Generative and reranker models

Embedding models / Vectorizers

Visit the Vectorizer and vector config guide to find out how to configure an embedding model for your collection or to fine-tune the vector index settings.

Specify a reranker model integration

Configure a reranker model integration for reranking retrieved results.

Related pages

• Available reranker model integrations

PythonJavaScript/TypeScriptGoJava v6 (Beta)C# (Beta)

  API docs

More infoCode snippets in the documentation reflect the latest client library and Weaviate Database version. Check the Release notes for specific versions.

If a snippet doesn't work or you have feedback, please open a GitHub issue.

from weaviate.classes.config import Configure, Property, DataType

client.collections.create(
    "Article",
    vector_config=Configure.Vectors.text2vec_openai(),
    reranker_config=Configure.Reranker.cohere(),
)

import { vectors, reranker } from 'weaviate-client';

await client.collections.create({
  name: 'Article',
  vectorizers: vectors.text2VecOpenAI(),
  reranker: reranker.cohere(),
})

  articleClass := &models.Class{
    Class:       "Article",
    Description: "Collection of articles",
    Vectorizer:  "text2vec-openai",
    ModuleConfig: map[string]interface{}{
      "reranker-cohere": map[string]interface{}{
        "model": "rerank-v3.5",
      },
    },
  }

client.collections.create("Article",
    col -> col.vectorConfig(VectorConfig.text2vecTransformers())
        .rerankerModules(Reranker.cohere())
        .properties(Property.text("title")));

await client.Collections.Create(new CollectionConfig
{
    Name = "Article",
    VectorConfig = new VectorConfigList
    {
        new VectorConfig("default", new Vectorizer.Text2VecTransformers())
    },
    RerankerConfig = new Reranker.Cohere(),
    Properties =
    [
        Property.Text("title")
    ]
});

Update the reranker model integration

Available from v1.25.23, v1.26.8 and v1.27.1

The reranker and generative configurations are mutable from v1.25.23, v1.26.8 and v1.27.1.

Update the reranker model integration for reranking retrieved results.

PythonJavaScript/TypeScriptGoJava v6 (Beta)C# (Beta)

  API docs

More infoCode snippets in the documentation reflect the latest client library and Weaviate Database version. Check the Release notes for specific versions.

If a snippet doesn't work or you have feedback, please open a GitHub issue.

from weaviate.classes.config import Reconfigure

collection = client.collections.use("Article")

collection.config.update(
    reranker_config=Reconfigure.Reranker.cohere()  # Update the reranker module
)

import { reconfigure } from 'weaviate-client';

  const collection = client.collections.use('Article')

  await collection.config.update({
    reranker: reconfigure.reranker.voyageAI()  // Update the reranker module
  })

  updatedArticleClassConfig := &models.Class{
    // Note: The new collection config must be provided in full,
    // including the configuration that is not being updated.
    // We suggest using the original class config as a starting point.
    Class: "Article",
    ModuleConfig: map[string]interface{}{
      "reranker-cohere": map[string]interface{}{
        "model": "rerank-v3.5",
      },
    },
  }

var collection = client.collections.use("Article");
collection.config.update(col -> col.rerankerModules(Reranker.cohere()));

var collection = client.Collections.Use<object>("Article");
await collection.Config.Update(c =>
{
    c.RerankerConfig = new Reranker.Cohere();
});

Specify a generative model integration

Specify a generative model integration for a collection (for RAG).

Related pages

• Available generative model integrations

PythonJavaScript/TypeScriptGoJavaJava v6 (Beta)C# (Beta)

  API docs

More infoCode snippets in the documentation reflect the latest client library and Weaviate Database version. Check the Release notes for specific versions.

