| name | retrieval-search-orchestration |
| description | Designs retrieval strategies for querying knowledge graphs in RAG systems, covering pattern selection (global-first, local-first, U-shaped hybrid), query decomposition for multi-hop reasoning, ranking and constraint configuration, and provenance tracking for citation. Use when designing retrieval pipelines, orchestrating search over knowledge graphs, or when user mentions retrieval strategy, search orchestration, query decomposition, multi-hop reasoning, provenance tracking, or citation in GraphRAG. |
Table of Contents
Retrieval & Search Orchestration
Workflow
Copy this checklist and work through each step:
Step Descriptions
Step 1: Analyze Query Types and Requirements
Classify the queries your system must handle. Common categories include:
- Factoid lookups: single-entity, single-hop (e.g., "What is the capital of France?")
- Exploratory/thematic: broad questions requiring aggregation across many entities (e.g., "What are the main themes in this corpus?")
- Multi-hop reasoning: questions requiring traversal of multiple relationships (e.g., "Which drugs treat diseases caused by gene X?")
- Temporal queries: questions bounded by time ranges or requiring sequence understanding
- Constrained queries: questions with type or attribute filters (e.g., "List all clinical trials after 2020 for drug Y")
Identify the distribution of these query types in your use case to guide pattern selection.
Step 2: Select Retrieval Pattern
Use the Retrieval Pattern Selection Guide below to choose the right approach for your query distribution. See resources/methodology.md for detailed implementation guidance on each pattern.
Step 3: Design Query Decomposition Strategy
For complex queries, break them into sub-queries that can be independently resolved and then aggregated. Approaches include LLM-as-controller decomposition, self-ask chains, and ReAct-style interleaved reasoning and retrieval. See the Query Decomposition Patterns section below and resources/methodology.md for details.
Step 4: Configure Ranking and Constraints
Define how retrieved results are scored, ranked, and filtered:
- Embedding similarity thresholds
- Graph distance penalties
- Type-based pre-filters or post-filters
- Confidence score minimums
- Temporal decay functions for time-sensitive data
Step 5: Design Provenance Tracking
Ensure every piece of retrieved information carries metadata about its origin. This includes source document IDs, extraction timestamps, confidence scores, and evidence chain construction. See resources/provenance-patterns.md for annotation approaches, confidence scoring, and LLM integration patterns.
Step 6: Define Fallback Strategies
Design what happens when primary retrieval fails or returns insufficient results:
- Iterative deepening (expand hop count)
- Query relaxation (remove constraints progressively)
- Parallel exploration (try multiple patterns simultaneously)
- Graceful degradation (return partial results with confidence indicators)
Step 7: Produce Retrieval Strategy Specification
Compile the full specification using the Output Template below.
Retrieval Pattern Selection Guide
| Pattern | Best For | Mechanism | Trade-offs | Section Ref |
|---|
| Global-First | Broad thematic queries, corpus-level summaries | Community detection, top-down traversal of summarized indexes | High-level coverage; may miss specific details | 3.1 |
| Local-First | Entity-centric lookups, neighborhood exploration | Seed entity linking, 1-2 hop neighborhood expansion with embedding gates | High precision for known entities; limited scope | 3.2 |
| U-Shaped Hybrid | Complex queries needing both breadth and depth | Coarse-to-fine bidirectional search, top-down then bottom-up refinement | Best coverage; higher latency and complexity | 3.3 |
| Query Decomposition | Multi-hop reasoning, composite questions | LLM breaks query into sub-queries, sequential retrieval, aggregation | Handles complex questions; depends on decomposition quality | 3.4 |
| Temporal | Time-bounded or sequence-dependent queries | Time-slice filtering, episodic windowing, time-decay ranking | Captures temporal dynamics; needs temporal metadata | 3.5 |
| Constraint-Guided | Type-filtered or rule-bounded queries | Pre-filter + vector search, symbolic query then neural re-rank | Reduces search space; requires well-typed schema | 3.6 |
Selection heuristic: Start with the dominant query type in your system. If queries are mixed, consider U-Shaped Hybrid as a default with fallback to specialized patterns.
Query Decomposition Patterns
LLM-as-Controller
The LLM receives the original query and generates a plan of sub-queries:
Original: "Which drugs treat diseases linked to mutations in BRCA1?"
Sub-query 1: "What diseases are linked to mutations in BRCA1?"
Sub-query 2: "What drugs treat [diseases from sub-query 1]?"
Aggregation: Combine results, deduplicate, rank by evidence strength
Self-Ask Chain
The model iteratively asks itself follow-up questions, retrieving after each:
Q: "What is the relationship between company X and technology Y?"
Follow-up 1: "What products does company X produce?" -> retrieve
Follow-up 2: "Which of those products use technology Y?" -> retrieve
Follow-up 3: "What partnerships exist between X and Y providers?" -> retrieve
Synthesize: Combine all retrieved evidence into final answer
ReAct Pattern
Interleave reasoning and retrieval actions:
Thought: I need to find the connection between entity A and entity C
Action: Search KG for paths between A and C (max 3 hops)
Observation: Found path A -> B -> C via relationship R1 and R2
Thought: I should verify this path with supporting evidence
Action: Retrieve source documents for edges A-B and B-C
Observation: Edge A-B supported by [doc1, doc2], edge B-C supported by [doc3]
Answer: A connects to C through B, supported by 3 source documents
Tool-Augmented Retrieval
Generate formal queries (Cypher, SPARQL) for structured graph traversal:
LLM generates: MATCH (d:Drug)-[:TREATS]->(dis:Disease)<-[:CAUSES]-(g:Gene {name: 'BRCA1'})
RETURN d.name, dis.name, g.name
Execute against graph database
Post-process results with LLM for natural language answer
Output Template
# Retrieval Strategy Specification
## System Context
- **Domain**: [e.g., biomedical, legal, financial]
- **Knowledge Graph Type**: [e.g., property graph, RDF, hybrid]
- **Primary Query Types**: [list dominant query categories]
- **Scale**: [approximate node/edge counts, query volume]
## Retrieval Pattern
- **Primary Pattern**: [selected pattern from guide]
- **Rationale**: [why this pattern fits the query distribution]
- **Secondary/Fallback Pattern**: [if applicable]
## Query Decomposition
- **Strategy**: [LLM-as-controller / self-ask / ReAct / tool-augmented / none]
- **Max Sub-queries**: [limit per original query]
- **Aggregation Method**: [union / intersection / ranked merge / LLM synthesis]
## Ranking & Constraints
- **Similarity Threshold**: [minimum embedding similarity score]
- **Max Hop Distance**: [maximum graph traversal depth]
- **Type Filters**: [entity/relationship type constraints]
- **Temporal Constraints**: [time windows, decay functions]
- **Confidence Minimum**: [minimum source confidence for inclusion]
## Provenance Design
- **Annotation Method**: [metadata fields / evidence nodes / named graphs / reification]
- **Confidence Scoring**: [source reliability tiers, aggregation rules]
- : [inline / post-hoc / both]
: [timestamp priority / source authority / LLM adjudication]
: [iterative deepening / query relaxation / parallel exploration]
: [number of fallback attempts]
: [partial results with confidence / explicit uncertainty / escalation]
Reference:
: [minimum acceptable weighted score]
