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Documentation Index

Fetch the complete documentation index at: https://docs.praison.ai/llms.txt

Use this file to discover all available pages before exploring further.

Late chunking embeds the entire document first, then splits. This produces chunks with better individual embeddings.

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Quick Start

from praisonaiagents import Agent

agent = Agent(
    instructions="Answer questions with high precision.",
    knowledge={
        "sources": ["technical_docs/"],
        "chunker": {
            "type": "late",
            "chunk_size": 512,
            "embedding_model": "all-MiniLM-L6-v2"
        }
    }
)

response = agent.start("Explain the architecture")

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When to Use

  • High-precision retrieval needed
  • Quality matters more than speed
  • Complex technical documents
  • Semantic similarity search critical

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Parameters

ParameterTypeDefaultDescription
chunk_sizeint512Max tokens per chunk
embedding_modelstrautoEmbedding model

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How It Works

Traditional chunking: Split → Embed each chunk Late chunking: Embed full doc → Split with context awareness This preserves document-level context in each chunk’s embedding.