Enhancing Accuracy in RAG Applications with AWS Bedrock: The Role of Re-Ranking

This blog addresses a key challenge in Retrieval-Augmented Generation (RAG) for NLP applications: ensuring relevance and accuracy in the information retrieved for response generation. Traditional approaches often retrieve data through semantic search, which, while effective, can lack precise contextual alignment, leading to irrelevant chunks being passed to the language model (LLM). This not only affects the quality of responses but also increases token consumption and computational costs. 

To solve this, we explore the introduction of a Re-Ranker layer between data retrieval and response generation. This layer filters and prioritizes the most relevant chunks, ensuring that only contextually appropriate information reaches the LLM. The result is improved accuracy, reduced token usage, and better overall performance of RAG-based systems. 

EXISTING RAG BLOCK DIAGRAM

RAG WITH RE-RANKER BLOCK DIAGRAM 

Requirement Description 

When developing a virtual assistant or an AI-based information retrieval system, the key goal is to provide users with quick, accurate answers to their queries. A critical aspect of this process is retrieving relevant data from large knowledge bases. The solution must: 

Retrieve Relevant Information: When a query is made, the system should fetch the most relevant data about the topic in question (e.g., product details, specifications, prices). 

Reduce Irrelevant Data: To ensure response quality, irrelevant or excessive information should be minimized. 

Efficient Use of Resources: Reducing unnecessary token usage is crucial, as large amounts of irrelevant data lead to higher computational costs and longer processing times. 

Given the vast amount of data typically involved in such systems, ensuring that only the most relevant pieces of information are processed is essential for delivering an effective user experience. 

Existing Approach 

In the existing approach, we implemented a Retrieval-Augmented Generation (RAG) system for chatbot-based interactions, which relies heavily on a knowledge base to provide relevant answers to user queries. This system is designed to retrieve information from a large corpus of data and generate a response using a large language model (LLM), enhancing the overall chatbot’s capabilities. The process follows a sequence of steps, but it faced significant challenges in ensuring the accuracy and relevance of the generated responses. 

Here’s how the process works in the current setup: 

1. User Query Submission 

When a user submits a query through the chatbot interface, the query is forwarded to an API Gateway. The API Gateway then routes the request to an AWS Lambda function for processing. The Lambda function acts as an intermediary that handles the logic of interacting with the knowledge base and preparing the query for retrieval. 

2. Hybrid Search in Knowledge Base 

Once the Lambda function receives the user query, the next step is to retrieve relevant information from the knowledge base. To do this, the system performs a hybrid search approach. This hybrid search combines two types of search methods: 

Keyword-based Search: Traditional search techniques rely on matching keywords from the user’s query with the indexed content in the knowledge base. This is a straightforward approach to identify potentially relevant chunks of information. 

Semantic Vector Search: Complementing the keyword-based search, semantic vector search uses embedding models (such as those based on BERT or other transformer-based architectures) to understand the meaning behind the user’s query and match it with similar semantic content in the knowledge base. This search method focuses on the context and intent of the query, ensuring a more nuanced understanding of the data. 

By combining these two techniques, the system ensures that a wide range of potentially relevant chunks—both semantically and contextually related to the query—are retrieved from the knowledge base. This helps cast a wide net for relevant information. 

3. Metadata Filtering 

Once the chunks are retrieved, the system applies metadata filtering to further refine the results. The metadata may include attributes like: 

Metadata Key Associated with the document: Only the documents tagged with the identified metadata will be retrieved. 

The purpose of metadata filtering is to exclude irrelevant, outdated, or low-confidence chunks that were returned by the hybrid search. It is an essential step for reducing noise and improving the quality of the retrieved data. 

4. Passing Data to the LLM for Response Generation 

After the retrieval and metadata filtering, the relevant chunks are then passed to the LLM, along with the user’s original query. The LLM processes this input and uses the information to generate a natural language response. Ideally, the LLM would use the provided chunks to craft an accurate and informative response that directly addresses the user’s query. 

