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

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

Chunking is not just splitting text—it’s how your system thinks.

  • Retrieval accuracy
  • Cost efficiency
  • Hallucination rate
  • Security (ACL filtering)

✅ Production Chunking Checklist

  • Chunk size: 300–900 tokens
  • Overlap: 10–20%
  • Structure-aware splitting
  • Metadata (document_id, section, ACL)
  • Deduplication
  • Token-aware splitting

📄 Example Input File

Document: AI Platform Guide

Section: Introduction
AI platforms enable scalable machine learning.

Section: Architecture
Includes ingestion, processing, inference.

Section: Security
Includes identity, access control, audit logs.

🧪 Python Example (With Debug)

def chunk_text(text, chunk_size=1200, overlap=200):
    chunks = []
    start = 0

    while start < len(text):
        end = min(len(text), start + chunk_size)
        chunk = text[start:end]
        chunks.append(chunk)

        print(f"[DEBUG] Start: {start}, End: {end}")
        print(f"[DEBUG] Chunk: {chunk[:50]}...")

        start = end - overlap
        if start < 0:
            start = 0

    return chunks

🎯 Summary

Chunking = how your RAG remembers context without losing meaning.

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