KnowledgeConfig

Configure RAG and knowledge retrieval for agents

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

Quick Start

1. Simple Usage

   Add knowledge sources directly:

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

   Configure retrieval settings:

   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

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

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

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.

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"
        }
    )
)

Only chroma ships as a top-level Knowledge adapter. qdrant, pinecone, weaviate, and similar stores need register_knowledge_adapter(name, adapter_cls) first — see Custom Knowledge Adapters. For a copy-paste-runs example, use "provider": "chroma".

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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.

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Related

Knowledge Overview

Learn about the knowledge system

RAG

RAG configuration and features