Document Indexing

Learn how MINDTRICKS AI prepares your documents for search: chunking, embeddings, and storage in your knowledge base. Indexing is the first step in retrieval augmented generation (RAG); see also Document Retrieval and AI Response Generation.

How Indexing Works

Before a query can retrieve relevant text, your sources must be ingested and turned into searchable representations.

The Indexing Pipeline

  1. Ingestion: Documents are read from uploads or connected sources
  2. Chunking: Text is split into segments sized for embedding models and retrieval
  3. Embedding: Each chunk is converted to a vector that captures semantic meaning
  4. Storage: Vectors and metadata are stored for fast similarity search at query time

Chunking & Embeddings

Good indexing balances chunk size, overlap, and embedding choice so retrieval returns coherent, relevant passages.

Best Practices

  • Keep chunks aligned with natural sections where possible
  • Use overlap so ideas split across boundaries stay findable
  • Match embedding models to your domain and languages
  • Refresh indexes when source documents change materially

Common Issues

  • Chunks too large—noise dilutes similarity scores
  • Chunks too small—missing surrounding context
  • Stale index—answers reflect old versions of docs
  • Mixed formats—tables and lists need sensible splitting

Why Indexing Matters

  • Grounds answers in your data instead of the model's training snapshot alone
  • Improves factual accuracy when retrieval and generation follow quality indexing
  • Supports transparency when chunks map back to source documents

Relationship to retrieval & generation

Indexing builds the corpus that retrieval searches and that generation uses as context. Tuning indexing improves downstream answer quality as much as choosing a strong language model.