onyx-ai
Deploy and configure Onyx, a self-hosted AI chat platform with RAG and 25+ data connectors. Use when: setting up private ChatGPT alternative, connecting AI to company documents, building enterprise AI assistant with Confluence/Slack/Drive access.
What this skill does
# Onyx AI ## Overview Onyx (formerly Danswer) is a self-hosted AI platform that connects to your company's data sources and provides ChatGPT-like chat with retrieval-augmented generation. It supports any LLM provider and includes 25+ connectors for Confluence, Slack, Google Drive, GitHub, Notion, and more. ## Instructions ### Deployment Deploy Onyx using Docker Compose: ```bash git clone https://github.com/onyx-dot-app/onyx.git cd onyx/deployment/docker_compose docker compose up -d ``` Access the admin panel at `http://localhost:3000`. Create an admin account on first visit. ### Configure LLM Provider In Admin → LLM Configuration, add your provider: - **OpenAI**: Enter API key, select model (gpt-4o recommended) - **Anthropic**: Enter API key, select Claude model - **Azure OpenAI**: Enter endpoint URL, API key, deployment name - **Ollama**: Point to local Ollama instance for fully private setup ### Connect Data Sources In Admin → Connectors, add sources: 1. **Confluence**: Enter base URL + API token. Onyx indexes all spaces. 2. **Slack**: Install Onyx Slack app, select channels to index. 3. **Google Drive**: OAuth connection, select shared drives. 4. **GitHub**: Personal access token, select repos. 5. **Web scraping**: Enter URLs to crawl and index. Each connector runs on a configurable schedule (hourly, daily, weekly). ### Create Assistants Create specialized assistants with different system prompts and document access: - **Engineering Assistant**: Access to GitHub + Confluence tech docs - **Sales Assistant**: Access to CRM data + product docs - **HR Assistant**: Access to policies + handbook (restricted access) ## Examples **Example 1: Company knowledge search** User prompt: "What is our refund policy for enterprise customers?" The assistant searches across Confluence docs and Google Drive, finds the relevant policy document, and responds with the answer including a citation link to the source document. **Example 2: Code question** User prompt: "How does our authentication middleware work?" The assistant searches GitHub repos, finds the auth middleware files, and explains the implementation with code references. ## Guidelines - Start with 1-2 connectors, verify quality before adding more - Set document permissions to match your org structure - Use Ollama for fully air-gapped deployments (no data leaves your network) - Monitor token usage in Admin → Analytics to control costs - Schedule connector syncs during off-hours to avoid load spikes - Test RAG quality with known questions before rolling out to team
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