLM_MAX_BATCH_SIZE` | `10000` | Max chunks per LLM batch (summary or doc-comment). Clamped to `[1, 100_000]`. When the cap is reached, remaining chunks are picked up on the next run. |
| `CQS_LLM_MAX_CONTENT_CHARS` | `8000` | Max content chars in LLM prompts |
| `CQS_LLM_MAX_TOKENS` | `100` | Max tokens for LLM summary generation |
| `CQS_LLM_MODEL` | `claude-haiku-4-5` | LLM model name for summaries. Required when `CQS_LLM_PROVIDER=local`; must match a model your server exposes. |
| `CQS_LLM_PROVIDER` | `anthropic` | LLM provider: `anthropic` (Messages Batches API) or `local` (any OpenAI-compat `/v1/chat/completions` endpoint — llama.cpp, vLLM, Ollama, LMStudio). |
| `CQS_LOCAL_LLM_CONCURRENCY` | `4` | Worker pool size for `CQS_LLM_PROVIDER=local`. Clamped to `[1, 64]`. |
| `CQS_LOCAL_LLM_MAX_BODY_BYTES` | `4194304` (4 MiB) | Max response body bytes accepted from a `CQS_LLM_PROVIDER=local` server. Larger bodies are a sign of a misbehaving or hostile endpoint and abort with a clear error rather than OOMing the daemon. Must be > 0. |
| `CQS_LOCAL_LLM_TIMEOUT_SECS` | `120` | Per-request timeout (seconds) for `CQS_LLM_PROVIDER=local`. Local inference can be slow, so the default is 2× the Anthropic 60s ceiling. |
| `CQS_MAX_CONNECTIONS` | `4` | SQLite write-pool max connections |
| `CQS_MAX_REFERENCES` | `20` | Max number of reference indexes loaded from `[references]` blocks. Each reference holds a separate SQLite DB + HNSW index (~50-100 MB RAM each). Hit on a `cqs ref`-heavy workspace? Bump it; `0`/garbage falls back to default. SHL-V1.30-6. |
| `CQS_GATHER_DEPTH` | `1` (`gather` default) | BFS expansion depth for the shared `gather` pipeline. Honored as a fallback by `task` when `CQS_TASK_GATHER_DEPTH` is unset. SHL-V1.30-4. |
| `CQS_TASK_GATHER_DEPTH` | `2` | BFS expansion depth used inside `cqs task` (number of call-graph hops from each modify target). Takes precedence over `CQS_GATHER_DEPTH` for the task pipeline only. SHL-V1.30-4. |
| `CQS_ONBOARD_CALLEE_FETCH` | `30` | Max callees `cqs onboard` fetches content for after BFS. Excess callees are surfaced as `summary.callees_truncated` in JSON and a `tracing::warn!`. SHL-V1.30-5. |
| `CQS_ONBOARD_CALLER_FETCH` | `15` | Max callers `cqs onboard` fetches content for. Truncation surfaces as `summary.callers_truncated`. SHL-V1.30-5. |
| `CQS_NOTES_MAX_FILE_SIZE` | `10485760` (10 MiB) | Max size of `notes.toml` accepted by both read and rewrite paths. A larger file is rejected with `InvalidData`. Bump on workspaces with very large note collections. SHL-V1.30-7. |
| `CQS_NOTES_MAX_ENTRIES` | `10000` | Max number of notes parsed from a single `notes.toml`. Excess entries are dropped with a `tracing::warn!` (previously silent). SHL-V1.30-7. |
| `CQS_ENRICHMENT_PAGE_SIZE` | `500` | Chunks per page during the second-pass enrichment loop. Smaller = lower per-batch RAM (callers/callees maps), larger = fewer SQLite round-trips. SHL-V1.30-8. |
| `CQS_WATCH_PRUNE_SIZE_THRESHOLD` | `5000` | Size threshold that triggers the watch loop's `last_indexed_mtime` recency prune. Larger maps (e.g. `cqs ref`-heavy projects) need this lifted to keep dedup working past the default. SHL-V1.30-9. |
| `CQS_BATCH_MAX_LINE_LEN` | `52428800` (50 MiB) | Max bytes per batch-mode line (`cqs batch` stdin and the daemon socket request). Aligned with `CQS_MAX_DIFF_BYTES` so batch-routed diffs aren't capped 50× sooner than the CLI path. |
| `CQS_MAX_CONTRASTIVE_CHUNKS` | `30000` | Max chunks for contrastive summary matrix (memory = N*N*4 bytes) |
| `CQS_MAX_DIFF_BYTES` | `52428800` (50 MiB) | Max bytes accepted on stdin (`cqs review --stdin`, `cqs impact --diff`) and from `git diff` subprocess. Long-running feature branches with multi-MB diffs need this lifted. |
