RAG for Customer Support: A Practical Walkthrough

My journey into the world of AI has been driven by a passion for building tools that solve real-world problems. Often when building hands-on tutorials on developing LLM-based solutions, customer support is often used a showcase usecase. It is a space where providing quick, accurate responses is important to cut support cost while not compromising customer satisfaction and delight.

This walkthrough shares with you my personal learning process, from setting up the knowledge base to integrating retrieval and generation, using tools like LangChain, FAISS, and OpenAI.

What Is RAG?

When I started exploring RAG, I realized its strength lies in its hybrid nature. It combines the knowledge retrieval of search engines with the language generation of models like GPT. Instead of relying only on pre-trained knowledge, which might be outdated, RAG fetches the most relevant, real-time information and uses it to generate meaningful responses. This is a game-changer for customer support, where accuracy and relevance are non-negotiable.

For example, imagine a customer asking about a return policy. A traditional AI model might not have the latest details, but RAG retrieves the most current policy from a database and crafts a helpful response.

Why Use RAG for Customer Support?

Building a RAG solution for customer support is becoming a popular trend because it addresses key challenges:

It ensures accuracy by pulling the latest information.
It’s scalable, handling large volumes of queries effortlessly.
It allows customization, making it adaptable to specific industries.
It cuts support cost while not compromising customer delight.

Tools and Libraries

These are the tools I used to implement the solution

LangChain: A framework to integrate retrieval and generation seamlessly. It simplifies connecting knowledge bases to language models. LangChain Documentation

FAISS: A library that handles similarity search efficiently. It indexes and retrieves relevant information quickly. FAISS GitHub

Hugging Face models: I chose Hugging Face for text embeddings. Hugging Face Transformers
OpenAI API: I used OpenAI API to integrate its GPT models OpenAI API Docs
ChromaDB: A vector embedding database is needed to manage embeddings and retrievals. I used Chroma Docs.

Step-by-Step Implementation

1. Setup and Installation

I started by installing the required libraries. This provided the foundation for building the solution.

pip install langchain faiss-cpu openai chromadb

2. Preparing the Knowledge Base

To simulate customer support FAQs, I created a small knowledge base. I saved it in JSON format for easy retrieval.

knowledge_base = [
    {"question": "What are your store hours?", "answer": "Our store is open from 9 AM to 9 PM daily."},
    {"question": "How can I return a product?", "answer": "You can return a product within 30 days with a receipt."},
    {"question": "What is your support email?", "answer": "You can contact us at support@example.com."},
]

import json
with open("knowledge_base.json", "w") as f:
    json.dump(knowledge_base, f)

3. Indexing the Knowledge Base

To ensure fast and relevant retrieval, I indexed the data using FAISS. This step felt similar to organizing a library, where every book is placed for quick access.

from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import FAISS

# Load knowledge base
with open("knowledge_base.json") as f:
    data = json.load(f)

# Create embeddings and index
documents = [{"content": f"Q: {item['question']} A: {item['answer']}"} for item in data]
embeddings = OpenAIEmbeddings(model="text-embedding-ada-002")
index = FAISS.from_texts([doc["content"] for doc in documents], embeddings)

4. Building the RAG Pipeline

I connected the retrieval system to OpenAI’s GPT model using LangChain. This allowed the AI to generate responses based on retrieved information.

from langchain.chains import RetrievalQA
from langchain.llms import OpenAI

llm = OpenAI(model="gpt-3.5-turbo")
rag_chain = RetrievalQA(llm=llm, retriever=index.as_retriever())

# Query example
query = "How do I return an item?"
response = rag_chain.run(query)

print("Response:", response)

5. Testing and Iteration

When I ran the solution, I tested it with several queries and fine-tuned the retrieval parameters to improve relevance. Seeing the system provide accurate answers felt rewarding, knowing it could genuinely help users.

Further Reads

If you want to follow these steps atere are some resources I used as reference: