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

Description

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