Ollama 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 Ollama's models allows you to access their models' capabilities directly from Weaviate.

Configure a Weaviate collection to use an Ollama generative AI model, and Weaviate will perform retrieval augmented generation (RAG) using the specified model via your local Ollama instance.

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

Requirements

Ollama

This integration requires a locally running Ollama instance with your selected model available. Refer to the Ollama documentation for installation and model download instructions.

Weaviate configuration

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

For Weaviate Cloud (WCD) users

This integration is not available for Weaviate Cloud (WCD) serverless instances, as it requires a locally running Ollama instance.

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.

Your Weaviate instance must be able to access the Ollama endpoint. If you area a Docker user, specify the Ollama endpoint using host.docker.internal alias to access the host machine from within the container.

Credentials

As this integration connects to a local Ollama container, no additional credentials (e.g. API key) are required. Connect to Weaviate as usual, such as in the examples below.

Python API v4

import weaviate

client = weaviate.connect_to_local()

# Work with Weaviate

client.close()

JS/TS API v3

import weaviate from 'weaviate-client'

const client = await weaviate.connectToLocal()

// Work with Weaviate

client.close()

Configure collection

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

Python API v4

from weaviate.classes.config import Configure

client.collections.create(
    "DemoCollection",
    generative_config=Configure.Generative.ollama(
        api_endpoint="http://host.docker.internal:11434",  # If using Docker, use this to contact your local Ollama instance
        model="llama3"  # The model to use, e.g. "phi3", or "mistral", "command-r-plus", "gemma"
    )
    # Additional parameters not shown
)

JS/TS API v3

await client.collections.create({
  name: 'DemoCollection',
  generative: weaviate.configure.generative.ollama({
    apiEndpoint: 'http://host.docker.internal:11434',  // If using Docker, use this to contact your local Ollama instance
    model: 'llama3',  // The model to use, e.g. 'phi3', or 'mistral', 'command-r-plus', 'gemma'
  }),
  // Additional parameters not shown
});

The default model 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

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

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

JS/TS API v3

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

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"])

JS/TS API v3

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']);
}

References

Available models

See the Ollama documentation for a list of available models. Note that this list includes both generative models and embedding models; specify a generative model for the generative-ollama module.

Download the desired model with ollama pull <model-name>.

Further resources

Other integrations

• Ollama embedding models + Weaviate.

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

• Ollama models
• Ollama repository
• How to change the host and port of the Ollama server

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

• Technical questions
• Documentation feedback