Quality-Based RAG

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

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")

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:

Layer	Mechanism
Knowledge	`KnowledgeConfig(rerank=True)` reranks chunks before injection
Memory search	`min_quality` filters by stored quality metadata
Memory store	`compute_quality_score()` combines completeness, relevance, clarity, accuracy
Task output	`quality_check=True` on tasks stores high-scoring outputs (threshold 0.7)

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

Configuration Options

KnowledgeConfig (retrieval)

Option	Type	Default	Description
`rerank`	`bool`	`False`	Rerank retrieved chunks
`rerank_model`	`str`	`None`	Model for reranking
`retrieval_k`	`int`	`5`	Chunks to retrieve
`retrieval_threshold`	`float`	`0.0`	Minimum similarity score

Memory search

Parameter	Type	Default	Description
`min_quality`	`float`	`0.0`	Minimum quality metadata
`rerank`	`bool`	`False`	Rerank search results
`relevance_cutoff`	`float`	`0.0`	Minimum relevance score

Best Practices

Enable rerank for accuracy-critical tasks

Reranking adds an LLM call — disable for high-throughput pipelines.

Set retrieval_threshold for noisy corpora

Raise retrieval_threshold to 0.6–0.7 when documents overlap heavily.

Combine with task quality_check

Use Task(quality_check=True) so only high-quality outputs enter long-term memory.

Weight metrics for your domain

Pass custom weights to compute_quality_score() — weight accuracy higher for factual Q&A.

Quality Checking

Automatic task output assessment

Knowledge

Knowledge base setup and retrieval

Getting Started

Install

Core Concepts

Guides

Features

Models

Databases

Observability

Memory

Telemetry & Analytics

Knowledge

RAG

Persistence

Tools

Other Features

Developers

Configuration

Best Practices

Getting Started (No Code)

How It Works

Quick Start

How It Works

Configuration Options

KnowledgeConfig (retrieval)

Memory search

Best Practices

Quality Checking

Knowledge

​How It Works

​Quick Start

​How It Works

​Configuration Options

​KnowledgeConfig (retrieval)

​Memory search

​Best Practices

​Related

Quality Checking

Knowledge

How It Works

Quick Start

How It Works

Configuration Options

KnowledgeConfig (retrieval)

Memory search

Best Practices

Related