'query engines' 검색 결과

Build LlamaIndex applications that retrieve relevant material before generating answers, a method called RAG. Connect documents, websites, and databases to support document questions, conversational search, structured summaries, and checks of answer relevance and source consistency.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Building question answering over internal documents
• Creating a chatbot that remembers the conversation
• Searching information across files, websites, and databases

Guides building AI applications that search documents before answering questions, an approach called RAG. Covers importing files and web data, creating searchable indexes, adding conversational interfaces, and evaluating whether answers match the retrieved material.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build a question-answering system for internal documents
• Create a chatbot that remembers conversation context
• Search information across files, web pages, and databases

数据工程 — 数据平台从业者的认知操作系统, 覆盖把数据从源系统搬运成可靠 / 可查询 / 可信赖形态供分析 / ML / 数据产品消费的全生命周期 (生成 → 摄取 → 存储 → 转换 → 服务 + 安全/数据管理/DataOps/数据架构/编排/软件工程 六条暗流, Reis & Housley 框架): 摄取与集成 (批 + CDC 变更数据捕获 Debezium + EL 工具 Fivetran/Airbyte/Meltano/dlt + Kafka Connect + schema drift) / 存储与文件表格式 (对象存储数据湖 + 列存 Parquet/ORC/Arrow/Avro + 开放表格式 Apache Iceberg/Delta Lake/Apache Hudi + lakehouse + 分区/compaction) / 转换与建模 (ELT dbt/SQLMesh + Spark + 维度建模 Kimball + Inmon + Data Vault + 大宽表 OBT + 渐变维 SCD + 增量模型 + 语义/指标层) / 编排与工作流 (Apache Airflow/Dagster/Prefect/Mage/Kestra/Apache DolphinScheduler + DAG + 幂等 + 回填 backfill + 数据资产调度) / 批流与实时 (Apache Kafka/Apache Flink/Spark Structured Streaming/Kinesis/Pulsar/Redpanda + Lambda vs Kappa + watermark/窗口/exactly-once + 流式 SQL Materialize/RisingWave + 实时 OLAP ClickHouse/Apache Druid/Apache Pinot/StarRocks/Apache Doris) / 数仓与查询引擎 (Snowflake/BigQuery/Redshift/Databricks SQL/Trino/Presto/DuckDB/Polars + 存算分离 + MPP) / 数据质量测试与可观测性 (dbt tests/Great Expectations/Soda + 数据契约 + Monte Carlo data downtime + 新鲜度/量/schema 异常检测) / 数据治理编目与血缘 (DataHub/Amundsen/OpenMetadata/Unity Catalog + 列级血缘 + PII 分类 + 访问控制 + GDPR) / DataOps 与可靠性 (数据 CI/CD + 转换版本控制 + 环境隔离 + 幂等重处理 + 数据 SLA/SLO + 计算存储 FinOps) / 数据架构范式 (现代数据栈 + lakehouse + data mesh + data fabric + 去中心化 vs 中心化所有权) / 分析工程角色 (dbt 时代连接数据工程与分析的桥) — 不含 数据科学/ML 建模本身 (是下游消费者) / BI 仪表盘制作 (serving 下游) / 数据分析报表为终点 / 'data engineer = 跑 Hadoop 的' 过时窄化 / 通用后端应用开发 (平行学科) (Data Engineering — the cognitive operating system of practitioners who design, build, and operate the data platform: moving data from source systems into reliable, queryable, trustworthy form for analytics / ML / products, covering (a) the data engineering lifecycle (generation → ingestion → storage → transformation → serving, with the undercurrents security / data management / DataOps / data architecture / orchestration / software engineering — Reis & Housley framing), (b) ingestion & integration (batch + CDC change-data-capture with Debezium, EL tools Fivetran / Airbyte / Meltano / dlt, Kafka Connect, API + file + database sources, schema drift handling), (c) storage & file/table formats (object storage data lakes, columnar formats Parquet / ORC / Arrow / Avro, open table formats Apache Iceberg / Delta Lake / Apache Hudi, lakehouse architecture, partitioning / compaction / Z-ordering), (d) transformation & modeling (ELT with dbt / SQLMesh, Spark, dimensional modeling Kimball, Inmon CIF, Data Vault, One Big Table / wide tables, normalization vs denormalization, slowly changing dimensions, incremental models, the semantic / metrics layer), (e) orchestration & workflow (Apache Airflow, Dagster, Prefect, Mage, Kestra, Apache DolphinScheduler, DAGs, idempotency, backfills, data-aware / asset-based scheduling), (f) batch vs streaming & real-time (Apache Kafka, Apache Flink, Spark Structured Streaming, Kinesis / Pulsar / Redpanda, the Lambda vs Kappa debate, watermarks / windowing / exactly-once, streaming SQL Materialize / RisingWave, real-time OLAP ClickHouse / Apache Druid / Apache Pinot / StarRocks / Apache Doris), (g) warehouses & query engines (Snowflake, BigQuery, Redshift, Databricks SQL, Trino / Presto, DuckDB, Polars, decoupled storage & compute, MPP), (h) data quality, testing & observability (dbt tests, Great Expectations, Soda, data contracts, Monte Carlo / data downtime, freshness / volume / schema anomaly detection, unit / integration testing of pipelines), (i) data governance, catalog & lineage (DataHub, Amundsen, OpenMetadata, Unity Catalog, column-level lineage, PII / data classification, access control, GDPR / data privacy), (j) DataOps & reliability (CI/CD for data, version control of transformations, environments, idempotent reprocessing, SLAs / SLOs for data, cost / FinOps for compute & storage), (k) data architecture paradigms (modern data stack, data lakehouse, data mesh, data fabric, decentralized vs centralized ownership), (l) the analytics engineering role (the dbt-era bridge between data engineering and analysis); N

