Amity Document Search Optimizer

Improve RAG Search Accuracy for your Generative AI Agents
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Why Generative AI Needs Context
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Generative AI models require additional external context to produce accurate and relevant responses. Even the most sophisticated models trained on fixed datasets may lack specific information not included during training. Enterprises often have specialized data essential for providing accurate and tailored responses. Generative AI needs access to this proprietary information to ensure precise and relevant answers based on the enterprise's unique data.

What is Retrieval-Augmented Generation (RAG)

Retrieval-Augmented Generation (RAG) combines retrieval-based and generative models in AI. The system first retrieves relevant documents based on the user's query, ensuring access to the most pertinent information. These documents are then used by a generative model to create comprehensive and accurate responses. This hybrid approach ensures AI responses are both broadly informed and specifically relevant.
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Why is Document Search Accuracy Important to RAG Application

Document search accuracy is crucial for RAG systems. The relevance of retrieved documents directly impacts the quality of responses. Accurate retrieval ensures users receive accurate answers, leading to higher trust in the AI system. Enhancing document search accuracy is key to improving RAG applications' overall performance and reliability.

Introducing Amity Document Search Optimizer

The Amity Document Search Optimizer significantly improves the accuracy of document search in a Retrieval-Augmented Generation (RAG) workflow by leveraging agentic workflow and document reranking techniques. Designed to be search system agnostic, it seamlessly integrates with various search technologies, including Azure Cognitive Search and Google Vertex AI Search, among others.

To evaluate our optimizer’s performance, we tested our optimizer against 100 chunks of Thai language uncleaned documents related to customer service in the financial industry, one of the most complex use cases in Q&A RAG chatbots. A set of 90 of Thai language test questions is then used to determine if the search system could accurately retrieve the correct document chunks.

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The results of our evaluation demonstrate the significant impact of the Amity Document Search Optimizer:
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- Azure Cognitive Search: Accuracy increases from 42.22% to 55.56% for documents with a max token length of 4500. Accuracy improves from 47.78% to 70.00% for a max token length of 7500.
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- Google Vertex AI Search:Accuracy rises from 65.56% to 76.67% for 4500 tokens. Accuracy enhances from 72.22% to 84.44% for 7500 tokens.

These improvements highlight the optimizer's effectiveness in enhancing document retrieval accuracy across different search systems and token lengths. By leveraging the Amity Document Search Optimizer, businesses can boost the accuracy of their RAG systems by up to 26% increasing it from 48% to an impressive 70% on Azure Cognitive Search and to 84.44% on Google Vertex AI. This significant improvement in document retrieval accuracy translates to higher-quality generated responses, enabling businesses to provide more accurate and relevant information to their users.

Please note that the results were tested using uncleaned financial industry documents, which are among the most complex and challenging use cases for Q&A chatbots. This approach ensures that our evaluation reflects a worst-case scenario, demonstrating the optimizer's capability to handle real-world data. The Amity Document Search Optimizer is also industry agnostic, making it deployable across various sectors such as automotive, retail, insurance, healthcare, HR, and more. In actual deployments, where cleaner data and additional prompt engineering techniques are applied, we typically achieve accuracy rates of 90% or higher. Therefore, the 84.44% accuracy shown in the graph represents a conservative estimate of the optimizer's potential, underscoring its robustness in challenging conditions across multiple industries.

Key Features of Amity Document Search Optimizer

Advanced Query Understanding

Our optimizer employs sophisticated natural language processing techniques to deeply understand the user's query intent. It goes beyond simple keyword matching by analyzing the semantic meaning, context, and relationships between the query terms. This enables the optimizer to capture the true essence of the user's information needs, even in the presence of ambiguous or complex queries.

Dynamic Document Chunk Retrieval

The Amity Document Search Optimizer introduces a revolutionary feature that allows for dynamic document chunking based on the question context. Unlike traditional methods that require pre-chunking of documents, our optimizer intelligently determines the most relevant chunks of information on-the-fly. This dynamic chunking capability ensures that the retrieved documents are highly specific to the user's query, improving the accuracy and relevance of the generated responses.

Trainable with Adaptable Architecture

The Amity Document Search Optimizer is designed to be highly adaptable and trainable to your specific domain and use case. With its trainable architecture, our optimizer can be continuously trained on the context documents that are expected to be relevant to your question context. By providing a training dataset, you can fine-tune the optimizer to achieve exceptional performance, with the potential to reach up to 100% accuracy on the training data. Moreover, the trained optimizer generalizes well to validation and real production data, ensuring consistent and reliable results in real-world scenarios.

Vector Search
Azure Cognitive Search
Google Vertex AI Search
Google Vertex AI Search
+ Amity Search Optimizer
Vector Search
Azure Cognitive Search
Google Vertex AI Search
Google Vertex AI Search
+ Amity Search Optimizer
Vector Search
Azure Cognitive Search
Google Vertex AI Search
Google Vertex AI Search
+ Amity Search Optimizer

Benefits of Amity Document Search Optimizer

Improved Response Accuracy

By significantly enhancing the accuracy of document retrieval, the Amity Document Search Optimizer directly contributes to the generation of more accurate and relevant responses. With access to the most pertinent information, the RAG system can produce responses that precisely address the user's query, leading to higher user satisfaction and trust.

Scalability and Easy to Use

The Amity Document Search Optimize is designed to scale seamlessly with growing document collections and evolving user needs. Our optimizer can handle large-scale document repositories, making it suitable for businesses of all sizes. Additionally, the optimizer's automatic document processing capabilities and minimal setup requirements make it easy to integrate and use, saving you time and effort in document preparation and maintenance.

Adaptability

The Amity Document Search Optimizer is highly adaptable to changing document sets and domain-specific requirements. With its trainable architecture, our optimizer can be continuously fine-tuned on evolving context documents, ensuring that it stays up-to-date with the latest information and maintains high accuracy in dynamic environments. This adaptability allows the optimizer to deliver consistent performance even as your document collection grows and changes over time.

Get Started with Amity Document Search Optimizer

Unlock the full potential of your RAG system with the Amity Document Search Optimizer. Our team of experts is ready to work with you to integrate our cutting-edge optimizer into your existing RAG setup, enabling you to experience the benefits of enhanced document search accuracy and improved response generation.

Contact us today to schedule a demo and learn more about how the Amity Document Search Optimizer can transform your document search process and elevate your RAG system's performance.

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