Results for “query-engine”

Provides ClickHouse-specific patterns for schema design, query optimization, data ingestion, and analytics, including materialized views and performance monitoring.

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

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

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.

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.

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.

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

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.

Guides database index selection, creation, and maintenance with SQL examples for PostgreSQL and MySQL.

Provides PostgreSQL query patterns covering SELECT, JOINs, subqueries, CTEs, window functions, and advanced SQL techniques.

Optimizes SQL queries, designs database schemas, and troubleshoots performance issues. Use when a user asks why their query is slow, needs help writing complex joins or aggregations, mentions database performance issues, or wants to design or migrate a schema. Invoke for complex queries, window functions, CTEs, indexing strategies, query plan analysis, covering index creation, recursive queries, EXPLAIN/ANALYZE interpretation, before/after query benchmarking, or migrating queries between database dialects (PostgreSQL, MySQL, SQL Server, Oracle).

Monitors and manages Coveo enterprise search platform: sources, indexes, query pipelines, ML models, usage analytics, and security providers, with health checks and license utilization.

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

Connects LLMs with user data for RAG applications, document Q&A, and knowledge retrieval using 300+ data connectors and vector indices.

Use DuckDB from MATLAB via Database Toolbox (R2026a+) as a non-math operations engine on large tabular files (CSV/Parquet/JSON) and as a zero-config embedded database. Use when connecting to DuckDB, querying CSV, Parquet, and JSON files directly with SQL, reducing or profiling large data before MATLAB analysis, creating portable development databases, or installing DuckDB extensions. Triggers on: DuckDB, duckdb(), large CSV/Parquet/JSON, file too large for readtable, filter/aggregate at source, deduplicate, reduce before analysis, profile large file, persistent file import, analytical engine, SQL on CSV, SQL on Parquet, SQL on JSON, query CSV with SQL, query Parquet with SQL, run SQL on files, SQL queries on files, query files directly, SQL without database, in-process SQL.

Use for writing, reviewing, debugging, testing, or optimizing Python code that embeds DuckDB, executes analytical SQL, manages DuckDB connections and transactions, builds DuckDB relations, queries Parquet/CSV/Arrow/pandas/Polars inputs, or exports query results. Trigger on connection scope, parameters, replacement scans, materialization, concurrency, extensions, and query plans. Do not use for generic SQL with another engine, DuckDB CLI-only work, server-database administration, dbt-only projects, or dataframe work that does not call DuckDB.