RAG Architecture Builder

What the RAG Architecture Builder — Free Online Tool does

The RAG Architecture Builder helps you design a retrieval-augmented generation pipeline step by step. Choose an embedding model, a vector store, a chunking strategy, and a retrieval method such as hybrid search with reranking, and see how the pieces connect. It is for developers and architects planning search or question-answering systems before writing code. Plugsky provides embedding and chat models through one OpenAI-compatible API, which keeps the generation side simple.

How to use it

1. Choose the embedding model for your content and language.
2. Choose the vector store that matches your scale and filters.
3. Set chunk size, overlap, and how the text is split.
4. Choose retrieval, such as vector search, hybrid search, or reranking.
5. Assemble the prompt and generate an answer from retrieved passages.

FAQ

What are the main components of a RAG pipeline?

Ingestion, chunking, embedding, storage, retrieval, reranking, and generation. Each stage has trade-offs: chunk size affects context quality, while retrieval and reranking determine whether the right passage reaches the model at all.

How should I choose a chunking strategy?

Start with structure-aware chunks of a few hundred tokens and modest overlap, then test on real queries. Headings, tables, and code usually deserve their own boundaries, and smaller chunks raise recall but can fragment context.

When do I need a reranker?

Add a cross-encoder reranker when top-k retrieval returns the right passage but ranks it too low. Retrieve a wider candidate set, rerank it, and pass only the best few passages to the model to cut noise and token cost.

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Canonical pricing and plans: plugsky.com/#sec-pricing · Terms · SLA · Docs

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