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Headlines · 4

  1. RAG Query Expansion with HyDE and Multi-Query Rewriting

    This tutorial demonstrates how HyDE and multi-query rewriting can expand retrieval entry points for complete queries whose wording differs from knowledge-base documents. Using PostgreSQL 16, pgvector 0.8, bge-m3, and RRF fusion, the author reports that multi-query expansion plus the original query achieved 86% recall@5, compared with 72% without expansion and 80% for HyDE alone, though at higher latency. The evaluation covers 100 queries from the author's device knowledge base and therefore is not a general benchmark.

    掘金 · 人工智能 · 🔥 0
  2. NVIDIA open-sources Auto Ontology for governed NL-to-SQL

    The supplied reports describe NVIDIA’s open-source Auto Ontology as a semantic catalog and business-ontology system for natural-language SQL generation over structured data. It ingests database metadata to draft terms, column mappings, relationships and reusable SQL attributes, which users can revise and certify before query agents use them to retrieve context, generate and check SQL, and execute queries. The project exposes Web, authenticated REST API, CLI and MCP interfaces. The reports caution that generated definitions require business-owner approval, final SQL is still model-generated, and successful execution does not guarantee a correct business answer. They also note validation, retry and fallback paths, meaning returned SQL should not automatically be treated as a successfully executed or authoritative result.

    掘金 · 人工智能 · 🔥 0
  3. A Beginner’s Guide to Spring AI Application Development

    A Juejin article presents a beginner-oriented introduction to AI application development for backend developers familiar with Spring Boot. It explains architecture, RAG, Tool Calling, MCP, Spring AI Advisors, memory, vector retrieval and Agent/ReAct concepts, alongside runnable code and five implementation pitfalls. The article also compares Alibaba Cloud Bailian and DashScope and discusses how models can be accessed through cloud services, self-hosted deployments, platforms, clients and code.

    掘金 · 人工智能 · 🔥 0
  4. Lakebase Postgres reaches general availability on Google Cloud

    Databricks Blog says Lakebase Postgres is now generally available on Google Cloud, enabling teams to run serverless Postgres workloads on the Databricks Data + AI Platform. The release includes autoscaling, scale to zero, instant branching, point-in-time recovery, standard Postgres tooling, and governance through Unity Catalog. Databricks also claims Lakebase Search delivers twice the throughput of pgvector at one-quarter the cost and that direct writes can load 1TB in under five minutes, but the report provides no independent validation for these figures.

    Databricks Blog · 🔥 0

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