codexlens-search 0.4.1

Lightweight semantic code search engine — 2-stage vector + FTS + RRF fusion + MCP server

codexlens-search

Semantic code search engine with MCP server for Claude Code.

2-stage vector search + FTS + RRF fusion + reranking — install once, configure API keys, ready to use.

Quick Start (Claude Code MCP)

Add to your project .mcp.json:

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

Install

# Standard install (includes vector search + API clients)
uv pip install codexlens-search

# With MCP server for Claude Code
uv pip install codexlens-search[mcp]

Optional extras:

Extra	Description
mcp	MCP server (codexlens-mcp command)
gpu	GPU-accelerated embedding (onnxruntime-gpu)
faiss-cpu	FAISS ANN backend
watcher	File watcher for auto-indexing

MCP Tools

Tool	Description
search_code	Semantic search with hybrid fusion + reranking
index_project	Build or rebuild the search index
index_status	Show index statistics
index_update	Incremental sync (only changed files)
find_files	Glob file discovery
list_models	List models with cache status
download_models	Download local fastembed models

MCP Configuration Examples

API Embedding Only (simplest)

{
  "mcpServers": {
    "codexlens": {
      "command": "uvx",
      "args": ["--from", "codexlens-search[mcp]", "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"
      }
    }
  }
}

API Embedding + API Reranker (best quality)

{
  "mcpServers": {
    "codexlens": {
      "command": "uvx",
      "args": ["--from", "codexlens-search[mcp]", "codexlens-mcp"],
      "env": {
        "CODEXLENS_EMBED_API_URL": "https://api.openai.com/v1",
        "CODEXLENS_EMBED_API_KEY": "${OPENAI_API_KEY}",
        "CODEXLENS_EMBED_API_MODEL": "text-embedding-3-small",
        "CODEXLENS_EMBED_DIM": "1536",
        "CODEXLENS_RERANKER_API_URL": "https://api.jina.ai/v1",
        "CODEXLENS_RERANKER_API_KEY": "${JINA_API_KEY}",
        "CODEXLENS_RERANKER_API_MODEL": "jina-reranker-v2-base-multilingual"
      }
    }
  }
}

Multi-Endpoint Load Balancing

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

Local Models (Offline, No API)

uv pip install codexlens-search[mcp]
codexlens-search download-models

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

Pre-installed (no uvx)

{
  "mcpServers": {
    "codexlens": {
      "command": "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"
      }
    }
  }
}

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	Example
CODEXLENS_EMBED_API_URL	Embedding API base URL	https://api.openai.com/v1
CODEXLENS_EMBED_API_KEY	API key	sk-xxx
CODEXLENS_EMBED_API_MODEL	Model name	text-embedding-3-small
CODEXLENS_EMBED_API_ENDPOINTS	Multi-endpoint: url|key|model,...	See above
CODEXLENS_EMBED_DIM	Vector dimension	1536

Reranker

Variable	Description	Example
CODEXLENS_RERANKER_API_URL	Reranker API base URL	https://api.jina.ai/v1
CODEXLENS_RERANKER_API_KEY	API key	jina-xxx
CODEXLENS_RERANKER_API_MODEL	Model name	jina-reranker-v2-base-multilingual

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	Max texts per API batch (auto-splits on 413)
CODEXLENS_EMBED_MAX_TOKENS	8192	Max tokens per text (truncate if exceeded, 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
         ├→ [BinaryStore] → candidates (Hamming)
         │     └→ [ANNIndex] → ranked IDs (cosine)
         ├→ [FTS exact] → exact matches
         └→ [FTS fuzzy] → fuzzy 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