# cqs ("seeks")

Code intelligence and RAG for AI agents. Semantic search, call graph analysis, impact tracing, type dependencies, and smart context assembly — all in single tool calls. Local ML embeddings, GPU-accelerated.

**TL;DR:** Code intelligence toolkit for Claude Code. Instead of grep + sequential file reads, cqs understands what code *does* — semantic search finds functions by concept, call graph commands trace dependencies, and `gather`/`impact`/`context` assemble the right context in one call. 17-41x token reduction vs full file reads. 94.5% Recall@1 (BGE-large) on confusable function retrieval. 51 languages, GPU-accelerated.

[![Crates.io](https://img.shields.io/crates/v/cqs.svg)](https://crates.io/crates/cqs)
[![CI](https://github.com/jamie8johnson/cqs/actions/workflows/ci.yml/badge.svg)](https://github.com/jamie8johnson/cqs/actions/workflows/ci.yml)
[![CodeQL](https://github.com/jamie8johnson/cqs/actions/workflows/codeql.yml/badge.svg)](https://github.com/jamie8johnson/cqs/actions/workflows/codeql.yml)

---

> **`<claude>`**
>
> You're an AI assistant reading this. cqs is a code intelligence and RAG toolkit — semantic search, call graphs, impact analysis, type dependencies, and smart context assembly via CLI.
>
> **Why you want this:** Single tool calls replace 5-10 sequential file reads. `cqs gather` assembles relevant context via search + call graph BFS. `cqs impact` shows what breaks if you change a function. `cqs trace` follows call chains. `cqs deps` maps type relationships. All searchable by concept, not just name.
>
> **Setup:** Add cqs commands to your project's CLAUDE.md so Claude Code uses them automatically. See Claude Code Integration section below.
>
> **`</claude>`**

---

## Install

**Requires Rust 1.93+**

```bash
cargo install cqs
```

**Upgrading?** Schema changes require rebuilding the index:
```bash
cqs index --force  # Run after upgrading from older versions (current schema: v16)
```

## Quick Start

```bash
# Download model and initialize
cqs init

# Index your project
cd /path/to/project
cqs index

# Search
cqs "retry with exponential backoff"
cqs "validate email with regex"
cqs "database connection pool"
```

### Embedding Model

cqs ships with E5-base-v2 (768-dim) as the default. Alternative models can be configured:

```bash
# Built-in preset
export CQS_EMBEDDING_MODEL=bge-large
cqs index --force  # reindex required after model change

# Or via CLI flag
cqs index --force --model bge-large

# Or in cqs.toml
[embedding]
model = "bge-large"
```

For custom ONNX models, see `cqs export-model --help`.

## Filters

```
# By language
cqs --lang rust "error handling"
cqs --lang python "parse json"

# By path pattern
cqs --path "src/*" "config"
cqs --path "tests/**" "mock"
cqs --path "**/*.go" "interface"

# By chunk type
cqs --chunk-type function "retry logic"
cqs --chunk-type struct "config"
cqs --chunk-type enum "error types"

# By structural pattern
cqs --pattern async "request handling"
cqs --pattern unsafe "memory operations"
cqs --pattern recursion "tree traversal"
# Patterns: builder, error_swallow, async, mutex, unsafe, recursion

# Combined
cqs --lang typescript --path "src/api/*" "authentication"
cqs --lang rust --chunk-type function --pattern async "database query"

# Hybrid search tuning
cqs --name-boost 0.2 "retry logic"   # Semantic-heavy (default)
cqs --name-boost 0.8 "parse_config"  # Name-heavy for known identifiers
cqs "query" --expand                  # Expand results via call graph

# Show surrounding context
cqs -C 3 "error handling"       # 3 lines before/after each result

# Token budgeting (cross-command: query, gather, context, explain, scout, onboard)
cqs "query" --tokens 2000     # Limit output to ~2000 tokens
cqs gather "auth" --tokens 4000
cqs explain func --tokens 3000

# Output options
cqs --json "query"           # JSON output
cqs --no-content "query"     # File:line only, no code
cqs -n 10 "query"            # Limit results
cqs -t 0.5 "query"           # Min similarity threshold
cqs --no-stale-check "query" # Skip staleness checks (useful on NFS)
cqs --no-demote "query"      # Disable score demotion for low-quality matches
```

## Configuration

Set default options via config files. CLI flags override config file values.

**Config locations (later overrides earlier):**
1. `~/.config/cqs/config.toml` - user defaults
2. `.cqs.toml` in project root - project overrides

**Example `.cqs.toml`:**

```toml
# Default result limit
limit = 10

# Minimum similarity threshold (0.0 - 1.0)
threshold = 0.4

# Name boost for hybrid search (0.0 = pure semantic, 1.0 = pure name)
name_boost = 0.2

# HNSW search width (higher = better recall, slower queries)
ef_search = 100

# Skip index staleness checks on every query (useful on NFS or slow disks)
stale_check = true

# Output modes
quiet = false
verbose = false

# Embedding model (optional — defaults to e5-base)
[embedding]
model = "bge-large"              # built-in preset
# model = "custom"               # for custom ONNX models:
# repo = "org/model-name"
# onnx_path = "model.onnx"
# tokenizer_path = "tokenizer.json"
# dim = 1024
# query_prefix = "query: "
# doc_prefix = "passage: "
```

## Watch Mode

Keep your index up to date automatically:

```bash
cqs watch              # Watch for changes and reindex
cqs watch --debounce 1000  # Custom debounce (ms)
```

Watch mode respects `.gitignore` by default. Use `--no-ignore` to index ignored files.

