Cohere Embeddings with Weaviate
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 text object embeddings and saves them into the index. For vector and hybrid search operations, Weaviate converts text queries into embeddings.
Requirements
Weaviate configuration
Your Weaviate instance must be configured with the Cohere vectorizer integration (text2vec-cohere
) module.
For Weaviate Cloud (WCD) users
This integration is enabled by default on Weaviate Cloud (WCD) serverless 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.
- Python API v4
- JS/TS API v3
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
Set the vectorizer to configure Weaviate to use a Cohere embedding model:
- Python API v4
- JS/TS API v3
from weaviate.classes.config import Configure
client.collections.create(
"DemoCollection",
vectorizer_config=[
Configure.NamedVectors.text2vec_cohere(
name="title_vector",
source_properties=["title"]
)
],
# Additional parameters not shown
)
await client.collections.create({
name: 'DemoCollection',
properties: [
{
name: 'title',
dataType: 'text' as const,
},
],
vectorizers: [
weaviate.configure.vectorizer.text2VecCohere({
name: 'title_vector',
sourceProperties: ['title'],
}),
],
// Additional parameters not shown
});
If you don't specify an embedding model, the vectorizer uses the default model.
For more information on configuring a vector embedding model, see Specify a vectorizer.
Select a model
You can specify one of the available models for the vectorizer to use, as shown in the following configuration example.
- Python API v4
- JS/TS API v3
from weaviate.classes.config import Configure
client.collections.create(
"DemoCollection",
vectorizer_config=[
Configure.NamedVectors.text2vec_cohere(
name="title_vector",
source_properties=["title"],
model="embed-multilingual-light-v3.0"
)
],
# Additional parameters not shown
)
await client.collections.create({
name: 'DemoCollection',
properties: [
{
name: 'title',
dataType: 'text' as const,
},
],
vectorizers: [
weaviate.configure.vectorizer.text2VecCohere({
name: 'title_vector',
sourceProperties: ['title'],
model: 'embed-multilingual-light-v3.0'
}),
],
// Additional parameters not shown
});
The default model is used if no model is specified.
Data import
After configuring the vectorizer, import data into Weaviate. Weaviate generates embeddings for text objects using the specified model.
- Python API v4
- JS/TS API v3
collection = client.collections.get("DemoCollection")
with collection.batch.dynamic() as batch:
for src_obj in source_objects:
weaviate_obj = {
"title": src_obj["title"],
"description": src_obj["description"],
}
# 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.get(collectionName)
let dataObjects = []
for (let srcObject of srcObjects) {
dataObject.push({
title: srcObject.title,
description: srcObject.description,
});
}
const response = await myCollection.data.insertMany(dataObjects);
console.log(response);
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
.
- Python API v4
- JS/TS API v3
collection = client.collections.get("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.get(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
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
.
- Python API v4
- JS/TS API v3
collection = client.collections.get("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.get(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));
References
Vectorizer parameters
The following examples show how to configure Cohere-specific options.
- Python API v4
- JS/TS API v3
from weaviate.classes.config import Configure
client.collections.create(
"DemoCollection",
vectorizer_config=[
Configure.NamedVectors.text2vec_cohere(
name="title_vector",
source_properties=["title"],
# 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: 'text' as const,
},
],
vectorizers: [
weaviate.configure.vectorizer.text2VecCohere({
name: 'title_vector',
sourceProperties: ['title'],
// // Further options
// model: 'embed-multilingual-v3.0',
// truncate: 'END',
// baseURL: '<custom_cohere_url>',
// vectorizeClassName: true,
}),
],
// Additional parameters not shown
});
For further details on model parameters, see the Cohere API documentation.
Available models
embed-multilingual-v3.0
(Default)embed-multilingual-light-v3.0
embed-multilingual-v2.0
(previouslyembed-multilingual-22-12
)embed-english-v3.0
embed-english-light-v3.0
embed-english-v2.0
embed-english-light-v2.0
Deprecated models
The following models are available, but deprecated:
multilingual-22-12
large
medium
small
Further resources
Other integrations
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 data guides show how to perform data operations (i.e. create, update, delete).
- The how-to: 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.