Challenges in the Existing Approach 

Despite the hybrid search and metadata filtering, several issues impacted the effectiveness of the existing approach: 

Irrelevant Chunks Passed to the LLM: 

Even with metadata filtering, some irrelevant chunks (e.g., outdated data, unrelated topics) still made it to the LLM, affecting the relevance of the response. 

Inaccurate Responses: 

The presence of irrelevant data in the input often led to inaccurate or incomplete responses, as the LLM generated answers based on extraneous information. 

Increased Token Consumption: 

The inclusion of unnecessary chunks increased the number of tokens passed to the LLM, leading to higher computational costs and slower response times. 

Existing RAG Chatbot Architecture 

RAG With Re Ranker Approach 

The current approach builds upon the foundation of the existing system, retaining the hybrid search for data retrieval and the use of a large language model (LLM) for response generation. However, to improve accuracy and efficiency, we have introduced a significant enhancement: an intermediate Re-Ranker layer placed between the retrieval process and the response generation LLM. This additional layer serves to refine the data passed to the LLM, ensuring that only the most relevant chunks are used in response generation. 

Here’s a detailed look at the flow of the current approach:                                                         

1. Hybrid Search in Knowledge Base 

As with the previous approach, when the user submits a query, the system performs a hybrid search within the knowledge base. The hybrid search employs both semantic vector search and keyword-based search to gather a set of relevant chunks based on the user’s query. This ensures that the retrieved data encompasses a wide range of relevant results. 

2. Re-Ranker Layer 

Unlike the previous system, where all retrieved chunks were passed directly to the response generation LLM, the chunks are now first sent to an intermediate Re-Ranker layer. The Re-Ranker layer processes the user’s query and the retrieved chunks to assess the relevance of each chunk. 

• The Re-Ranker evaluates each chunk based on how closely it matches the context of the user’s query. 

• It uses advanced algorithms to rank the chunks by relevance and selects the top N most relevant chunks for further processing. 

• Only these high-relevance chunks are forwarded to the response generation LLM. 

The number of chunks passed to the LLM can be dynamically controlled, allowing for fine-tuning of the number of retrieved chunks based on the complexity of the query. 

3. Passing Reranked Chunks to the LLM 

Once the Re-Ranker has filtered and ranked the chunks, the top N chunks—those with the highest relevance to the user’s query—are sent to the LLM. The LLM processes these chunks along with the user query to generate a well-informed, relevant, and accurate response. 

RAG Chatbot With Re-Ranking Flow Architecture

Advantages Of Using Re-Ranker In RAG Applications 

Conclusion

The introduction of the Re-Ranker layer in the Retrieval-Augmented Generation (RAG) architecture marks a significant improvement in ensuring the accuracy and efficiency of virtual assistants. By filtering the most relevant chunks before passing them to the response generation LLM, the Re-Ranker addresses key challenges of the existing approach, such as irrelevant data influencing responses and high token usage. This enhancement ensures that only precise and contextually appropriate information informs the generated answers, improving both the quality of responses and user satisfaction. 

This approach not only enhances accuracy but also optimizes resource usage by reducing the number of tokens processed, leading to cost efficiency and faster response times. Additionally, the system’s flexibility to dynamically adjust the number of chunks based on query complexity makes it highly adaptable to various use cases. These improvements result in a virtual assistant that is better equipped to handle complex queries while maintaining scalability and reliability, providing superior user experience. 

Overall, the integration of the Re-Ranker layer demonstrates the value of targeted optimizations in AI-driven conversational systems. It bridges the gap between data retrieval and response generation, setting a strong foundation for future advancements in virtual assistants. This project exemplifies how refining intermediate layers can transform existing architectures into smarter, more effective, and user-centric solutions, paving the way for more intelligent and impactful AI applications. 

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