## Call Graph

Find function call relationships:

```bash
cqs callers <name>   # Functions that call <name>
cqs callees <name>   # Functions called by <name>
cqs deps <type>      # Who uses this type?
cqs deps --reverse <fn>  # What types does this function use?
cqs impact <name> --format mermaid   # Mermaid graph output
```

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 --include-types  # 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`. 94.5% Recall@1 (BGE-large) on confusable functions.
- **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 read <path>` - file with context notes injected as comments
- `cqs read --focus <function>` - function + type dependencies only
- `cqs stats` - index stats, chunk counts, HNSW index status
- `cqs callers <function>` - find functions that call a given function
- `cqs callees <function>` - find functions called by a given function
- `cqs deps <type>` - 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 <function>` - find functions similar to a given function
- `cqs explain <function>` - function card: signature, callers, callees, similar
- `cqs diff <ref>` - semantic diff between indexed snapshots
- `cqs drift <ref>` - semantic drift: functions that changed most between reference and project
- `cqs trace <source> <target>` - follow call chain (BFS shortest path)
- `cqs impact <function>` - 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 <function>` - map functions to tests that exercise them
- `cqs context <file>` - module-level: chunks, callers, callees, notes
- `cqs context <file> --compact` - signatures + caller/callee counts only
- `cqs gather "query"` - smart context assembly: seed search + call graph BFS
- `cqs related <function>` - 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 <function>` - 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 <path>` - convert PDF/HTML/CHM/Markdown to cleaned Markdown for indexing
- `cqs train-data` - generate fine-tuning training data from git history
- `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 <org/model>` - export a HuggingFace model to ONNX format for use with cqs
- `cqs doctor` - check model, index, hardware (execution provider, CAGRA availability)
- `cqs completions <shell>` - generate shell completions (bash, zsh, fish, powershell, elvish)

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

<details>
<summary><h2>Supported Languages (51)</h2></summary>

- 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)
- Elixir (functions, modules, protocols, implementations, macros, pipe calls)
- 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 `<script>` extracts JS/TS functions, `<style>` extracts CSS rules via multi-grammar injection)
- 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, constants, and documentation across 51 languages. 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 (SQ-11). 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 (E5-base-v2 default, BGE-large preset, or custom ONNX) generates embeddings locally. 94.5% Recall@1 (BGE-large) on confusable function retrieval — outperforms code-specific models because NL descriptions play to general-purpose model strengths. Optional HyDE query predictions (`--hyde-queries`) generate synthetic search queries per function for improved 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

Evaluated on a hard eval suite of 55 queries across 5 languages (Rust, Python, TypeScript, JavaScript, Go) with 15 confusable functions per language (6 sort variants, 4 validators, etc.):

| Metric | BGE-large (cqs default) | E5-base-v2 (cqs) | jina-v2-base-code |
|--------|------------------------|-------------------|-------------------|
| **Recall@1** | **94.5%** | 83.6% | 80.0% |
| **Recall@5** | **98.2%** | 98.2% | 94.5% |
| **MRR** | **0.966** | 0.909 | — |

E5 column includes the full cqs enrichment stack (contrastive summaries, type-aware embeddings, hybrid RRF). jina column is raw embeddings without enrichment.

General-purpose E5 outperforms code-specific jina because cqs generates natural language descriptions of each code element — doc comments, parameter types, return types, parent type context — transforming the retrieval task from code→code to NL→NL, where general-purpose models excel.

E5 reaches perfect recall at Recall@5 with the enrichment stack. jina catches up at Recall@10 (100%) but ranks results lower, reflected in its lower NDCG@10.

## 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

Benchmarked on a 4,110-chunk Rust project (202 files, 12 languages) with CUDA GPU (RTX A6000):

| Metric | Value |
|--------|-------|
| **Search latency (hot, p50)** | 45ms |
| **Search latency (cold, p50)** | 1,767ms |
| **Throughput (batch mode)** | 22 queries/sec |
| **Index build (203 files)** | 36 sec |
| **Index size** | ~8 KB/chunk (31 MB for 4,110 chunks) |

Cold latency includes process startup, model init, and DB open. Batch mode (`cqs batch`) amortizes startup across queries — use it for pipelines and agent workloads.

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

| Mode | Single Query | Batch (50 docs) |
|------|--------------|-----------------|
| CPU  | ~20ms        | ~15ms/doc       |
| CUDA | ~3ms         | ~0.3ms/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 gpu-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. This requires the `gpu-index` feature flag and the cuVS library from conda:

```bash
conda install -c rapidsai -c conda-forge libcuvs
cargo install cqs --features gpu-index
```

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

### 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

MIT
