Skip to main content

Cohere Generative AI with Weaviate

New Documentation

The model provider integration pages are new and still undergoing improvements. We appreciate any feedback on this forum thread.

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

Configure a Weaviate collection to use a Cohere generative AI model, and Weaviate will perform retrieval augmented generation (RAG) using the specified model and your Cohere API key.

More specifically, Weaviate will perform a search, retrieve the most relevant objects, and then pass them to the Cohere generative model to generate outputs.

RAG integration illustration

Requirements

Weaviate configuration

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

For Weaviate Cloud (WCD) users

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

For self-hosted users

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.
import weaviate
from weaviate.auth import AuthApiKey
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_wcs(
cluster_url=weaviate_url, # `weaviate_url`: your Weaviate URL
auth_credentials=AuthApiKey(weaviate_key), # `weaviate_key`: your Weaviate API key
headers=headers
)

# Work with Weaviate

client.close()

Configure collection

Configure a Weaviate collection to use a Cohere generative AI model as follows:

from weaviate.classes.config import Configure

client.collections.create(
"DemoCollection",
generative_config=Configure.Generative.cohere()
# Additional parameters not shown
)

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

Retrieval augmented generation

After configuring the generative AI integration, perform RAG operations, either with the single prompt or grouped task method.

Single prompt

Single prompt RAG integration generates individual outputs per search result

To generate text for each object in the search results, use the single prompt method.

The example below generates outputs for each of the n search results, where n is specified by the limit parameter.

When creating a single prompt query, use braces {} to interpolate the object properties you want Weaviate to pass on to the language model. For example, to pass on the object's title property, include {title} in the query.

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

response = collection.generate.near_text(
query="A holiday film", # The model provider integration will automatically vectorize the query
single_prompt="Translate this into French: {title}",
limit=2
)

for obj in response.objects:
print(obj.properties["title"])
print(f"Generated output: {obj.generated}") # Note that the generated output is per object

Grouped task

Grouped task RAG integration generates one output for the set of search results

To generate one text for the entire set of search results, use the grouped task method.

In other words, when you have n search results, the generative model generates one output for the entire group.

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

response = collection.generate.near_text(
query="A holiday film", # The model provider integration will automatically vectorize the query
grouped_task="Write a fun tweet to promote readers to check out these films.",
limit=2
)

print(f"Generated output: {response.generated}") # Note that the generated output is per query
for obj in response.objects:
print(obj.properties["title"])

References

Generative parameters

Configure the following generative parameters to customize the model behavior.

from weaviate.classes.config import Configure

client.collections.create(
"DemoCollection",
generative_config=Configure.Generative.cohere(
# # These parameters are optional
# model="command-r",
# temperature=0.7,
# max_tokens=500,
# k=5,
# stop_sequences=["\n\n"],
# return_likelihoods="GENERATION"
)
# Additional parameters not shown
)

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

Available models

  • command-r-plus (Added in v1.25.0)
  • command-r (default) (Added in v1.25.0)
  • command-xlarge
  • command-xlarge-beta
  • command-xlarge-nightly
  • command-medium
  • command-medium-nightly
  • command
  • command-nightly
  • command-light
  • command-light-nightly

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. Accordingly, please refer to the following examples, which are model-agnostic:

  • 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.

References

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