KnowledgeConfig

Configure RAG and knowledge retrieval for agents

Configure knowledge sources, embedding, chunking, and retrieval settings for RAG-powered agents.

Diagram

Quick Start

Simple Usage

Add knowledge sources directly:

python

from praisonaiagents import Agent

agent = Agent(
    name="Knowledge Agent",
    instructions="Answer questions from the documentation",
    knowledge=["docs/", "data.pdf"]
)

With Configuration

Configure retrieval settings:

python

from praisonaiagents import Agent
from praisonaiagents.config import KnowledgeConfig

agent = Agent(
    name="Knowledge Agent",
    instructions="Answer questions from the documentation",
    knowledge=KnowledgeConfig(
        sources=["docs/"],
        embedder="openai",
        chunking_strategy="semantic",
        retrieval_k=5,
        rerank=True
    )
)

Configuration Options

python

from praisonaiagents.config import KnowledgeConfig, ChunkingStrategy

config = KnowledgeConfig(
    # Knowledge sources
    sources=["docs/", "data.pdf"],

    # Embedder configuration
    embedder="openai",
    embedder_config=None,

    # Chunking settings
    chunking_strategy=ChunkingStrategy.SEMANTIC,
    chunk_size=1000,
    chunk_overlap=200,
    chunker=None,  # Alternative dict config

    # Retrieval settings
    retrieval_k=5,
    retrieval_threshold=0.0,

    # Reranking
    rerank=False,
    rerank_model=None,

    # Auto-retrieval
    auto_retrieve=True,

    # Vector store
    vector_store=None,

    # Advanced config
    config=None
)

Parameter	Type	Default	Description
`sources`	`List[str]`	`[]`	Files, directories, or URLs
`embedder`	`str`	`"openai"`	Embedding provider
`embedder_config`	`Dict \	None`	`None`	Embedder-specific settings
`chunking_strategy`	`str \	ChunkingStrategy`	`"semantic"`	Chunking method (`fixed`, `semantic`, `sentence`, `paragraph`)
`chunk_size`	`int`	`1000`	Target chunk size in tokens
`chunk_overlap`	`int`	`200`	Overlap between chunks
`chunker`	`Dict \	None`	`None`	Alternative chunker config dict
`retrieval_k`	`int`	`5`	Number of chunks to retrieve
`retrieval_threshold`	`float`	`0.0`	Minimum similarity threshold
`rerank`	`bool`	`False`	Enable result reranking
`rerank_model`	`str \	None`	`None`	Model for reranking
`auto_retrieve`	`bool`	`True`	Auto-inject relevant context
`vector_store`	`Dict \	None`	`None`	Vector database settings
`config`	`Dict \	None`	`None`	Advanced configuration dict

Common Patterns

Pattern 1: High-Precision Retrieval

python

from praisonaiagents import Agent
from praisonaiagents.config import KnowledgeConfig

agent = Agent(
    name="Precise Agent",
    instructions="Provide accurate answers",
    knowledge=KnowledgeConfig(
        sources=["docs/"],
        retrieval_k=10,
        rerank=True,
        retrieval_threshold=0.7
    )
)

Pattern 2: Custom Chunking

python

from praisonaiagents import Agent
from praisonaiagents.config import KnowledgeConfig

agent = Agent(
    name="Custom Chunk Agent",
    instructions="Process large documents",
    knowledge=KnowledgeConfig(
        sources=["large_docs/"],
        chunker={
            "type": "semantic",
            "chunk_size": 512,
            "chunk_overlap": 100
        }
    )
)

Pattern 3: External Vector Store

chroma is the shipped default. Providers like qdrant are plugin adapters — register them first.

python

from praisonaiagents import Agent
from praisonaiagents.config import KnowledgeConfig
from praisonaiagents.knowledge.adapters import register_knowledge_adapter

# qdrant is a plugin adapter — register it before use
register_knowledge_adapter("qdrant", MyQdrantAdapter)

agent = Agent(
    name="Qdrant Agent",
    instructions="Use external vector database",
    knowledge=KnowledgeConfig(
        sources=["docs/"],
        vector_store={
            "provider": "qdrant",
            "url": "http://localhost:6333",
            "collection": "my_docs"
        }
    )
)

Best Practices

Use Semantic Chunking for Documents

Semantic chunking preserves context better than fixed-size chunks, especially for technical documentation.

Enable Reranking for Accuracy

Reranking improves retrieval precision by using a cross-encoder to reorder results.

Set Retrieval Threshold

Use retrieval_threshold to filter low-quality matches and prevent hallucination.

Auto-Retrieve by Default

Keep auto_retrieve=True to automatically inject relevant context into prompts.

Knowledge Overview

Learn about the knowledge system

RAG

RAG configuration and features

KnowledgeConfig

Quick Start

Configuration Options

Common Patterns

Pattern 1: High-Precision Retrieval

Pattern 2: Custom Chunking

Pattern 3: External Vector Store

Best Practices

Related