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Data framework for building LLM applications with RAG. Specializes in document ingestion (300+ connectors), indexing, and querying. Features vector indices, query engines, agents, and multi-modal support. Use for document Q&A, chatbots, knowledge retrieval, or building RAG pipelines. Best for data-centric LLM applications.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Operating-system distillation of LlamaIndex — the leading RAG / document-agent framework. Activate when the calling agent must build, debug, harden, or evaluate a Retrieval-Augmented Generation pipeline over unstructured/private data, decide between RAG primitives (Index types, retrievers, query engines, routers, agents), or pick LlamaIndex vs LangChain / Haystack / raw vector store for a coding task. Encodes the 5-layer mental model (Documents → Nodes → Indices → Retrievers → Query Engines / Response Synthesizers), the canonical RAG bootstrap SOP from baseline `VectorStoreIndex` through hybrid + reranker + eval-loop hardening, the official 13-failure-mode checklist, and 5 dilemma cases distilled from docs, GitHub issues, and 2025 production post-mortems.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

MySQL과 MariaDB의 테이블 설계와 느린 SQL을 검토합니다. 인덱스, 갱신 처리, 연결 관리, 복제 지연을 점검하고 구체적인 수정안과 검증 절차를 정리합니다.

• 테이블과 인덱스 설계
• 느린 SQL과 교착 상태 조사
• 대규모 운영 테이블 변경 검토

Web search, online retrieval, real-time information query, search engine search, Reddit and other community platform discussions, external site posts and trending topics. Trigger when users need to search for the latest information online, look up recent news or trends, query real-time data, research topics via search engines, browse Reddit or community discussions, discover external site posts and trending topics, obtain any online content, web search, online lookup, real-time information, news search, Reddit, trending topics, search engine. Even if the user does not explicitly say "search", trigger this skill whenever their intent involves obtaining real-time web information or verifying current facts.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

LlamaIndex.TS data framework for RAG, indexing, retrieval, query engines, chat engines, and agentic workflows in TypeScript

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Build document search with AgentDB that finds related content by meaning. Store documents as numerical representations, combine similarity search with metadata filters, and retrieve relevant passages for systems that generate answers using source material.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build answers grounded in retrieved documents
• Add meaning-based search to a knowledge base
• Find documents with similar content

Build document search with AgentDB that finds related content by meaning. Store documents as numerical representations, combine similarity search with metadata filters, and retrieve relevant passages for systems that generate answers using source material.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build answers grounded in retrieved documents
• Add meaning-based search to a knowledge base
• Find documents with similar content

Analyze CSV, Parquet, and JSON files to answer questions through grouping, joins, cleaning, time series, and charts. Chooses DuckDB or Polars and a suitable processing location based on the task and available memory.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Summarizing and joining data files
• Exploring trends and distributions
• Cleaning data before analysis

Set up a local web search server using Bright Data SERP API and unfancy-search. Retrieve results from multiple engines, optionally expand queries with AI, and receive combined rankings with duplicates removed and results grouped by domain.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adding web search to a Claude Code agent
• Researching a topic through multiple related queries
• Filtering searches by region or domain

Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Selects, investigates, and compares AWS object, file, and block storage services, and answers cost, performance, configuration, security, and troubleshooting questions about storage services. Applies when a user asks where to store or archive data based on their usage patterns; which storage service to choose or how two compare; how to migrate data from on-premises or between AWS services; how to protect, replicate, or recover data; how to optimize storage costs; where to deploy shared NFS, SMB, or POSIX file systems; where to store vector embeddings or tabular data; what storage backs enterprise file shares, self-managed databases on EC2, VMware, or stateful containers; or asks what an AWS storage service can do or how it works. Relevant for storage needs for workloads such as AI/ML, analytics, EDA, HPC, media, genomics, or financial trading. Not applicable for SQL query engines (Athena, Spark, Redshift, EMR), ETL (Glue), streaming (Kafka, MSK, Kinesis), or managed database services (RDS, Aurora, DynamoDB).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Graph database implementation for relationship-heavy data models. Use when building social networks, recommendation engines, knowledge graphs, or fraud detection. Covers Neo4j (primary), ArangoDB, Amazon Neptune, Cypher query patterns, and graph data modeling.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Web search using curl + multiple search engines (Baidu, Google, Bing, DuckDuckGo). Activates when user asks to search, look up, or query something online. Includes security enhancements: input sanitization, command injection protection, and URL encoding.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Designs lakehouse platforms across Iceberg, Delta, Hudi, and Paimon. Use when choosing catalogs, CDC paths, query engines, governance, or cost controls.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.