If a snippet doesn't work or you have feedback, please open a GitHub issue.

from weaviate.classes.config import Configure, Property, DataType

client.collections.create(
    "Article",
    vector_config=Configure.Vectors.text2vec_openai(),
    generative_config=Configure.Generative.openai(
        model="gpt-4o"  # set your generative model (optional parameter)
    ),
)

import { vectors, generative } from 'weaviate-client';

await client.collections.create({
  name: 'Article',
  vectorizers: vectors.text2VecOpenAI(),
  generative: generative.openAI({
    model: "gpt-4o"  // set your generative model (optional parameter)
  }),
})

  articleClass := &models.Class{
    Class:       "Article",
    Description: "Collection of articles",
    Vectorizer:  "text2vec-openai",
    ModuleConfig: map[string]interface{}{
      "generative-openai": map[string]interface{}{
        "model": "gpt-4o",
      },
    },
  }

// Configure OpenAI text2vec module settings
Map<String, Object> text2vecOpenAI = new HashMap<>();
Map<String, Object> text2vecOpenAISettings = new HashMap<>();
text2vecOpenAISettings.put("model", "text-embedding-3-small"); // or your preferred embedding model
text2vecOpenAI.put("text2vec-openai", text2vecOpenAISettings);

// Configure OpenAI generative module settings
Map<String, Object> generativeOpenAI = new HashMap<>();
Map<String, Object> generativeOpenAISettings = new HashMap<>();
generativeOpenAISettings.put("model", "gpt-4");
generativeOpenAI.put("generative-openai", generativeOpenAISettings);

// Combine module configurations
Map<String, Object> moduleConfig = new HashMap<>();
moduleConfig.put("text2vec-openai", text2vecOpenAI);
moduleConfig.put("generative-openai", generativeOpenAI);

// Create the Article collection with vectorizer and generative configuration
WeaviateClass articleCollection = WeaviateClass.builder()
    .className(collectionName)
    .vectorizer("text2vec-openai") // Set the vectorizer
    .moduleConfig(moduleConfig) // Set both vectorizer and generative configs
    .build();

// Add the collection to the schema
Result<Boolean> result = client.schema().classCreator()
    .withClass(articleCollection)
    .run();

client.collections.create("Article",
    col -> col.vectorConfig(VectorConfig.text2vecTransformers())
        .generativeModule(Generative.cohere())
        .properties(Property.text("title")));

await client.Collections.Create(new CollectionConfig
{
    Name = "Article",
    VectorConfig = new VectorConfigList
    {
        new VectorConfig("default", new Vectorizer.Text2VecTransformers())
    },
    GenerativeConfig = new GenerativeConfig.Cohere(),
    Properties =
    [
        Property.Text("title")
    ]
});

Update the generative model integration

Available from v1.25.23, v1.26.8 and v1.27.1

The reranker and generative configurations are mutable from v1.25.23, v1.26.8 and v1.27.1.

Update a generative model integration.

PythonJavaScript/TypeScriptGoJava v6 (Beta)C# (Beta)

  API docs

More infoCode snippets in the documentation reflect the latest client library and Weaviate Database version. Check the Release notes for specific versions.

If a snippet doesn't work or you have feedback, please open a GitHub issue.

from weaviate.classes.config import Reconfigure

collection = client.collections.use("Article")

collection.config.update(
    generative_config=Reconfigure.Generative.cohere()  # Update the generative module
)

import { reconfigure } from 'weaviate-client';

const collection = client.collections.use("Article")

await collection.config.update({
  generative: weaviate.reconfigure.generative.openAI()  // Update the generative module
})

  updatedArticleClassConfig := &models.Class{
    Class: "Article",
    ModuleConfig: map[string]interface{}{
      "generative-cohere": map[string]interface{}{},
    },
  }

var collection = client.collections.use("Article");
collection.config.update(col -> col.generativeModule(Generative.cohere()));

var collection = client.Collections.Use<object>("Article");
await collection.Config.Update(c =>
{
    c.GenerativeConfig = new GenerativeConfig.Cohere();
});

tip

You can override the generative integration settings at query time without updating it in the collection configuration.

Further resources

• API References: REST: Schema

• References: Configuration: Schema
• Concepts: Data structure

Questions and feedback

If you have any questions or feedback, let us know in the user forum.

Technical questions

If you have questions feel free to post on our Community forum.

Documentation feedback

Leave feedback by opening a GitHub issue.