# Welcome to Kallia

Welcome to the Kallia documentation! Kallia is an advanced semantic document processing library that transforms documents into intelligent, queryable knowledge bases.

## What is Kallia?

Kallia is a powerful document processing framework that:

* **Converts documents to structured markdown** with intelligent parsing using Docling
* **Generates semantic chunks** optimized for retrieval and understanding
* **Creates contextual memories** that capture conversation history and insights
* **Provides RESTful APIs** for easy integration with FastAPI
* **Supports interactive playground** with Chainlit for document Q\&A
* **Handles multiple document formats** with extensible architecture

## Quick Start

1. **Installation**: Set up Kallia using Docker or pip
2. **Configuration**: Configure your environment variables
3. **API Usage**: Start processing documents via REST API
4. **Integration**: Integrate with your applications

## Documentation Structure

* [**Overview**](https://github.com/kallia-project/kallia/blob/main/docs/overview/README.md) - Introduction and core concepts
* [**Fundamentals**](https://github.com/kallia-project/kallia/blob/main/docs/fundamentals/README.md) - Setup, API reference, and Docker deployment
* [**Use Cases**](https://github.com/kallia-project/kallia/blob/main/docs/use-cases/README.md) - Practical examples and implementations

## Key Features

### 🔄 Document Processing

Transform any document into structured, searchable content with intelligent parsing and formatting using Docling library.

### 🧠 Memory Generation

Create contextual memories from conversation histories that understand relationships and provide intelligent insights.

### 🔍 Semantic Chunking

Break documents into meaningful chunks with summaries, questions, and answers optimized for retrieval and comprehension.

### 🚀 RESTful API

FastAPI-based service with comprehensive endpoints for document processing, chunking, and memory creation.

### 🎮 Interactive Playground

Chainlit-powered chat interface for real-time document Q\&A with memory management and source citations.

### 📊 Benchmark Performance

![Benchmark Results](https://raw.githubusercontent.com/kallia-project/kallia/refs/tags/v0.1.6/benchmark/results.png)

Proven superior performance with 4.6/5.0 mean score and 81% perfect score rate in comprehensive RAG evaluations.

## Getting Started

### Prerequisites

* Python 3.11 or higher
* Docker (optional, for containerized deployment)

### Quick Installation

```bash
pip install kallia
```

### Docker Deployment

```bash
# Core API Service
cd kallia/core
docker-compose up -d

# Interactive Playground
cd kallia/playground
docker-compose up -d
```

## Core Concepts

### Document Processing Pipeline

1. **Input**: PDF documents (extensible to other formats)
2. **Conversion**: Document to structured markdown using Docling
3. **Chunking**: Semantic segmentation with AI-powered analysis
4. **Output**: Structured chunks with summaries, Q\&A pairs

### Memory System

* **Short-term Memory**: Recent conversation context
* **Long-term Memory**: Extracted insights and patterns
* **Contextual Understanding**: Relationship mapping between conversations

### API Architecture

* **RESTful Design**: Standard HTTP methods and status codes
* **Pydantic Models**: Type-safe request/response validation
* **Error Handling**: Comprehensive exception management
* **Scalable**: Designed for production deployment

## Next Steps

* Read the [Introduction](/docs/overview/introduction.md) for detailed concepts
* Check the [API Reference](/docs/fundamentals/rest-api.md) for endpoint documentation
* Explore [Use Cases](https://github.com/kallia-project/kallia/blob/main/docs/use-cases/README.md) for practical implementations
* Try the [Docker Setup](/docs/fundamentals/docker.md) for quick deployment

## Support

For issues, questions, or contributions:

* GitHub: <https://github.com/kallia-project/kallia>
* Email: <ck@kallia.net>

***

Built with ❤️ for intelligent document processing


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://kallia.gitbook.io/docs/readme.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.