v6.2.0
This release adds binary quantization, bind parameters for multimedia SQL queries and performance improvements
⚡ Scalar quantization. Supports 1 bit (binary) through 8 bit quantization. Can dramatically reduce vector storage requirements.
🚀 SQL bind parameters. Enables searching binary content with SQL statements, along with being a standard best practice.
See below for full details on the new features, improvements and bug fixes.
New Features
- Add scalar quantization support to vectors (#583)
- Feature request: Bind variable support when searching with SQL using Content=True mode (#564)
- Add cls pooling option (#565)
- Add prefix parameter for object storage (#568)
- Add parameter to RetrieveTask to disable directory flattening (#569)
- Add support for binary indexes to Faiss ANN (#585)
- Add support for scalar data to torch and numpy ANN backends (#587)
- Add quantization notebook (#588)
- Add API extensions notebook (#591)
- Add env variable to disable macOS MPS devices (#592)
Improvements
- Allow searching for images (#404)
- Update LLM pipeline to support template parameter (#566)
- Update recommended models (#573)
- Is it possible to add chat history to extractor workflow? (#575)
- Extractor pipeline improvements (#577)
- Update documentation (#582)
- Move vector normalization to vectors module (#584)
- Update benchmarks to read configuration (#586)
- Update torch version in Dockerfile (#589)
- Update Faiss ANN to support IVF strings without number of cells (#594)
- Update documentation to note SQL bind parameters (#596)
Bug Fixes
- Inconsistency in Embeddings behavior in Applications (#571)