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

npm install n8n-nodes-query-retriever-rerank

Usage

Basic Setup

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

Query Strategies

🎯 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

Advanced Configuration

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

Debugging & Performance Analysis

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

{
  "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 in strategies/
2. Extend BaseStrategy class
3. Register in strategies/index.ts
4. Zero changes to main node required!

License

MIT

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