Smart Retrieval

Give agents sharper answers from large knowledge bases by combining keyword matching, semantic search, and reranking.

from praisonaiagents import Agent

agent = Agent(
    name="Research Agent",
    instructions="Answer from the knowledge base.",
    knowledge={"sources": ["docs/"], "retrieval_k": 10, "rerank": True},
)
response = agent.start("How does API authentication work?")

The user asks a question; hybrid retrieval and reranking surface the best knowledge snippets.

How It Works

Quick Start

Simple Usage

Enable reranking on the agent’s knowledge config:

from praisonaiagents import Agent

agent = Agent(
    name="Research Agent",
    instructions="Answer from the knowledge base.",
    knowledge={"sources": ["docs/"], "retrieval_k": 10, "rerank": True},
)
response = agent.start("How does API authentication work?")

With Configuration

Use KnowledgeConfig for chunking, vector store, and rerank model:

from praisonaiagents import Agent, KnowledgeConfig

agent = Agent(
    name="Research Agent",
    instructions="Answer from the knowledge base.",
    knowledge=KnowledgeConfig(
        sources=["docs/"],
        retrieval_k=10,
        rerank=True,
        rerank_model="cohere/rerank-english-v3.0",
    ),
)
response = agent.start("How does API authentication work?")

How It Works

Stage	What it does
Keyword search	Fast lexical matching (BM25-style)
Semantic search	Embedding similarity over chunks
Score fusion	Combines keyword and semantic scores
Reranking	Re-scores top candidates for relevance

Strategy selection scales with corpus size — see Retrieval Strategies.

Advanced: SmartRetriever

For direct control over retrieval strategy and filters, use SmartRetriever with a Knowledge instance:

from praisonaiagents import Knowledge
from praisonaiagents.rag import SmartRetriever

knowledge = Knowledge(sources=["docs/"])
retriever = SmartRetriever(knowledge=knowledge)

result = retriever.retrieve(
    query="API authentication method",
    strategy="reranked",
    top_k=5,
)

for chunk in result.chunks:
    print(chunk.get("text", "")[:100])

CLI Usage

praisonai knowledge search "API authentication" --rerank --top-k 10

Best Practices

Enable rerank for large corpora

Reranking adds latency but improves relevance once you have hundreds of chunks. Start with rerank=True when answers feel noisy.

Tune retrieval_k before reranking

Fetch more candidates (retrieval_k=10–20) so the reranker has enough to choose from, then let the agent inject the top results.

Use hybrid strategies at scale

For technical docs with exact terms, rely on automatic strategy selection — hybrid and reranked modes combine keyword and semantic signals.

Scope by user or agent

Pass user_id or agent_id to SmartRetriever.retrieve() when isolating knowledge per tenant or sub-agent.

Retrieval Strategies

Automatic strategy selection by corpus size

Retrieval Configuration

Configure retrieval behaviour on agents

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

Advanced: SmartRetriever

CLI Usage

Best Practices

Retrieval Strategies

Retrieval Configuration

​How It Works

​Quick Start

​How It Works

​Advanced: SmartRetriever

​CLI Usage

​Best Practices

​Related

Retrieval Strategies

Retrieval Configuration

How It Works

Quick Start

How It Works

Advanced: SmartRetriever

CLI Usage

Best Practices

Related