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. Given a query and a set of documents, it will output similarity scores.

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.

output

LocalAI supports reranker models, and you can use them by using the rerankers backend, which uses rerankers.

Usage

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:

  name: jina-reranker-v1-base-en
backend: rerankers
parameters:
  model: cross-encoder

# optionally:
# type: flashrank
# diffusers:
#  pipeline_type: en # to specify the english language

and test it with:

  
    curl http://localhost:8080/v1/rerank \
      -H "Content-Type: application/json" \
      -d '{
      "model": "jina-reranker-v1-base-en",
      "query": "Organic skincare products for sensitive skin",
      "documents": [
        "Eco-friendly kitchenware for modern homes",
        "Biodegradable cleaning supplies for eco-conscious consumers",
        "Organic cotton baby clothes for sensitive skin",
        "Natural organic skincare range for sensitive skin",
        "Tech gadgets for smart homes: 2024 edition",
        "Sustainable gardening tools and compost solutions",
        "Sensitive skin-friendly facial cleansers and toners",
        "Organic food wraps and storage solutions",
        "All-natural pet food for dogs with allergies",
        "Yoga mats made from recycled materials"
      ],
      "top_n": 3
    }'
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Last updated 25 May 2024, 16:11 +0200 . history