Weaviate Blog

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.

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.

This article explores how AI can solve retrieval problems in creative workflows through semantic search and vector databases, rather than replacing creatives. The author argues that the main friction in creative work is organizing, finding, and reusing existing assets, and AI as an infrastructure layer can improve accessibility and reduce redundant effort.

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.

Most vector database prototypes fail at ingest. This guide covers server-side batching, error handling, blobHash data type, and multimodal ingestion for Weaviate, with code examples and production-ready patterns.

Weaviate Cloud now offers free tiers across its entire product suite, including the managed database, Query Agent, and Engram, with no credit card required and no time limit, enabling users to build prototypes and use them indefinitely.

Weaviate announces the general availability of Engram, a managed memory and context service for agentic applications. It addresses long-context degradation, messy raw data, and multi-agent context fragmentation through asynchronous pipelines, templates, and built-in scopes, helping agents compound value over time.

Use Weaviate's built-in MCP server to give Claude Code, Cursor, and VS Code hybrid search over your codebase and docs. No glue code.

A researcher argues retrieval quality is the most critical factor in RAG systems, outweighing model size or prompt design. Poor retrieval leads to undetectable hallucinations. The article identifies five common failure modes and offers practical tips for improving retrieval, including hybrid search, cross-encoder re-ranking, and continuous evaluation.

This release introduces the built-in MCP Server, Extensible Tokenizers, Diversity Search (MMR), and Query Profiling as previews, along with Incremental Backups, Gemini audio support for multi2vec-google, and the new BlobHash property type.

A deep dive into Engram, our managed memory service for agents which is simple to get started but adaptable to any use case.

Weaviate Shared Cloud is now generally available on AWS in US East and Europe, providing teams with a fully managed, AI-native database on the provider and region that works best for them.

Two weeks of dogfooding Engram, Weaviate's memory product, in daily Claude Code sessions. This surfaced where a dedicated memory product adds value, and the specific mechanics that prevent integration with coding assistants from working well.

Multimodal embeddings allow AI systems to search and reason across text, images, audio, and video in their native formats. This blog covers the key intuitions behind how this all works and walks through three practical implementations using Weaviate and Gemini.

The Weaviate Managed .NET Client brings Entity Framework Core-like experience to C# developers, enabling attribute-driven schema, type-safe queries, and automatic migrations for vector databases.

A comprehensive guide to securing Weaviate enterprise deployments using OIDC, RBAC, multi-tenant isolation, audit logging, and network security, illustrated through the fictional MedVector Health case study.

Learn how we built a production-ready, end-to-end RAG application in just 36 hours using the Query Agent and the new Weaviate Agent Skills library. The post explains the architecture, comparison with naive RAG, and step-by-step instructions.