Results for “query-engine”

ClickHouse database patterns, query optimization, analytics, and data engineering best practices for high-performance analytical workloads.

ClickHouse database patterns, query optimization, analytics, and data engineering best practices for high-performance analytical workloads.

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.

Diagnoses and improves Qdrant search relevance by isolating embedding model, configuration, or query strategy issues.

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.

为X、LinkedIn、TikTok、YouTube、新闻通讯和跨平台重新利用的多平台活动创建平台原生内容系统。适用于当用户需要社交媒体帖子、帖子串、脚本、内容日历，或一个源资产在多个平台上清晰适配时。

Provides the shared STORM methodology, artifact layout, stage-gating contract, citation hygiene, and retrieval fallback. Use when executing any /storm:* skill (generate, research, outline, write, polish). Internal knowledge; never user-invocable.

Diagnose and optimize existing slow SQL queries using execution plans, indexing strategies, query rewriting, and ORM tuning. Use when the user provides a query, performance symptom, or EXPLAIN plan; use sql-query-generation when creating a new query from requirements.

Search YouTube SERP results through AISA for video research, channel discovery, trend checking, and competitor tracking. Use when: you need ranked YouTube results for a query, optionally filtered by country or language.

Find keyword gaps, SERP opportunities, and low-competition high-intent keywords

Build production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations. Use PROACTIVELY for LLM features, chatbots, AI agents, or AI-powered applications.

Kysely type-safe SQL query builder - End-to-end type safety from schema to queries, migrations, transactions, plugins

Generate SQL queries from natural-language requirements using SELECT, JOIN, GROUP BY, window functions, CTEs, and subqueries. Use when the user needs a new query from a business question or schema; use query-optimization when an existing query or execution plan is slow.

Diagnoses and resolves slow Qdrant search performance issues including high latency, low throughput, and slow filtered searches.

Optimize Qdrant vector search performance through indexing strategies, query tuning, memory management, and hardware considerations.

Manage datasets, tables, and jobs in BigQuery. Run SQL queries, manage BigQuery resources, and perform basic data ingestion and analysis.

Detects and triages explicitly-unbounded AEM queries (p.limit=-1 or setLimit(-1)) that cause OOMs, safely capping only where provably safe and escalating others for human pagination.

Answers questions using a structured wiki knowledge base, returning cited synthesis with wikilinks, counter-arguments, and knowledge gaps.

Layer 1d of the keyword research pipeline. Mines question-shaped keywords from Semrush phrase_questions (per surviving seed) plus People-Also-Ask strings from the SERP. Appends rows to keyword-ideas.csv with source=question_mining (or merges to source=both when the keyword already exists) and a question_subtype column. Cap of 100 rows per run.

Manages vector embeddings with Pinecone for semantic search, recommendation, and RAG pipelines.

Use when querying a Rulebase workspace with SQL through the MCP server's query tool — writing queries that finish inside the statement timeout, and avoiding the join fan-outs that silently inflate QA evaluation counts. Trigger for "query my Rulebase data", "the query timed out", "statement timeout", "canceling statement due to statement timeout", counts that don't reconcile between two Rulebase queries, criterion counts exceeding team counts, or any multi-step analysis over Rulebase conversations and evaluations.

Optimizes SQL queries with execution plan analysis, index suggestions, and query rewriting

Generate optimized SQL queries from natural language descriptions across multiple database platforms including BigQuery, PostgreSQL, MySQL, and Snowflake. Reads database schemas from uploaded files or diagrams to produce accurate, production-ready queries.

Deploys Weaviate vector database with hybrid search, modules, and GraphQL API.

Automates question submission to Amazon's Alexa/Rufus AI shopping assistant and collects structured response data, with optional keyword search context for category-specific answers.

Build production RAG and semantic search systems with a high-performance vector database written in Rust, supporting hybrid search, filtering, and horizontal scaling.

研究优先于编码的工作流程。在编写自定义代码之前，搜索现有的工具、库和模式。调用研究员代理。

Create platform-native content systems for X, LinkedIn, TikTok, YouTube, newsletters, and repurposed multi-platform campaigns. Use when the user wants social posts, threads, scripts, content calendars, or one source asset adapted cleanly across platforms.

Optimizes database queries and improves performance across PostgreSQL and MySQL systems by analyzing execution plans, designing index strategies, and tuning configurations.

Analiza datos en bases de datos relacionales con consultas SQL eficientes y legibles, incluyendo joins, window functions, CTEs y subqueries, para extraer insights directamente de la base de datos.