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57 lines
2 KiB
Markdown
57 lines
2 KiB
Markdown
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+++
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disableToc = false
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title = "📈 Reranker"
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weight = 11
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url = "/features/reranker/"
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+++
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A **reranking** model, often referred to as a cross-encoder, is a core component in the two-stage retrieval systems used in information retrieval and natural language processing tasks.
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Given a query and a set of documents, it will output similarity scores.
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We can use then the score to reorder the documents by relevance in our RAG system to increase its overall accuracy and filter out non-relevant results.
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LocalAI supports reranker models, and you can use them by using the `rerankers` backend, which uses [rerankers](https://github.com/AnswerDotAI/rerankers).
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## Usage
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You can test `rerankers` by using container images with python (this does **NOT** work with `core` images) and a model config file like this, or by installing `cross-encoder` from the gallery in the UI:
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```yaml
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name: jina-reranker-v1-base-en
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backend: rerankers
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parameters:
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model: cross-encoder
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# optionally:
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# type: flashrank
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# diffusers:
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# pipeline_type: en # to specify the english language
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```
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and test it with:
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```bash
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curl http://localhost:8080/v1/rerank \
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-H "Content-Type: application/json" \
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-d '{
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"model": "jina-reranker-v1-base-en",
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"query": "Organic skincare products for sensitive skin",
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"documents": [
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"Eco-friendly kitchenware for modern homes",
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"Biodegradable cleaning supplies for eco-conscious consumers",
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"Organic cotton baby clothes for sensitive skin",
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"Natural organic skincare range for sensitive skin",
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"Tech gadgets for smart homes: 2024 edition",
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"Sustainable gardening tools and compost solutions",
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"Sensitive skin-friendly facial cleansers and toners",
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"Organic food wraps and storage solutions",
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"All-natural pet food for dogs with allergies",
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"Yoga mats made from recycled materials"
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],
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"top_n": 3
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}'
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```
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