# Vexor [![Python](https://img.shields.io/badge/python-3.9%2B-blue)](https://www.python.org/downloads/) [![PyPI](https://img.shields.io/pypi/v/vexor.svg)](https://pypi.org/project/vexor/) [![CI](https://img.shields.io/github/actions/workflow/status/scarletkc/vexor/publish.yml?branch=main)](https://github.com/scarletkc/vexor/actions/workflows/publish.yml) [![Codecov](https://img.shields.io/codecov/c/github/scarletkc/vexor/main)](https://codecov.io/github/scarletkc/vexor) [![License](https://img.shields.io/github/license/scarletkc/vexor.svg)](https://github.com/scarletkc/vexor/blob/main/LICENSE) [![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/scarletkc/vexor)

--- **Vexor** is a semantic search engine that builds reusable indexes over files and code. It supports configurable embedding and reranking providers, and exposes the same core through a Python API, a CLI tool, and an optional desktop frontend.

## Featured In Vexor has been recognized and featured by the community: - **[Ruan Yifeng's Weekly (Issue #379)](https://github.com/ruanyf/weekly/blob/master/docs/issue-379.md#ai-%E7%9B%B8%E5%85%B3)** - A leading tech newsletter in the Chinese developer community. - **[Awesome Claude Skills](https://github.com/VoltAgent/awesome-claude-skills?tab=readme-ov-file#development-and-testing)** - Curated list of best-in-class skills for AI agents. ## 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](https://github.com/scarletkc/vexor/releases) (no Python required), or: ```bash pip install vexor # also works with pipx, uv ``` ## Quick Start ### 0. Guided Setup (Recommended) ```bash vexor init ``` The wizard also runs automatically on first use when no config exists. ### 1. Search ```bash vexor "api client config" # defaults to search current directory # or explicit path: vexor search "api client config" --path ~/projects/demo --top 5 # in-memory search only: vexor search "api client config" --no-cache ``` 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 ``` ### 2. Explicit Index (Optional) ```bash 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. ## Desktop App (Experimental) > The desktop app is experimental and not actively maintained. > It may be unstable. For production use, prefer the CLI. ![GUI](https://raw.githubusercontent.com/scarletkc/vexor/refs/heads/main/assets/gui_demo.png) Download the desktop app from [releases](https://github.com/scarletkc/vexor/releases). ## Python API Vexor can also be imported and used directly from Python: ```python from vexor import index, search index(path=".", mode="head") response = search("config loader", path=".", mode="name") for hit in response.results: print(hit.path, hit.score) ``` By default it reads `~/.vexor/config.json`. For runtime config overrides, cache controls, and per-call options, see [`docs/api/python.md`](https://github.com/scarletkc/vexor/tree/main/docs/api/python.md). ## AI Agent Skill This repo includes a skill for AI agents to use Vexor effectively: ```bash vexor install --skills claude # Claude Code vexor install --skills codex # Codex ``` Skill source: [`plugins/vexor/skills/vexor-cli`](https://github.com/scarletkc/vexor/raw/refs/heads/main/plugins/vexor/skills/vexor-cli/SKILL.md) ## Configuration ```bash vexor config --set-provider openai # default; also supports gemini/voyageai/custom/local vexor config --set-model text-embedding-3-small vexor config --set-provider voyageai # uses voyage defaults when model/base_url are unset vexor config --set-batch-size 0 # 0 = single request vexor config --set-embed-concurrency 4 # parallel embedding requests vexor config --set-extract-concurrency 4 # parallel file extraction workers vexor config --set-extract-backend auto # auto|thread|process (default: auto) vexor config --set-embedding-dimensions 1024 # optional, model/provider dependent vexor config --clear-embedding-dimensions # reset to model default dimension vexor config --set-auto-index true # auto-index before search (default) vexor config --rerank bm25 # optional BM25 rerank for top-k results vexor config --rerank flashrank # FlashRank rerank (requires optional extra) vexor config --rerank remote # remote rerank via HTTP endpoint vexor config --set-flashrank-model ms-marco-MultiBERT-L-12 # multilingual model vexor config --set-flashrank-model # reset FlashRank model to default vexor config --clear-flashrank # remove cached FlashRank models vexor config --set-remote-rerank-url https://proxy.example.com/v1/rerank vexor config --set-remote-rerank-model bge-reranker-v2-m3 vexor config --set-remote-rerank-api-key $VEXOR_REMOTE_RERANK_API_KEY # or env var vexor config --clear-remote-rerank # clear remote rerank config 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 ``` Rerank defaults to `off`. **It is highly recommended to configure the Reranker in advance to improve search accuracy.