Cohere Multimodal Embeddings with Weaviate

Added in 1.25.25, 1.26.9 and v1.27.2

Weaviate's integration with Cohere's APIs allows you to access their models' capabilities directly from Weaviate.

Configure a Weaviate vector index to use a Cohere embedding model, and Weaviate will generate embeddings for various operations using the specified model and your Cohere API key. This feature is called the vectorizer.

At import time, Weaviate generates multimodal object embeddings and saves them into the index. For vector and hybrid search operations, Weaviate converts text queries into embeddings. Multimodal search operations are also supported.

Requirements

Weaviate configuration

Your Weaviate instance must be configured with the Cohere vectorizer integration (multi2vec-cohere) module.

For Weaviate Cloud (WCD) users

This integration is enabled by default on Weaviate Cloud (WCD) instances.

For self-hosted users

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

API credentials

You must provide a valid Cohere API key to Weaviate for this integration. Go to Cohere to sign up and obtain an API key.

Provide the API key to Weaviate using one of the following methods:

• Set the COHERE_APIKEY environment variable that is available to Weaviate.
• Provide the API key at runtime, as shown in the examples below.

PythonJavaScript/TypeScript

  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.

import weaviate
from weaviate.classes.init import Auth
import os

# Recommended: save sensitive data as environment variables
cohere_key = os.getenv("COHERE_APIKEY")
headers = {
    "X-Cohere-Api-Key": cohere_key,
}

client = weaviate.connect_to_weaviate_cloud(
    cluster_url=weaviate_url,                       # `weaviate_url`: your Weaviate URL
    auth_credentials=Auth.api_key(weaviate_key),      # `weaviate_key`: your Weaviate API key
    headers=headers
)

# Work with Weaviate

client.close()

import weaviate from 'weaviate-client'

const cohereApiKey = process.env.COHERE_APIKEY || '';  // Replace with your inference API key

const client = await weaviate.connectToWeaviateCloud(
  'WEAVIATE_INSTANCE_URL',  // Replace with your instance URL
  {
    authCredentials: new weaviate.ApiKey('WEAVIATE_INSTANCE_APIKEY'),
    headers: {
      'X-Cohere-Api-Key': cohereApiKey,
    }
  }
)

// Work with Weaviate

client.close()

Configure the vectorizer

Configure a Weaviate index as follows to use a Cohere embedding model:

PythonJavaScript/TypeScript

  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, DataType, Multi2VecField, Property

client.collections.create(
    "DemoCollection",
    properties=[
        Property(name="title", data_type=DataType.TEXT),
        Property(name="poster", data_type=DataType.BLOB),
    ],
    vector_config=[
        Configure.Vectors.multi2vec_cohere(
            name="title_vector",
            # Define the fields to be used for the vectorization - using image_fields, text_fields
            image_fields=[
                Multi2VecField(name="poster", weight=0.9)
            ],
            text_fields=[
                Multi2VecField(name="title", weight=0.1)
            ],
        )
    ],
    # Additional parameters not shown
)

await client.collections.create({
  name: "DemoCollection",
  properties: [
    {
      name: 'title',
      dataType: weaviate.configure.dataType.TEXT,
    },
    {
      name: 'poster',
      dataType: weaviate.configure.dataType.BLOB,
    },
  ],
  vectorizers: [
    weaviate.configure.vectors.multi2VecCohere({
      name: "title_vector",
      // Define the fields to be used for the vectorization - using imageFields, textFields
      imageFields: [{
        name: "poster",
        weight: 0.9
      }],
      textFields: [{
        name: "title",
        weight: 0.1
      }]
    })],
    // Additional parameters not shown
})

Select a model

You can specify one of the available models for the vectorizer to use, as shown in the following configuration example.

