vector-database

The author shares a learning roadmap for RAG and memory systems geared toward Agent development and interviews, covering chunking, hybrid retrieval, reranking, vector database selection, and short-term/long-term memory management, along with reference materials including the original RAG paper by Lewis et al.

Qdrant tested Perplexity's new visual document embeddings on the ViDoRe v3 benchmarks, finding that 1-bit TurboQuant compressed vectors from 856 KB to 40 KB per page while retaining 96.5% of retrieval accuracy (99.9% with rescoring), though original vectors must still be stored for rescoring.

文章论述检索型智能体不应将所有数据压平为文本块，而应暴露 KB、DB、Graph、Skills、Memory 等多类型数据界面，由检索代理按需选择最小充分集合，并已在 OpenDCAI/DataMind 中实现。

An open-source collective memory network (Agentflow) for autonomous agents using FastMCP and Supabase pgvector, enabling cross-agent knowledge sharing of verified remedies to avoid redundant trial-and-error across agent instances.

Zilliz has open-sourced DeepSearcher, an Apache-2.0 deep research framework combining LLMs, vector databases like Milvus, and agents to let companies run private, on-prem research over their own knowledge bases instead of sending data to online AI services.

Qdrant publishes a case study on Lucy, which built a source-of-truth retrieval layer over 2.8M+ SEC and Korea DART filings using hybrid lanes and in-query payload filters, achieving 94.7% Recall@10 on FinanceBench and +0.160 answer accuracy with the answer model held fixed.

Gergely Orosz highlights turbopuffer openly sharing their v3 architecture migration, where the new design was initially up to 128x slower than the old one, with each optimization (like batched reads yielding a ~9x improvement) publicly documented.

Turbopuffer announced a major storage architecture change ("turbopuffer v3") that demotes the ANN vector index from primary to secondary index, evolving the serverless database from a specialized vector search engine into a generalized search database supporting many more query plans at greater scale.

Gergely Orosz highlights turbopuffer publicly documenting its in-progress rewrite of the tpuf storage engine (v3), grinding query plans in public rather than only celebrating after the rewrite is finished and fast.

Researchers have developed a new RAG approach called PageIndex that bypasses traditional dependencies like vector databases and embeddings, potentially disrupting the RAG industry.

LEANN is a new vector indexing method that reduces storage requirements by about 97% through graph simplification and real-time embedding computation, while maintaining retrieval recall close to full HNSW. It is suitable for use on laptops and won the Best Paper award at MLSys 2026.

James Luan, CTO of Zilliz, argues that data infrastructure becomes more critical as AI agents act on enterprise data, emphasizing the need for data quality, freshness, and proper permissions.

MongoDB Atlas introduces auto-embedding, enabling databases to generate and manage embeddings internally for semantic search, eliminating the need for external services and improving data synchronization.

The author updated the 'embedflow' embedding migration project with new features including support for multiple vector databases, a migration planner, shadow mode, and persistent caching to make it production-ready for embedding model transitions.

James, CTO of Zilliz, introduces Vector Lakebase and the concepts of One Data and One Index to unify data and index management for AI workloads, changing how agents retrieve information by leveraging lake-native storage.

The article discusses how agent memory transforms vector search into a long-lived systems problem, emphasizing the need for scalable, reliable infrastructure beyond just search algorithms.

@memory is an archiving agent in the AIPass open-source framework that manages memory for AI agents by vectorizing older entries and storing them in ChromaDB, ensuring long-term persistence and recall without data loss.

The article argues that cosine similarity in vector databases is a mathematical property that does not ensure factual accuracy, rendering retrieval systems susceptible to poisoning attacks where malicious documents can surpass legitimate ones in ranking.

Weaviate introduces a method to search PDFs without text extraction by embedding each page as an image using late-interaction multi-vector retrieval, demonstrated on NVIDIA investor decks.

The article warns about the limitations of free-tier vector databases, highlighting issues like data deletion and deployment constraints, and advises choosing based on where your AI agent runs.