Nosia

Self-hosted AI RAG + MCP Platform

Nosia is a platform that allows you to run AI models on your own data with complete privacy and control. Beyond traditional RAG capabilities, Nosia integrates the Model Context Protocol (MCP) to connect AI models with external tools, services, and data sources. It is designed to be easy to install and use, providing OpenAI-compatible APIs that work seamlessly with existing AI applications.

Features

• 🔒 Private & Secure - Your data stays on your infrastructure
• 🤖 OpenAI-Compatible API - Drop-in replacement for OpenAI clients
• 📚 RAG-Powered - Augment AI responses with your documents
• 🔌 MCP Integration - Connect AI to external tools and services via Model Context Protocol
• 🔄 Real-time Streaming - Server-sent events for live responses
• 📄 Multi-format Support - PDFs, text files, websites, and Q&A pairs
• 🎯 Semantic Search - Vector similarity search with pgvector
• 🐳 Easy Deployment - Docker Compose with one-command setup
• 🔑 Multi-tenancy - Account-based isolation for secure data separation

Preview

Install

curl -fsSL https://get.nosia.ai | sh

Start and First Run

docker compose up -d

https://nosia.localhost

Quick Links

• 📖 Nosia Guides - Step-by-step tutorials
• 🏗️ Architecture Documentation - Technical deep dive
• 💬 Community Support - Get help

Documentation

• 📐 Architecture - Detailed system design and implementation
• 📊 System Diagrams - Visual representations of system components
• 🚀 Deployment Guide - Production deployment strategies and best practices
• 📋 Documentation Index - Complete documentation overview
• 🤝 Code of Conduct - Community guidelines

Table of Contents

• Quickstart
  • One Command Installation
  • Custom Installation
  • Advanced Installation
• Configuration
• Using Nosia
  • Web Interface
  • API Access
  • MCP Integration
• Managing Your Installation
  • Start
  • Stop
  • Upgrade
  • Logs
• Troubleshooting
• Contributing
• License

Quickstart

One Command Installation

Get Nosia up and running in minutes on macOS, Debian, or Ubuntu.

Prerequisites

• macOS, Debian, or Ubuntu operating system
• Internet connection
• sudo/root access (for Docker installation if needed)

Installation

The installation script will:

1. Install Docker and Docker Compose if not already present
2. Download Nosia configuration files
3. Generate a secure .env file
4. Pull all required Docker images

curl -fsSL https://get.nosia.ai | sh

You should see the following output:

Setting up prerequisites
Setting up Nosia
Generating .env file
Pulling latest Nosia
[+] Pulling 6/6
 ✔ llm Pulled
 ✔ embedding Pulled
 ✔ web Pulled
 ✔ reverse-proxy Pulled
 ✔ postgres-db Pulled
 ✔ solidq Pulled

Starting Nosia

Start all services with:

docker compose up
# OR run in the background
docker compose up -d

Accessing Nosia

Once started, access Nosia at:

• Web Interface: https://nosia.localhost
• API Endpoint: https://nosia.localhost/v1

Note: The default installation uses a self-signed SSL certificate. Your browser will show a security warning on first access. For production deployments, see the Deployment Guide for proper SSL certificate configuration.

Custom Installation

Default Models

By default, Nosia uses:

• Completion model: ai/granite-4.0-h-tiny
• Embeddings model: ai/granite-embedding-multilingual

Using a Custom Completion Model

You can use any completion model available on Docker Hub AI by setting the LLM_MODEL environment variable during installation.

