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

Vector Store (required): Connect your indexed document store
LLM (required): Language model for answer generation and reasoning
Embed (required): Embeddings model for document reranking
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:

Create new strategy file in strategies/
Extend BaseStrategy class
Register in strategies/index.ts
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.

Package detail

n8n-nodes-query-retriever-rerank

readme