QueryRewriterAgent Module

Transform user queries to improve retrieval quality in RAG applications

QueryRewriterAgent Module

The QueryRewriterAgent class transforms user queries to improve retrieval quality in RAG applications.

Supported Strategies

• BASIC: Simple rephrasing for clarity and keyword optimization
• HYDE: Hypothetical Document Embeddings - generates a hypothetical answer
• STEP_BACK: Generates higher-level concept questions for complex queries
• SUB_QUERIES: Decomposes multi-part questions into focused sub-queries
• MULTI_QUERY: Generates multiple paraphrased versions for ensemble retrieval
• CONTEXTUAL: Uses conversation history to resolve references and context
• AUTO: Automatically selects the best strategy

Import

from praisonaiagents import QueryRewriterAgent, RewriteStrategy

Quick Example

from praisonaiagents import QueryRewriterAgent, RewriteStrategy

agent = QueryRewriterAgent()

# Basic rewriting
result = agent.rewrite("AI trends")
print(result.rewritten_queries)

# HyDE for better semantic matching
result = agent.rewrite(
    "What is quantum computing?",
    strategy=RewriteStrategy.HYDE
)
print(result.hypothetical_document)

Constructor

QueryRewriterAgent()

Creates a new QueryRewriterAgent instance.

Parameters:

Parameter	Type	Default	Description
name	str	"Query Rewriter"	Agent name
model	str	"gpt-4o-mini"	LLM model to use
verbose	bool	False	Enable verbose logging
tools	list	[]	Optional tools for context-aware rewriting

Methods

rewrite(query, strategy, chat_history)

Rewrites a query using the specified strategy.

Parameters:

• query (str): The original user query
• strategy (RewriteStrategy): Rewriting strategy to use (default: AUTO)
• chat_history (list): Optional conversation history for contextual rewriting

Returns: RewriteResult - Contains rewritten queries and metadata

RewriteStrategy Enum

class RewriteStrategy(Enum):
    BASIC = "basic"
    HYDE = "hyde"
    STEP_BACK = "step_back"
    SUB_QUERIES = "sub_queries"
    MULTI_QUERY = "multi_query"
    CONTEXTUAL = "contextual"
    AUTO = "auto"

RewriteResult

@dataclass
class RewriteResult:
    original_query: str
    rewritten_queries: List[str]
    strategy_used: RewriteStrategy
    hypothetical_document: Optional[str] = None
    step_back_question: Optional[str] = None
    sub_queries: Optional[List[str]] = None
    metadata: Dict[str, Any]
    
    @property
    def primary_query(self) -> str:
        """Returns the primary rewritten query."""
        
    @property
    def all_queries(self) -> List[str]:
        """Returns all queries including original and rewritten."""

Examples

Contextual Rewriting with Chat History

from praisonaiagents import QueryRewriterAgent

agent = QueryRewriterAgent()

chat_history = [
    {"role": "user", "content": "Tell me about Python"},
    {"role": "assistant", "content": "Python is a programming language..."}
]

result = agent.rewrite(
    "What about its performance?",
    chat_history=chat_history
)
# Resolves "its" to "Python's"

With Search Tools

from praisonaiagents import QueryRewriterAgent
from praisonaiagents import internet_search

agent = QueryRewriterAgent(tools=[internet_search], )
result = agent.rewrite("latest AI developments")
# Searches first, then rewrites with context

Related

• Agent Module - Base Agent class
• PromptExpanderAgent Module - Expand prompts for tasks
• Knowledge Module - Knowledge base integration