PythonJavaScript/TypeScript

  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, DataType, Multi2VecField, Property

client.collections.create(
    "DemoCollection",
    properties=[
        Property(name="title", data_type=DataType.TEXT),
        Property(name="poster", data_type=DataType.BLOB),
    ],
    vector_config=[
        Configure.Vectors.multi2vec_cohere(
            name="title_vector",
            model="embed-multilingual-v3.0",
            # Define the fields to be used for the vectorization - using image_fields, text_fields
            image_fields=[
                Multi2VecField(name="poster", weight=0.9)
            ],
            text_fields=[
                Multi2VecField(name="title", weight=0.1)
            ],
        )
    ],
    # Additional parameters not shown
)

await client.collections.create({
  name: "DemoCollection",
  properties: [
    {
      name: 'title',
      dataType: weaviate.configure.dataType.TEXT,
    },
    {
      name: 'poster',
      dataType: weaviate.configure.dataType.BLOB,
    },
  ],
  vectorizers: [
    weaviate.configure.vectors.multi2VecCohere({
      name: "title_vector",
      model: "embed-multilingual-v3.0",
      // Define the fields to be used for the vectorization - using imageFields, textFields
      imageFields: [{
        name: "poster",
        weight: 0.9
      }],
      textFields: [{
        name: "title",
        weight: 0.1
      }]
    })],
    // Additional parameters not shown
})

You can specify one of the available models for Weaviate to use. The default model is used if no model is specified.

Vectorization behavior

Weaviate follows the collection configuration and a set of predetermined rules to vectorize objects.

Unless specified otherwise in the collection definition, the default behavior is to:

• Only vectorize properties that use the text or text[] data type (unless skipped)
• Sort properties in alphabetical (a-z) order before concatenating values
• If vectorizePropertyName is true (false by default) prepend the property name to each property value
• Join the (prepended) property values with spaces
• Prepend the class name (unless vectorizeClassName is false)
• Convert the produced string to lowercase

Vectorizer parameters

The following examples show how to configure Cohere-specific options.

PythonJavaScript/TypeScript

  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, DataType, Multi2VecField, Property

client.collections.create(
    "DemoCollection",
    properties=[
        Property(name="title", data_type=DataType.TEXT),
        Property(name="poster", data_type=DataType.BLOB),
    ],
    vector_config=[
        Configure.Vectors.multi2vec_cohere(
            name="title_vector",
            # Define the fields to be used for the vectorization - using image_fields, text_fields
            image_fields=[
                Multi2VecField(name="poster", weight=0.9)
            ],
            text_fields=[
                Multi2VecField(name="title", weight=0.1)
            ],
            # Further options
            # model="embed-multilingual-v3.0",
            # truncate="END",  # "NONE", "START" or "END"
            # base_url="<custom_cohere_url>"
        )
    ],
    # Additional parameters not shown
)

await client.collections.create({
  name: "DemoCollection",
  properties: [
    {
      name: 'title',
      dataType: weaviate.configure.dataType.TEXT,
    },
    {
      name: 'poster',
      dataType: weaviate.configure.dataType.BLOB,
    },
  ],
  vectorizers: [
    weaviate.configure.vectors.multi2VecCohere({
      name: "title_vector",
      // Define the fields to be used for the vectorization - using imageFields, textFields
      imageFields: [{
        name: "poster",
        weight: 0.9
      }],
      textFields: [{
        name: "title",
        weight: 0.1
      }],
      // Further options
      // model: "embed-multilingual-v3.0",
      // truncate: "END",  // "NONE", "START" or "END"
      // baseURL: "<custom_cohere_url>"
    })],
    // Additional parameters not shown
})

For further details on model parameters, see the Cohere API documentation.

Data import

After configuring the vectorizer, import data into Weaviate. Weaviate generates embeddings for text objects using the specified model.

PythonJavaScript/TypeScript

  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.