** FlashRank requires `pip install "vexor[flashrank]"` and caches models under `~/.vexor/flashrank`. Config stored in `~/.vexor/config.json`. ### Configure API Key ```bash vexor config --set-api-key "YOUR_KEY" ``` Or via environment: `VEXOR_API_KEY`, `OPENAI_API_KEY`, `GOOGLE_GENAI_API_KEY`, or `VOYAGE_API_KEY`. ### Rerank Rerank reorders the semantic results with a secondary ranker. Candidate sizing uses `clamp(int(--top * 2), 20, 150)`. Recommended defaults: - Keep `off` unless you want extra precision. - Use `bm25` for lightweight lexical boosts; it is fast and lightweight. - BM25 uses a multilingual tokenizer (Bert pre-tokenizer), so it can handle CJK better. - Use `flashrank` for stronger reranking (requires `pip install "vexor[flashrank]"` and downloads a model to `~/.vexor/flashrank`). - Use `remote` to call a hosted reranker that accepts `{model, query, documents}` and returns ranked indexes. - For Chinese or multi-language content, set `--set-flashrank-model ms-marco-MultiBERT-L-12`. - If unset, FlashRank defaults to `ms-marco-TinyBERT-L-2-v2`. ### Providers: Remote vs Local Vexor supports both remote API providers (`openai`, `gemini`, `voyageai`, `custom`) and a local provider (`local`): - Remote providers use `api_key` and optional `base_url`. - `voyageai` defaults to `https://api.voyageai.com/v1` when `base_url` is not set. - `custom` is OpenAI-compatible and requires both `model` and `base_url`. - Local provider ignores `api_key/base_url` and only uses `model` plus `local_cuda` (CPU/GPU switch). ### Embedding Dimensions Embedding dimensions are optional. If unset, the provider/model default is used. Custom dimensions are validated for: - OpenAI `text-embedding-3-*` - Voyage `voyage-3*` and `voyage-code-3*` ```bash vexor config --set-embedding-dimensions 1024 vexor config --clear-embedding-dimensions ``` If you change dimensions after an index is built, rebuild the index: ```bash vexor index --path . ``` ### Local Model (Offline) Install the lightweight local backend: ```bash pip install "vexor[local]" ``` GPU backend (requires CUDA drivers): ```bash pip install "vexor[local-cuda]" ``` Download a local embedding model and auto-configure Vexor: ```bash 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`, `--exclude-pattern`. Keep flags consistent to reuse cache; changing flags creates a separate index. ```bash 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 init` | Run the interactive setup wizard | | `vexor QUERY` | Shortcut for `vexor search QUERY` | | `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 config --clear-flashrank` | Remove cached FlashRank models under `~/.vexor/flashrank` | | `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`) | | `vexor alias` | Print a shell alias for `vx` and optionally apply it | ### 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) | | `--exclude-pattern PATTERN` | Exclude paths by gitignore-style pattern (repeatable; `.js` treated as `**/*.js`) | | `--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 | | `--no-cache` | In-memory only; do not read/write index cache | Porcelain output fields: `rank`, `similarity`, `path`, `chunk_index`, `start_line`, `end_line`, `preview` (line fields are `-` when unavailable). ## Documentation See [docs](https://github.com/scarletkc/vexor/tree/main/docs) for more details. ## Contributing Contributions, issues, and PRs welcome! Star if you find it helpful. ## Star History [![Star History Chart](https://api.star-history.com/svg?repos=scarletkc/vexor&type=date&legend=top-left)](https://www.star-history.com/#scarletkc/vexor&type=date&legend=top-left) ## License [MIT](http://github.com/scarletkc/vexor/blob/main/LICENSE)