“RAG”

Runs workflows for AIEWF 2024 RAG talks — layout-aware ingest, GraphRAG, eval-driven tuning, Pydantic structured IO, MongoDB vectors, EMT memory, construction…

Citation-guarded retrieval over 22M Taiwan court judgments and administrative interpretations

RAG setup with Elasticsearch as the retrieval backend

Acts as the verification and grounding layer in Retrieval-Augmented Generation (RAG) pipelines.

Author rules from policy docs, then decide: a Rete engine gives the verdict, an LLM explains why.

Use when Codex needs to build or query a local RAG knowledge base for academic papers, literature matrices, public author guidelines, GB/T7714 rules, CSL…

INVOKE THIS SKILL when building ANY retrieval-augmented generation (RAG) system.

Use this skill for requests about RAG design, chunking, retrieval, reranking, and related implementation trade-offs.

Build RAG (Retrieval Augmented Generation) pipelines with web search and LLMs.

AIChat is a comprehensive LLM command-line tool written in Rust that combines chat-REPL, shell command generation, RAG, AI tools, and multi-provider support…

Private knowledge base for indexing multimodal files or folders into a knowledge graph, supporting multi-hop graph retrieval

Guides evaluation of RAG pipeline retrieval and generation quality.

Grounded knowledge retrieval, multi-source synthesis, and citation verification

Search the hybrid RAG (BM25 + vector, RRF-merged) over 17.6M chunks of historical sell-side research (Nomura, JPM, MS, BofA and hundreds more, 1982–2026).

Query the local RAG knowledge base using a python script.

GitHub repository vectorizer and context synthesizer for RAG pipelines.

Optional local retrieval (M9-OPT / ADR-003): use retrieve_knowledge_snippets when present to ground notes in files under knowledge/; map source_id to existing…

Use when | Pipeline para integrar documentos de OnlyOffice con el sistema RAG de KAI.

Efficiently perform web searches using the mcp-local-rag server with semantic similarity ranking.

Expert in building Retrieval-Augmented Generation systems.

Rag Knowledge MCP Server by MEOK AI Labs

Academic paper knowledge-base MCP server: PDF ingest, vector search with reranking, KB management.

Patterns for RAG pipelines, vector databases, embedding strategies, context-window management, and LLM orchestration.

Permission-aware RAG for agents — ingest docs, hybrid search, cited answers (hosted API).