Building a Customer Service Chatbot with LLM: A Step-by-Step Guide

Customer service chatbots built on large language models have moved from proof-of-concept to production infrastructure. The difference between a demo and a reliable system usually comes down to retrieval accuracy, tool integration, and context management. This guide walks through a production-ready architecture using a standard OpenAI-compatible SDK, and we will use Oxlo.ai as the inference backend because its request-based pricing and broad model catalog simplify cost forecasting for conversational workloads that often carry long context windows.

Architecture Overview

A robust customer service bot typically combines three layers: retrieval to ground responses in your documentation, function calling to perform actions like looking up order status, and a reasoning layer to synthesize answers. For the reasoning layer, you need a model that supports tool use and long context. Oxlo.ai offers several candidates: Llama 3.3 70B for general-purpose dialog, Qwen 3 32B for multilingual support, and DeepSeek R1 671B MoE when the bot must reason through complex policy logic. Because Oxlo.ai charges per request rather than per token, expanding the prompt with retrieved documentation and conversation history does not linearly increase cost. This makes it significantly cheaper for long-context and agentic workloads compared to token-based providers.

Prerequisites and SDK Setup

You only need an HTTP client or the OpenAI Python SDK. Oxlo.ai exposes a fully compatible endpoint at https://api.oxlo.ai/v1, so existing code requires only a base URL change.

import os
import openai

client = openai.OpenAI(
    base_url="https://api.oxlo.ai/v1",
    api_key=os.environ["OXLO_API_KEY"]
)

Building the Knowledge Base with Embeddings

Store your FAQs, policies, and product manuals in a vector database. First, generate embeddings. Oxlo.ai provides embedding endpoints via models such as BGE-Large and E5-Large.

def embed_chunks(chunks: list[str])