Handle customer support queries with cache-first RAG using Redis, LangCache and OpenAI

An end-to-end Retrieval-Augmented Generation (RAG) customer support workflow for n8n, using a cache-first strategy (LangCache) combined with a Redis vector store powered by OpenAI embeddings.
This template is designed for fast, accurate, and cost-efficient customer support chatbots, internal help desks, and knowledge-base assistants.

Overview

This workflow implements a production-ready RAG architecture optimized for customer support use cases. Incoming chat messages are processed through a structured pipeline that prioritizes cached answers, falls back to semantic vector search when needed, and validates response quality before returning a final answer.

The workflow supports:

• Multi-question user inputs
• Intelligent query decomposition
• Cache reuse to reduce latency and cost
• High-precision retrieval from a Redis vector database
• Quality evaluation and controlled retries
• Final answer synthesis into a single, coherent response

Key Features

• Chat-based RAG pipeline using n8n’s Chat Trigger
• Query decomposition for multi-topic questions
• LangCache integration (search + save)
• Redis Vector Store for semantic retrieval
• OpenAI embeddings and chat models
• Quality scoring with retry logic
• Session memory buffers for contextual continuity
• Fallback-safe behavior (no hallucinations)

How the Workflow Works

1. Chat Trigger

The workflow starts when a new chat message is received.

2. Configuration Setup

A centralized configuration node defines:

• LangCache base URL
• Cache ID
• Similarity threshold (default: 0.75)
• Maximum retrieval iterations (default: 2)

3. Query Decomposition

The user message is analyzed and decomposed into:

• A single focused question, or
• Multiple independent sub-questions

This improves retrieval accuracy and cache reuse.

4. Cache-First Retrieval

Each sub-question is processed independently:

• The workflow first searches LangCache
• If a high-similarity cached answer is found, it is reused immediately

5. Vector Retrieval (Cache Miss)

If no cache hit exists:

• The query is embedded using OpenAI embeddings
• A semantic search is executed against the Redis vector index
• Retrieved knowledge-base documents are passed to a research-only agent

6. Knowledge-Only Answering

The research agent:

• Answers strictly from the retrieved knowledge
• Returns "no info found" if no relevant data exists

7. Quality Evaluation

Each generated answer is evaluated by a dedicated quality-check node:

• Outputs a numerical SCORE (0.0 – 1.0)
• Provides textual feedback
• Low scores can trigger limited retries

8. Cache Update

High-quality answers are saved back to LangCache for future reuse.

9. Aggregation & Synthesis

All sub-answers are aggregated and synthesized into:

• One final, user-facing response, or
• A polite fallback message if information is insufficient

Main Nodes & Responsibilities

• When Chat Message Received — Entry point for user messages
• LangCache Config — Centralized configuration values
• Decompose Query (LangChain Agent) — Splits complex queries
• Structured Output Parser — Ensures valid JSON output
• Search LangCache — Cache lookup via HTTP
• Redis Vector Store — Semantic retrieval from Redis
• Embeddings OpenAI — Vector generation
• Research Agent — KB-only answering (no hallucinations)
• Quality Evaluator — Scores answer relevance
• Save to LangCache — Stores validated answers
• Memory Buffers — Session context handling
• Response Synthesizer — Final message generation

Setup Instructions

1. Configure Credentials

Create the following credentials in n8n:

• OpenAI API
• Redis
• HTTP Bearer Auth (for LangCache)

2. Prepare the Knowledge Base

• Embed your documents using OpenAI embeddings
• Insert them into the configured Redis vector index
• Ensure documents are concise and well-structured

3. Configure LangCache

Update the configuration node with:

• langcacheBaseUrl
• langcacheCacheId
• Optional tuning for similarity threshold and iterations

4. Test the Workflow

• Use the example data loader or schedule trigger
• Send test chat messages
• Validate cache hits, vector retrieval, and final responses

Recommended Tuning

• Similarity Threshold: 0.7 – 0.85
• Max Iterations: 1 – 3
• Quality Score Cutoff: 0.7
• Model Choice: Use faster models for low latency, stronger models for accuracy
• Cache Policy: Cache only high-confidence answers

Security & Compliance Notes

• Store API keys securely using n8n credentials
• Avoid caching sensitive or personally identifiable information
• Apply least-privilege access to Redis and LangCache
• Consider logging cache writes for audit purposes

Common Use Cases

• Customer support chatbots
• Internal help desks
• Knowledge-base assistants
• Self-service support portals
• AI-powered FAQ systems

Template Metadata (Recommended)

• Template Name: AI Customer Support — Redis RAG (LangCache + OpenAI)
• Category: Customer Support / AI / RAG
• Tags:
  customer-support, RAG, knowledge-base, redis, openai, langcache, chatbot, n8n-template
• Difficulty Level: Intermediate
• Required Integrations: OpenAI, Redis, LangCache