“RAG”

Retrieve relevant excerpts from a project's text files without loading entire files into context.

Answer questions from indexed document evidence.

Semantic search over locally indexed documents, notes, images, and videos

Responde preguntas sobre documentación técnica, CVEs, políticas y contenido indexado en ChromaDB.

从知识库检索扫地机器人专业资料。当用户询问产品功能、故障排除、使用技巧、保养维护、选购建议等专业问题时使用。

Build retrieval-augmented workflows with clear ingestion, retrieval, and answer boundaries.

Local knowledge retrieval and indexing skill.

Build retrieval and RAG with Anvia — chunking, embeddings, vector stores, knowledge graphs, filters, and search tools.

Designs and implements production-grade RAG systems by chunking documents, generating embeddings, configuring vector stores, building hybrid search pipelines…

Build a local RAG index by splitting a document and embedding its chunks using the on-device engine (private/offline).

Use when building self-correcting retrieval systems for AI agents.

Manage and query Agent Platform RAG Engine Corpora and retrieve grounded contexts using the Google GenAI SDK.

Query historical test runs, failures, and API specs from the SQLite RAG index via MCP.

Design RAG pipelines: chunking, retrieval evaluation, and architecture.

Use when comparing basic, enhanced, GraphRAG, or agentic RAG designs, evaluating domain-specific RAG quality, tuning retrieval components, or deciding whether…

Triggers on keywords RAG, GraphRAG, vector database, agentic RAG, semantic search

Use when needing global or relational understanding of codebases or knowledge corpora.

Use this skill when you need evidence-bounded grounding, relevance, completeness, citation support, abstention, and answer-level evidence in RAG outputs…

RAG 模式 —— 检索增强生成架构设计、分块策略、向量检索优化与评估方法

Agentic RAG 检索、查询改写、证据判断与引用规则

Query a RAG (Retrieval-Augmented Generation) knowledge base to find relevant information from ingested documents.

Use quando precisar buscar contexto vetorial, gerar embeddings, ou consultar o RAG.

RAG pipeline design — chunking, embeddings, retrieval strategies, evaluation, and demo patterns.

Systematic evaluation of Retrieval-Augmented Generation (RAG) pipelines measuring faithfulness, answer relevance, context recall, and precision.