# Ollama + Weaviate

<!-- Note: for images, use https://docs.google.com/presentation/d/15opIcJuaIjEEcs_1Zm8B6pccox2p7_MHSjCnRv4dPfU/edit?usp=sharing -->

The Ollama library allows you to easily run a wide range of models on your own device. Weaviate seamlessly integrates with the Ollama library, allowing users to leverage compatible models directly from the Weaviate Database.

These integrations empower developers to build sophisticated AI-driven applications with ease.

## Integrations with Ollama

Weaviate integrates with compatible Ollama models by accessing the locally hosted Ollama API.

### Embedding models for vector search

![Embedding integration illustration](/assets/docs/weaviate/model-providers/_includes/integration_ollama_embedding.png)

Ollama's embedding models transform text data into vector embeddings, capturing meaning and context.

[Weaviate integrates with Ollama's embedding models](ollama-embeddings.md) to enable seamless vectorization of data. This integration allows users to perform semantic and hybrid search operations without the need for additional preprocessing or data transformation steps.

[Ollama embedding integration page](ollama-embeddings.md)

### Generative AI models for RAG

![Single prompt RAG integration generates individual outputs per search result](/assets/docs/weaviate/model-providers/_includes/integration_ollama_rag_single.png)

Ollama's generative AI models can generate human-like text based on given prompts and contexts.

[Weaviate's generative AI integration](ollama-generative.md) enables users to perform retrieval augmented generation (RAG) directly from the Weaviate Database. This combines Weaviate's efficient storage and fast retrieval capabilities with Ollama's generative AI models to generate personalized and context-aware responses.

[Ollama generative AI integration page](ollama-generative.md)

## Summary

These integrations enable developers to leverage powerful Ollama models from directly within Weaviate.

In turn, they simplify the process of building AI-driven applications to speed up your development process, so that you can focus on creating innovative solutions.

## Get started

A locally hosted Weaviate instance is required for these integrations so that you can host your own Ollama models.

Go to the relevant integration page to learn how to configure Weaviate with the Ollama models and start using them in your applications.

- [Text Embeddings](ollama-embeddings.md)
- [Generative AI](ollama-generative.md)

## Questions and feedback

Have a question or feedback? Here's how to reach us.

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Ask questions and connect with other developers on our **Community forum**.
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## Related pages

- [Agents](./agents-index.md)
- [AI-assisted Weaviate code generation](./ai-assisted-vibe-coding-index.md)
- [APIs](./apis-index.md)
- [Authorization and authentication](./authorization-and-authentication-index.md)
- [Benchmarks](./benchmarks-index.md)
- [Best practices](./best-practices-index.md)
- [Client libraries](./clients-index.md)
- [Client Libraries / SDKs](./client-libraries-index.md)
- [Cloud](./cloud-index.md)
- [Cloud account management](./cloud-account-management-index.md)

# Agent Instructions

This portal answers questions programmatically. 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=<what-you-are-trying-to-do>` steers the answer toward your
  objective (e.g. `&goal=write+a+python+client`).
- `&version=<label>` scopes the answer to a mounted version when the
  portal publishes more than one.

The response is `text/markdown`: the answer followed by a `# Sources` list
of the portal pages it was grounded in. Status codes are the contract:

- `200` — the answer; `402` — the portal owner’s plan or answer credits are
  exhausted (surface this to your operator; do NOT retry); `429` — you are
  rate-limited; back off for the `Retry-After` seconds; `503` — the answer
  lane is temporarily unavailable; fall back to crawling the `.md` pages.

For the full corpus map read `llms.txt` at the site root; for the tool
surface (search + page fetch as MCP tools) see `/mcp`.