Architect scalable RAG pipelines with chunking, embeddings, and hybrid retrieval.
npx skills add https://github.com/mnsgrosa/skills --skill rag-architect-mnsgrosa
Please help me install this Agent Skill. Skill: rag-architect Source: https://github.com/mnsgrosa/skills/tree/main/skills/rag-architect Command: npx skills add https://github.com/mnsgrosa/skills --skill rag-architect-mnsgrosa
Architect scalable RAG pipelines that reliably fetch relevant context for LLMs by coordinating chunking, embeddings, vector stores, and ranking.
Configure your data sources, chunk documents, generate embeddings, index them into a vector store, and connect a retrieval-augmented pipeline with reranking to answer questions.
High-intent search queries and answers about installing and using this skill.
Build a RAG pipeline by chunking documents, generating embeddings, indexing them into a vector store, and configuring hybrid retrieval with reranking. This approach coordinates semantic search and context augmentation for reliable LLM grounding.
Hybrid retrieval combines vector search with other matching methods to fetch relevant context. Reranking is needed to evaluate retrieval quality and ensure reliable ranking across large document stores.
Yes, the RAG architecture supports multi-model embedding choices and vector store configuration. It applies to enterprise data and knowledge bases requiring deterministic chunking and idempotent ingestion for scalable semantic search.
The best way to chunk documents for idempotent ingestion is applying deterministic chunking strategies. This ensures consistent embedding generation and reliable indexing into vector databases for retrieval-augmented generation.
Evaluate retrieval quality metrics by applying reranking and measuring how effectively the vector store returns relevant context. This evaluation ensures reliable ranking for retrieval-augmented generation across knowledge bases.
Yes, this RAG architecture works for context-augmented search across large document stores. It scales by coordinating chunking, embeddings, vector stores, and ranking to reliably fetch relevant context for LLMs.
NelkitEmbed text data into ChromaDB with deterministic IDs and chunking.
apassuelloBuild RAG systems grounding LLM outputs in external knowledge sources.
lagarcessDesign and optimize retrieval-augmented generation pipelines for document indexing and vector search.
ai-kitchen-incChunk documents, generate embeddings, and index vectors in Pinecone for RAG.
