codexlens-search 0.6.9

Lightweight semantic code search engine — hybrid vector + FTS + AST graph + regex fusion + MCP server

codexlens-search

Semantic code search engine with MCP server for Claude Code.

Hybrid search: vector + FTS + AST graph + ripgrep regex — with RRF fusion and reranking.

Quick Start

pip install codexlens-search[all]

Add to your project .mcp.json:

{
  "mcpServers": {
    "codexlens": {
      "command": "uvx",
      "args": ["--from", "codexlens-search[all]", "codexlens-mcp"],
      "env": {
        "CODEXLENS_EMBED_API_URL": "https://api.openai.com/v1",
        "CODEXLENS_EMBED_API_KEY": "${OPENAI_API_KEY}",
        "CODEXLENS_EMBED_API_MODEL": "text-embedding-3-small",
        "CODEXLENS_EMBED_DIM": "1536"
      }
    }
  }
}

That's it. Claude Code will auto-discover the tools: index_project -> Search.

Install

Choose the install that matches your platform:

# Minimal — CPU inference (fastembed bundles onnxruntime CPU)
pip install codexlens-search

# Windows GPU — DirectML, any DirectX 12 GPU (NVIDIA/AMD/Intel)
pip install codexlens-search[directml]

# Linux/Windows NVIDIA GPU — CUDA (requires CUDA + cuDNN)
pip install codexlens-search[cuda]

# Auto-select — DirectML on Windows, CPU elsewhere
pip install codexlens-search[all]

Platform Recommendations

Platform	Recommended	Command
Windows + any GPU	[directml]	pip install codexlens-search[directml]
Windows CPU only	base	pip install codexlens-search
Linux + NVIDIA GPU	[cuda]	pip install codexlens-search[cuda]
Linux CPU / AMD GPU	base	pip install codexlens-search
macOS (Apple Silicon)	base	pip install codexlens-search
Don't know / CI	[all]	pip install codexlens-search[all]

Note: On Windows, if you install the base package without [directml], the MCP server will auto-detect the missing GPU runtime and install onnxruntime-directml on first launch. GPU takes effect from the second start.

What's Included

All install variants include:

• MCP server — codexlens-mcp command
• AST parsing — tree-sitter symbol extraction + graph search
• USearch — high-performance HNSW ANN backend (default)
• FAISS — ANN + binary index backend (Hamming coarse search)
• File watcher — watchdog auto-indexing
• Gitignore filtering — recursive .gitignore support
• Focused search — when no index exists, greps relevant files, indexes only those (~10s), then runs semantic search — no waiting for full index build

ANN Backend Selection

Three backends for approximate nearest neighbor search, auto-selected in order:

Backend	Install	Best for
usearch (default)	Included	Cross-platform, fastest CPU HNSW
faiss	Included	GPU acceleration, binary Hamming search
hnswlib	Included	Lightweight fallback

Override with CODEXLENS_ANN_BACKEND:

CODEXLENS_ANN_BACKEND=faiss    # use FAISS (GPU when available)
CODEXLENS_ANN_BACKEND=usearch  # use USearch (default)
CODEXLENS_ANN_BACKEND=hnswlib  # use hnswlib
CODEXLENS_ANN_BACKEND=auto     # auto-select (usearch > faiss > hnswlib)

MCP Tools

Search

Hybrid code search combining semantic vector, FTS, AST graph, and ripgrep regex.

Mode	Description	Requires
auto (default)	Semantic + regex parallel. No index? Focused grep-index-search in ~10s.
symbol	Find definitions by exact/fuzzy name match	Index
refs	Find cross-references — incoming and outgoing edges	Index
regex	Ripgrep regex on live files	rg

Parameters: project_path, query, mode, scope (restricts auto/regex to subdirectory)

Results capped by CODEXLENS_TOP_K env var (default 10).

