codexlens-search 0.6.4

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

uv pip install codexlens-search

Add to your project .mcp.json:

{
  "mcpServers": {
    "codexlens": {
      "command": "uvx",
      "args": ["--from", "codexlens-search", "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.

All features are included by default — MCP server, AST parsing, USearch + FAISS backends, file watcher, gitignore filtering. GPU acceleration is auto-detected when available.

Install

# One command — GPU auto-detected on Windows, no extra steps
uv pip install codexlens-search

# Linux with NVIDIA GPU (requires CUDA + cuDNN)
uv pip install codexlens-search[gpu]

Default install includes:

• MCP server — codexlens-mcp command
• AST parsing — tree-sitter symbol extraction + graph search (on by default)
• USearch — high-performance HNSW ANN backend (default, cross-platform)
• FAISS — ANN + binary index backend (Hamming coarse search)
• File watcher — watchdog auto-indexing
• Gitignore filtering — recursive .gitignore support (on by default)
• GPU acceleration — Windows auto-installs onnxruntime-directml, works with any DirectX 12 GPU (NVIDIA/AMD/Intel), no CUDA needed. GPU is auto-detected at runtime — no config needed

[gpu] adds onnxruntime-gpu + faiss-gpu for Linux CUDA setups.

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:

# Force specific 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. Auto-triggers background indexing if none exists.
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).

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 — BFS expansion from matches, fused 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)

codexlens-search download-models

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

GPU

Windows: GPU acceleration is included by default — onnxruntime-directml is auto-installed and works with any DirectX 12 GPU (NVIDIA/AMD/Intel). No CUDA, no extra install, no config.

Linux: uv pip install codexlens-search[gpu] 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

Embedding

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,...
CODEXLENS_EMBED_DIM	Vector dimension (e.g. 1536)

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 / 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 (BFS)
         └→ [ripgrep] → regex matches
              └→ [RRF Fusion] → merged ranking
                    └→ [Reranker] → final top-k

Development

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

License

MIT