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Documentation Index

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Smart Retrieval

Smart retrieval combines multiple search techniques for optimal relevance: keyword prefiltering, semantic search, and reranking.

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Overview

The SmartRetriever provides:
  • Hybrid search combining keyword (BM25) and semantic search
  • Keyword prefiltering for efficient candidate selection
  • Semantic reranking for improved relevance
  • Score normalization across different search methods

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Quick Start

from praisonaiagents.rag import SmartRetriever, RetrievalResult

retriever = SmartRetriever()

# Basic search
results = retriever.search(
    query="What is the API authentication method?",
    top_k=5,
)

for result in results:
    print(f"Score: {result.score:.3f} - {result.text[:100]}...")

from praisonaiagents.rag import SmartRetriever

retriever = SmartRetriever(
    use_hybrid=True,
    keyword_weight=0.3,
    semantic_weight=0.7,
)

results = retriever.search(
    query="authentication API key",
    top_k=10,
)

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How Hybrid Search Works

  1. Keyword Search (BM25) - Fast lexical matching
  2. Semantic Search - Embedding-based similarity
  3. Score Fusion - Weighted combination of scores
  4. Deduplication - Remove duplicate results

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Reranking

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Using the Reranker

from praisonaiagents.rag import SmartRetriever, RetrievalResult

retriever = SmartRetriever()

# Initial search with more candidates
initial_results = retriever.search(query, top_k=20)

# Rerank to get best results
reranked = retriever.rerank(
    query=query,
    results=initial_results,
    top_k=5,
)

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Custom Reranker

from praisonaiagents.rag import RerankerProtocol, RetrievalResult
from typing import List

class CustomReranker(RerankerProtocol):
    def rerank(
        self,
        query: str,
        results: List[RetrievalResult],
        top_k: int = 5,
    ) -> List[RetrievalResult]:
        # Custom reranking logic
        scored = []
        for result in results:
            # Calculate custom relevance score
            score = self._calculate_relevance(query, result.text)
            scored.append((score, result))
        
        # Sort by score and return top_k
        scored.sort(key=lambda x: x[0], reverse=True)
        return [r for _, r in scored[:top_k]]
    
    def _calculate_relevance(self, query: str, text: str) -> float:
        # Implement custom scoring
        overlap = len(set(query.lower().split()) & set(text.lower().split()))
        return overlap / max(len(query.split()), 1)

​
Retrieval Results

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RetrievalResult Structure

from dataclasses import dataclass
from typing import Dict, Any

@dataclass
class RetrievalResult:
    text: str
    score: float
    metadata: Dict[str, Any] = None
    source: str = None
    chunk_id: str = None

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Working with Results

results = retriever.search(query, top_k=5)

for result in results:
    print(f"Score: {result.score:.3f}")
    print(f"Text: {result.text[:200]}...")
    print(f"Source: {result.metadata.get('source', 'unknown')}")
    print("---")

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CLI Usage

# Basic search
praisonai knowledge search "API authentication"

# Search with reranking
praisonai knowledge search "API authentication" --rerank

# Search with specific top_k
praisonai knowledge search "API authentication" --top-k 10

# Verbose output
praisonai knowledge search "API authentication" --rerank --verbose

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Integration with Agents

from praisonaiagents import Agent

agent = Agent(
    name="SmartSearchAgent",
    instructions="Answer questions using the knowledge base.",
    knowledge={
        "sources": ["./docs"],
        "retrieval_k": 10,
        "rerank": True,
        "retrieval_threshold": 0.5,
    }
)

response = agent.chat("How do I authenticate with the API?")

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Best Practices

  1. Use hybrid search for diverse corpora with technical terms
  2. Enable reranking for improved relevance at slight latency cost
  3. Adjust weights based on your content type
  4. Set min_score to filter low-quality results

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API Reference

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SmartRetriever

class SmartRetriever:
    def __init__(
        self,
        use_hybrid: bool = False,
        keyword_weight: float = 0.3,
        semantic_weight: float = 0.7,
        use_reranking: bool = False,
    ):
        """Initialize smart retriever."""
    
    def search(
        self,
        query: str,
        top_k: int = 5,
        min_score: float = 0.0,
    ) -> List[RetrievalResult]:
        """Search for relevant documents."""
    
    def rerank(
        self,
        query: str,
        results: List[RetrievalResult],
        top_k: int = 5,
    ) -> List[RetrievalResult]:
        """Rerank results for improved relevance."""

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RetrieverProtocol

class RetrieverProtocol(Protocol):
    def search(
        self,
        query: str,
        top_k: int = 5,
    ) -> List[RetrievalResult]:
        """Search for relevant documents."""

​
RerankerProtocol

class RerankerProtocol(Protocol):
    def rerank(
        self,
        query: str,
        results: List[RetrievalResult],
        top_k: int = 5,
    ) -> List[RetrievalResult]:
        """Rerank results."""