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29 lines
3.1 KiB
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29 lines
3.1 KiB
Plaintext
---
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title: "Rankers"
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id: rankers
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slug: "/rankers"
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description: "Rankers are a group of components that order documents by given criteria. Their goal is to improve your document retrieval results."
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---
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# Rankers
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Rankers are a group of components that order documents by given criteria. Their goal is to improve your document retrieval results.
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| Ranker | Description |
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| --- | --- |
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| [AmazonBedrockRanker](rankers/amazonbedrockranker.mdx) | Ranks documents based on their similarity to the query using Amazon Bedrock models. |
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| [CohereRanker](rankers/cohereranker.mdx) | Ranks documents based on their similarity to the query using Cohere rerank models. |
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| [FastembedRanker](rankers/fastembedranker.mdx) | Ranks documents based on their similarity to the query using cross-encoder models supported by FastEmbed. |
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| [FastembedLateInteractionRanker](rankers/fastembedlateinteractionranker.mdx) | Ranks documents based on their similarity to the query using late interaction models supported by FastEmbed. |
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| [HuggingFaceTEIRanker](rankers/huggingfaceteiranker.mdx) | Ranks documents based on their similarity to the query using a Text Embeddings Inference (TEI) API endpoint. |
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| [JinaRanker](rankers/jinaranker.mdx) | Ranks documents based on their similarity to the query using Jina AI models. |
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| [LLMRanker](rankers/llmranker.mdx) | Ranks documents for a query using a Large Language Model, which returns ranked document indices as JSON. |
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| [LostInTheMiddleRanker](rankers/lostinthemiddleranker.mdx) | 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](https://arxiv.org/abs/2307.03172). |
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| [MetaFieldRanker](rankers/metafieldranker.mdx) | A lightweight Ranker that orders documents based on a specific metadata field value. |
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| [MetaFieldGroupingRanker](rankers/metafieldgroupingranker.mdx) | Reorders the documents by grouping them based on metadata keys. |
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| [NvidiaRanker](rankers/nvidiaranker.mdx) | Ranks documents using large-language models from [NVIDIA NIMs](https://ai.nvidia.com) . |
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| [PyversityRanker](rankers/pyversityranker.mdx) | Reranks documents by balancing relevance and diversity using pyversity's diversification algorithms. |
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| [SentenceTransformersDiversityRanker](rankers/sentencetransformersdiversityranker.mdx) | A Diversity Ranker based on Sentence Transformers. |
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| [SentenceTransformersSimilarityRanker](rankers/sentencetransformerssimilarityranker.mdx) | 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. <br /> <br />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. |
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| [VLLMRanker](rankers/vllmranker.mdx) | Ranks documents based on their similarity to the query using reranker models served with vLLM. |
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