RAG is the most common pattern for building AI applications that need to work with specific data — company documents, knowledge bases, product catalogs, or user content. It reduces hallucinations and improves accuracy.
A RAG pipeline has four stages: chunking (splitting documents into segments), embedding (converting chunks to vectors), storage (indexing vectors in a database), and retrieval (finding relevant chunks at query time).
Plugsky provides built-in RAG support: embedding models, vector storage, and retrieval APIs — all through a single endpoint. No separate vector database or embedding service needed.
For production RAG, consider chunk size (larger chunks capture more context but dilute relevance), overlap (helps maintain context across chunk boundaries), and hybrid search (combining vector and keyword retrieval for better results).
Plugsky's knowledge base feature handles chunking and indexing automatically. Upload documents through the playground or API, and they're ready for semantic search within seconds.
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