> ## Documentation Index
> Fetch the complete documentation index at: https://docs.3ntrop1a.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Introduction to OpenRAG

> Complete production-ready RAG (Retrieval-Augmented Generation) system

# Welcome to OpenRAG 🚀

OpenRAG is a complete, modular, and production-ready RAG (Retrieval-Augmented Generation) solution. It allows you to query your documents using advanced language models with precise and relevant context.

## What is a RAG system?

A RAG system combines **information retrieval** with **text generation** to provide accurate answers based on your own documents:

1. **Retrieval**: Finds relevant passages in your document base
2. **Augmented**: Enriches the query with the found context
3. **Generation**: Generates a coherent response with an LLM

### RAG Workflow

```mermaid theme={null}
graph LR
    Q[Question] --> R[Vector Search]
    R --> D[Relevant Documents]
    D --> A[Context Augmentation]
    A --> G[LLM Generation]
    G --> Answer[Answer with Sources]
```

## Main Features

<CardGroup cols={2}>
  <Card title="Document Upload" icon="file-upload">
    PDF, DOCX, TXT, Markdown - Automatic processing
  </Card>

  <Card title="Semantic Search" icon="magnifying-glass">
    Advanced vector search with Qdrant
  </Card>

  <Card title="Answer Generation" icon="robot">
    Ollama, OpenAI, Anthropic Claude
  </Card>

  <Card title="Modular Architecture" icon="cubes">
    Decoupled microservices with Docker
  </Card>
</CardGroup>

## Main Components

OpenRAG consists of **10 Docker services**:

| Service             | Port      | Role                                          |
| ------------------- | --------- | --------------------------------------------- |
| **frontend-nextjs** | 3000      | User chat interface (Next.js + ShadcnUI)      |
| **api**             | 8000      | REST API (FastAPI)                            |
| **orchestrator**    | 8001      | RAG workflow coordination                     |
| **embedding**       | 8002      | Embeddings generation (sentence-transformers) |
| **ollama**          | 11434     | Local LLM server                              |
| **qdrant**          | 6333      | Vector database                               |
| **postgres**        | 5432      | Metadata and history                          |
| **redis**           | 6379      | Cache and queues                              |
| **minio**           | 9000/9001 | File storage (S3-compatible)                  |

## Use Cases

<AccordionGroup>
  <Accordion icon="building" title="Enterprise Knowledge Base">
    Create an AI assistant that knows all your internal documents, procedures, and company policies.
  </Accordion>

  <Accordion icon="scale-balanced" title="Legal Assistance">
    Quickly query contracts, case law, and legal documents with precise citations.
  </Accordion>

  <Accordion icon="graduation-cap" title="Customer Support">
    Automatically answer questions based on your product documentation and FAQ.
  </Accordion>

  <Accordion icon="book" title="Academic Research">
    Explore and synthesize large collections of scientific research papers.
  </Accordion>
</AccordionGroup>

## Next Steps

<CardGroup cols={2}>
  <Card title="Quick Start Guide" icon="rocket" href="/quickstart">
    Complete installation in 5 minutes
  </Card>

  <Card title="Detailed Architecture" icon="sitemap" href="/architecture">
    Understand the internal workings
  </Card>

  <Card title="System Requirements" icon="server" href="/installation/requirements">
    Minimum configuration: 16 GB RAM, optional GPU
  </Card>

  <Card title="API Reference" icon="code" href="/api-reference/introduction">
    Complete REST API documentation
  </Card>
</CardGroup>

## Technical Characteristics

<AccordionGroup>
  <Accordion icon="gauge-high" title="Performance">
    * **With GPU:** 1-3s per query
    * **Without GPU:** 5-15s per query (after warm-up)
    * **Vector search:** 100-200ms
    * **Indexing:** 10-30s per PDF document
  </Accordion>

  <Accordion icon="database" title="Scalability">
    * Horizontally scalable microservices architecture
    * Support for millions of documents
    * Redis for distributed cache
    * PostgreSQL for metadata
    * Qdrant for high-performance vector search
  </Accordion>

  <Accordion icon="shield-halved" title="Security">
    * Data stored locally (no third-party cloud)
    * Docker service isolation
    * Support for local LLMs (Ollama) for total privacy
    * Compatible with cloud LLMs (OpenAI, Claude) if desired
  </Accordion>

  <Accordion icon="puzzle-piece" title="Extensibility">
    * Easy integration of new document formats
    * Multi-LLM support (Ollama, OpenAI, Anthropic)
    * REST API for integration into your applications
    * Multiple collections for document segmentation
  </Accordion>
</AccordionGroup>

## Support and Contributions

* 🐛 **Issues:** [GitHub Issues](https://github.com/3ntrop1a/openrag/issues)
* 💻 **Source Code:** [github.com/3ntrop1a/openrag](https://github.com/3ntrop1a/openrag)
* 📖 **Documentation:** This Mintlify documentation