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Spring Boot integrates LangChain to build a Rag Application
1. What is rag?
Retrieval-augmented generation (RAG) is the process of optimizing large language model outputs to reference authoritative knowledge bases outside of the training data before generating a response. Large language models (LLMs) are trained with massive amounts of data, using billions of parameters to produce raw output for tasks such as answering questions, translating languages, and completing sentences. Building on the already powerful capabilities of LLMs, RAG extends them to access internal knowledge bases for specific domains or organizations, all without retraining the model. This is a cost-effective way to improve LLM outputs so that they remain relevant, accurate, and useful in a variety of contexts.
Why is retrieval-enhanced generation important?
LLM is a key artificial intelligence (AI) technology that powers intelligent chatbots and other natural language processing (NLP) applications. The goal is to create bots that can answer user questions in a variety of contexts by cross-referencing authoritative knowledge sources. Unfortunately, the nature of LLM technology introduces unpredictability in LLM responses. Additionally, LLM training data is static and introduces a deadline for the knowledge it possesses. Known challenges with LLM include:
- Providing false information without an answer.
- Providing outdated or generic information when users need a specific, current response.
- Create a response from a non-authoritative source.
- Because of terminology confusion, different training sources use the same term to talk about different things, which can result in inaccurate responses.
You can think of a large language model as an overly enthusiastic new employee who refuses to stay up to date on current events, but always answers every question with absolute confidence. Unfortunately, this attitude can negatively impact user trust, which is something you don’t want your chatbot to emulate! RAG is one way to address some of these challenges. It redirects the LLM to retrieve relevant information from authoritative, predetermined knowledge sources. Organizations have more control over the generated text output, and users gain insight into how the LLM generated responses.
