Harnessing RAG for Intelligent Document Analysis: A Proof of Concept

As an AI researcher and developer, I am thrilled to share the exciting journey we are embarking on with a proof of concept that leverages the power of Retrieval-Augmented Generation (RAG). This innovative approach promises to revolutionize how we interact with domain-specific texts, particularly annual reports, and generate insightful comparisons and actionable intelligence.

Use Case

Our focus is on annual reports within a specific industry sector to ensure our data is both relevant and comparable. We will investigate queries such as:

• Comparing earnings per share (EPS) of companies over the last two years.
• Highlighting new projects or planned changes in company strategy for the upcoming 12 months.

Technical Approach

We are building our infrastructure using the Azure Ecosystem, which provides scalability and seamless integration:

• Blob Storage: Text data like annual reports and transcriptions will be securely stored in Azure Blob Storage for efficient retrieval.
• Chunking Model: To ensure compatibility with token limitations of large language models, the text will undergo preprocessing into smaller, manageable chunks.
• Vector Embeddings: These chunks will then be converted into vector embeddings using a state-of-the-art model, establishing a searchable knowledge base.

RAG Workflow

The workflow for our proof of concept involves several stages:

1. Document Ingestion: Upload annual reports and convert them into vector embeddings.
2. Query Processing: User queries will interact with the embedded data through a retrieval mechanism.
3. Augmented Response Generation: The AI model will combine retrieved data with its generative capabilities to produce informed, contextual responses.

Benefits

This RAG system enables several value propositions:

• Quick identification of trends, patterns, and actionable insights from extensive text sources.
• Enhanced decision-making by summarizing and comparing critical metrics effectively.
• Scalability, allowing seamless operation with text, video, and audio transcriptions.

Next Steps

Our next steps involve:

1. Setting up Azure Blob Storage and the vector embedding model.
2. Training and testing the chunking process for optimal text preparation.
3. Building a user interface for submitting queries and receiving comparisons or summaries.
4. Testing the RAG system with various datasets, starting with annual reports and potentially expanding to legal documents and training materials.

Through this proof of concept, we aim to showcase the capabilities of RAG as a robust tool for intelligent document analysis and decision-making, pushing the frontier of what AI can achieve in specialized domains.