Semantic Chunking

Split documents based on meaning and topic coherence

Uses embeddings to split text at semantic boundaries. Groups related content together for better retrieval.

Quick Start

Agent with Semantic Chunking

from praisonaiagents import Agent

agent = Agent(
    instructions="Answer questions from research papers.",
    knowledge={
        "sources": ["papers/"],
        "chunker": {
            "type": "semantic",
            "chunk_size": 512,
            "embedding_model": "all-MiniLM-L6-v2"
        }
    }
)

response = agent.start("What methodology did they use?")

Direct API

from praisonaiagents.knowledge.chunking import Chunking

chunker = Chunking(
    chunker_type="semantic",
    chunk_size=512,
    embedding_model="all-MiniLM-L6-v2"
)

chunks = chunker.chunk("Your research paper content here...")
for chunk in chunks:
    print(chunk.text)

When to Use

Good For

Research papers
Topic-dense content
Multi-subject documents
Quality over speed

Consider Alternatives

Speed-critical pipelines
Uniform chunk sizes needed
Simple structured content
Very short documents

Parameters

Parameter	Type	Default	Description
`chunk_size`	int	512	Max tokens per chunk
`embedding_model`	str	auto	Model for semantic similarity

Examples

Research Analysis

python

agent = Agent(
    instructions="Analyze academic papers.",
    knowledge={
        "sources": ["research/"],
        "chunker": {
            "type": "semantic",
            "chunk_size": 512,
            "embedding_model": "all-MiniLM-L6-v2"
        }
    }
)

Knowledge Base

python

agent = Agent(
    instructions="Answer from knowledge base.",
    knowledge={
        "sources": ["wiki/", "faq.txt"],
        "chunker": {
            "type": "semantic",
            "chunk_size": 256  # Smaller for precise retrieval
        }
    }
)

How It Works

Diagram

Semantic chunking:

Splits document into sentences
Generates embeddings for each sentence
Groups consecutive similar sentences
Creates new chunk when topic changes

Performance Note

Embedding Models

The default embedding model is all-MiniLM-L6-v2. You can use any model supported by the chonkie library:

python

knowledge={
    "sources": ["docs/"],
    "chunker": {
        "type": "semantic",
        "embedding_model": "all-MiniLM-L6-v2"  # Default
        # Or: "text-embedding-3-small", etc.
    }
}