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Generative AI

Added in 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.

RAG integration illustration

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

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.
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()
py docs  API docs

Configure collection​

Generative model integration mutability

A collection's generative model integration configuration is mutable from v1.25.23, v1.26.8 and v1.27.1. See this section for details on how to update the collection configuration.

Configure a Weaviate collection to use a Databricks generative AI endpoint as follows:

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
)
py docs  API docs

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.

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
)
py docs  API docs

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​

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
py docs  API docs

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"])
py docs  API docs

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.