Rankers
Rankers rank documents by specific criteria using pre-trained models. They're used in query pipelines after retrievers to improve retrieval results.
Available rankers:
- CohereRanker: Ranks documents based on their similarity to the query using Cohere models.
- LostInTheMiddleRanker: Puts the most relevant documents and the beginning and at the end of input for an LLM.
- MetaFieldRanker: Ranks documents based on a value of their metadata field.
- SentenceTransformersDiversityRanker: Ranks documents to maximize their diversity.
- TopPSampler: Uses nucleus sampling to select the most relevant documents based on their similarity to a query.
- TransformersSimilarityRanker: Uses a cross-encoder model to embed both the query and the documents and then rank documents by their similarity to the query.
Updated 4 months ago