Rankers
Rankers are a group of components that order documents by given criteria. Their goal is to improve your document retrieval results.
Ranker | Description |
|---|---|
Ranks documents based on their similarity to the query using Cohere rerank models. | |
Ranks documents based on their similarity to the query using Jina AI models. | |
Positions the most relevant documents at the beginning and at the end of the resulting list while placing the least relevant documents in the middle, based on a research paper. | |
A lightweight Ranker that orders documents based on a specific metadata field value. | |
Ranks documents using large-language models from NVIDIA NIMs. | |
A model-based Ranker that orders documents based on their relevance to the query. It uses a cross-encoder model to produce query and document embeddings. It then compares the similarity of the query embedding to the document embeddings to produce a ranking with the most similar documents appearing first. It's a powerful Ranker that takes word order and syntax into account. You can use it to improve the initial ranking done by a weaker Retriever, but it's also more expensive computationally than the Rankers that don't use models. | |
A Diversity Ranker based on Sentence Transformers. |
Updated about 5 hours ago