Example with Granite 4.0 32B:

curl -fsSL https://get.nosia.ai | LLM_MODEL=ai/granite-4.0-h-small sh

Model options:

• ai/granite-4.0-h-micro - 3B long-context instruct model by IBM
• ai/granite-4.0-h-tiny - 7B long-context instruct model by IBM (default)
• ai/granite-4.0-h-small - 32B long-context instruct model by IBM
• ai/mistral - Efficient open model (7B) with top-tier performance and fast inference by Mistral AI
• ai/magistral-small-3.2 - 24B multimodal instruction model by Mistral AI
• ai/devstral-small - Agentic coding LLM (24B) fine-tuned from Mistral-Small 3.1 by Mistral AI
• ai/llama3.3 - Meta's Llama 3.3 model
• ai/gemma3 - Google's Gemma 3 model
• ai/qwen3 - Alibaba's Qwen 3 model
• ai/deepseek-r1-distill-llama - DeepSeek's distilled Llama model
• Browse more at Docker Hub AI

Using a Custom Embeddings Model

By default, Nosia uses ai/granite-embedding-multilingual for generating document embeddings.

To change the embeddings model:

1. Update the environment variables in your .env file:

   EMBEDDING_MODEL=your-preferred-embedding-model
   EMBEDDING_DIMENSIONS=768  # Adjust based on your model's output dimensions
   

2. Restart Nosia to apply changes:

   docker compose down
   docker compose up -d
   

3. Update existing embeddings (if you have documents already indexed):

   docker compose run web bin/rails embeddings:change_dimensions
   

Important: Different embedding models produce vectors of different dimensions. Ensure EMBEDDING_DIMENSIONS matches your model's output size, or vector search will fail.

Advanced Installation

With Docling Document Processing

Docling provides enhanced document processing capabilities for complex PDFs and documents.

To enable Docling:

1. Start Nosia with the Docling serve compose file:

    # For NVIDIA GPUs
   docker compose -f docker-compose-docling-serve-nvidia.yml up -d
    # OR for AMD GPUs
   docker compose -f docker-compose-docling-serve-amd.yml up -d
    # OR for CPU only
   docker compose -f docker-compose-docling-serve-cpu.yml up -d
   

2. Configure the Docling URL in your .env file:

   DOCLING_SERVE_BASE_URL=http://localhost:5001
   

This starts a Docling serve instance on port 5001 that Nosia will use for advanced document parsing.

With Augmented Context (RAG)

Enable Retrieval Augmented Generation to enhance AI responses with relevant context from your documents.

To enable RAG:

Add to your .env file:

AUGMENTED_CONTEXT=true

When enabled, Nosia will:

1. Search your document knowledge base for relevant chunks
2. Include the most relevant context in the AI prompt
3. Generate responses grounded in your specific data

Additional RAG configuration:

RETRIEVAL_FETCH_K=3          # Number of document chunks to retrieve
LLM_TEMPERATURE=0.1          # Lower temperature for more factual responses

Configuration

Environment Variables

Nosia validates required environment variables at startup to prevent runtime failures. If any required variables are missing or invalid, the application will fail to start with a clear error message.

Required Variables

Optional Variables with Defaults

See .env.example for a complete list of configuration options.

Setting Up Your Environment

For Docker Compose (Recommended)

The installation script automatically generates a .env file. To customize:

1. Edit the .env file in your installation directory:

   nano .env
   

2. Update values as needed and restart:

   docker compose down
   docker compose up -d
   

For Manual/Development Setup

1. Copy the example environment file:

   cp .env.example .env
   

2. Generate a secure secret key:

   SECRET_KEY_BASE=$(bin/rails secret)
   echo "SECRET_KEY_BASE=$SECRET_KEY_BASE" >> .env
   

3. Update other required values in .env:

   AI_BASE_URL=http://your-ai-service:11434/v1
   LLM_MODEL=ai/mistral
   EMBEDDING_MODEL=ai/granite-embedding-multilingual
   EMBEDDING_DIMENSIONS=768
   

4. Test your configuration:

   bin/rails runner "puts 'Configuration valid!'"
   

If validation fails, you'll see a detailed error message indicating which variables are missing or invalid.

Using Nosia

Web Interface

After starting Nosia, access the web interface at https://nosia.localhost:

1. Create an account or log in
2. Upload documents - PDFs, text files, or add website URLs
3. Create Q&A pairs - Add domain-specific knowledge
4. Start chatting - Ask questions about your documents

API Access

Nosia provides an OpenAI-compatible API that works with existing OpenAI client libraries.