| `CQS_MAX_DISPLAY_FILE_SIZE` | `10485760` (10 MiB) | Max file size that `read_context_lines` (snippet extraction for search results) will open. |
| `CQS_MAX_FILE_SIZE` | `1048576` (1 MB) | Per-file size cap (bytes) for indexing. Files above this are skipped with an `info!` log; bump for generated code (`bindings.rs`, compiled TS, migrations). |
| `CQS_MAX_QUERY_BYTES` | `32768` | Max query input bytes for embedding |
| `CQS_MAX_SEQ_LENGTH` | (auto) | Override max sequence length for custom ONNX models |
| `CQS_MD_MAX_SECTION_LINES` | `150` | Max markdown section lines before overflow split |
| `CQS_MD_MIN_SECTION_LINES` | `30` | Min markdown section lines (smaller sections merge) |
| `CQS_MIGRATE_REQUIRE_BACKUP` | `1` | Migration-time DB backup is required by default; a backup failure aborts the migration with `StoreError::Io` so the destructive v18→v19 rebuild never runs without a recovery snapshot. Set to `0` to downgrade to a `warn!` and proceed without a snapshot (accept data-loss risk on a subsequent commit failure). |
| `CQS_MMAP_SIZE` | `268435456` (256 MB) | SQLite memory-mapped I/O size |
| `CQS_NO_DAEMON` | (none) | Set to `1` to force CLI mode (skip daemon connection attempt) |
| `CQS_ONNX_DIR` | (auto) | Custom ONNX model directory (must contain `model.onnx` + `tokenizer.json`) |
| `CQS_PARSE_CHANNEL_DEPTH` | `512` | Parse pipeline channel depth |
| `CQS_PARSER_MAX_CHUNK_BYTES` | `100000` (100 KiB) | Per-chunk byte cap inside the parser. Chunks above this are dropped before windowing sees them; per-file warn summarises the count. Distinct from `CQS_MAX_FILE_SIZE` (file-discovery gate) so per-stage knobs stay independent. |
| `CQS_PARSER_MAX_FILE_SIZE` | `52428800` (50 MiB) | Per-file size cap inside the parser. Files above this are skipped with a warn. Distinct from `CQS_MAX_FILE_SIZE` (which gates file enumeration before the parser even runs). |
| `CQS_PDF_SCRIPT` | (auto) | Path to `pdf_to_md.py` for PDF conversion |
| `CQS_QUERY_CACHE_SIZE` | `128` | Embedding query cache entries |
| `CQS_RAYON_THREADS` | (auto) | Rayon thread pool size for parallel operations |
| `CQS_READ_MAX_FILE_SIZE` | `10485760` (10 MiB) | Max file size that `cqs read` will open (full-file body emit + note injection). Distinct from `CQS_MAX_DISPLAY_FILE_SIZE` because `cqs read` emits the entire file, not just a snippet. |
| `CQS_REFS_LRU_SIZE` | `2` | Slots in the batch-mode reference-index LRU cache (sibling projects loaded via `@name`). |
| `CQS_RERANKER_BATCH` | `32` | Cross-encoder batch size per ORT run (reduce if reranker OOMs on large `--rerank-k`) |
| `CQS_RERANKER_MAX_LENGTH` | `512` | Max input length for cross-encoder reranker |
| `CQS_RERANKER_MODEL` | `cross-encoder/ms-marco-MiniLM-L-6-v2` | Cross-encoder model for `--rerank` |
| `CQS_RERANK_OVER_RETRIEVAL` | `4` | Multiplier on `--limit` for the reranker over-retrieval pool. At `--rerank --limit N`, stage-1 returns `N * MULTIPLIER` candidates so the cross-encoder has recall headroom. Bump for projects where the right answer routinely sits past rank-20 in stage-1. |
| `CQS_RERANK_POOL_MAX` | `20` | Hard cap on the reranker pool regardless of multiplier. Caps ORT memory + per-batch latency, and avoids weak cross-encoders shuffling noise at deep ranks. Bump on workstations running a known-strong reranker. |
| `CQS_RRF_K` | `60` | RRF fusion constant (higher = more weight to top results) |
| `CQS_SERVE_BLOCKING_PERMITS` | `32` | Max concurrent blocking tasks the `cqs serve` HTTP layer will dispatch (heavy DB reads, embedding inference). Clamped to `[1, 1024]`. SEC-3. |
