RAG

1. Indexing Indexing prepares the knowledge base for efficient retrieval by processing and storing documents in a structured format. Load Purpose: Ingest raw data from various sources and normalize it for further processing. Common Data Sources: Structured: Databases, APIs Semi-structured: JSON, XML, CSV Unstructured: PDFs, DOCX, HTML, scraped web pages Best Practices: Extract metadata (e.g., author, timestamps) to enhance retrieval. Normalize text (remove special characters, fix encoding issues). Detect language and preprocess accordingly (stemming, lemmatization). Split Purpose: Break documents into manageable chunks for improved retrieval accuracy. Splitting Strategies: Fixed-size chunking (e.g., 512 tokens per chunk). Overlapping chunks (ensures context continuity). Semantic chunking (splitting based on topic shifts or sentence boundaries). Best Practices: Ensure chunks are not too large (to avoid irrelevant retrieval) or too small (to retain context). Use metadata tags to track chunk origin and relationships. Documents chunking strategies and best practices ...