Learn how to use Engram effectively, from writing topic descriptions that control extraction to choosing bounded topics and scopes, selecting a retrieval mode, and placing memories in your prompt without hurting prompt-cache efficiency.
4-bit Rotational Quantization in Weaviate 1.39: the SIMD performance work, a centered tier, scaling analysis and a TurboQuant comparison.
HFresh is Weaviate's disk-based vector index for memory-efficient vector search, combining low heap usage with incremental background maintenance.
Part 3: We turn a messy creative archive into a searchable library using a manifest, Weaviate, and hybrid search.
PDFs full of charts and tables are notoriously hard to put through a traditional RAG pipeline. In this post, we show how late-interaction multi-vector retrieval lets you search PDFs by what the page looks like: no OCR, no chunking, no text extraction.
Weaviate 1.39 promotes the Boost API and MMR diversity selection to GA, previews 4-bit Rotational Quantization, and ships an experimental Search REST API.
Part 2: Why folders, tags, and keyword search break down in real creative workflows and what retrieval needs to do instead.
Introducing medium, high, and ultrahigh effort tiers to the Query Agent's Search Mode.
Why AI won’t replace creatives, and how it can remove friction from messy workflows, lost files, and creative processes.
When a Weaviate query is slow, the first question is where the time went. Query profiling returns a per-stage, per-shard timing breakdown, making query performance issues visible.
This release brings the HFresh disk-based vector index and the built-in MCP Server to general availability, rebuilds cluster-wide async replication to run from a single scheduler (on by default), and adds two previews: the Boost API and Nested Object Filtering.
Server-side batching, retries, the blobHash data type, and multimodal ingestion — what to use when, with code.