| `CQS_SERVE_CHUNK_DETAIL_CALLEES` | `50` | Cap on callees returned by `/api/chunk/{id}` detail. Clamped to `[1, 1000]`. SEC-3. |
| `CQS_SERVE_CHUNK_DETAIL_CALLERS` | `50` | Cap on callers returned by `/api/chunk/{id}` detail. Clamped to `[1, 1000]`. SEC-3. |
| `CQS_SERVE_CHUNK_DETAIL_TESTS` | `20` | Cap on tests returned by `/api/chunk/{id}` detail. Clamped to `[1, 1000]`. SEC-3. |
| `CQS_SERVE_CLUSTER_MAX_NODES` | `50000` | Cap on `/api/embed/2d` nodes (cluster view). Clamped to `[1, 1_000_000]`. SEC-3. |
| `CQS_SERVE_GRAPH_MAX_EDGES` | `500000` | Cap on `/api/graph` edges. Clamped to `[1, 10_000_000]`. SEC-3. |
| `CQS_SERVE_GRAPH_MAX_NODES` | `50000` | Cap on `/api/graph` nodes. Clamped to `[1, 1_000_000]`. SEC-3. |
| `CQS_SLOT` | (unset) | Slot to use for this invocation. Overridden by `--slot` flag, overrides `.cqs/active_slot`. See `cqs slot --help`. |
| `CQS_CACHE_ENABLED` | `1` | Set `0` to disable the project-scoped embeddings cache for this run (benchmark / debug). Cache lives at `<project>/.cqs/embeddings_cache.db`. |
| `CQS_CACHE_MAX_BYTES` | (unset) | Soft cap; emits `tracing::warn!` when the embeddings cache DB exceeds this many bytes. Does NOT auto-prune — use `cqs cache prune` / `cqs cache compact`. |
| `CQS_SKIP_ENRICHMENT` | (none) | Comma-separated enrichment layers to skip (e.g. `llm,hyde,callgraph`) |
| `CQS_SKIP_INTEGRITY_CHECK` | (none) | Set to `1` to skip `PRAGMA quick_check` on write-mode store opens |
| `CQS_SPARSE_CHUNKS_PER_TX` | `50` | Chunks per sub-transaction during `upsert_sparse_vectors`. Each sub-tx commits independently and bumps `splade_generation`, so a long-running incremental SPLADE upsert never holds `WRITE_LOCK` long enough to starve queries. Lower = more frequent commits / less lock pressure / more I/O; raise on fast NVMe to amortize commit overhead. |
| `CQS_SPLADE_ALPHA` | (per-category default) | Global SPLADE fusion alpha override (0.0 = pure sparse, 1.0 = pure dense) |
| `CQS_SPLADE_ALPHA_{CATEGORY}` | (per-category default) | Per-category SPLADE alpha override (e.g. `CQS_SPLADE_ALPHA_CONCEPTUAL`); takes precedence over `CQS_SPLADE_ALPHA` |
| `CQS_SPLADE_BATCH` | `32` | Initial chunk batch size for SPLADE encoding during indexing |
| `CQS_SPLADE_MAX_CHARS` | `4000` | Max chars per chunk for SPLADE encoding |
| `CQS_SPLADE_MAX_INDEX_BYTES` | `2147483648` (2 GB) | Max `splade.index.bin` size before index build refuses to persist |
| `CQS_SPLADE_MAX_SEQ` | `256` | Max sequence length (tokens) for SPLADE ONNX inference |
| `CQS_SPLADE_MODEL` | (auto) | Path to SPLADE ONNX model directory (supports `~`-prefixed paths) |
| `CQS_SPLADE_RESET_EVERY` | `0` | Reset the ORT session every N SPLADE batches to bound arena growth (0 = disabled) |
| `CQS_SPLADE_THRESHOLD` | `0.01` | SPLADE sparse activation threshold |
| `CQS_SQLITE_CACHE_SIZE` | `-16384` (`-4096` for `open_readonly`) | SQLite `cache_size` PRAGMA. Negative = kibibytes, positive = page count. |
| `CQS_TELEMETRY` | `0` | Set to `1` to enable command usage telemetry |
| `CQS_TEST_MAP_MAX_NODES` | `10000` | Max BFS nodes in test-map traversal |
| `CQS_MMR_LAMBDA` | unset (disabled) | Maximum Marginal Relevance λ ∈ `[0.0, 1.0]` for opt-in result diversification. `1.0` = pure relevance (no-op), `0.0` = pure diversity. Disabled by default. |
| `CQS_TRACE_MAX_NODES` | `10000` | Max nodes in call chain trace |
| `CQS_TRT_ENGINE_CACHE` | `1` (on) | Persist compiled TensorRT engines + timing cache to `~/.cache/cqs/trt-engine-cache/` so daemon restarts reuse the engine instead of paying the 4–90 s per-model compile cost again. Set to `0` to opt out (forces re-compile every session — useful for validating that a driver upgrade invalidated the cache). Cache invalidates automatically when (model bytes, GPU SM, TRT version) changes. |
