Chained Insights: the chatbot that leverages large language models and the Langchain library to explore knowledge across multiple sources. Reason through complex problems and uncover new insights with ease.
Welcome to the Chained Insights tutorial, where we'll explore how to build a chatbot that can leverage large language models, multiple data sources, and advanced chaining techniques to reason through complex problems and uncover new insights.
In this tutorial, we'll be using the LangChain library to help us explore topics such as query decomposition, memory as part of the chatbot, and chaining of agents. We'll start by introducing the basics of chatbot development and how we can use large language models, such as ChatGPT, to generate natural and human-like responses to user input.
From there, we'll dive into the LangChain library and explore how we can use it to integrate multiple data sources and APIs into our chatbot. We'll cover topics such as query decomposition, which involves breaking down complex user queries into smaller, more manageable sub-queries, and memory as part of the chatbot, which involves storing and recalling information from previous interactions to provide more personalized responses.
We'll also explore advanced chaining techniques, such as chaining of agents, which involves orchestrating multiple chatbots or agents to work together to solve complex problems. By the end of this tutorial, you'll have a solid understanding of how to build a chatbot that can leverage large language models and the LangChain library to reason through complex problems and provide insightful responses.