Retrieval, RAG & Search

What Happened Retrieval‑Augmented Generation (RAG) has moved from prototypes to production patterns: embedding pipelines, ANN vector stores, hybrid BM25+vector retrieval, and reranking are now standard. Frameworks and orchestration layers (LlamaIndex, LangChain, Haystack) provide connectors and orchestration for embedding, retrieval and LLM chaining. Vector database options now span fully managed services and open source projects (Pinecone,…

What Happened A recent engineering project built a publicly hosted Model Context Protocol (MCP) server for Vespa Cloud and evaluated an agent that used the MCP server vs an agent given a Vespa CLI/terminal. The MCP-based agent passed 97% of deterministic assertions vs 95% for the CLI agent, required fewer deployment attempts, resolved issues faster,…

What Happened Recent engineering work shows how an agentic optimizer can safely and repeatedly tune a search backend (Elasticsearch) when paired with a disciplined harness: treat agent proposals as noisy, verify wins with staged benchmarks, and enforce strict guardrails and ownership before production changes. The approach uses a proposer–verifier–referee loop, exploration/exploitation/benchmark task types, machine‑readable verdicts,…

What Happened Three technology developments change practical choices for retrieval‑augmented generation (RAG) and semantic search: Elasticsearch launched a serverless Elasticsearch Vector Database with vector‑first index modes, built‑in hybrid search, managed GPU embeddings, automatic vector compression (BBQ / DiskBBQ), and predictable line‑item pricing — designed to host hundreds of billions of vectors without adding…

What Happened Recent infrastructure and search-engine updates change practical trade-offs for retrieval‑augmented generation (RAG) systems and enterprise search: Vespa added a time‑bounded ANN search parameter to stop HNSW lookups when a latency budget is reached, plus richer labeled‑query and tensor ranking features and cloud provisioning improvements for operational resilience [1]. Elasticsearch…

What Happened Search and vector-retrieval tooling is converging on hybrid patterns: dense-vector nearest-neighbor retrieval for semantic recall, plus structured/keyword filters and fast substring checks for precision and cost control. Separately, Elasticsearch introduced runtime query rewrite rules and a columnar doc-values mode that materially reduce scan and decompression cost for common query shapes: a Wildcard->Contains rewrite…

What Happened Retrieval-augmented generation (RAG) is now a mature pattern: application logic orchestrates chunking, embeddings, ANN search, metadata filtering and neural reranking to provide high-precision grounding for LLMs. The ecosystem contains orchestration libraries (LlamaIndex, LangChain, Haystack), many managed and open vector stores (Pinecone, Qdrant, Weaviate, Milvus, Elasticsearch, Vespa) and specialized runtime features (GPU indexing, hybrid…

What Happened Recent practical work shows two important advances for production RAG systems. First, late-interaction multi-vector retrieval (ColBERT/ColPali style) lets you index pages or documents as many vectors (token or image-patch level) and use MaxSim-style scoring so queries match specific regions such as charts, table cells or diagram patches without brittle OCR/chunking pipelines. Weaviate Cloud…

What Happened Two converging advances changed practical design for retrieval-augmented generation (RAG): 1) Elasticsearch introduced an AI Index pattern that precomputes concise, fact-level Knowledge Indicators (KIs) so agents retrieve grounded facts instead of full documents, dramatically lowering token use, tool calls and latency [1]; 2) Weaviate 1.39 promoted query-time rescoring (Boost API) and MMR diversity…

What Happened Elasticsearch 9.5 introduced an unmapped_fields option for ES|QL that prevents queries from failing when they reference fields missing from index mappings. The option accepts NULLIFY (return NULLs) or LOAD (read values from _source) and resolves partially unmapped non-keyword fields (PUNKs) by injecting an “unmapped” field into the query plan so the planner and…

What Happened Teams tuning vector search often find that changing one index parameter moves an evaluation score by a small amount (for example, 0.01) and it’s unclear whether the change actually improved relevance or just reflected ranking variability. The underlying APIs define knobs such as HNSW ef, reciprocal‑rank fusion k, and quantization oversampling, but the…

What Happened Recent engineering results show that reducing repeated retrievals inside RAG agent loops materially improves throughput and GPU efficiency: Qdrant combined with Minima reported a 2.92× increase in agentic RAG tasks per GPU‑hour by addressing the search → inspect → retry pattern that compounds latency, context consumption and inference cost [1]. At the same…