| `CQS_TRUST_DELIMITERS` | `1` (on) | Wraps every chunk's `content` in `<<<chunk:{id}>>> ... <<</chunk:{id}>>>` markers so prompt-injection guards downstream of cqs detect content boundaries when the agent inlines the rendered string into a larger prompt. Set to `0` to opt out (raw text). Default flipped on in v1.30.2. (#1167, #1181) |
| `CQS_TRAIN_BM25_B` | `0.75` | BM25 length-normalisation parameter for training-data hard-negative mining. Standard Robertson-Walker default. (P3-13 / SHL-V1.33-7) |
| `CQS_TRAIN_BM25_K1` | `1.2` | BM25 term-frequency saturation parameter for training-data hard-negative mining. Standard Robertson-Walker default. (P3-13 / SHL-V1.33-7) |
| `CQS_TRAIN_GIT_DIFF_TREE_MAX_BYTES` | `268435456` (256 MiB) | Max bytes retrieved from `git diff-tree` during training-data extraction. Diffs above the cap cause the producer to bail (rather than truncate) so a malformed or unexpectedly large commit can't OOM the training generator. (P3-39 / RM-V1.33-6) |
| `CQS_TRAIN_GIT_SHOW_MAX_BYTES` | `52428800` (50 MiB) | Max bytes retrieved per file via `git show` during training-data extraction. Files above the cap are skipped; bump to capture larger generated files (schema dumps, vendored corpora). |
| `CQS_TYPE_BOOST` | `1.2` | Multiplier applied to chunks whose type matches the query filter (e.g. `--include-type function`) |
| `CQS_TYPE_GRAPH_MAX_EDGES` | `500000` | Max `type_edges` rows loaded into the in-memory type graph. Sibling of `CQS_CALL_GRAPH_MAX_EDGES` for type-dependency analysis. |
| `CQS_WAL_AUTOCHECKPOINT_PAGES` | `1000` | SQLite `wal_autocheckpoint` ceiling (pages) applied via every connection's `after_connect` hook. Caps WAL growth between commits so an abrupt shutdown leaves a bounded recovery walk. Lower for tighter WAL bounds; raise on long write-heavy reindex sessions to amortize checkpoint cost. (P2-25 / DS-V1.33-8) |
| `CQS_WATCH_DEBOUNCE_MS` | `500` (inotify) / `1500` (WSL/poll auto) | Watch debounce window (milliseconds). Takes precedence over `--debounce`. |
| `CQS_WATCH_INCREMENTAL_SPLADE` | `1` | Set to `0` to disable inline SPLADE encoding in `cqs watch`. Daemon then runs dense-only and sparse coverage drifts until a manual `cqs index`. |
| `CQS_WATCH_MAX_PENDING` | `10000` | Max pending file changes before watch forces flush |
| `CQS_WATCH_POLL_MS` | `5000` | Poll-watcher tick interval (milliseconds). Only used on WSL `/mnt/c/` and other non-inotify filesystems where notify-rs falls back to polling. Lower = faster reaction; higher = less idle CPU walking the tree. Min 100. |
| `CQS_WATCH_REBUILD_THRESHOLD` | `100` | Files changed before watch triggers full HNSW rebuild |
| `CQS_WATCH_RECONCILE` | `1` | Set to `0` to disable Layer 2's periodic full-tree reconciliation (#1182). When on, `cqs watch --serve` walks the working tree on the cadence below and queues files whose stored mtime lags the disk mtime — catches missed events from bulk git operations and WSL `/mnt/c/` 9P drops. |
| `CQS_WATCH_RECONCILE_SECS` | `30` | Cadence (seconds) for Layer 2 periodic full-tree reconciliation. Lower = faster catch-up after missed events at the cost of more idle CPU; higher = quieter daemon. Idle-gated: tick only fires after `daemon_periodic_gc_idle_secs` of quiet so a long edit burst never triggers a reconcile mid-burst. |
| `CQS_WATCH_RESPECT_GITIGNORE` | `1` | Set to `0` to stop `cqs watch` from honoring `.gitignore`. Defaults on — prevents ignored paths (e.g. `.claude/worktrees/*`) from polluting the index. |

