⚡ RAG Pipeline Engineering Tool
RAG Chunking Strategy & Vector Index Sizer
Estimate vector database RAM requirements, total chunk counts, HNSW index graph overhead, and expected retrieval recall@K across OpenAI, BGE, and Nomic embeddings.
⚙️ Corpus & Model Parameters
50M Tokens
Vector Index RAM
1.82 GB
Embeddings + HNSW Graph
Total Vectors Generated
115,000
Chunk Overlap Applied
Hybrid Recall @ 10
94.5%
BM25 + Semantic Search
📊 RAG Index Capacity & Memory Allocation Calculated Values
RAG Pipeline Metric Sizing Value
Raw Embedding Vector Size 706 MB
HNSW Graph Overhead (M=16, ef=200) 1,114 MB
P99 Vector Query Latency < 4.2 ms
Monthly OpenAI Embedding API Cost $1.00
Target Vector DB Instance Spec 2 vCPU / 4 GB RAM (Qdrant / Milvus)
💡 RAG Chunking Recommendations
- 512-token chunks with 15% overlap yield optimal precision-recall balance for technical documentation.
- Enabling Scalar Quantization (SQ8) reduces vector RAM by 75% with under 1% drop in recall@10.