RAG Planner
Plan and estimate RAG system requirements
Document Configuration
Storage Breakdown
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What is RAG Planning?
RAG (Retrieval-Augmented Generation) systems require careful planning to balance performance, cost, and quality. This planner helps you estimate resource requirements—chunks, storage, embedding costs—before building your system.
Input your document corpus characteristics and chunking strategy to see projected requirements.
Key Parameters
Chunk Size
Tokens per chunk. Smaller = more precise retrieval but less context. Typical: 256-512.
Embedding Dimension
Vector size from your embedding model. OpenAI: 1536/3072. Open source: 384-768.
Top K
How many chunks to retrieve per query. More = better recall, higher cost.
FAQ
How accurate are cost estimates?
Rough estimates based on OpenAI ada-002 pricing. Actual costs vary by provider and model.
What about query costs?
This estimates ingestion costs. Query costs depend on traffic volume and aren't included here.