vexor 0.12.0

A vector-powered CLI for semantic search over files.

Vexor

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Vexor is a vector-powered CLI for semantic file search. It uses configurable embedding models and ranks results by cosine similarity.

Why Vexor?

When you remember what a file does but forget its name or location, Vexor finds it instantly—no grep patterns or directory traversal needed.

Designed for both humans and AI coding assistants, enabling semantic file discovery in autonomous agent workflows.

Install

Download standalone binary from releases (no Python required), or:

pip install vexor  # also works with pipx, uv

Quick Start

1. Configure API Key

vexor config --set-api-key "YOUR_KEY"

Or via environment: VEXOR_API_KEY, OPENAI_API_KEY, or GOOGLE_GENAI_API_KEY.

2. Search

vexor search "api client config"  # searches current directory
# or explicit path:
vexor search "api client config" --path ~/projects/demo --top 5

Vexor auto-indexes on first search. Example output:

Vexor semantic file search results
──────────────────────────────────
#   Similarity   File path                       Lines   Preview
1   0.923        ./src/config_loader.py          -       config loader entrypoint
2   0.871        ./src/utils/config_parse.py     -       parse config helpers
3   0.809        ./tests/test_config_loader.py   -       tests for config loader

3. Explicit Index (Optional)

vexor index  # indexes current directory
# or explicit path:
vexor index --path ~/projects/demo --mode code

Useful for CI warmup or when auto_index is disabled.

Configuration

vexor config --set-provider openai          # default; also supports gemini/local
vexor config --set-model text-embedding-3-small
vexor config --set-batch-size 0             # 0 = single request
vexor config --set-auto-index true          # auto-index before search (default)
vexor config --set-base-url https://proxy.example.com  # optional proxy
vexor config --clear-base-url               # reset to official endpoint
vexor config --show                         # view current settings

Config stored in ~/.vexor/config.json.

Providers: Remote vs Local

Vexor supports both remote API providers (openai, gemini) and a local provider (local):

• Remote providers use api_key and optional base_url.
• Local provider ignores api_key/base_url and only uses model plus local_cuda (CPU/GPU switch).

Local Model (Offline)

Install the lightweight local backend:

pip install "vexor[local]"

GPU backend (requires CUDA drivers):

pip install "vexor[local-cuda]"

Download a local embedding model and auto-configure Vexor:

vexor local --setup --model intfloat/multilingual-e5-small

Then use vexor search / vexor index as usual.

Local models are stored in ~/.vexor/models (clear with vexor local --clean-up).

GPU (optional): install onnxruntime-gpu (or vexor[local-cuda]) and use vexor local --setup --cuda (or vexor local --cuda). Switch back with vexor local --cpu.

Index Modes

Control embedding granularity with --mode:

Mode	Description
auto	Default. Smart routing: Python/JS/TS → code, Markdown → outline, small files → full, large files → head
name	Embed filename only (fastest, zero content reads)
head	Extract first snippet for lightweight semantic context
brief	Extract high-frequency keywords from PRDs/requirements docs
full	Chunk entire content; long documents searchable end-to-end
code	AST-aware chunking by module/class/function boundaries for Python and JavaScript/TypeScript; other files fall back to full
outline	Chunk Markdown by heading hierarchy with breadcrumbs; non-.md falls back to full

Cache Behavior

Index cache keys derive from: --path, --mode, --include-hidden, --no-recursive, --no-respect-gitignore, --ext.

Keep flags consistent to reuse cache; changing flags creates a separate index.

vexor config --show-index-all    # list all cached indexes
vexor config --clear-index-all   # clear all cached indexes
vexor index --path . --clear     # clear index for specific path

Re-running vexor index only re-embeds changed files; >50% changes trigger full rebuild.

Command Reference

Command	Description
vexor search QUERY --path PATH	Semantic search (auto-indexes if needed)
vexor index --path PATH	Build/refresh index manually
vexor config --show	Display current configuration
vexor local --setup [--model MODEL]	Download a local model and set provider to local
vexor local --clean-up	Remove local model cache under ~/.vexor/models
vexor local --cuda	Enable CUDA for local embeddings (requires onnxruntime-gpu)
vexor local --cpu	Disable CUDA and use CPU for local embeddings
vexor install --skills claude	Install Agent Skill for Claude Code
vexor install --skills codex	Install Agent Skill for Codex
vexor doctor	Run diagnostic checks (command, config, cache, API key, API connectivity)
vexor update [--upgrade] [--pre]	Check for new version (optionally upgrade; --pre includes pre-releases)
vexor feedback	Open GitHub issue form (or use gh)

Common Flags

Flag	Description
--path PATH	Target directory (default: current working directory)
--mode MODE	Index mode (auto/name/head/brief/full/code/outline)
--top K / -k	Number of results (default: 5)
--ext .py,.md / -e	Filter by extension (repeatable)
--include-hidden / -i	Include hidden files
--no-recursive / -n	Don't recurse into subdirectories
--no-respect-gitignore	Include gitignored files
--format porcelain	Script-friendly TSV output
--format porcelain-z	NUL-delimited output

Porcelain output fields: rank, similarity, path, chunk_index, start_line, end_line, preview (line fields are - when unavailable).

AI Agent Skill

This repo includes a skill for AI agents to use Vexor effectively:

vexor install --skills claude  # Claude Code
vexor install --skills codex   # Codex

Skill source: plugins/vexor/skills/vexor-cli

Documentation

See docs for more details.

Contributions, issues, and PRs welcome! Star if you find it helpful.

License

MIT