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In the ever-evolving landscape of information retrieval, the quest for more nuanced and accurate search methodologies has led to the emergence of hybrid search techniques. Hybrid search, particularly when utilizing Llamaindex, stands at the forefront of this evolution, seamlessly amalgamating sparse and dense vectors to refine search results and enhance user experiences.
Hybrid Search: Hybrid search is an information retrieval technique that combines results obtained from two different types of vectors, namely dense and sparse. These vectors capture different aspects of the text: dense vectors focus on semantic meaning, while sparse vectors emphasize lexical similarity.
Dense Vectors: Vectors generated from embedding models (e.g., text-embedding-ada-002, bge) that encapsulate semantic nuances and contextual embeddings across the entirety of the text. They aim to represent the overall semantic meaning of the content.
Sparse Vectors: Vectors produced by specialized models (e.g., BM25, SPLADE) that predominantly consist of 0s and 1s, where each number corresponds to a specific term. Sparse vectors highlight lexical similarities and identify matching keywords within the text.
Relative Score Fusion: A method of combining dense and sparse vectors by introducing an alpha term (usually between 0 and 1) to weigh the similarity derived from each vector type. This technique allows for adjusting the balance between semantic coherence and lexical relevance in the search results.
Reciprocal Rank Fusion: An approach to fusion that involves augmenting the ranked list by adding the reciprocal of the rank for both sparse and dense retrieval. This method strengthens the rankings of relevant results across both vector types, aiming to provide a more comprehensive set of search outcomes.
The marriage of these vectors grants a holistic view of the content, where dense vectors provide a nuanced…