Getting an API Token

1. Log in to Nosia web interface
2. Navigate to https://nosia.localhost/api_tokens
3. Click "Generate Token" and copy your API key
4. Store it securely - it won't be shown again

Using the API

Configure your OpenAI client to use Nosia:

Python Example:

from openai import OpenAI

client = OpenAI(
    base_url="https://nosia.localhost/v1",
    api_key="your-nosia-api-token"
)

response = client.chat.completions.create(
    model="default",  # Nosia uses your configured model
    messages=[
        {"role": "user", "content": "What is in my documents about AI?"}
    ],
    stream=True
)

for chunk in response:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="")

cURL Example:

curl https://nosia.localhost/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer your-nosia-api-token" \
  -d '{
    "model": "default",
    "messages": [
      {"role": "user", "content": "Summarize my documents"}
    ]
  }'

Node.js Example:

import OpenAI from 'openai';

const client = new OpenAI({
  baseURL: 'https://nosia.localhost/v1',
  apiKey: 'your-nosia-api-token'
});

const response = await client.chat.completions.create({
  model: 'default',
  messages: [
    { role: 'user', content: 'What information do you have about my project?' }
  ]
});

console.log(response.choices[0].message.content);

For more API examples and details, see the API Guide.

MCP Integration

Nosia supports the Model Context Protocol (MCP), allowing AI models to interact with external tools, services, and data sources. MCP servers can provide tools, prompts, and resources that extend the AI's capabilities beyond text generation.

What is MCP?

The Model Context Protocol is an open protocol that standardizes how applications provide context to Large Language Models (LLMs). MCP enables AI to:

• Execute Tools - Perform actions in external systems (calendars, file storage, databases)
• Access Resources - Read from various data sources in real-time
• Use Prompts - Leverage pre-configured prompt templates
• Extend Capabilities - Add custom functionality without modifying core code

Using MCP Servers

1. Navigate to MCP Settings in the web interface

2. Browse the MCP Catalog - Pre-configured servers for popular services:

   • Productivity: Infomaniak Calendar, kDrive file storage
   • Communication: kChat messaging
   • And more - Extensible catalog of integrations

3. Activate an MCP Server:

   • Click on a server from the catalog
   • Provide required configuration (API keys, tokens)
   • Test the connection
   • Enable it for your chats

4. Add MCP to Chat:

   • Open or create a chat session
   • Select which MCP servers to use
   • The AI can now use tools from connected servers

Example: Using Calendar MCP

from openai import OpenAI

client = OpenAI(
    base_url="https://nosia.localhost/v1",
    api_key="your-nosia-api-token"
)

# The AI can now use calendar tools if enabled in the chat
response = client.chat.completions.create(
    model="default",
    messages=[
        {"role": "user", "content": "Schedule a meeting tomorrow at 2pm"}
    ]
)

print(response.choices[0].message.content)

When MCP servers are enabled, the AI can:

• Search your calendar for availability
• Create new events
• Access file storage
• Post messages to chat systems
• And execute any tools provided by connected MCP servers

Custom MCP Servers

Beyond the catalog, you can add custom MCP servers:

1. Navigate to MCP Settings → Custom Servers

2. Choose transport type:

   • stdio - Local processes (NPX, Python scripts)
   • SSE - Server-sent events over HTTP
   • HTTP - Standard HTTP endpoints

3. Configure connection:

   • Provide endpoint or command
   • Add authentication credentials
   • Test connection

4. Use in chats - Enable the custom server for your conversations

For more details on MCP integration, see the MCP Documentation.

