How do domain-specific chatbots work? A retrieval augmented generation overview

LangChain is an open-source library that allows developers to create chatbots with Q&A capabilities using just three lines of code. The library utilizes a process called retrieval augmented generation (RAG) to supplement user input with additional information retrieved from a knowledge base. This additional information is used to augment the response generated by a large language model (LLM) like ChatGPT. The retrieval step involves searching for the most relevant content based on the user’s input, while the generation step involves using the LLM to answer the question using the retrieved information. The process relies on the use of embeddings, which represent human language as vectors of numbers, and semantic search to find the most relevant information. Indexing the knowledge base and splitting it into smaller snippets is a crucial step in the process, and there are built-in loaders in LangChain that can extract content from various sources. Overall, LangChain simplifies the creation of Q&A chatbots by providing an easy-to-use framework for developers.

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