mirror of https://github.com/catlog22/Claude-Code-Workflow.git synced 2026-03-19 18:58:47 +08:00

Files

catlog22 5a4b18d9b1 feat: enhance search, ranking, reranker and CLI tooling across ccw and codex-lens

Major improvements to smart-search, chain-search cascade, ranking pipeline,
reranker factory, CLI history store, codex-lens integration, and uv-manager.
Simplify command-generator skill by inlining phases. Add comprehensive tests.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

2026-03-16 20:35:08 +08:00

2.7 KiB

Raw Blame History

CodexLens

CodexLens is a multi-modal code analysis platform designed to provide comprehensive code understanding and analysis capabilities.

Features

Multi-language Support: Analyze code in Python, JavaScript, TypeScript and more using Tree-sitter parsers
Semantic Search: Find relevant code snippets using semantic understanding with fastembed and HNSWLIB
Code Parsing: Advanced code structure parsing with tree-sitter
Flexible Architecture: Modular design for easy extension and customization

Installation

Basic Installation

pip install codex-lens

With Semantic Search

pip install codex-lens[semantic]

With GPU Acceleration (NVIDIA CUDA)

pip install codex-lens[semantic-gpu]

With DirectML (Windows - NVIDIA/AMD/Intel)

pip install codex-lens[semantic-directml]

With All Optional Features

pip install codex-lens[full]

Local ONNX Reranker Bootstrap

Use the pinned bootstrap flow when you want the local-only reranker backend in an existing CodexLens virtual environment without asking pip to resolve the whole project extras set at once.

Start from the CodexLens repo root and create or activate the project venv.
Review the pinned install manifest in scripts/requirements-reranker-local.txt.
Render the deterministic setup plan:

python scripts/bootstrap_reranker_local.py --dry-run

The bootstrap script always targets the selected venv Python, installs the local ONNX reranker stack in a fixed order, and keeps the package set pinned to the validated Python 3.13-compatible combination:

numpy==2.4.0
onnxruntime==1.23.2
huggingface-hub==0.36.2
transformers==4.53.3
optimum[onnxruntime]==2.1.0

When you are ready to apply it to the CodexLens venv, use:

python scripts/bootstrap_reranker_local.py --apply

To pre-download the default local reranker model (Xenova/ms-marco-MiniLM-L-6-v2) into the repo-local Hugging Face cache, use:

python scripts/bootstrap_reranker_local.py --apply --download-model

The dry-run plan also prints the equivalent explicit model download command. On Windows PowerShell with the default repo venv, it looks like:

.venv/Scripts/hf.exe download Xenova/ms-marco-MiniLM-L-6-v2 --local-dir .cache/huggingface/models/Xenova--ms-marco-MiniLM-L-6-v2

After installation, probe the backend from the same venv:

python scripts/bootstrap_reranker_local.py --apply --probe

Requirements

Python >= 3.10
See pyproject.toml for detailed dependency list

Development

This project uses setuptools for building and packaging.

License

MIT License

Authors

CodexLens Contributors

2.7 KiB Raw Blame History