AVAILABLE FOR WEB, FLUTTER & QA PROJECTS
How to use this tool
1. Specify Document Volume
Enter total document size in words, tokens, or pages (e.g. 50,000 words / 100 pages).
2. Configure Chunk Parameters
Set target Chunk Size (e.g. 256, 512, 1024 tokens) and Chunk Overlap (e.g. 50 tokens or 10-20%).
3. Choose Vector Embedding Model
Select embedding dimensions (1536 for OpenAI, 3072 for Large, 768 for Gemini) to calculate storage.
4. Review Architecture Metrics
Analyze total vector chunks generated, stride step, overlap redundancy ratio, and vector DB RAM/disk footprint.
Formula or logic used
Chunking & Stride Mathematics
Recursive character text splitters slice continuous documents using a sliding window with overlap to prevent context fracture at chunk boundaries.
Stride (Effective Step) = Chunk Size - Overlap TokensTotal Chunks Generated = Math.max(1, Math.ceil((Total Document Tokens - Overlap) / Stride))Total Indexed Tokens = Total Chunks × Chunk SizeRedundancy Ratio (%) = ((Total Indexed Tokens - Total Document Tokens) / Total Document Tokens) × 100Vector Storage Size (Bytes) = Total Chunks × Embedding Dimensions × 4 bytes (Float32) + Metadata Overhead (~500 bytes/chunk)
Examples
Example 1: Enterprise Knowledge Base (100,000 Tokens)
512 tokens provides sufficient semantic richness for question answering while keeping retrieval focused and affordable.
Example 2: Dense Legal Contracts (500,000 Tokens)
Larger 1,024 chunk sizes preserve clause context across complex contractual definitions.
Common use cases
LangChain & LlamaIndex Pipeline Configuration
Tune RecursiveCharacterTextSplitter chunk_size and chunk_overlap parameters scientifically.
Vector Database Capacity Planning
Estimate index memory sizing for Pinecone, pgvector, Qdrant, Milvus, and Weaviate.
Context Window Top-K Retrieval Sizing
Ensure Top-K (e.g. 5 chunks of 512 tokens = 2,560 tokens) fits comfortably inside generation prompts.
Retrieval Accuracy vs Redundancy Optimization
Balance semantic continuity against token bloat and repetitive context retrieval.
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Frequently asked questions
What is the best chunk size for RAG?
For most general question-answering systems, 512 tokens with a 10% to 15% overlap (50-75 tokens) provides the optimal balance between semantic specificity and surrounding context. For short FAQs, 256 tokens is ideal, while dense legal or financial filings perform best at 1,024 tokens.
Why is chunk overlap necessary in RAG?
Chunk overlap prevents critical context from being fractured mid-sentence or mid-argument at an arbitrary token boundary. Overlap ensures that entities, definitions, and dependent clauses are fully represented in at least one retrieved chunk.
How much vector storage does 10,000 chunks require?
For 1,536-dimensional embeddings (OpenAI text-embedding-3-small), 10,000 chunks require approximately 61.4 MB of raw vector memory (10,000 × 1,536 × 4 bytes). With HNSW index graphs and metadata payloads, expect ~90 to 120 MB total database memory.
How does chunk size affect retrieval Top-K?
Smaller chunks (e.g. 256 tokens) allow you to retrieve a higher Top-K (e.g. Top 8-10 chunks from diverse sections) within a 2,500-token budget. Larger chunks (e.g. 1,024 tokens) limit you to Top 2-3 chunks before exhausting token budgets.
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