## Per-category SPLADE alpha

Hybrid retrieval fuses a dense (EmbeddingGemma-300m by default; configurable via `CQS_EMBEDDING_MODEL`) and sparse (SPLADE) candidate pool. The fusion weight `alpha` controls how much each side contributes to the final score: `alpha = 1.0` means pure dense, `alpha = 0.0` means pure sparse, and values in between interpolate ranks via RRF.

SPLADE is always generating candidates; `alpha` only weights the scoring. The defaults below are derived from a per-category sweep on the live eval set:

| Category | Default alpha | Rationale |
|----------|---------------|-----------|
| `identifier` | `1.00` | Pure dense; identifier semantics are what dense captures best |
| `structural` | `0.90` | Dense-heavy; structural language keywords (`async`, `trait`, `impl`) get a small sparse nudge |
| `conceptual` | `0.70` | Dense-dominant with sparse contribution for keyword-carrying concepts |
| `behavioral` | `0.00` | Pure sparse — action verbs match lexically better than semantically |
| `type_filtered` | `1.00` | Pure dense; the type filter already narrows candidates |
| `multi_step` | `1.00` | Pure dense; semantic chaining matters more than exact tokens |
| `negation` | `0.80` | Dense-heavy with a small sparse contribution for negation tokens (`not`, `null`, `avoid`) |
| `cross_language` | `0.10` | Heavy sparse; code tokens (function names, keywords like `async`/`await`) share across languages more reliably than translated semantics |
| `unknown` | `1.00` | Pure dense; safest default when the router can't classify |

**Override precedence** (highest to lowest):

1. `CQS_SPLADE_ALPHA_{CATEGORY}` (e.g. `CQS_SPLADE_ALPHA_CONCEPTUAL=0.95`) — per-category override
2. `CQS_SPLADE_ALPHA=<value>` — global override applied to every category
3. The per-category default from the table above

Overrides are clamped to `[0.0, 1.0]`. Non-finite or unparseable values fall through to the next layer with a `tracing::warn!`.

## RAG Efficiency

cqs is a retrieval component for RAG pipelines. Context assembly commands (`gather`, `task`, `scout --tokens`) deliver semantically relevant code within a token budget, replacing full file reads.

| Command | What it does | Token reduction |
|---------|-------------|-----------------|
| `cqs gather "query" --tokens 4000` | Seed search + call graph BFS | **17x** vs reading full files |
| `cqs task "description" --tokens 4000` | Scout + gather + impact + placement + notes | **41x** vs reading full files |

Measured on a 4,110-chunk project: `gather` returned 17 chunks from 9 files in 2,536 tokens where the full files total ~43K tokens. `task` returned a complete implementation brief (12 code chunks, 2 risk scores, 2 tests, 3 placement suggestions, 6 notes) in 3,633 tokens from 12 files totaling ~151K tokens.

Token budgeting works across all context commands: `--tokens N` packs results by relevance score into the budget, guaranteeing the most important context fits the agent's context window.

