Generative AI
v1.26.3
Databricks Generative AI with Weaviate
Weaviate's integration with Databricks' APIs allows you to access their models' capabilities directly from Weaviate.
Configure a Weaviate collection to use an Databricks' generative AI model, and Weaviate will perform retrieval augmented generation (RAG) using the specified endpoint and your Databricks token.
More specifically, Weaviate will perform a search, retrieve the most relevant objects, and then pass them to the Databricks generative model to generate outputs.
Requirements
Weaviate configuration
Your Weaviate instance must be configured with the Databricks generative AI integration (generative-databricks
) 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 Databricks Personal Access Token (PAT) to Weaviate for this integration. Refer to the Databricks documentation for instructions on generating your PAT in your workspace.
Provide the Dataricks token to Weaviate using one of the following methods:
- Set the
DATABRICKS_TOKEN
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
databricks_token = os.getenv("DATABRICKS_TOKEN")
headers = {
"X-Databricks-Token": databricks_token,
}
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 databricksToken = process.env.DATABRICKS_TOKEN || ''; // 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-Databricks-Token': databricksToken,
}
}
)
// Work with Weaviate
client.close()
Configure collection
Configure a Weaviate collection to use a Databricks generative AI endpoint as follows:
- Python API v4
- JS/TS API v3
from weaviate.classes.config import Configure
databricks_generative_endpoint = os.getenv("DATABRICKS_GENERATIVE_ENDPOINT")
client.collections.create(
"DemoCollection",
generative_config=Configure.Generative.databricks(endpoint=databricks_generative_endpoint)
# Additional parameters not shown
)
const databricksGenerativeEndpoint = process.env.DATABRICKS_VECTORIZER_ENDPOINT || ''; // If saved as an environment variable
await client.collections.create({
name: 'DemoCollection',
generative: weaviate.configure.generative.databricks({
endpoint: databricksGenerativeEndpoint, // Required for Databricks
}),
// Additional parameters not shown
});
This will configure Weaviate to use the generative AI model served through the endpoint you specify.
Generative parameters
Configure the following generative parameters to customize the model behavior.
- Python API v4
- JS/TS API v3
from weaviate.classes.config import Configure
databricks_generative_endpoint = os.getenv("DATABRICKS_GENERATIVE_ENDPOINT")
client.collections.create(
"DemoCollection",
generative_config=Configure.Generative.databricks(
endpoint=databricks_generative_endpoint
# # These parameters are optional
# max_tokens=500,
# temperature=0.7,
# top_p=0.7,
# top_k=0.1
)
# Additional parameters not shown
)
const databricksGenerativeEndpoint = process.env.DATABRICKS_VECTORIZER_ENDPOINT || ''; // If saved as an environment variable
await client.collections.create({
name: 'DemoCollection',
generative: weaviate.configure.generative.databricks({
endpoint: databricksGenerativeEndpoint, // Required for Databricks
// These parameters are optional
// maxTokens: 500,
// temperature: 0.7,
// topP: 0.7,
// topK: 0.1
}),
// Additional parameters not shown
});
For further details on model parameters, see the Databricks documentation.
Retrieval augmented generation
After configuring the generative AI integration, perform RAG operations, either with the single prompt or grouped task method.
Single prompt
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.
- Python API v4
- JS/TS API v3
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
let myCollection = client.collections.get('DemoCollection');
const singlePromptResults = await myCollection.generate.nearText(
['A holiday film'],
{
singlePrompt: `Translate this into French: {title}`,
},
{
limit: 2,
}
);
for (const obj of singlePromptResults.objects) {
console.log(obj.properties['title']);
console.log(`Generated output: ${obj.generated}`); // Note that the generated output is per object
}
Grouped task
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.
- Python API v4
- JS/TS API v3
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"])
let myCollection = client.collections.get('DemoCollection');
const groupedTaskResults = await myCollection.generate.nearText(
['A holiday film'],
{
groupedTask: `Write a fun tweet to promote readers to check out these films.`,
},
{
limit: 2,
}
);
console.log(`Generated output: ${groupedTaskResults.generated}`); // Note that the generated output is per query
for (const obj of groupedTaskResults.objects) {
console.log(obj.properties['title']);
}
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.
References
Questions and feedback
If you have any questions or feedback, let us know in the user forum.