Definition
A multi-stage sequence of operations—comprising document ingestion, parsing, chunking, embedding, and storage—that transforms unstructured data into a structured format optimized for semantic retrieval. Architectural trade-offs involve balancing chunk size (granularity vs. context) and embedding dimensionality (accuracy vs. latency/cost).
Unlike traditional database indexing for keyword matching, this creates high-dimensional vector representations for semantic similarity.
"An industrial sawmill processing raw timber (documents) into uniform, labeled planks (chunks) that are stored in a GPS-indexed warehouse (vector store)."
- Chunking(Component)
- Embedding Model(Component)
- Vector Database(Component)
- ETL (Extract, Transform, Load)(Prerequisite)
Conceptual Overview
A multi-stage sequence of operations—comprising document ingestion, parsing, chunking, embedding, and storage—that transforms unstructured data into a structured format optimized for semantic retrieval. Architectural trade-offs involve balancing chunk size (granularity vs. context) and embedding dimensionality (accuracy vs. latency/cost).
Disambiguation
Unlike traditional database indexing for keyword matching, this creates high-dimensional vector representations for semantic similarity.
Visual Analog
An industrial sawmill processing raw timber (documents) into uniform, labeled planks (chunks) that are stored in a GPS-indexed warehouse (vector store).