Overview
- The coverage formalizes a four-stage ingestion flow—Extract, Chunk, Embed, Store—that converts raw corporate content into vector records for semantic search.
- Chunking guidance recommends 256–512 tokens per chunk with about 10% overlap (example: 512-token chunks with a 50-token overlap) to keep meaning intact and avoid fragmented retrieval.
- Embedding guidance stresses using the same embedding model at index and query time because switching models invalidates vectors and typically requires full re-indexing; common dimension ranges are 1024–3072 with 1024–1536 often a practical sweet spot.
- Vector databases rely on Approximate Nearest Neighbour algorithms such as HNSW to return top-K similar chunks in milliseconds, and Azure AI Search’s hybrid BM25-plus-vector semantic ranker is highlighted as strong for technical and policy documents.
- Operational best practices include cleaning and deduplicating extracted text, using source-specific extractors (Microsoft Graph for SharePoint, PyMuPDF for PDFs, python-docx for Word, Jira REST API), automating ingestion on updates, and planning lifecycle costs for re-indexing; a hands-on Azure implementation is promised next.