Weaviate's integration with Ollama's models allows you to access their models' capabilities directly from Weaviate. [Configure a Weaviate collection](#configure-collection) to use a generative AI model with Ollama. 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. ![RAG integration illustration](/assets/docs/weaviate/model-providers/_includes/integration_ollama_rag.png) ## Requirements ### Ollama This integration requires a locally running Ollama instance with your selected model available. Refer to the [Ollama documentation](https://ollama.com/) for installation and model download instructions. ### Weaviate configuration Your Weaviate instance must be configured with the Ollama generative AI integration (`generative-ollama`) module. :::accordion{title="For Weaviate Cloud (WCD) users"} This integration is enabled by default on Weaviate Cloud (WCD) instances. To use Ollama with Weaviate Cloud, make sure your Ollama server is running and accessible from the Weaviate Cloud instance. If you are running Ollama on your own machine, you may need to expose it to the internet. Carefully consider the security implications of exposing your Ollama server to the internet. For use cases such as this, consider using a self-hosted Weaviate instance, or another API-based integration method. ::: :::accordion{title="For self-hosted users"} - Check the [cluster metadata](../monitoring-and-logging/status.md#cluster-metadata) to verify if the module is enabled. - Follow the [how-to configure modules](../how-to-configure-weaviate/modules.md) 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`](#configure-collection) 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. :::code-group{sync="languages"} ```python title="Python" ``` ```typescript title="JavaScript/TypeScript" ``` ::: ## Configure collection :::callout{intent="info" title="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](../how-to-manage-collections/generative-reranker-models.md#update-the-generative-model-integration) for details on how to update the collection configuration. ::: [Configure a Weaviate index](../how-to-manage-collections/generative-reranker-models.md#specify-a-generative-model-integration) as follows to use an Ollama generative model: :::code-group{sync="languages"} ```python title="Python" {5-8} 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 ) ``` ```typescript title="JavaScript/TypeScript" {3-6} await client.collections.create({ name: 'DemoCollection', generative: weaviate.configure.generative.ollama({ apiEndpoint: 'http://ollama:11434', // If using Docker you might need: http://host.docker.internal:11434 model: 'llama3', // The model to use, e.g. 'phi3', or 'mistral', 'command-r-plus', 'gemma' }), // Additional parameters not shown }); ``` ::: The Weaviate server has to be able to reach the Ollama API endpoint. If Weaviate is running in a Docker container and Ollama is running locally, use `host.docker.internal` to redirect Weaviate from `localhost` inside the container to `localhost` on the host machine. If your Weaviate instance and Ollama instance are hosted in a different way, adjust the API endpoint parameter so it points to your Ollama instance. The [default model](#available-models) is used if no model is specified. ## Select a model at runtime Aside from setting the default model provider when creating the collection, you can also override it at query time. :::code-group{sync="languages"} ```python title="Python" {9-13} from weaviate.classes.config import Configure from weaviate.classes.generate import GenerativeConfig collection = client.collections.use("DemoCollection") response = collection.generate.near_text( query="A holiday film", limit=2, grouped_task="Write a tweet promoting these two movies", generative_provider=GenerativeConfig.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 ) ``` ```typescript title="JavaScript/TypeScript" import { generativeParameters } from 'weaviate-client'; ``` ::: ## Retrieval augmented generation After configuring the generative AI integration, perform RAG operations, either with the [single prompt](#single-prompt) or [grouped task](#grouped-task) method. ### Single prompt ![Single prompt RAG integration generates individual outputs per search result](/assets/docs/weaviate/model-providers/_includes/integration_ollama_rag_single.png) 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. :::code-group{sync="languages"} ```python title="Python" {5-6} 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 ``` ```typescript title="JavaScript/TypeScript" let response; const myCollection = client.collections.use("DemoCollection"); ``` ::: ### Grouped task ![Grouped task RAG integration generates one output for the set of search results](/assets/docs/weaviate/model-providers/_includes/integration_ollama_rag_grouped.png) 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. :::code-group{sync="languages"} ```python title="Python" {5-6} 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 query for obj in response.objects: print(obj.properties["title"]) ``` ```typescript title="JavaScript/TypeScript" let response; const myCollection = client.collections.use("DemoCollection"); ``` ::: ### RAG with images You can also supply images as a part of the input when performing retrieval augmented generation in both single prompts and grouped tasks. :::code-group{sync="languages"} ```python title="Python" {9-11,18} import base64 import requests from weaviate.classes.generate import GenerativeConfig, GenerativeParameters src_img_path = "https://upload.wikimedia.org/wikipedia/commons/thumb/b/b0/Winter_forest_silver.jpg/960px-Winter_forest_silver.jpg" base64_image = base64.b64encode(requests.get(src_img_path).content).decode('utf-8') prompt = GenerativeParameters.grouped_task( prompt="Which movie is closest to the image in terms of atmosphere", images=[base64_image], # A list of base64 encoded strings of the image bytes # image_properties=["img"], # Properties containing images in Weaviate ) jeopardy = client.collections.use("DemoCollection") response = jeopardy.generate.near_text( query="Movies", limit=5, grouped_task=prompt, generative_provider=GenerativeConfig.ollama(), ) # Print the source property and the generated response for o in response.objects: print(f"Title property: {o.properties['title']}") print(f"Grouped task result: {response.generative.text}") ``` ```typescript title="JavaScript/TypeScript" ``` ::: ## References ### Available models See the [Ollama documentation](https://ollama.com/library) 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 `. If no model is specified, the default model (`llama3`) is used. ## Further resources ### Other integrations - [Ollama embedding models + Weaviate](ollama-embeddings.md). ### 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 collections](../how-to-manage-collections/index.md) and [How-to: Manage objects](../how-to-manage-objects/index.md) guides show how to perform data operations (i.e. create, read, update, delete collections and objects within them). - The [How-to: Query & Search](../how-to-query-search/index.md) guides show how to perform search operations (i.e. vector, keyword, hybrid) as well as retrieval augmented generation. ### References - [Ollama models](https://ollama.com/library) - [Ollama repository](https://github.com/ollama/ollama) - [How to change the host and port of the Ollama server](https://github.com/ollama/ollama/blob/main/docs/faq.md#how-can-i-expose-ollama-on-my-network) Have a question or feedback? 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To receive a synthesized, source-cited answer instead of crawling page by page, append the `?ask=` query parameter to any page URL on this site: /guides/quickstart?ask=how+do+I+authenticate Optional parameters: - `&goal=` steers the answer toward your objective (e.g. `&goal=write+a+python+client`). - `&version=