Test documentation pipeline #1883
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* [feat]: Add a new semantic_search_client crate that provides vector embedding and
semantic search capabilities for the Amazon Q CLI. This implementation:
- Supports text embedding generation using Candle and ONNX runtimes
- Provides hardware acceleration via Metal on macOS
- Implements efficient vector indexing for semantic search
- Includes file processing utilities for various file types
- Supports persistent storage of semantic contexts
- Includes comprehensive test coverage
This crate will enable memory bank functionality for Amazon Q, allowing
users to create, manage, and search through semantic memory contexts.
🤖 Assisted by [Amazon Q Developer](https://aws.amazon.com/q/developer)
* Update semantic_search_client dependencies in Cargo.toml
* Refactor embedder implementation for Linux platforms to use trait objects
This change modifies the semantic search client to use Box<dyn TextEmbedderTrait>
on Linux platforms instead of directly using CandleTextEmbedder. This provides
more flexibility and consistency with the implementation on macOS and Windows,
allowing for better extensibility and polymorphic behavior across all platforms.
* Update Cargo.lock file
* Remove redundant CandleTextEmbedder import for non-macOS/Windows platforms
* fix(semantic_search): Update conditional compilation flags for embedders
Update conditional compilation flags to match the new embedding model selection logic:
- Replace target_env="musl" conditions with target_os conditions
- Update TextEmbedder trait implementation to use macOS/Windows condition
- Ensure consistent conditions across all files
🤖 Assisted by [Amazon Q Developer](https://aws.amazon.com/q/developer)
---------
Co-authored-by: Kenneth Sanchez V <kennvene@amazon.com>
Co-authored-by: Kenneth Sanchez V <kennvene@amazon.com>
* fix Build * fix: Removes flakey test --------- Co-authored-by: Kenneth Sanchez V <kennvene@amazon.com>
…ection and selective regeneration
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