Cold Start Search

When no index exists, auto mode uses a focused search pipeline instead of waiting for a full index build:

1. Expand query — split camelCase/snake_case into search terms
2. Grep files — rg --count finds top 50 relevant files, ranked by match count
3. Index — embed only those 50 files (~8-10s with GPU)
4. Search — semantic vector search on the fresh index
5. Background — full index builds asynchronously for next queries

This gives semantic results in ~10s vs ~100s for a full index build.

index_project

Build, update, or inspect the search index.

Action	Description
sync (default)	Incremental — only changed files
rebuild	Full re-index from scratch
status	Index statistics (files, chunks, symbols, refs)

Parameters: project_path, action, scope

find_files

Glob-based file discovery. Parameters: project_path, pattern (default **/*)

Max results controlled by CODEXLENS_FIND_MAX_RESULTS env var (default 100).

watch_project

Manage file watcher for automatic re-indexing on file changes.

Parameters: project_path, action (start / stop / status)

AST Features

Enabled by default. Disable with CODEXLENS_AST_CHUNKING=false.

• Smart chunking — splits at symbol boundaries instead of fixed-size windows
• Symbol extraction — 12 kinds: function, class, method, module, variable, constant, interface, type_alias, enum, struct, trait, property
• Cross-references — import, call, inherit, type_ref edges
• Graph search — seeded from vector/FTS results, BFS expansion with adaptive weights

Languages: Python, JavaScript, TypeScript, Go, Java, Rust, C, C++, Ruby, PHP, Scala, Kotlin, Swift, C#, Bash, Lua, Haskell, Elixir, Erlang.

Configuration Examples

Reranker (best quality)

Add reranker API on top of the Quick Start config:

"CODEXLENS_RERANKER_API_URL": "https://api.jina.ai/v1",
"CODEXLENS_RERANKER_API_KEY": "${JINA_API_KEY}",
"CODEXLENS_RERANKER_API_MODEL": "jina-reranker-v2-base-multilingual"

Multi-Endpoint Load Balancing

"CODEXLENS_EMBED_API_ENDPOINTS": "https://api1.example.com/v1|sk-key1|model,https://api2.example.com/v1|sk-key2|model",
"CODEXLENS_EMBED_DIM": "1536"

Format: url|key|model,url|key|model,... — replaces single-endpoint EMBED_API_URL/KEY/MODEL.

Local Models (Offline)

No API needed — fastembed runs the model locally via ONNX runtime.

# Pre-download models (optional — auto-downloads on first use)
codexlens-search download-models

{
  "mcpServers": {
    "codexlens": {
      "command": "codexlens-mcp",
      "env": {
        "CODEXLENS_DEVICE": "directml"
      }
    }
  }
}

Default local model: BAAI/bge-small-en-v1.5 (384d, ~33MB). To use a different model:

{
  "mcpServers": {
    "codexlens": {
      "command": "codexlens-mcp",
      "env": {
        "CODEXLENS_EMBED_MODEL": "BAAI/bge-base-en-v1.5",
        "CODEXLENS_EMBED_DIM": "768",
        "CODEXLENS_DEVICE": "directml"
      }
    }
  }
}

Available Local Models

Model	Dim	Size	Notes
BAAI/bge-small-en-v1.5	384	~33MB	Default, fastest
BAAI/bge-base-en-v1.5	768	~130MB	Better quality
BAAI/bge-large-en-v1.5	1024	~335MB	Best English quality
BAAI/bge-small-zh-v1.5	512	~46MB	Chinese, fast
BAAI/bge-large-zh-v1.5	1024	~335MB	Chinese, best quality
sentence-transformers/all-MiniLM-L6-v2	384	~23MB	Lightweight general

CODEXLENS_EMBED_DIM must match the model's output dimension. Mismatched dim will cause indexing errors.

