Lang Chain & RAG (Retrieval-Augmented Generation

What is Lang Chain?

Lang Chain is an open source framework for building applications based on large language models (LLMs). LLMs are large deep-learning models pre-trained on large amounts of data that can generate responses to user queries — for example, answering questions or creating images from text-based prompts. Lang Chain provides tools and abstractions to improve the customization, accuracy, and relevancy of the information the models generate. For example, developers can use Lang Chain components to build new prompt chains or customize existing templates. Lang Chain also includes components that allow LLMs to access new data sets without retraining.

Why is Lang Chain important?

LLMs excel at responding to prompts in a general context, but struggle in a specific domain they were never trained on. Prompts are queries people use to seek responses from an LLM. For example, an LLM can provide an answer to how much a computer costs by providing an estimate.

To do that, machine learning engineers must integrate the LLM with the organization’s internal data sources and apply prompt engineering a practice where a data scientist refines inputs to a generative model with a specific structure and context.

Lang Chain streamlines intermediate steps to develop such data-responsive applications, making prompt engineering more efficient. It is designed to develop diverse applications powered by language models more effortlessly, including chatbots, and more.

What are the core components of Lang Chain?

Using Lang Chain, software teams can build context-aware language model systems with the following modules.

LLM interface

Lang Chain provides APIs with which developers can connect and query LLMs from their code. Developers can interface with public and proprietary models like GPT with Lang Chain by making simple API calls instead of writing complex code.

Prompt templates

Prompt templates are pre-built structures developers use to consistently and precisely format queries for AI models. Developers can create a prompt template for chatbot applications, few-shot learning, or deliver specific instructions to the language models. Moreover, they can reuse the templates across different applications and language models.

Retrieval modules

Lang Chain enables the architecting of RAG systems with numerous tools to transform, store, search, and retrieve information that refine language model responses. Developers can create semantic representations of information with word embeddings and store them in local or cloud vector databases.

Memory

Some conversational language model applications refine their responses with information recalled from past interactions. LangChain allows developers to include memory capabilities in their systems. It supports:

• Simple memory systems that recall the most recent conversations.
• Complex memory structures that analyze historical messages to return the most relevant results.

What is Retrieval-Augmented Generation?

Retrieval-Augmented Generation (RAG) is the process of optimizing the output of a large language model, so it references an authoritative knowledge base outside of its training data sources before generating a response. Large Language Models (LLMs) are trained on vast volumes of data and use billions of parameters to generate original output for tasks like answering questions, translating languages, and completing sentences. RAG extends the already powerful capabilities of LLMs to specific domains or an organization’s internal knowledge base, all without the need to retrain the model. It is a cost-effective approach to improving LLM output so it remains relevant, accurate, and useful in various contexts.

Why is Retrieval-Augmented Generation important?

LLMs are a key artificial intelligence (AI) technology powering intelligent chatbots and other natural language processing (NLP) applications. The goal is to create bots that can answer user questions in various 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 cut-off date on the knowledge it has.

Known challenges of LLMs include:

• Presenting false information when it does not have the answer.
• Presenting out-of-date or generic information when the user expects a specific, current response.
• Creating a response from non-authoritative sources.
• Creating inaccurate responses due to terminology confusion, wherein different training sources use the same terminology to talk about different things.

You can think of the LLM as an over-enthusiastic new employee who refuses to stay informed with current events but will always answer every question with absolute confidence. Unfortunately, such an attitude can negatively impact user trust and is not something you want your chatbots to emulate!

RAG is one approach to solving some of these challenges. It redirects the LLM to retrieve relevant information from authoritative, pre-determined knowledge sources. Organizations have greater control over the generated text output, and users gain insights into how the LLM generates the response.