A history-aware retriever is a type of AI model or system designed to improve information retrieval by considering the context or history of interactions, usually in a conversation or search. This is particularly useful in conversational systems or search engines where queries are often related to previous exchanges. Here's how it works:

Key Concepts of a History-Aware Retriever:
Contextual Awareness:

The retriever doesn't treat each query as isolated; instead, it maintains awareness of previous interactions or questions.
It uses prior inputs or exchanges to refine and better interpret the current query, allowing for more accurate and relevant retrieval of information.
Query Disambiguation:

Many queries are ambiguous or incomplete when taken out of context. A history-aware retriever looks back at the previous queries to resolve ambiguity and provide a more appropriate....read more