China Mirror

"CODEXLENS_HF_MIRROR": "https://hf-mirror.com"

Custom Model Cache

"CODEXLENS_MODEL_CACHE_DIR": "/path/to/cache"

GPU

Windows: pip install codexlens-search[directml] — works with any DirectX 12 GPU (NVIDIA/AMD/Intel). No CUDA needed. Even without [directml], the server auto-installs it on first launch.

Linux: pip install codexlens-search[cuda] adds CUDA support (requires CUDA + cuDNN).

Auto-detection priority: CUDA > DirectML > CPU

• Embedding — ONNX runtime selects best available GPU provider, ~12x faster than CPU
• FAISS — index auto-transfers to GPU 0 (CUDA only)

Force specific device: CODEXLENS_DEVICE=directml / cuda / cpu

CLI

codexlens-search --db-path .codexlens sync --root ./src
codexlens-search --db-path .codexlens search -q "auth handler" -k 10
codexlens-search --db-path .codexlens status
codexlens-search list-models
codexlens-search download-models

Environment Variables

Local Model

Variable	Default	Description
CODEXLENS_EMBED_MODEL	BAAI/bge-small-en-v1.5	Local fastembed model name
CODEXLENS_EMBED_DIM	384	Vector dimension (must match model)
CODEXLENS_MODEL_CACHE_DIR	fastembed default	Model download cache directory
CODEXLENS_HF_MIRROR		HuggingFace mirror (e.g. https://hf-mirror.com)

Embedding API (overrides local model)

Variable	Description
CODEXLENS_EMBED_API_URL	API base URL (e.g. https://api.openai.com/v1)
CODEXLENS_EMBED_API_KEY	API key
CODEXLENS_EMBED_API_MODEL	Model name (e.g. text-embedding-3-small)
CODEXLENS_EMBED_API_ENDPOINTS	Multi-endpoint: url|key|model,...

Reranker

Variable	Description
CODEXLENS_RERANKER_API_URL	Reranker API base URL
CODEXLENS_RERANKER_API_KEY	API key
CODEXLENS_RERANKER_API_MODEL	Model name

Features

Variable	Default	Description
CODEXLENS_AST_CHUNKING	true	AST chunking + symbol extraction
CODEXLENS_GITIGNORE_FILTERING	true	Recursive .gitignore filtering
CODEXLENS_DEVICE	auto	auto / cuda / directml / cpu
CODEXLENS_AUTO_WATCH	false	Auto-start file watcher after indexing

MCP Tool Defaults

Variable	Default	Description
CODEXLENS_TOP_K	10	Search result limit
CODEXLENS_FIND_MAX_RESULTS	100	find_files result limit

Tuning

Variable	Default	Description
CODEXLENS_BINARY_TOP_K	200	Binary coarse search candidates
CODEXLENS_ANN_TOP_K	50	ANN fine search candidates
CODEXLENS_FTS_TOP_K	50	FTS results per method
CODEXLENS_FUSION_K	60	RRF fusion k parameter
CODEXLENS_RERANKER_TOP_K	20	Results to rerank
CODEXLENS_EMBED_BATCH_SIZE	32	Texts per API batch
CODEXLENS_EMBED_MAX_TOKENS	8192	Max tokens per text (0=no limit)
CODEXLENS_INDEX_WORKERS	2	Parallel indexing workers
CODEXLENS_MAX_FILE_SIZE	1000000	Max file size in bytes

Architecture

Query -> [Embedder] -> query vector
          |-> [FAISS Binary] -> candidates (Hamming)
          |     +-> [USearch/FAISS HNSW] -> ranked IDs (cosine)
          |-> [FTS exact + fuzzy] -> text matches
          |-> [GraphSearcher] -> symbol neighbors (seeded from vector/FTS)
          +-> [ripgrep] -> regex matches
               +-> [RRF Fusion] -> merged ranking
                     +-> [Reranker] -> final top-k

Development

git clone https://github.com/catlog22/codexlens-search.git
cd codexlens-search
pip install -e ".[dev,all]"
pytest

License

MIT