Data Ingestion and Preparation
Overview
Data ingestion and preparation is a crucial step in building AI systems, involving the transformation of raw data into structured formats suitable for processing.
Key Components
Text Preprocessing
- Tokenization methods for different languages and contexts
- Normalization techniques for consistency
- Cleaning strategies for noise removal
- Special token handling for domain-specific needs
Chunking Strategies
- Size optimization for efficient processing
- Overlap techniques for context preservation
- Semantic chunking for meaningful segments
- Hierarchical chunking for complex documents
Vector Embeddings
- Word embeddings for token-level semantics
- Sentence embeddings for phrase understanding
- Document embeddings for full-text representation
- Cross-lingual embeddings for multiple languages
Knowledge Representation
- Knowledge graphs for relationship modeling
- Ontologies for domain structuring
- Semantic networks for concept linking
- Frame-based systems for structured knowledge