Retrieval, RAG & Search

What Happened Weaviate deprecated its GraphQL query API on October 26, 2026, without immediately disrupting existing users. New Weaviate Cloud clusters already have GraphQL disabled. Cloud will disable it on December 1, 2026 for clusters with no GraphQL use in the preceding 30 days, and on May 1, 2027 for all remaining clusters [1]. Applications…

What Happened Vespa added fast-search map fields for attributes stored as key-value pairs. Instead of finding documents with a matching key and value and then checking whether they occur at the same array position, Vespa indexes each pair as a combined term for direct lookup. In a benchmark on a Graviton 4 node, this improved…

What Happened Recent releases and implementations show RAG search moving beyond vector similarity alone. Qdrant 1.20 adds BM25 ranking over full-text payloads without requiring embeddings, a tokenizer for code search, faster indexing for nested filters, and vector-storage options including a 16-bit format that halves storage without reported accuracy loss [1]. An Elasticsearch demonstration turned a…

What Happened DMIS Lab’s UltraBERT-Embed-150M is a 151M-parameter English embedding model that accepts inputs up to 32K tokens and produces one 768-dimensional vector per input. That makes a single-vector representation possible for many documents that would otherwise be split into chunks. A Qdrant test compares document-level vectors, 512-token chunks, a combination of both, and compressed…

What Happened Retail discovery is shifting from showing more results to ranking the most useful ones. In a study of more than 21,500 consumers across 20 countries, 86% said whether a product works matters more than its brand. For grocery shopping and meal planning, 26% would let technology suggest options, but only 7% would let…

What Happened Weaviate fixed a medium-severity authorization flaw in the GraphQL nearObject operator in v1.39.3. In earlier versions, a user without read access to a target collection could infer an object’s vector from search distances if they knew its collection name and UUID, could read collection definitions, and had a separate collection in which to…

What Happened Weaviate 1.40 added PathSeer, a filtered vector-search strategy that adapts between distance-first and filter-first exploration as filter selectivity changes. It uses existing HNSW indices, so teams can test filterStrategy: pathseer without reindexing. ACORN remains the default, and the existing flat-search cutoff still applies [1]. In Weaviate’s tests, PathSeer was generally fastest at 0.90…

What Happened Perplexity released two pplx-embed-v2-late models that search PDF pages as images using late interaction. They can represent tables, charts, and page layout without an OCR step during indexing [1]. This addresses a common weakness of text-only retrieval: a financial figure is easy to misinterpret when its row, column, unit, or label is missing…

What Happened Elastic Agent Builder now supports live shared conversations on Elastic Cloud Serverless. Authorized teammates can read a thread’s history and continue questioning the same agent, rather than receiving a static transcript. Elastic describes incident-response handoffs as one use case. Support for Elastic Cloud Hosted and Self-Managed is planned for version 9.6, but timing…

What Happened Elastic described a context engine that prepares reusable knowledge about schemas, fields, entities and documents before an agent receives a question. It stores these “Knowledge Indicators” in an AI index and retrieves them through lexical, semantic, hybrid and agentic search. Agent traces feed a loop for improving the indicators over time [1]. In…

What Happened Google DeepMind’s EmbeddingGemma 2 can run on a phone, but serving its embeddings across a large document collection is a different resource problem. For 10 million documents, full-size float32 vectors alone require 30.7 GB of RAM, before indexes, metadata, replicas or application overhead [1]. In early Qdrant tests, quantized full-size vectors used 30…

What Happened Weaviate v1.39.3 fixes a high-severity credential disclosure vulnerability in its Google-backed embedding and generation modules. Earlier versions could send a configured Google API key or OAuth token to an attacker-controlled host if an unvalidated apiEndpoint was supplied. An attacker could set that endpoint through collection configuration with schema-write access or, for generative-google, through…