How to Chunk Files for a RAG Model (Production Checklist + Python)

Byadmin
How to Chunk Files for a RAG Model (Production Checklist + Python)

Chunking is not just splitting text—it’s deciding how your knowledge base will be retrieved, cited, and trusted. Below is a practical checklist we use in production RAG systems.

Production Chunking Checklist

  • Chunk size: 300–900 tokens (start at ~600)
  • Overlap: 10–20% to protect context
  • Structure-aware splitting: headings, sections, tables
  • Metadata: document, section, timestamp, ACL filters
  • Deduplication: checksum or content fingerprinting

Minimal Python Example

def chunk_text(text, chunk_size=1200, overlap=200):
    chunks = []
    start = 0
    while start < len(text):
        end = min(len(text), start + chunk_size)
        chunks.append(text[start:end])
        start = end - overlap
        if start < 0:
            start = 0
    return chunks

In production, replace character counts with token-aware splitting and keep metadata for security filters and relevance tuning.

]]>

Similar Posts