Weaviate's integration with OctoAI's APIs allows you to access open source and their models' capabilities directly from Weaviate.

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

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

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

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

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

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

# Recommended: save sensitive data as environment variables
octoai_key = os.getenv("OCTOAI_API_KEY")

const octoaiApiKey = process.env.OCTOAI_API_KEY || '';  // Replace with your inference API key

Configure a Weaviate index as follows to use an OctoAI generative AI model:

from weaviate.classes.config import Configure

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

await client.collections.create({  name: 'DemoCollection',  generative: weaviate.configure.generative.octoai(),  // Additional parameters not shown});

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

from weaviate.classes.config import Configure

client.collections.create(
    "DemoCollection",
    generative_config=Configure.Generative.octoai(
        model="meta-llama-3-70b-instruct"
    )
    # Additional parameters not shown
)

await client.collections.create({  name: 'DemoCollection',  generative: weaviate.configure.generative.octoai({    model: 'meta-llama-3-70b-instruct'  }),  // 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.

Configure the following generative parameters to customize the model behavior.

from weaviate.classes.config import Configure

client.collections.create(
    "DemoCollection",
    generative_config=Configure.Generative.octoai(
        # # These parameters are optional
        model = "meta-llama-3-70b-instruct",
        max_tokens = 500,
        temperature = 0.7,
        base_url = "https://text.octoai.run"
    )
)

await client.collections.create({  name: 'DemoCollection',  generative: weaviate.configure.generative.octoai({    model: 'meta-llama-3-70b-instruct',    maxTokens: 500,    temperature: 0.7,    baseURL: 'https://text.octoai.run'  }),  // Additional parameters not shown});

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

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

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.use("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 response;
const myCollection = client.collections.use("DemoCollection");

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.use("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.generative.text}")  # Note that the generated output is per queryfor obj in response.objects:    print(obj.properties["title"])

let response;
const myCollection = client.collections.use("DemoCollection");

• qwen1.5-32b-chat
• meta-llama-3-8b-instruct
• meta-llama-3-70b-instruct
• mixtral-8x22b-instruct
• nous-hermes-2-mixtral-8x7b-dpo
• mixtral-8x7b-instruct
• mixtral-8x22b-finetuned
• hermes-2-pro-mistral-7b
• mistral-7b-instruct (default)
• codellama-7b-instruct
• codellama-13b-instruct
• codellama-34b-instruct
• llama-2-13b-chat
• llama-2-70b-chat

• OctoAI embedding models + Weaviate.

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.

• OctoAI API documentation

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