SmartFAQs.ai
Back to Learn
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."

Key Tools
PineconeWeaviateMilvusLangChain Indexing APILlamaIndex (Data Ingestion Pipeline)Qdrant
Related Connections

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.

Related Articles