Final Output - Trainline App Review AI - Assisant
SITUATION
In our previous blogs, we demonstrated how to use ChatGPT API to deliver app review analysis by summarising each review from app users (link) and how to use OpenAI Assistants API to develop a very simple chatbot to answers questions from an uploaded document (link). In today’s blog, we combine what we learnt in previous blogs and deliver a full function chatbot to answer all the questions about the app reviews.
It is recommended that one has a basic Python coding experience and access on OpenAI API keys. Please refer to this blog about how to setup OpenAI API.
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💡 Build a fully functions App Review ChatBot Web Application…
- Feature 1: Answer users prompt
- Feature 2: Able to recall previous conversation
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We will share the end to end solution from data extraction to chatbot deployment. The blog is split into two parts. In Part I, we will go through how to develop the backend of the chatbot using LangChain. And Part II will focus on how to build the frontend interface using Streamlit, a free and open-source framework to rapidly build data science web apps.
Before we tap into the code, let’s understand the concept of the technologies we applied to the chatbot web app.
LangChain
What is LangChain?
LangChain is a framework designed to work with large language models (LLM). Think of a language model as a very smart computer program that can read, understand, and even write text, just like we do. LangChain takes these language models and makes them even more powerful and useful for a variety of tasks.
What is LangChain - AWS
Why is LangChain Important?
- Seamless Integration: LangChain helps different language models work together smoothly. It's like having various experts who can communicate and collaborate effectively, each bringing their unique strengths to solve problems.
- Enhanced Capabilities: By linking multiple models, LangChain can perform complex tasks that a single model might struggle with. For instance, it can handle a customer service chat where one part of the model understands the query, another fetches the right information, and a third one generates a helpful response.
- Customization: LangChain can be tailored to specific needs. Whether it's for business reports, creative writing, or technical support, it adapts to deliver the best outcomes.
Real-Life Applications of LangChain
- Customer Support: Businesses use LangChain to create smart chatbots. These chatbots can handle various customer queries efficiently, providing quick and accurate responses, just like a helpful customer service agent.
- Content Creation: Writers and marketers use LangChain to generate content ideas, write drafts, or even complete articles. It’s like having an assistant who knows your style and preferences.
- Education: Educators can use LangChain to develop interactive learning tools. Imagine a study buddy who can explain complex topics in simple terms, answer your questions, and quiz you on the material.