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

with collection.batch.fixed_size(batch_size=200) as batch:
    for src_obj in source_objects:
        poster_b64 = url_to_base64(src_obj["poster_path"])
        weaviate_obj = {
            "title": src_obj["title"],
            "poster": poster_b64  # Add the image in base64 encoding
        }

        # The model provider integration will automatically vectorize the object
        batch.add_object(
            properties=weaviate_obj,
            # vector=vector  # Optionally provide a pre-obtained vector
        )

const collectionName = 'DemoCollection'
const myCollection = client.collections.use(collectionName)

let multiModalObjects = new Array();

for (let mmSrcObject of mmSrcObjects) {
  multiModalObjects.push({
    title: mmSrcObject.title,
    poster: mmSrcObject.poster,  // Add the image in base64 encoding
  });
}

// The model provider integration will automatically vectorize the object
const mmInsertResponse = await myCollection.data.insertMany(dataObjects);

console.log(mmInsertResponse);

Re-use existing vectors

If you already have a compatible model vector available, you can provide it directly to Weaviate. This can be useful if you have already generated embeddings using the same model and want to use them in Weaviate, such as when migrating data from another system.

Searches

Once the vectorizer is configured, Weaviate will perform vector and hybrid search operations using the specified Cohere model.

Vector (near text) search

When you perform a vector search, Weaviate converts the text query into an embedding using the specified model and returns the most similar objects from the database.

The query below returns the n most similar objects from the database, set by limit.

PythonJavaScript/TypeScript

  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.

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

response = collection.query.near_text(
    query="A holiday film",  # The model provider integration will automatically vectorize the query
    limit=2
)

for obj in response.objects:
    print(obj.properties["title"])

const collectionName = 'DemoCollection'
const myCollection = client.collections.use(collectionName)

let result;

result = await myCollection.query.nearText(
  'A holiday film',  // The model provider integration will automatically vectorize the query
  {
    limit: 2,
  }
)

console.log(JSON.stringify(result.objects, null, 2));

Hybrid search

What is a hybrid search?

A hybrid search performs a vector search and a keyword (BM25) search, before combining the results to return the best matching objects from the database.

When you perform a hybrid search, Weaviate converts the text query into an embedding using the specified model and returns the best scoring objects from the database.

The query below returns the n best scoring objects from the database, set by limit.

PythonJavaScript/TypeScript

  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.

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

response = collection.query.hybrid(
    query="A holiday film",  # The model provider integration will automatically vectorize the query
    limit=2
)

for obj in response.objects:
    print(obj.properties["title"])

const collectionName = 'DemoCollection'
const myCollection = client.collections.use(collectionName)

result = await myCollection.query.hybrid(
  'A holiday film',  // The model provider integration will automatically vectorize the query
  {
    limit: 2,
  }
)

console.log(JSON.stringify(result.objects, null, 2));

Vector (near media) search

When you perform a media search such as a near image search, Weaviate converts the query into an embedding using the specified model and returns the most similar objects from the database.

To perform a near media search such as near image search, convert the media query into a base64 string and pass it to the search query.

The query below returns the n most similar objects to the input image from the database, set by limit.

PythonJavaScript/TypeScript

  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.

def url_to_base64(url):
    import requests
    import base64

    image_response = requests.get(url)
    content = image_response.content
    return base64.b64encode(content).decode("utf-8")


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

query_b64 = url_to_base64(src_img_path)

response = collection.query.near_image(
    near_image=query_b64,
    limit=2,
    return_properties=["title", "release_date", "tmdb_id", "poster"]  # To include the poster property in the response (`blob` properties are not returned by default)
)

for obj in response.objects:
    print(obj.properties["title"])

const base64String = 'SOME_BASE_64_REPRESENTATION';

result = await myCollection.query.nearImage(
  base64String,  // The model provider integration will automatically vectorize the query
  {
    limit: 2,
  }
)

console.log(JSON.stringify(result.objects, null, 2));

References

Available models

• embed-multilingual-v3.0 (Default)
• embed-multilingual-light-v3.0
• embed-english-v3.0
• embed-english-light-v3.0

Further resources

Other integrations

• Cohere text embedding models + Weaviate.
• Cohere generative models + Weaviate.
• Cohere reranker models + Weaviate.

Code examples

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.

External resources

• Cohere Embed API documentation

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.