Self-hosted Retrieval Augmented Generation (RAG) Platform
Nosia is a platform that allows you to run AI models on your own data with complete privacy and control. It is designed to be easy to install and use, providing OpenAI-compatible APIs that work seamlessly with existing AI applications.
- 🔒 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
- 🔄 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
curl -fsSL https://get.nosia.ai | sh
docker compose up -d
https://nosia.localhost
nosia-first-run.mp4
- 📖 Nosia Guides - Step-by-step tutorials
- 🏗️ Architecture Documentation - Technical deep dive
- 💬 Community Support - Get help
- 📐 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
- Quickstart
- Configuration
- Using Nosia
- Managing Your Installation
- Troubleshooting
- Contributing
- License
Get Nosia up and running in minutes on macOS, Debian, or Ubuntu.
- macOS, Debian, or Ubuntu operating system
- Internet connection
- sudo/root access (for Docker installation if needed)
The installation script will:
- Install Docker and Docker Compose if not already present
- Download Nosia configuration files
- Generate a secure
.env
file - 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
Start all services with:
docker compose up
# OR run in the background
docker compose up -d
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.
By default, Nosia uses:
- Completion model:
ai/granite-4.0-h-tiny
- Embeddings model:
ai/granite-embedding-multilingual
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 IBMai/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 IBMai/mistral
- Efficient open model (7B) with top-tier performance and fast inference by Mistral AIai/magistral-small-3.2
- 24B multimodal instruction model by Mistral AIai/devstral-small
- Agentic coding LLM (24B) fine-tuned from Mistral-Small 3.1 by Mistral AIai/llama3.3
- Meta's Llama 3.3 modelai/gemma3
- Google's Gemma 3 modelai/qwen3
- Alibaba's Qwen 3 modelai/deepseek-r1-distill-llama
- DeepSeek's distilled Llama model- Browse more at Docker Hub AI
By default, Nosia uses ai/granite-embedding-multilingual
for generating document embeddings.
To change the embeddings model:
-
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
-
Restart Nosia to apply changes:
docker compose down docker compose up -d
-
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.
Docling provides enhanced document processing capabilities for complex PDFs and documents.
To enable Docling:
-
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
-
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.
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:
- Search your document knowledge base for relevant chunks
- Include the most relevant context in the AI prompt
- 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
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.
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 |
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.
The installation script automatically generates a .env
file. To customize:
-
Edit the
.env
file in your installation directory:nano .env
-
Update values as needed and restart:
docker compose down docker compose up -d
-
Copy the example environment file:
cp .env.example .env
-
Generate a secure secret key:
SECRET_KEY_BASE=$(bin/rails secret) echo "SECRET_KEY_BASE=$SECRET_KEY_BASE" >> .env
-
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
-
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.
After starting Nosia, access the web interface at https://nosia.localhost
:
- Create an account or log in
- Upload documents - PDFs, text files, or add website URLs
- Create Q&A pairs - Add domain-specific knowledge
- Start chatting - Ask questions about your documents
Nosia provides an OpenAI-compatible API that works with existing OpenAI client libraries.
- Log in to Nosia web interface
- Navigate to
https://nosia.localhost/api_tokens
- Click "Generate Token" and copy your API key
- Store it securely - it won't be shown again
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.
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 all running services:
# Stop services (keeps data)
docker compose down
# Stop and remove all data (⚠️ destructive)
docker compose down -v
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:
- Backup your data before upgrading (see Deployment Guide)
- Review release notes for breaking changes
- Pull latest images
- Restart services
- Verify functionality
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
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"
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
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:
- Check background jobs:
https://nosia.localhost/jobs
- View job logs:
docker compose logs -f solidq
- 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')"
Documents not processing:
- Check background jobs:
https://nosia.localhost/jobs
- View processing logs:
docker compose logs -f web
- 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
Issue Type | Log Location | Command |
---|---|---|
Installation | ./log/production.log |
tail -f log/production.log |
Runtime errors | Docker logs | docker compose logs -f web |
Background jobs | Jobs dashboard | Visit https://nosia.localhost/jobs |
Database | PostgreSQL logs | docker compose logs postgres-db |
AI model | LLM container logs | docker compose logs llm |
If you need further assistance:
-
Check Documentation:
- Architecture Guide - Understand how Nosia works
- Deployment Guide - Advanced configuration
-
Search Existing Issues:
- GitHub Issues
- Someone may have encountered the same problem
-
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
- Include your Nosia version:
-
Community Support:
- GitHub Discussions
- Share your use case and get advice from the community
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.
Nosia is open source software. See LICENSE for details.
- 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