query-decomposition（查询分解）

Query Decomposition

Break complex queries into independent concepts for parallel retrieval and fusion.

Overview

• Complex queries spanning multiple topics or concepts
• Multi-hop questions requiring chained reasoning
• Queries where single retrieval misses relevant documents
• Improving recall for compound questions

Break complex queries into independent concepts for parallel retrieval and fusion.

The Problem

Complex queries span multiple topics that may not co-occur in single documents:

Query: "How do chunking strategies affect reranking in RAG?"
→ Single search may miss docs about chunking OR reranking
→ Poor coverage across all concepts

The Solution

Decompose into independent concepts, retrieve separately, then fuse:

Query: "How do chunking strategies affect reranking in RAG?"
→ Concepts: ["chunking strategies", "reranking methods", "RAG pipeline"]
→ Search each concept independently
→ Fuse results with Reciprocal Rank Fusion (RRF)
→ Full coverage across all topics

Implementation

1. Heuristic Detection (Fast Path)

MULTI_CONCEPT_INDICATORS = [
    " vs ", " versus ", " compared to ", " or ",
    " and ", " with ", " affect ", " impact ",
    "difference between", "relationship between",
]

def is_multi_concept_heuristic(query: str) -> bool:
    """Fast check for multi-concept indicators (<1ms)."""
    query_lower = query.lower()
    return any(ind in query_lower for ind in MULTI_CONCEPT_INDICATORS)

2. LLM Decomposition

from pydantic import BaseModel, Field
from openai import AsyncOpenAI

class ConceptExtraction(BaseModel):
    """LLM output schema for concept extraction."""
    concepts: list[str] = Field(
        ...,
        min_length=1,
        max_length=5,
        description="Distinct concepts from the query",
    )
    reasoning: str | None = None

async def decompose_query(
    query: str,
    llm: AsyncOpenAI,
) -> list[str]:
    """Extract independent concepts using LLM."""

    response = await llm.chat.completions.create(
        model="gpt-5.2-mini",
        messages=[
            {"role": "system", "content": """
Extract 2-4 independent concepts from this query.
Each concept should be searchable on its own.
Output JSON: {"concepts": ["concept1", "concept2"], "reasoning": "..."}
"""},
            {"role": "user", "content": query}
        ],
        response_format={"type": "json_object"},
        temperature=0,
    )

    result = ConceptExtraction.model_validate_json(
        response.choices[0].message.content
    )
    return result.concepts

3. Parallel Retrieval + RRF Fusion

import asyncio
from collections import defaultdict

async def decomposed_search(
    query: str,
    search_fn: callable,
    llm: AsyncOpenAI,
    top_k: int = 10,
) -> list[dict]:
    """Search with query decomposition and RRF fusion."""

    # Check if decomposition needed
    if not is_multi_concept_heuristic(query):
        return await search_fn(query, limit=top_k)

    # Decompose into concepts
    concepts = await decompose_query(query, llm)

    if len(concepts) <= 1:
        return await search_fn(query, limit=top_k)

    # Parallel retrieval for each concept
    tasks = [search_fn(concept, limit=top_k) for concept in concepts]
    results_per_concept = await asyncio.gather(*tasks)

    # RRF fusion
    return reciprocal_rank_fusion(results_per_concept, k=60)

def reciprocal_rank_fusion(
    result_lists: list[list[dict]],
    k: int = 60,
) -> list[dict]:
    """Combine ranked lists using RRF."""
    scores: defaultdict[str, float] = defaultdict(float)
    docs: dict[str, dict] = {}

    for results in result_lists:
        for rank, doc in enumerate(results, start=1):
            doc_id = doc["id"]
            scores[doc_id] += 1.0 / (k + rank)
            docs[doc_id] = doc

    # Sort by RRF score
    ranked_ids = sorted(scores.keys(), key=lambda x: scores[x], reverse=True)
    return [docs[doc_id] for doc_id in ranked_ids]

Complete Service

class QueryDecomposer:
    def __init__(self, llm, search_fn):
        self.llm = llm
        self.search_fn = search_fn
        self._cache: dict[str, list[str]] = {}

    async def search(
        self,
        query: str,
        top_k: int = 10,
    ) -> list[dict]:
        """Search with automatic decomposition."""

        # Fast path: single concept
        if not is_multi_concept_heuristic(query):
            return await self.search_fn(query, limit=top_k)

        # Check cache
        cache_key = query.lower().strip()
        if cache_key in self._cache:
            concepts = self._cache[cache_key]
        else:
            concepts = await decompose_query(query, self.llm)
            self._cache[cache_key] = concepts

        # Single concept after decomposition
        if len(concepts) <= 1:
            return await self.search_fn(query, limit=top_k)

        # Parallel retrieval
        tasks = [self.search_fn(c, limit=top_k) for c in concepts]
        results_per_concept = await asyncio.gather(*tasks)

        # Fuse with RRF
        return reciprocal_rank_fusion(results_per_concept)[:top_k]

Combining with HyDE

async def decomposed_hyde_search(
    query: str,
    decomposer: QueryDecomposer,
    hyde_service: HyDEService,
    vector_search: callable,
    top_k: int = 10,
) -> list[dict]:
    """Best of both: decomposition + HyDE for each concept."""

    # Decompose query
    concepts = await decomposer.get_concepts(query)

    # Generate HyDE for each concept in parallel
    hyde_results = await asyncio.gather(*[
        hyde_service.generate(concept) for concept in concepts
    ])

    # Search with HyDE embeddings
    search_tasks = [
        vector_search(embedding=hr.embedding, limit=top_k)
        for hr in hyde_results
    ]
    results_per_concept = await asyncio.gather(*search_tasks)

    # Fuse results
    return reciprocal_rank_fusion(results_per_concept)[:top_k]

When to Decompose

Performance Tips

• Use heuristics first (sub-millisecond)
• Cache decomposition results
• Limit to 2-4 concepts max
• Set timeout with fallback to original query
• Combine with HyDE for vocabulary bridging

Related Skills

• rag-retrieval - Core RAG patterns enhanced by query decomposition
• hyde-retrieval - Combine with HyDE for vocabulary bridging per concept
• reranking-patterns - Rerank fused results for final precision
• embeddings - Embedding strategies for parallel concept retrieval

Key Decisions

References

• Multi-hop Question Answering
• Query2Doc