🔧Core Library

The Kallia core library provides the fundamental building blocks for semantic document processing. This section covers the main components, their functionality, and how to use them programmatically.

Overview

The core library consists of several key modules:

• Documents: Document conversion and processing

• Chunker: Semantic chunking and content segmentation

• Memories: Conversation memory management

• Models: Data structures and validation

• Utils: Utility functions and helpers

Installation

Using pip

pip install kallia

From Source

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

Requirements

• Python 3.11 or higher

• FastAPI 0.115.14

• Docling 2.41.0

• Pydantic for data validation

Core Components

Documents Module

The Documents class handles document conversion from various formats to structured markdown.

Key Features

• PDF to markdown conversion using Docling

• Configurable page selection

• Image captioning support

• OCR capabilities

• Scalable image processing

Usage

from kallia_core.documents import Documents

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

Parameters

• source (str): Path or URL to the document

• page_number (int): Specific page to process (default: 1)

• temperature (float): AI model temperature for processing (default: 0.0)

• max_tokens (int): Maximum tokens for AI processing (default: 8192)

• include_image_captioning (bool): Enable image description generation (default: False)

Chunker Module

The Chunker class performs semantic segmentation of text content into meaningful chunks.

Key Features

• Semantic understanding of content structure

• Automatic summary generation

• Question-answer pair creation

• Context-aware segmentation

• Optimized for retrieval tasks

Usage

from kallia_core.chunker import Chunker

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

# Each chunk contains:
for chunk in chunks:
    print(f"Original: {chunk.original_text}")
    print(f"Summary: {chunk.concise_summary}")
    print(f"Question: {chunk.question}")
    print(f"Answer: {chunk.answer}")

Chunk Structure

Each chunk is a Chunk object with the following properties:

• original_text: The original text segment

• concise_summary: AI-generated summary

• question: Generated question about the content

• answer: Answer to the generated question

Memories Module

The Memories class extracts and manages conversational context and insights.

Key Features

• Short-term conversation context

• Long-term insight extraction

• Pattern recognition

• Contextual relationship mapping

• Memory persistence

Usage

from kallia_core.memories import Memories
from kallia_core.models import Message

# Create conversation messages
messages = [
    Message(role="user", content="What is this document about?"),
    Message(role="assistant", content="This document discusses semantic processing..."),
    Message(role="user", content="How does the chunking work?"),
    Message(role="assistant", content="The chunking algorithm analyzes...")
]

# Generate memories
memories = Memories.create(
    messages=messages,
    temperature=0.7,
    max_tokens=4000
)

Next Steps

• Explore the REST API for web service integration

• Learn about Docker deployment for production use

• Check out use cases for practical examples

Support

For technical issues or questions about the core library:

• GitHub Issues: https://github.com/kallia-project/kallia/issues

• Email: [e-mail]