Automating Customer Service using LangChain: Building custom open-source
  GPT Chatbot for organizations
Keivalya Pandya Mehfuza Holia
In the digital age, the dynamics of customer service are evolving, driven by
technological advancements and the integration of Large Language Models (LLMs).
This research paper introduces a groundbreaking approach to automating customer
service using LangChain, a custom LLM tailored for organizations. The paper
explores the obsolescence of traditional customer support techniques,
particularly Frequently Asked Questions (FAQs), and proposes a paradigm shift
towards responsive, context-aware, and personalized customer interactions. The
heart of this innovation lies in the fusion of open-source methodologies, web
scraping, fine-tuning, and the seamless integration of LangChain into customer
service platforms. This open-source state-of-the-art framework, presented as
"Sahaay," demonstrates the ability to scale across industries and
organizations, offering real-time support and query resolution. Key elements of
this research encompass data collection via web scraping, the role of
embeddings, the utilization of Google's Flan T5 XXL, Base and Small language
models for knowledge retrieval, and the integration of the chatbot into
customer service platforms. The results section provides insights into their
performance and use cases, here particularly within an educational institution.
This research heralds a new era in customer service, where technology is
harnessed to create efficient, personalized, and responsive interactions.
Sahaay, powered by LangChain, redefines the customer-company relationship,
elevating customer retention, value extraction, and brand image. As
organizations embrace LLMs, customer service becomes a dynamic and
customer-centric ecosystem.