# Building a Financial RAG System with Search-First Architecture ## Project Overview This case study details the implementation of a RAG-based chatbot system for financial executives, finance teams, and investor relations departments at a major client. The system was designed to help users quickly access and analyze information across SEC filings, earnings calls, analyst reports, and internal financial documents. ## Technical Architecture - Azure OpenAI Service with GPT-4-32k and GPT-4-Turbo models - Azure AI Search for document indexing and retrieval - Azure App Service for the web application backend - Python-based implementation using Microsoft's accelerator template ## Key Challenges and Solutions ### Initial Implementation Challenges - Context preservation issues when chunking documents - Incorrect attribution of quotes and financial data - Poor search result quality mixing disclaimers with actual content - Inability to handle multi-entity queries effectively - Lack of temporal understanding in search results ### Search Architecture Innovation The team developed a novel search-first approach: - Using GPT-4 to generate concise summaries of each document page - Adding summaries as searchable fields in Azure AI Search index - Vectorizing summaries instead of raw content - Matching user queries against summary vectors for improved relevance - Implementing custom scoring profiles based on document recency ### Content Processing Pipeline - Page-level chunking to preserve context instead of token-based chunking - Pre-processing step to remove problematic footers and headers - Summary generation with specialized prompts - Hybrid search combining vector and keyword approaches - Custom filters to remove disclaimer/certification pages ### Query Processing Improvements - Decision agent to determine optimal search strategy - Breaking complex multi-entity queries into sub-queries - Category-based filtering system - Implementation of recency boosts in search scoring ## Technical Implementation Details ### Document Processing - Modified chunking strategy to preserve page-level context - Added metadata extraction including creation dates and document categories - Implemented custom scoring profiles in Azure AI Search - Built specialized prompting system for page summarization ### Search Optimization - Hybrid search combining vector and keyword approaches - Custom boosting based on document recency - Category-based filtering system - Context-aware search routing based on query type ### LLM Integration - Multiple GPT-4 specialized agents: - Prompt engineering for detailed responses - Streaming response implementation ### System Architecture Improvements - Implementation of caching strategies - Addition of custom scoring profiles - Category-based document filtering - Temporal awareness through custom boost functions ## Project Outcomes ### Key Achievements - Successful deployment of production RAG system - Significant improvement in response accuracy and relevance - Reduction in manual research time for financial analysts - Positive user feedback on system performance ### Technical Learnings - Importance of search quality over LLM capabilities - Value of context preservation in document processing - Need for specialized handling of financial data - Benefits of hybrid search approaches ## Operational Considerations ### Production Readiness - Need for comprehensive testing framework - Importance of LLMOps practices - Requirements for automated data ingestion - Security model considerations ### Performance Optimization - Response latency improvements - Streaming implementation - Caching strategies - PTU considerations for Azure OpenAI ## Best Practices and Recommendations ### RAG Implementation - Focus on search quality first - Preserve document context - Implement hybrid search approaches - Use specialized agents for different tasks ### System Design - Plan for scalability - Consider security requirements early - Implement comprehensive testing - Build in monitoring and evaluation ### Data Processing - Carefully consider chunking strategies - Preserve metadata and context - Implement proper preprocessing - Consider domain-specific requirements ## Future Improvements ### Planned Enhancements - Implementation of vision capabilities - Database connectivity - Autonomous agents - Whole document summarization - User document upload capability ### Technical Debt - Testing framework implementation