Nosia

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
• 🤖 Agent Skills - Extend chat with custom LLM-driven and Ruby-based skills
• 🔄 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

macOS/Linux:

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

Windows:

Invoke-WebRequest https://get.nosia.ai/install.ps1 -OutFile install.ps1; .\install.ps1

Start and First Run

docker compose up -d

https://nosia.localhost

Quick Links

• 📖 Nosia Guides - Step-by-step tutorials
• 🏗️ [Architecture Documentation](docs/README.md) - Technical deep dive
• 💬 Community Support - Get help

Documentation

• [📐 Architecture](docs/ARCHITECTURE.md) - Detailed system design and implementation
• [📊 System Diagrams](docs/DIAGRAMS.md) - Visual representations of system components
• [🚀 Deployment Guide](docs/DEPLOYMENT.md) - Production deployment strategies and best practices
• [🤖 Agent Skills Development](docs/agent-skills-development.md) - Create custom skills for Nosia
• [📋 Documentation Index](docs/README.md) - Complete documentation overview
• [🤝 Code of Conduct](CODEOFCONDUCT.md) - Community guidelines

Table of Contents

• [Quickstart](#quickstart)
• [One Command Installation](#one-command-installation)
• [Custom Installation](#custom-installation)
• [Advanced Installation](#advanced-installation)
• [Configuration](#configuration)
• [Using Nosia](#using-nosia)
• [Web Interface](#web-interface)
• [API Access](#api-access)
• [MCP Integration](#mcp-integration)
• [Managing Your Installation](#managing-your-installation)
• [Start](#start)
• [Stop](#stop)
• [Upgrade](#upgrade)
• [Logs](#logs)
• [Troubleshooting](#troubleshooting)
• [Contributing](#contributing)
• [License](#license)

Quickstart

One Command Installation

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

Prerequisites

• macOS, Debian, Ubuntu, or Windows 10/11 (64-bit) operating system
• Internet connection
• sudo/root access (for Docker installation if needed on macOS/Linux)
• Docker Desktop installed and running (Windows)

Installation

The installation script will:

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

macOS/Linux:

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

Windows:

Invoke-WebRequest https://get.nosia.ai/install.ps1 -OutFile install.ps1; .\install.ps1

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](docs/DEPLOYMENT.md) for proper SSL certificate configuration.

> Windows Note: You may need to add nosia.localhost to your hosts file pointing to 127.0.0.1.

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:

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

1. Restart Nosia to apply changes:

``bash docker compose down docker compose up -d ``

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

``bash 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:

``bash # 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 ``

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

``env 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

| Variable | Description | Example | |----------|-------------|---------| | SECRET_KEY_BASE | Rails secret key for session encryption | Generate with bin/rails secret | | AI_BASE_URL | Base URL for OpenAI-compatible API | http://model-runner.docker.internal/engines/llama.cpp/v1 | | LLM_MODEL | Language model identifier | ai/mistral, ai/granite-4.0-h-tiny | | EMBEDDING_MODEL | Embedding model identifier | ai/granite-embedding-multilingual | | EMBEDDING_DIMENSIONS | Embedding vector dimensions | 768, 384, 1536 |

Optional Variables with Defaults

| Variable | Description | Default | Range/Options | |----------|-------------|---------|---------------| | AI_API_KEY | API key for the AI service | empty | Any string | | LLM_TEMPERATURE | Model creativity (lower = more factual) | 0.1 | 0.0 - 2.0 | | LLM_TOP_K | Top K sampling parameter | 40 | 1 - 100 | | LLM_TOP_P | Top P (nucleus) sampling | 0.9 | 0.0 - 1.0 | | RETRIEVAL_FETCH_K | Number of document chunks to retrieve for RAG | 3 | 1 - 10 | | AUGMENTED_CONTEXT | Enable RAG for chat completions | false | true, false | | DOCLING_SERVE_BASE_URL | Docling document processing service URL | empty | http://localhost:5001 |

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:

``bash nano .env ``

1. Update values as needed and restart:

``bash docker compose down docker compose up -d ``

For Manual/Development Setup

1. Copy the example environment file:

``bash cp .env.example .env ``

1. Generate a secure secret key:

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

1. Update other required values in .env:

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

1. Test your configuration:

``bash 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

1. 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

1. 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

1. Configure connection:

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

1. 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](docs/

…

Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: dilolabs
• Source: dilolabs/nosia
• License: MIT
• Homepage: https://hub.docker.com/r/dilolabs/nosia

Install and usage instructions live in the source repository linked above.