Dev #135
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Dev #135
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This commit introduces a new RELEASES.md file detailing the release history of the AgentMemory system, including the features and validation of the latest version (v0.1.1) which adds semantic similarity search capabilities. The document outlines key features, validation results, and getting started instructions for users, enhancing the overall documentation and clarity of the project's evolution.
This commit introduces two new files: `initial_findings.md`, which summarizes the analysis of various vector compression methods and their impact on semantic search performance, and `measure_semantic_loss.py`, a script that evaluates these methods, measuring semantic loss and search accuracy. Key findings highlight the effectiveness of 4-bit quantization for balancing storage efficiency and accuracy, while random projection methods are deemed unsuitable for semantic search. Additionally, a visualization of vector relationships is included as `vector_relationships.png` to support the analysis.
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This pull request introduces significant updates to the AgentMemory system, focusing on semantic search capabilities, vector compression analysis, and experimental validation. The changes include a new release for the Similarity Search Strategy, detailed findings on vector compression methods, and an experimental script for measuring semantic loss during search operations.
Enhancements to Semantic Search Capabilities:
RELEASES.md: Added release notes for v0.1.1, introducing the SimilaritySearchStrategy with features like semantic similarity search, multi-tier search, advanced scoring, and metadata filtering. This builds upon the earlier AttributeSearchStrategy in v0.1.0.Vector Compression Analysis:
experiments/compression/initial_findings.md: Documented findings on vector compression methods, identifying 4-bit quantization as the optimal balance between storage efficiency and search accuracy. Includes recommendations for production use and next steps for implementation.Experimental Validation:
experiments/compression/measure_semantic_loss.py: Added a script to evaluate the impact of vector compression on semantic search. The script tests quantization and random projection methods, measures reconstruction quality, and analyzes vector relationships and clustering.