Reranker

Weaviate's integration with the Hugging Face Transformers library allows you to access their models' capabilities directly from Weaviate.

Configure a Weaviate collection to use Transformers integration, and configure the Weaviate instance with a model image, and Weaviate will use the specified model in the Transformers inference container to rerank search results.

This two-step process involves Weaviate first performing a search and then reranking the results using the specified model.

Your Weaviate instance must be configured with the Transformers reranker integration (reranker-transformers) module.

• Check the cluster metadata to verify if the module is enabled.
• Follow the how-to configure modules guide to enable the module in Weaviate.

To use this integration, configure the container image of the Hugging Face Transformers model and the inference endpoint of the containerized model.

The following example shows how to configure the Hugging Face Transformers integration in Weaviate:

services:
  weaviate:
    # Other Weaviate configuration
    environment:
      RERANKER_INFERENCE_API: http://reranker-transformers:8080  # Set the inference API endpoint
  reranker-transformers:  # Set the name of the inference container
    image: cr.weaviate.io/semitechnologies/reranker-transformers:cross-encoder-ms-marco-MiniLM-L-6-v2
    environment:
      ENABLE_CUDA: 0  # Set to 1 to enable

modules:

  reranker-transformers:

    enabled: true
    tag: cross-encoder-ms-marco-MiniLM-L-6-v2
    repo: semitechnologies/reranker-transformers
    registry: cr.weaviate.io
    envconfig:
      enable_cuda: true

As this integration runs a local container with the transformers model, no additional credentials (e.g. API key) are required. Connect to Weaviate as usual, such as in the examples below.

Configure a Weaviate collection to use a Transformer reranker model as follows:

from weaviate.classes.config import Configureclient.collections.create(    "DemoCollection",    reranker_config=Configure.Reranker.transformers()    # Additional parameters not shown)

await client.collections.create({  name: 'DemoCollection',  reranker: weaviate.configure.reranker.transformers(),});

Once the reranker is configured, Weaviate performs reranking operations using the specified reranker model.

More specifically, Weaviate performs an initial search, then reranks the results using the specified model.

Any search in Weaviate can be combined with a reranker to perform reranking operations.

from weaviate.classes.query import Rerankcollection = client.collections.use("DemoCollection")response = collection.query.near_text(    query="A holiday film",  # The model provider integration will automatically vectorize the query    limit=2,    rerank=Rerank(        prop="title",                   # The property to rerank on        query="A melodic holiday film"  # If not provided, the original query will be used    ))for obj in response.objects:    print(obj.properties["title"])

let myCollection = client.collections.use('DemoCollection');const results = await myCollection.query.nearText(  ['A holiday film'],  {    limit: 2,    rerank: {      property: 'title',                // The property to rerank on      query: 'A melodic holiday film'   // If not provided, the original query will be used    }  });for (const obj of results.objects) {  console.log(obj.properties['title']);}

• cross-encoder/ms-marco-MiniLM-L-6-v2
• cross-encoder/ms-marco-MiniLM-L-2-v2
• cross-encoder/ms-marco-TinyBERT-L-2-v2

These pre-trained models are open-sourced on Hugging Face. The cross-encoder/ms-marco-MiniLM-L-6-v2 model, for example, provides approximately the same benchmark performance as the largest model (L-12) when evaluated on MS-MARCO (39.01 vs. 39.02).

We add new model support over time. For the latest list of available models, see the Docker Hub tags for the reranker-transformers container.

• Transformers embedding models + Weaviate.
• Transformers multi-modal embedding models + Weaviate.

Once the integrations are configured at the collection, the data management and search operations in Weaviate work identically to any other collection. See the following model-agnostic examples:

• The How-to: Manage collections and How-to: Manage objects guides show how to perform data operations (i.e. create, read, update, delete collections and objects within them).
• The How-to: Query & Search guides show how to perform search operations (i.e. vector, keyword, hybrid) as well as retrieval augmented generation.

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