Query Retriever with Rerank

An advanced n8n community node for intelligent document retrieval and question answering with multiple query strategies, reranking, and comprehensive debugging.

Features

- 🧠 Multi-Strategy Architecture: Four distinct query approaches for different use cases
- 🔄 Progressive Reasoning: Multi-step query decomposition with context building
- ⚡ Intelligent Reranking: Embeddings-based relevance scoring for improved results
- 🎯 Smart Early Stopping: Automatic termination when sufficient information is gathered
- 🐛 Advanced Debugging: Memory-based debug storage with optional AI analysis
- 🏗️ Modular Design: Clean, extensible architecture (61% code reduction from refactoring)

Installation

``bash npm install n8n-nodes-query-retriever-rerank`

`Usage`

`$3`


1. Vector Store (required): Connect your indexed document store
2. LLM (required): Language model for answer generation and reasoning
3. Embed (required): Embeddings model for document reranking
4. Debug (optional): Memory node for storing debug data
$3
#### 🎯 Simple Query
Direct retrieval with intelligent reranking
- Retrieves documents using the original query
- Reranks results using embeddings similarity
- Generates answer from top-ranked documents
- Best for: Straightforward questions with clear intent
#### 🔀 Multi-Query 
Enhanced retrieval with query variations
- Generates multiple query variations using LLM
- Retrieves documents for each variation independently
- Combines and deduplicates results across all queries
- Applies final reranking against original query
- Best for: Complex questions that benefit from multiple perspectives
#### 🧠 Multi-Step Query ⭐ NEW
Progressive reasoning with context accumulation
- Breaks complex queries into sequential reasoning steps
- Each step builds on previous context and findings
- Intelligent early stopping when sufficient information is gathered
- Comprehensive synthesis from all reasoning steps
- Best for: Complex analytical questions requiring step-by-step reasoning
#### 📄 None
Document retrieval without answer generation
- Returns ranked documents without generating an answer
- Best for: Citation systems, document discovery, or custom processing
$3
#### Retrieval Options
- Documents to Retrieve: Initial retrieval count (1-100, default: 10)
- Documents to Return: Final count after reranking (1-50, default: 4)
- Return Ranked Documents: Include source documents in response
#### Multi-Query Options
- Query Variations: Number of alternative queries (2-8, default: 3)
- Include Original Query: Add original to variations (default: true)
#### Multi-Step Options ⭐ NEW
- Max Reasoning Steps: Sequential reasoning limit (1-8, default: 3)
- Enable Early Stopping: Stop when sufficient info gathered (default: true)
#### Prompt Customization
- Query Prompt Template: Custom templates for answer generation or query generation
$3
#### Memory-Based Debug Storage ⭐ NEW
Connect a memory node to store comprehensive debug data:
- System Performance: Detailed timing for each operation
- Strategy Effectiveness: Analysis of chosen approach
- Document Flow: Tracking of retrieval and reranking
- Step-by-Step Analysis: For multi-step queries, see each reasoning step
- AI-Generated Insights: Optional LLM analysis of performance data
#### Debug Configuration
- Debugging: Enable comprehensive metrics collection
- LLM Debug Analysis: Generate AI-powered performance insights (⚠️ slower)

#### What You'll Find in Debug Data`json { "strategy": "multi_step_query", "timing": { "step_1": "17626ms", "step_2": "37228ms", "finalSynthesis": "15219ms", "total": "70074ms" }, "queryDetails": { "original": "your question", "stoppedEarly": true, "stoppedAtStep": 2 }, "stepResults": [ { "step": 1, "subQuery": "generated sub-question", "documentsRetrieved": 5, "stepAnswer": "intermediate answer..." } ] }`

`Strategy Selection Guide`

| Use Case | Recommended Strategy | Why | |----------|---------------------|-----| | Simple facts | Simple Query | Direct and efficient | | Complex topics | Multi-Query | Multiple perspectives | | Analytical research | Multi-Step Query | Progressive reasoning | | Document discovery | None | Just the documents |

`Architecture`

Modular Strategy System:`QueryRetrieverRerank/ ├── strategies/ # Individual query strategies │ ├── SimpleQueryStrategy.ts │ ├── MultiQueryStrategy.ts │ ├── MultiStepQueryStrategy.ts ⭐ NEW │ └── NoneStrategy.ts ├── shared/ # Reusable utilities │ ├── debugging.ts # Debug data management │ ├── reranking.ts # Document reranking logic │ └── types.ts # Shared interfaces └── QueryRetrieverRerank.node.ts # Clean orchestration`

`Performance`

Intelligent Optimizations: - Embeddings Reranking: Improves relevance over distance-based similarity - Document Deduplication: Prevents redundant content across strategies - Early Stopping: Reduces unnecessary processing in multi-step queries - Memory Debugging: Persistent analysis without performance impact (when disabled)

`Requirements`

- n8n: Workflow automation platform - Vector Store: Pre-indexed document collection - Language Model: For answer generation and reasoning - Embeddings Model: For document reranking - Memory Node: (Optional) For debug data storage

`Development`

Adding New Strategies: 1. Create new strategy file instrategies/2. ExtendBaseStrategyclass 3. Register instrategies/index.ts`
4. Zero changes to main node required!

License

MIT

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Built with a modular architecture for maximum extensibility and maintainability. The multi-step reasoning capability brings sophisticated analytical processing to n8n workflows.