Files
Claude-Code-Workflow/codex-lens/pyproject.toml
catlog22 5e91ba6c60 Implement ANN index using HNSW algorithm and update related tests
- Added ANNIndex class for approximate nearest neighbor search using HNSW.
- Integrated ANN index with VectorStore for enhanced search capabilities.
- Updated test suite for ANN index, including tests for adding, searching, saving, and loading vectors.
- Modified existing tests to accommodate changes in search performance expectations.
- Improved error handling for file operations in tests to ensure compatibility with Windows file locks.
- Adjusted hybrid search performance assertions for increased stability in CI environments.
2025-12-19 10:35:29 +08:00

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TOML

[build-system]
requires = ["setuptools>=61.0"]
build-backend = "setuptools.build_meta"
[project]
name = "codex-lens"
version = "0.1.0"
description = "CodexLens multi-modal code analysis platform"
readme = "README.md"
requires-python = ">=3.10"
license = { text = "MIT" }
authors = [
{ name = "CodexLens contributors" }
]
dependencies = [
"typer>=0.9",
"rich>=13",
"pydantic>=2.0",
"tree-sitter>=0.20",
"tree-sitter-python>=0.25",
"tree-sitter-javascript>=0.25",
"tree-sitter-typescript>=0.23",
"pathspec>=0.11",
]
[project.optional-dependencies]
# Semantic search using fastembed (ONNX-based, lightweight ~200MB)
semantic = [
"numpy>=1.24",
"fastembed>=0.2",
"hnswlib>=0.8.0",
]
# Encoding detection for non-UTF8 files
encoding = [
"chardet>=5.0",
]
# Full features including tiktoken for accurate token counting
full = [
"tiktoken>=0.5.0",
]
[project.urls]
Homepage = "https://github.com/openai/codex-lens"
[tool.setuptools]
package-dir = { "" = "src" }