AI's Next Leap: Understanding and Implementing RAG for Better Outcomes

Traditional text generation models, often using encoder-decoder architectures, can perform tasks like translating languages, adapting different styles, and answering basic questions. However, because these models depend on patterns in their training data, they can sometimes generate incorrect or irrelevant responses, a phenomenon known as “hallucination”.

Generative AI employs Large Language Models (LLMs) that are trained on extensive publicly available information from the internet. Companies like Microsoft, Google, AWS, and Meta provide LLMs but can't frequently update them due to the high cost and time required. As a result, LLMs are not always updated since they only include data up to a certain point in time.

Solving AI Hallucinations: Understanding RAG's Role in Reliable AI

Retrieval-Augmented Generation (RAG) is a popular approach for reducing the hallucinations often seen in large language models. RAG is a Generative AI (GenAI) architecture that improves a Large Language Model (LLM) by using fresh, reliable data from trusted internal knowledge bases and enterprise systems. This helps create more accurate and dependable responses.

The term "RAG" comes from a 2020 paper titled “Retrieval-Augmented Generation for Knowledge-Intensive Tasks,” submitted by Facebook AI Research (now Meta AI). The paper describes RAG as a "general-purpose fine-tuning recipe" because it connects any LLM with any internal data source. As its name suggests, retrieval-augmented generation includes a data retrieval step in the response generation process to make answers more relevant and reliable. The RAG pipeline consists of three key components: retrieval, augmentation, and generation.

1. Retrieval: This component finds relevant information from an external knowledge base, like a vector database, based on the user's query. It's a crucial first step in creating meaningful and contextually accurate responses.

2. Augmentation: This step involves adding more relevant context to the information retrieved, ensuring the response is tailored to the user's query.

3. Generation: The final output is created using a large language model (LLM). The LLM combines its own knowledge with the provided context to generate a suitable response for the user’s question.

Image Source - arxiv.org

Implementing RAG with LangChain

Implementing a Retrieval-Augmented Generation (RAG) model involves various methods, each with its own strengths and suitability for different applications. Let’s do a basic RAG implementation with LangChain.

LangChain is a library designed to help build applications with Large Language Models (LLMs) by providing tools to easily integrate them with external data sources. Implementing Retrieval-Augmented Generation (RAG) using LangChain involves setting up a system where a language model is augmented with retrieved data to improve its responses.

Prerequisites

• Python: Make sure you have Python installed on your system.

• LangChain: Install the LangChain library.

• OpenAI: Access to OpenAI API for language models like GPT-3/4.

• ElasticSearch/FAISS: Optionally set up for indexing and retrieval if you are using a custom knowledge base.

Step-by-Step Implementation

• Install LangChain

• Set Up the Environment: Ensure you have access to an LLM API like OpenAI's GPT-3/4. Set up your API keys and necessary environment variables

• Initialize LangChain and LLM: Set up your LangChain environment and initialize the LLM you will use for generation

• Create a Retrieval Component: Define your retriever component. This could be a simple document store or a more complex system using ElasticSearch or FAISS for efficient retrieval

• Combine Retriever and LLM for RAG: Create a LangChain that integrates the retriever and the LLM to form the RAG pipeline

• Execute Queries: Now you can execute queries using the RAG chain, which retrieves relevant documents and generates a response based on them

Summing Up

Retrieval Augmented Generation (RAG) is a major advancement in language models. By combining retrieval systems with sequence-to-sequence generation, RAG models can deliver more detailed and relevant responses. As the technology progresses, we can expect even more advanced combinations of these components, leading to AI models that are not only knowledgeable but also resourceful.