Rankers
Rankers are a group of components that order documents by given criteria. Their goal is to improve your document retrieval results.
Ranker | Description |
---|---|
CohereRanker | Ranks documents based on their similarity to the query using Cohere rerank models. |
JinaRanker | Ranks documents based on their similarity to the query using Jina AI models. |
LostInTheMiddleRanker | 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. |
MetaFieldRanker | A lightweight Ranker that orders documents based on a specific metadata field value. |
NvidiaRanker | Ranks documents using large-language models from NVIDIA NIMs . |
TransformersSimilarityRanker | 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. |
SentenceTransformersDiversityRanker | A Diversity Ranker based on Sentence Transformers. |
Updated about 2 months ago
Related Links