Quality-Based RAG

Score, filter, and rerank retrieved content for higher-quality answers

Retrieve with quality scoring — filter low-quality chunks and rerank before the agent answers.

from praisonaiagents import Agent, KnowledgeConfig

agent = Agent(
    name="Quality RAG",
    instructions="Answer from documents. Prefer high-quality sources.",
    knowledge=KnowledgeConfig(
        sources=["docs/"],
        rerank=True,
        retrieval_k=5,
        retrieval_threshold=0.7,
    ),
)

agent.start("What are the key product features?")

The user asks a question; retrieval scores and filters chunks before the agent answers from high-quality sources.

How It Works

Quick Start

1. Simple Usage

   Enable reranking on the agent’s knowledge base:

   from praisonaiagents import Agent, KnowledgeConfig
   
   agent = Agent(
       name="RAG Agent",
       instructions="Answer from the knowledge base.",
       knowledge=KnowledgeConfig(sources=["docs/"], rerank=True),
   )
   
   agent.start("Summarise the onboarding guide")

2. With Configuration

   Filter memory search by quality score:

   from praisonaiagents import Agent, Memory
   
   agent = Agent(
       name="Memory RAG",
       instructions="Use only high-quality past outputs.",
       memory=Memory(),
   )
   
   results = agent.memory.search_short_term(
       "previous research on AI",
       min_quality=0.7,
       rerank=True,
       limit=5,
   )

How It Works

Quality spans retrieval and memory:

score = memory.compute_quality_score(
    completeness=0.9,
    relevance=0.85,
    clarity=0.8,
    accuracy=0.9,
)

Configuration Options

KnowledgeConfig (retrieval)

Memory search

Best Practices

Related