kallia 0.1.6

Semantic Document Processing Library

Kallia

Kallia is a semantic document processing library that converts documents into intelligent semantic chunks. The library specializes in extracting meaningful content segments from documents while preserving context and semantic relationships.

🚀 Features

• Document-to-Markdown Conversion: Standardized processing pipeline for various document formats
• Semantic Chunking: Intelligent content segmentation that respects document structure and meaning
• PDF Support: Robust PDF processing with extensible architecture for additional formats
• RESTful API: FastAPI-based service with comprehensive error handling
• Interactive Playground: Chainlit-powered chat interface for document Q&A
• Memory Management: Long-term and short-term memory systems for conversational context
• Configurable Processing: Adjustable parameters (temperature, token limits, page selection)
• Docker Support: Containerized deployment for both core API and playground

📋 Requirements

• Python 3.11 or higher
• FastAPI 0.115.14
• Docling 2.41.0

🛠️ Installation

Using pip

pip install kallia

From Source

git clone https://github.com/kallia-project/kallia.git
cd kallia
pip install -e .

🏗️ Project Structure

kallia/
├── kallia/
│   ├── core/                    # Core API service
│   │   ├── kallia_core/         # Main library modules
│   │   │   ├── main.py          # FastAPI application
│   │   │   ├── documents.py     # Document processing
│   │   │   ├── chunker.py       # Semantic chunking
│   │   │   ├── memories.py      # Memory management
│   │   │   ├── models.py        # Data models
│   │   │   └── ...
│   │   ├── requirements.txt     # Core dependencies
│   │   ├── Dockerfile          # Core service container
│   │   └── docker-compose.yml  # Core service orchestration
│   └── playground/             # Interactive chat interface
│       ├── kallia_playground/  # Playground modules
│       │   ├── main.py         # Chainlit application
│       │   ├── qa.py           # Q&A functionality
│       │   └── ...
│       ├── requirements.txt    # Playground dependencies
│       ├── Dockerfile         # Playground container
│       └── docker-compose.yml # Playground orchestration
├── tests/                     # Test suite
├── assets/                    # Sample documents
└── pyproject.toml            # Project configuration

🚀 Quick Start

1. Core API Service

Start the FastAPI service:

cd kallia/core
pip install -r requirements.txt
uvicorn kallia_core.main:app --reload

The API will be available at http://localhost:8000

API Endpoints

Process Documents

POST /documents

Request body:

{
  "url": "path/to/document.pdf",
  "page_number": 1,
  "temperature": 0.7,
  "max_tokens": 4000
}

Create Memories

POST /memories

Request body:

{
  "messages": [
    { "role": "user", "content": "Hello" },
    { "role": "assistant", "content": "Hi there!" }
  ],
  "temperature": 0.7,
  "max_tokens": 4000
}

2. Interactive Playground

Start the Chainlit chat interface:

cd kallia/playground
pip install -r requirements.txt
chainlit run kallia_playground/main.py

The playground will be available at http://localhost:8000

3. Docker Deployment

Core Service

cd kallia/core
docker-compose up -d

Playground

cd kallia/playground
docker-compose up -d

💡 Usage Examples

Python API

from kallia_core.documents import Documents
from kallia_core.chunker import Chunker
from kallia_core.memories import Memories

# Convert document to markdown
markdown_content = Documents.to_markdown(
    source="document.pdf",
    page_number=1,
    temperature=0.7,
    max_tokens=4000
)

# Create semantic chunks
chunks = Chunker.create(
    text=markdown_content,
    temperature=0.7,
    max_tokens=4000
)

# Generate memories from conversation
messages = [
    {"role": "user", "content": "What is this document about?"},
    {"role": "assistant", "content": "This document discusses..."}
]
memories = Memories.create(messages)

REST API

# Process a document
curl -X POST "http://localhost:8000/documents" \
  -H "Content-Type: application/json" \
  -d '{
    "url": "https://raw.githubusercontent.com/kallia-project/kallia/refs/tags/v0.1.6/assets/pdf/01.pdf",
    "page_number": 1,
    "temperature": 0.7,
    "max_tokens": 4000
  }'

# Create memories
curl -X POST "http://localhost:8000/memories" \
  -H "Content-Type: application/json" \
  -d '{
    "messages": [
      {"role": "user", "content": "Hello"},
      {"role": "assistant", "content": "Hi there!"}
    ],
    "temperature": 0.7,
    "max_tokens": 4000
  }'

📊 Benchmark Results

Kallia has been extensively benchmarked against other popular document processing libraries using a comprehensive RAG (Retrieval-Augmented Generation) evaluation framework. The benchmark evaluates the quality of document chunking and retrieval performance across 100 test questions.

Performance Comparison

System	Mean Score	Perfect Score Rate	Ranking
Kallia	4.600	81.0%	🥇 1st
LlamaIndex	4.300	71.0%	🥈 2nd
PyMuPDF	4.060	65.0%	🥉 3rd
Unstructured	3.950	63.0%	4th

Key Advantages

• Highest Accuracy: Kallia achieves the highest mean score of 4.6/5.0
• Superior Perfect Score Rate: 81% of questions received perfect scores vs. 71% for the next best
• Semantic Chunking: Uses intelligent semantic chunking vs. fixed 500-character chunks with 0 overlap used by competitors

Benchmark Details

• Evaluation Model: Qwen3 30B A3B Instruct 2507
• Test Questions: 100 comprehensive questions across various document types
• Scoring: 1-5 scale (1=Poor, 2=Below Average, 3=Average, 4=Good, 5=Excellent)
• Chunking Method: Kallia uses semantic chunking with Qwen2.5 VL 32B Instruct
• Competitor Methods: Fixed 500-character chunks with 0 overlap

The benchmark results demonstrate Kallia's superior performance in document processing and retrieval tasks, making it the optimal choice for applications requiring high-quality document understanding and semantic chunking.

For detailed benchmark results and visualizations, see the benchmark/ directory.

🧪 Testing

Run the test suite:

python -m pytest tests/

Available tests:

• test_pdf_to_markdown.py - Document conversion tests
• test_markdown_to_chunks.py - Chunking functionality tests
• test_histories_to_memories.py - Memory creation tests

🔧 Configuration

Environment Variables

Create a .env file based on the provided .env.example template in each directory:

Core Service:

cd kallia/core
cp .env.example .env
# Edit .env with your configuration

Playground:

cd kallia/playground
cp .env.example .env
# Edit .env with your configuration

Supported File Formats

Currently supported:

• PDF documents

The architecture is designed to be extensible for additional formats.

📝 License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

🔗 Links

• Homepage: https://github.com/kallia-project/kallia
• Docker Hub: https://hub.docker.com/r/overheatsystem/kallia
• Issues: https://github.com/kallia-project/kallia/issues
• Documentation: https://kallia.gitbook.io/docs

👨‍💻 Author

CK - [e-mail]

🏷️ Keywords

• document-processing
• semantic-chunking
• document-analysis
• text-processing
• machine-learning
• fastapi
• chainlit
• pdf-processing
• nlp
• ai

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