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Intermediate

Incremental Learning

The architectural approach of updating a RAG knowledge base or an agent's internal model by processing new data streams sequentially without re-indexing the entire corpus or retraining from scratch. This method optimizes for low latency and resource efficiency while managing the trade-off between immediate data availability and potential index fragmentation or 'embedding drift'.

Definition

The architectural approach of updating a RAG knowledge base or an agent's internal model by processing new data streams sequentially without re-indexing the entire corpus or retraining from scratch. This method optimizes for low latency and resource efficiency while managing the trade-off between immediate data availability and potential index fragmentation or 'embedding drift'.

Disambiguation

In RAG, this refers to 'upserting' new documents into a vector store rather than performing a full batch re-index.

Visual Metaphor

"A loose-leaf binder where new pages are inserted into specific sections as information arrives, rather than reprinting the entire textbook for every update."

Conceptual Overview

The architectural approach of updating a RAG knowledge base or an agent's internal model by processing new data streams sequentially without re-indexing the entire corpus or retraining from scratch. This method optimizes for low latency and resource efficiency while managing the trade-off between immediate data availability and potential index fragmentation or 'embedding drift'.

Disambiguation

In RAG, this refers to 'upserting' new documents into a vector store rather than performing a full batch re-index.

Visual Analog

A loose-leaf binder where new pages are inserted into specific sections as information arrives, rather than reprinting the entire textbook for every update.

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