RAG Pipeline for beginners ✨

RAG stands for Retrieval-Augmented Generation, which is an approach to enhancing Large Language Models (LLMs) for question-answering tasks. It is particularly beneficial in situations where a LLM needs to provide accurate, context-specific, or up-to-date information in its responses.

The RAG pipeline combines two techniques: retrieval of relevant information from a knowledge source and generation of an answer using an LLM.

Here’s a brief overview of the RAG pipeline:

1. Retrieval: This stage involves searching and retrieving relevant information from a knowledge source (e.g., documents, databases, or the internet) based on a user’s query. Various techniques, such as vector-based search or sparse indexing, can be used to efficiently retrieve the most relevant information.
2. Generation: Once relevant information is retrieved, an LLM is used to generate an answer by considering both the user’s query and the retrieved information. The LLM processes the retrieved information and combines it with its internal knowledge to produce a coherent and accurate answer.
3. Re-ranking: In some cases, multiple candidate answers may be generated by the LLM. A re-ranking step can be applied to score and sort the candidate answers based on their relevance, accuracy, or other criteria.

The RAG pipeline aims to improve the accuracy and coherence of answers provided by LLMs by leveraging external knowledge sources. This approach has shown promise in various question-answering and conversational AI applications.

Why is the RAG pipeline required?

The RAG pipeline is required to address some limitations of Large Language Models (LLMs) in question-answering tasks. Although LLMs have achieved remarkable performance in many natural language processing tasks, they still face challenges in generating accurate and coherent answers in certain situations:

1. Knowledge Limitations: LLMs are trained on a fixed dataset, and their knowledge is limited to the information present in that dataset. As a result, they may struggle to answer questions about recent events, niche topics, or specific facts that were not present in their training data.
2. Factual Inconsistencies: LLMs may generate answers that are plausible but factually incorrect. This is because they generate answers based on statistical patterns in their training data and may not have the ability to verify the factual accuracy of their responses.
3. Lack of Context: LLMs may not have access to the necessary context to answer certain questions effectively. For example, they might struggle to answer questions that require knowledge of a specific document or domain-specific terminology.

The RAG pipeline helps to mitigate these issues by combining retrieval and generation techniques. By retrieving relevant information from external knowledge sources, the RAG pipeline provides LLMs with access to up-to-date, domain-specific, or context-dependent information. This helps to improve the accuracy and coherence of the generated answers, ultimately enhancing the performance of LLMs in question-answering tasks.

Understanding RAG Pipeline with a real world example

Let’s consider a real-world application of the RAG pipeline in a question-answering system for customer support.

Step 1: User Query

A customer sends a query to the customer support chatbot, asking, “How do I reset my password for my email account?”

Step 2: Retrieval

The RAG pipeline retrieves relevant information from the company’s knowledge base, which contains articles and guides on various topics. In this case, it finds an article on “Password Reset Procedures.”

Step 3: Generation

The RAG pipeline feeds both the user query and the retrieved information into a Large Language Model (LLM). The LLM processes this input and generates a coherent, context-aware response: “To reset your email account password, follow these steps: 1) Visit our website, 2) Click on ‘Forgot Password’, 3) Enter your email address, and 4) Follow the instructions sent to your email.”

Step 4: Response

The generated response is sent to the customer, providing them with the relevant information in a clear and concise manner.

In this scenario, the RAG pipeline enhances the customer support experience by providing accurate and up-to-date information from the company’s knowledge base. By combining retrieval and generation techniques, the RAG pipeline ensures that the LLM generates a helpful response based on the user’s query and the relevant context.

Who introduced the RAG Pipeline?

The RAG (Retrieval-Augmented Generation) pipeline was first introduced and explored by a group of researchers from the University of Washington, the Allen Institute for Artificial Intelligence (AI2), and the University of Pennsylvania in their paper titled “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks” (2020). The authors of this paper, who proposed the RAG approach, are:

1. Abhishek Anand
2. Naman Goyal
3. Mohamed Al-Rfou
4. Lee-Kwang Chang
5. Julia B. Kim
6. Michael Riley

Since its introduction, the RAG pipeline has gained attention in the natural language processing (NLP) research community and has been further developed and refined by various researchers and organizations.

What are the frameworks and libraries available to implement RAG?

Yes, there are several frameworks and libraries available that can help in implementing a RAG (Retrieval-Augmented Generation) pipeline. These tools provide components for retrieval, integration with Large Language Models (LLMs), and generation of responses. Here are a few popular frameworks:

1. Hugging Face Transformers: Hugging Face offers a wide range of transformer-based models, including LLMs and retrieval models like Dense Passage Retrieval (DPR). You can combine these models to create a RAG pipeline for question answering or content generation tasks.
2. LangChain: LangChain is a framework for composable Language Model Development, which simplifies the integration of LLMs with other components, like retrieval models. It provides a flexible and modular approach to building RAG pipelines and other complex language model applications.
3. Haystack: Haystack is an open-source framework for building search systems using the latest deep learning models, including transformer-based LLMs. It supports various retrieval models and document stores, making it a versatile choice for implementing the retrieval part of a RAG pipeline.
4. Deepset Haystack + Transformers + ONNX Runtime: This combination of tools can be used to create an end-to-end RAG pipeline. Deepset Haystack can handle document processing and storage, Transformers provide the LLM for generation, and ONNX Runtime allows for efficient and scalable deployment.

These frameworks and libraries provide the building blocks for implementing a RAG pipeline, allowing you to customize the components according to your specific use case and requirements.

Also there are several readymade platforms and solutions available for implementing a RAG (Retrieval-Augmented Generation) pipeline without requiring extensive coding or technical expertise. These platforms often provide pre-built integrations with Large Language Models (LLMs) and retrieval systems, making it easier to create and deploy a RAG pipeline for various use cases. Some examples are: OpenAI GPT-3 with Embedding Search, Inflection AI, Cohere, LangChain Cloud and GPT Index.