Deep Dive into Advanced RAG Applications in LLM Based Systems

Exploring Sentence Window Retrieval and Seamless Auto Merging Retrieval

Source: Image from haystack.deepset.ai

Introduction

Retrieval Augmented Generation (RAG) is an innovative method that harnesses the strengths of both retrieval-based and generative systems. By retrieving relevant documents from a vast database and subsequently utilizing a generative model to formulate a response, RAG proves to be a powerful tool in LLM-based systems.

Before diving into its advanced applications, it’s essential to understand the fundamentals. Below article acts as an essential reference for individuals aiming to understand the fundamentals of RAG.

Retrieval Augmented Generation (RAG)

Although the baseline RAG model has exhibited impressive performance, researchers have been investigating advanced approaches to augment its capabilities. Among these, Sentence-Window Retrieval and Auto-Merging Retrieval have emerged as promising methods. This article will dive into these techniques and explore their potential to elevate the performance of the RAG pipeline beyond its baseline.

Sentence-Window Retrieval

Sentence-Window Retrieval is a method that focuses on extracting relevant information from a larger context by considering a window of sentences instead of just a single sentence. By expanding the retrieval scope, the RAG model gains access to a broader context, enabling it to generate more accurate and contextually appropriate responses. This technique allows the model to capture long-range dependencies and understand the nuances of the given prompt, resulting in improved performance.

For example, consider a chatbot designed to assist users with technical troubleshooting.

prompt = "I'm encountering an error message 'Error 404: File Not Found' 
          when trying to open the application. How can I fix this?"

When a user submits a query about a software issue, the chatbot utilizes Sentence-Window Retrieval to extract relevant information from a broader context, such as user manuals, troubleshooting guides, and forums.

The process of Sentence Window Retrieval progresses through the following stages:

1. Tokenization: The document or corpus is tokenized into individual sentences or segments to prepare it for retrieval and generation.
2. Query Formation: A query or keyword is used to search for relevant information within the document. This query guides the retrieval and generation process.
3. Window Selection: A window of sentences is selected around the query to capture the relevant context. The size of the window is determined based on the specific requirements and can be adjusted to include more or fewer sentences.
4. Scoring and Ranking: The selected sentences within the window are scored based on their relevance to the query using RAG’s retrieval and ranking mechanisms. This may involve leveraging pre-trained language models and fine-tuning them for retrieval tasks.
5. Retrieval and Generation: The top-ranked sentences are retrieved and used as context for generation. RAG then generates responses or summaries based on the retrieved context, providing relevant and coherent outputs.

Auto-Merging Retrieval

Auto-Merging Retrieval is a strategy designed to improve the retrieval process by automatically consolidating multiple retrieved passages. Rather than depending on a single retrieved passage, this method integrates information from various sources to produce a more comprehensive and cohesive response. By harnessing the strengths of multiple passages, the RAG model can address challenges such as incomplete or biased information found in individual retrievals. This approach results in more resilient and precise generation, enhancing the overall performance of the RAG pipeline.

Auto-Merging Retrieval — Source: Image original from Author

The process of Auto Merging Retrieval progresses through the following stages:

1. Retrieval of Multiple Passages: The RAG model retrieves multiple passages from the document or corpus based on the user query or keyword. These passages are selected to capture diverse and relevant information.
2. Passage Ranking: Each retrieved passage is scored and ranked based on its relevance to the query. This ranking is determined using retrieval mechanisms such as semantic similarity, keyword matching, or machine learning algorithms.
3. Auto-Merging: The top-ranked passages are automatically merged to create a consolidated context for generation. This merging process aims to combine the strengths of individual passages and overcome limitations such as incomplete or biased information.
4. Contextual Generation: The merged context is used as input for the generation process. The RAG model leverages the consolidated information to generate responses or summaries that are more comprehensive and coherent.
5. Output Refinement: The generated output is refined and optimized to ensure that it accurately reflects the merged context and provides a high-quality response to the user query.

The Advantages of Contextual Expansion and Retrieval Consolidation in RAG

By incorporating a wider context and merging multiple retrievals, these methods address some of the limitations of the baseline RAG model, resulting in the following improvements:

Contextual Relevance: The expanded context provided by Sentence-Window Retrieval allows the RAG model to generate responses that are more contextually relevant. This enables the model to understand the prompt in a more holistic manner, leading to more accurate and coherent generation.

Coherence and Completeness: Auto-Merging Retrieval ensures that the generated responses are more coherent and complete by combining information from multiple sources. This technique mitigates the risk of generating fragmented or biased responses, resulting in more robust and comprehensive output.

Long-Range Dependencies: Sentence-Window Retrieval enables the RAG model to capture long-range dependencies within the prompt. This helps the model understand the broader context and make connections between different parts of the text, leading to more subtle and insightful generation.

Conclusion

The advanced methods such as Sentence-Window Retrieval and Auto-Merging Retrieval have the potential to significantly enhance the performance of the RAG pipeline beyond its baseline. By incorporating a wider context and merging multiple retrievals, these techniques improve the contextual relevance, coherence, and completeness of the generated text. Real-world applications such as customer support chatbots, content generation, and dialogue systems can greatly benefit from these advancements, providing more accurate and contextually appropriate responses.

If you are interested in learning more about AI and how we can guide the AI agents based on human intuition and expertise, I encourage you to read the following article:

Exploring Reinforcement Learning from Human Feedback (RLHF)

Do you want the article delivered directly to your inbox? Subscribe to my newsletter here — AI Stratus Insights or Subscribe on LinkedIn

References

1. Rag 101: Demystifying retrieval-augmented generation pipelines. NVIDIA Technical Blog. (2023, December 18). https://developer.nvidia.com/blog/rag-101-demystifying-retrieval-augmented-generation-pipelines/
2. Retrieval augmented generation (RAG) for llms — nextra. Prompt Engineering Guide. (n.d.). https://www.promptingguide.ai/research/rag