By the end of this guide you will have TrinaxAI running on your machine, the PWA open in your browser, your first chat sent to a local Ollama model, the CLI installed, and your first project indexed and queryable with RAG — all without touching a cloud service or entering a credit card.Documentation Index
Fetch the complete documentation index at: https://mintlify.com/TrinaxCode/TrinaxAI/llms.txt
Use this file to discover all available pages before exploring further.
Install TrinaxAI
Run the one-command installer for your OS. It downloads Ollama, pulls the right models for your hardware, sets up the RAG API and PWA, and configures auto-start.
Platform-specific guides: Linux · macOS · Windows
Before piping any script to your shell, review it first:Alternatively, clone the repo and run
./install.sh locally so you can inspect every line before execution.Install flags
Pass flags to tailor the install to your environment:| Flag | Description |
|---|---|
--non-interactive | Automatic install for CI/scripts — accepts all defaults |
--no-models | Skip downloading Ollama models (useful when re-installing) |
--no-vision | Skip the vision model download |
--no-autostart | Do not enable boot auto-start |
--no-start | Do not start TrinaxAI after the install finishes |
--profile 8gb|16gb|max|ultra | Override the auto-detected hardware profile |
--lan-system | Enable LAN system-control endpoints and auto-generate an admin token |
Open the PWA
Once the installer finishes, open your browser and navigate to:The 7-step onboarding wizard will guide you through selecting a default model, language preference, and dark/light theme. You can revisit any setting later from the Settings panel.
The PWA is served over HTTPS using a self-signed certificate generated at install time. Your browser will show a security warning on first visit — this is expected. Click Advanced → Proceed to localhost (Chrome/Edge) or Accept the Risk (Firefox) to continue. You only need to do this once per browser.From a phone or tablet on the same Wi-Fi, visit
https://[YOUR-LAN-IP]:3334 and install the app from the browser’s Add to Home Screen prompt.Send your first chat message
In the PWA chat interface, type a question and press Enter (or the send button). TrinaxAI’s auto-router picks the best local model for your query — no configuration needed.Try asking:The response streams in real time from your local Ollama instance. No request leaves your machine.
Install the CLI
The Verify the entry point is on your PATH:Available commands:
trinaxai CLI is a Python package in the same repo. Install it in editable mode from the repo root:ask, chat, index, browse, research, memory, collections, watch, export, obsidian, doctor, start, stopRun the health check
Confirm that all services are running and models are loaded:
doctor checks Ollama connectivity, the RAG API health endpoint, model availability, storage layout, and embedding readiness. It prints a colour-coded report and exits non-zero if anything needs attention.Index your first project
Point TrinaxAI at a directory of code or documents. The indexer uses AST-aware chunking for 15+ programming languages and is incremental — re-running only processes changed files.You can also index from the PWA: open Settings → Indexing and enter the path, or drag-and-drop files onto the Knowledge Browser.Watch the progress bar as chunks are embedded with
bge-m3 (or nomic-embed-text on the 8gb profile) and persisted to storage/. Large repositories finish in seconds on subsequent runs thanks to the manifest-based change detection.Ask a question with RAG
Once indexing is complete, query your codebase with the RAG engine. TrinaxAI retrieves the most relevant chunks via hybrid vector + BM25 search, reranks them, and synthesises a grounded answer with source citations.The CLI prints the answer followed by a Sources block showing the file path, collection, and relevance score for each cited chunk. You can browse and explore chunks interactively with:In the PWA, citations appear as expandable Source cards beneath each RAG response.
What to explore next
| Topic | Link |
|---|---|
| RAG indexing options, collections, and the file watcher | RAG & Indexing |
| Voice mode and vision (image analysis) | Voice & Vision |
| Persistent memory and knowledge collections | Memory & Collections |
| PWA install on iOS, Android, and desktop | PWA |
Security model, LAN hardening, and .env reference | Security Model |
| Full CLI command reference | CLI Reference |