# FriendliAI + Weaviate

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FriendliAI offers a wide range of models for natural language processing and generation. Weaviate seamlessly integrates with FriendliAI APIs, allowing users to leverage FriendliAI's inference engine within the Weaviate Database.

FriendliAI integration empowers developers to build sophisticated AI-driven applications with ease.

## Integrations with FriendliAI

### Generative AI models for RAG

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

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

[Weaviate's generative AI integration](friendliai-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 FriendliAI's generative AI models to generate personalized and context-aware responses.

Visit [FriendliAI generative AI integration page](friendliai-generative.md) for more information on our integrations with FriendliAI.

## Summary

This integration enables developers to harness the power of FriendliAI's inference engine within Weaviate.

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

## Get started

You must provide a valid Friendli token (aka Personal Access Token) to Weaviate for these integrations. Go to [Friendli Suite](https://suite.friendli.ai/) to sign up and obtain a personal access token.

Then, go to the relevant integrations page to learn how to configure Weaviate with the FriendliAI models and start using them in your applications.

- [Generative AI](friendliai-generative.md)

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`.