Memvid Files

Release Date: January 25, 2026

Overview

This release includes significant performance improvements, bug fixes, and feature enhancements. Highlights include HNSW vector search implementation, SIMD acceleration, Windows test fixes, and improved query precision with implicit AND operators.

🚀 New Features

HNSW Vector Search Implementation (PR [#185])

• Applied HNSW (Hierarchical Navigable Small World) implementation patch
• Enables fast approximate nearest neighbor search for large vector indexes
• Significant performance improvement for vector search operations
• Better scalability for memories with many vector embeddings

SIMD Acceleration (PR [#176])

• Added SIMD acceleration for vector distance calculations
• Optimized L2 distance computations using SIMD instructions
• Faster vector similarity searches
• Improved performance on modern CPUs with SIMD support

Extraction Cache Improvements (PR [#175])

• Added LRU (Least Recently Used) eviction to extraction cache
• Better memory management for document extraction
• Prevents cache from growing unbounded
• Improved performance for repeated document processing

Query Precision Enhancement (PR [#178])

• Changed implicit query operator from OR to AND for precision
• Multi-word queries now require all terms to match (implicit AND)
• More precise search results
• Explicit OR operator still available when needed
• Better user experience for targeted searches

🐛 Bug Fixes

Windows Test Fixes (PR [#186])

• Fixed Windows test failures by adding delay for Tantivy file handle release
• Resolved file locking issues on Windows during test cleanup
• Tests now pass reliably on Windows platforms
• Improved cross-platform test stability

Clippy Safety Overhaul (PR [#180])

• Comprehensive safety improvements based on Clippy linter recommendations
• Fixed potential safety issues across the codebase
• Improved code quality and maintainability
• Enhanced memory safety guarantees

📝 Documentation

Internationalization

• Added Bengali (bn) README translation (PR [#182])
• Added Japanese README translation (PR [#177])
• Improved accessibility for non-English speakers
• Expanded documentation coverage

Documentation Improvements (PR [#181])

• Added HTML markers to all README files to make updates easier
• Improved documentation maintenance workflow
• Better structure for automated documentation updates

🔧 Developer Experience

Build & Development Tools (PR [#184])

• Created script to add flags for easier development workflow
• Streamlined feature flag management
• Improved developer productivity

📊 Performance Improvements

• HNSW Implementation: Faster vector search for large indexes
• SIMD Acceleration: Optimized distance calculations
• LRU Cache: Better memory utilization
• Query Precision: More accurate search results

🙏 Contributors

Thank you to all contributors who made this release possible:

• @sharafdin - Release manager
• @0x-pankaj - HNSW implementation, SIMD acceleration, extraction cache, Windows fixes, Clippy safety
• @Abhisheklearn12 - Query operator precision fix
• @Adam-Elmi - Documentation improvements, build scripts
• @krishnaK-D-Bair - Bengali translation
• @yukaty - Japanese translation

📚 Related Pull Requests

• [#186] - fix(tests): add Windows delay for Tantivy file handle release
• [#185] - feat: apply HNSW implementation patch
• [#184] - Created a script to add flags
• [#182] - docs: add Bengali (bn) README translation
• [#181] - Added HTML markers to all README files to make updates easier
• [#180] - Fix/clippy safety overhaul
• [#178] - Fix: Change implicit query operator from OR to AND for precision
• [#177] - docs(i18n): add Japanese README translation
• [#176] - feat: add SIMD acceleration for vector distance calculations
• [#175] - feat(extract): add LRU eviction to extraction cache

🎯 Migration Notes

For Users

• No breaking changes in this release
• All existing .mv2 files remain compatible
• Query behavior change: Multi-word queries now use implicit AND (more precise)
• Use explicit OR operator if you need the old behavior
• Example: "machine learning" now requires both words (was: either word)
• Example: "machine OR learning" still works for either word

For Developers

• Windows developers: Test stability improved
• Performance: Vector search is significantly faster with HNSW
• Memory: Extraction cache now has bounded memory usage

📚 Documentation

• Full Documentation
• API Reference
• Migration Guide

🔗 Related

• GitHub Repository
• Issue Tracker
• Discussions