Managing Your Installation

Start

Start all Nosia services:

# Start in foreground (see logs in real-time)
docker compose up

# Start in background (detached mode)
docker compose up -d

Check that all services are running:

docker compose ps

Stop

Stop all running services:

# Stop services (keeps data)
docker compose down

# Stop and remove all data (⚠️ destructive)
docker compose down -v

Upgrade

Keep Nosia up to date with the latest features and security fixes:

# Pull latest images
docker compose pull

# Restart services with new images
docker compose up -d

# View logs to ensure successful upgrade
docker compose logs -f web

Upgrade checklist:

1. Backup your data before upgrading (see Deployment Guide)
2. Review release notes for breaking changes
3. Pull latest images
4. Restart services
5. Verify functionality

Logs

View logs for troubleshooting:

# All services
docker compose logs -f

# Specific service
docker compose logs -f web
docker compose logs -f postgres-db
docker compose logs -f llm

# Last 100 lines
docker compose logs --tail=100 web

Health Check

Verify Nosia is running correctly:

# Check service status
docker compose ps

# Check web application health
curl -k https://nosia.localhost/up

# Check background jobs
docker compose exec web bin/rails runner "puts SolidQueue::Job.count"

Troubleshooting

Common Issues

Installation Problems

Docker not found:

# Verify Docker is installed
docker --version

# Install Docker if needed (Ubuntu/Debian)
curl -fsSL https://get.docker.com | sh

Permission denied:

# Add your user to docker group
sudo usermod -aG docker $USER

# Log out and back in, then try again

Runtime Issues

Services won't start:

# Check logs for errors
docker compose logs

# Verify .env file exists and has required variables
cat .env | grep -E 'SECRET_KEY_BASE|AI_BASE_URL|LLM_MODEL'

# Restart services
docker compose down && docker compose up -d

Slow AI responses:

1. Check background jobs: https://nosia.localhost/jobs
2. View job logs:

   docker compose logs -f solidq
   

3. Ensure your hardware meets minimum requirements (see Deployment Guide)

Can't access web interface:

# Check if services are running
docker compose ps

# Verify reverse-proxy is healthy
docker compose logs reverse-proxy

# Test connectivity
curl -k https://nosia.localhost/up

Database connection errors:

# Check PostgreSQL is running
docker compose ps postgres-db

# View database logs
docker compose logs postgres-db

# Test database connection
docker compose exec web bin/rails runner "ActiveRecord::Base.connection.execute('SELECT 1')"

Document Processing Issues

Documents not processing:

1. Check background jobs: https://nosia.localhost/jobs
2. View processing logs:

   docker compose logs -f web
   

3. Verify embedding service is running:

   docker compose ps embedding
   

Embedding errors:

# Verify EMBEDDING_DIMENSIONS matches your model
docker compose exec web bin/rails runner "puts ENV['EMBEDDING_DIMENSIONS']"

# Rebuild embeddings if dimensions changed
docker compose run web bin/rails embeddings:change_dimensions

Log Locations

Getting Help

If you need further assistance:

1. Check Documentation:

   • Architecture Guide - Understand how Nosia works
   • Deployment Guide - Advanced configuration

2. Search Existing Issues:

   • GitHub Issues
   • Someone may have encountered the same problem

3. Open a New Issue:

   • Include your Nosia version: docker compose images | grep web
   • Describe the problem with steps to reproduce
   • Include relevant logs (remove sensitive information)
   • Specify your OS and Docker version

4. Community Support:

   • GitHub Discussions
   • Share your use case and get advice from the community

Contributing

We welcome contributions! Here's how you can help:

• Report bugs - Open an issue with details and reproduction steps
• Suggest features - Share your ideas in GitHub Discussions
• Improve documentation - Submit PRs for clarity and accuracy
• Write code - Fix bugs or implement new features
• Share your experience - Write blog posts or tutorials

See CONTRIBUTING.md if available, or start by opening an issue to discuss your ideas.

License

Nosia is open source software. See LICENSE for details.

Additional Resources

• Website: nosia.ai
• Documentation: guides.nosia.ai
• Source Code: github.com/nosia-ai/nosia
• Docker Hub: hub.docker.com/u/ai

Built with ❤️ by the Nosia community