## Performance

Measured 2026-04-16 on the cqs codebase itself (562 files, 15,516 chunks) with CUDA GPU (NVIDIA RTX A6000, 48 GB) on WSL2 Ubuntu. Embedder: BGE-large (1024-dim). SPLADE: ensembledistil (110M, off-the-shelf). Raw measurements: [`evals/performance-v1.27.0.json`](evals/performance-v1.27.0.json).

| Metric | Value |
|--------|-------|
| **Daemon query (graph ops, p50)** | 99 ms |
| **Daemon query (search, warm p50)** | 200 ms |
| **Daemon query (impact, p50)** | 199 ms |
| **Daemon query (search, first call after idle)** | 1.7–12 s (lazy ONNX init) |
| **CLI cold (no daemon, p50)** | 10.5 s |
| **Batch throughput (50 mixed ops)** | 2 ops/sec |
| **Index size** | 2.4 GB DB (~157 KB/chunk, dominated by LLM enrichments) + 73 MB HNSW (~4.7 KB/chunk) |

**Daemon mode** (`cqs watch --serve`) keeps the store, HNSW index, embedder, SPLADE, and reranker loaded across queries — agents pay startup once and amortize over thousands of calls. Graph operations (`callers`, `callees`, `impact`) hit the in-memory call graph; search adds ONNX dense + SPLADE sparse retrieval and RRF fusion.

CLI cold latency includes process spawn, ONNX model load, DB open, and HNSW load. The 10× gap vs daemon is the cost of doing all of that per query — `cqs batch` amortizes startup across queries when the daemon isn't running.

Mixed-batch throughput (~2 ops/sec) is dominated by search operations (~200 ms each via daemon). Pure call-graph throughput is much higher — `callers` alone runs at ~10 ops/sec via daemon.

**Embedding latency (GPU vs CPU):**

| Mode | Single Query | Batch (50 docs) |
|------|--------------|-----------------|
| CPU | ~20 ms | ~15 ms/doc |
| CUDA | ~3 ms | ~0.3 ms/doc |

<details>
<summary><h2>GPU Acceleration (Optional)</h2></summary>

cqs works on CPU out of the box. GPU acceleration has two independent components:

- **Embedding (ORT CUDA)**: 5-7x embedding speedup. Works with `cargo install cqs` -- just needs CUDA 12 runtime and cuDNN.
- **Index (CAGRA)**: GPU-accelerated nearest neighbor search via cuVS. Requires `cargo install cqs --features cuda-index` plus the cuVS conda package.

You can use either or both.

### Embedding GPU (CUDA 12 + cuDNN)

```bash
# Add NVIDIA CUDA repo
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2404/x86_64/cuda-keyring_1.1-1_all.deb
sudo dpkg -i cuda-keyring_1.1-1_all.deb
sudo apt update

# Install CUDA 12 runtime and cuDNN 9
sudo apt install cuda-cudart-12-6 libcublas-12-6 libcudnn9-cuda-12
```

Set library path:
```bash
export LD_LIBRARY_PATH=/usr/local/cuda-12.6/lib64:/usr/lib/x86_64-linux-gnu:$LD_LIBRARY_PATH
```

### CAGRA GPU Index (Optional, requires conda)

CAGRA uses cuVS for GPU-accelerated approximate nearest neighbor search, with native bitset filtering for type/language queries. Requires the `cuda-index` feature flag (the legacy `gpu-index` name is preserved as an alias) and matching libcuvs from conda:

```bash
conda install -c rapidsai libcuvs=26.04 libcuvs-headers=26.04
cargo install cqs --features cuda-index
```

`cuvs-sys` does strict version matching — the conda `libcuvs` version must match the Rust `cuvs` crate version (currently `=26.4`).

Building from source:
```bash
cargo build --release --features cuda-index
```

> **Note:** cqs uses a patched cuvs crate that exposes `search_with_filter` for GPU-native bitset filtering. This is applied transparently via `[patch.crates-io]`. Once upstream rapidsai/cuvs#2019 merges, the patch will be removed.

### WSL2

Same as Linux, plus:
- Requires NVIDIA GPU driver on Windows host
- Add `/usr/lib/wsl/lib` to `LD_LIBRARY_PATH`
- Dual CUDA setup: CUDA 12 (system, for ORT embedding) and CUDA 13 (conda, for cuVS). Both coexist via `LD_LIBRARY_PATH` ordering -- conda paths first for cuVS, system paths for ORT.
- Tested working with RTX A6000, CUDA 13.1 driver, cuDNN 9.19

### Verify

```bash
cqs doctor # Shows execution provider (CUDA or CPU) and CAGRA availability
```

</details>

## Contributing

Issues and PRs welcome at [GitHub](https://github.com/jamie8johnson/cqs).

## License