- Introduced APIEmbedder class to handle embeddings via a remote HTTP API.
- Implemented token packing to optimize batch sizes based on token limits.
- Added support for multiple API endpoints with round-robin dispatching.
- Included retry logic for API calls with exponential backoff on failures.
- Enhanced indexing pipeline with file exclusion checks and smart chunking strategies.
- Updated tests to cover new APIEmbedder functionality and ensure robustness.
- Updated agent spawning from `Task()` to `Agent()` across various files to align with new standards.
- Enhanced the `code-developer` agent description to clarify its invocation context and responsibilities.
- Introduced a new `delegation-check` skill to validate command delegation prompts against agent role definitions, ensuring content separation and conflict detection.
- Established comprehensive separation rules for command delegation prompts and agent definitions, detailing ownership and conflict patterns.
- Improved documentation for command and agent design specifications to reflect the updated spawning patterns and validation processes.
Rename the v2 search engine package to `codexlens-search` (import as
`codexlens_search`) so it can be installed independently and consumed
by the original codex-lens as a dependency. This avoids package path
conflicts since both previously used `src/codexlens/`.
Changes:
- Rename src/codexlens/ -> src/codexlens_search/
- Update pyproject.toml: name=codexlens-search, version=0.2.0
- Update all imports across source, tests, and scripts
- Add public API exports in __init__.py (Config, SearchPipeline,
IndexingPipeline, SearchResult, IndexStats)
37/37 tests pass. No functional changes.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Added BaseReranker abstract class for defining reranking interfaces.
- Implemented FastEmbedReranker using fastembed's TextCrossEncoder for scoring document-query pairs.
- Introduced FTSEngine for full-text search capabilities using SQLite FTS5.
- Developed SearchPipeline to integrate embedding, binary search, ANN indexing, FTS, and reranking.
- Added fusion methods for combining results from different search strategies using Reciprocal Rank Fusion.
- Created unit and integration tests for the new search and reranking components.
- Established configuration management for search parameters and models.
