Retrieval Strategies

Automatic strategy selection based on corpus size and complexity

PraisonAI picks the best retrieval strategy from your indexed corpus size — from direct loading for tiny folders to hierarchical search for massive libraries.

from praisonaiagents import Agent

agent = Agent(
    name="Researcher",
    instructions="Answer from the knowledge base.",
    knowledge={"sources": ["docs/"], "retrieval_k": 10, "rerank": True},
)
response = agent.start("What are the key features?")

The user asks a question; PraisonAI selects the best retrieval strategy for the indexed corpus size.

How It Works

Quick Start

1. Simple Usage

   Strategy selection is automatic when you attach knowledge to an agent:

   from praisonaiagents import Agent
   
   agent = Agent(
       name="Researcher",
       instructions="Answer from the knowledge base.",
       knowledge={"sources": ["docs/"], "retrieval_k": 10, "rerank": True},
   )
   response = agent.start("What are the key features?")

2. With Configuration

   Override the auto-selected strategy via KnowledgeConfig.config:

   from praisonaiagents import Agent, KnowledgeConfig
   
   agent = Agent(
       name="Researcher",
       instructions="Answer from documents.",
       knowledge=KnowledgeConfig(
           sources=["docs/"],
           rerank=True,
           config={"strategy": "hybrid", "top_k": 10, "hybrid": True},
       ),
   )

Strategy Selection

from praisonaiagents.rag import select_strategy
from praisonaiagents import CorpusStats

stats = CorpusStats(file_count=500)
strategy = select_strategy(stats)
print(strategy.value)  # hybrid

Configuration Options

CLI Usage

praisonai knowledge search "query" --strategy hybrid
praisonai knowledge search "query" --rerank --compress --max-context-tokens 4000

Best Practices

Related