mirror of
https://github.com/catlog22/Claude-Code-Workflow.git
synced 2026-02-05 01:50:27 +08:00
31a45f1f30
- Implemented GraphExpander to enhance search results with related symbols using precomputed neighbors. - Added CrossEncoderReranker for second-stage search ranking, allowing for improved result scoring. - Created migrations to establish necessary database tables for relationships and graph neighbors. - Developed tests for graph expansion functionality, ensuring related results are populated correctly. - Enhanced performance benchmarks for cross-encoder reranking latency and graph expansion overhead. - Updated schema cleanup tests to reflect changes in versioning and deprecated fields. - Added new test cases for Treesitter parser to validate relationship extraction with alias resolution.
73 lines
1.6 KiB
TOML
73 lines
1.6 KiB
TOML
[build-system]
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requires = ["setuptools>=61.0"]
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build-backend = "setuptools.build_meta"
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[project]
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name = "codex-lens"
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version = "0.1.0"
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description = "CodexLens multi-modal code analysis platform"
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readme = "README.md"
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requires-python = ">=3.10"
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license = { text = "MIT" }
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authors = [
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{ name = "CodexLens contributors" }
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]
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dependencies = [
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"typer>=0.9",
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"rich>=13",
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"pydantic>=2.0",
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"tree-sitter>=0.20",
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"tree-sitter-python>=0.25",
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"tree-sitter-javascript>=0.25",
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"tree-sitter-typescript>=0.23",
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"pathspec>=0.11",
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]
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[project.optional-dependencies]
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# Semantic search using fastembed (ONNX-based, lightweight ~200MB)
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semantic = [
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"numpy>=1.24",
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"fastembed>=0.2",
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"hnswlib>=0.8.0",
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]
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# GPU acceleration for semantic search (NVIDIA CUDA)
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# Install with: pip install codexlens[semantic-gpu]
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semantic-gpu = [
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"numpy>=1.24",
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"fastembed>=0.2",
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"hnswlib>=0.8.0",
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"onnxruntime-gpu>=1.15.0", # CUDA support
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]
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# GPU acceleration for Windows (DirectML - supports NVIDIA/AMD/Intel)
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# Install with: pip install codexlens[semantic-directml]
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semantic-directml = [
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"numpy>=1.24",
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"fastembed>=0.2",
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"hnswlib>=0.8.0",
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"onnxruntime-directml>=1.15.0", # DirectML support
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]
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# Cross-encoder reranking (second-stage, optional)
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# Install with: pip install codexlens[reranker]
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reranker = [
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"sentence-transformers>=2.2",
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]
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# Encoding detection for non-UTF8 files
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encoding = [
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"chardet>=5.0",
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]
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# Full features including tiktoken for accurate token counting
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full = [
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"tiktoken>=0.5.0",
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]
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[project.urls]
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Homepage = "https://github.com/openai/codex-lens"
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[tool.setuptools]
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package-dir = { "" = "src" }
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