RAG Regulatory Copilot: Practical Implementation of a Retrieval-Augmented Generation System for Financial Regulatory Documents

This article introduces an end-to-end RAG system implementation specifically designed for financial regulatory document query scenarios. It combines semantic search with large language models and provides a complete Docker containerization and AWS EKS cloud-native deployment solution.

In the financial industry, regulatory compliance is an extremely important yet complex task. Financial institutions need to process massive amounts of regulatory documents, laws and regulations, compliance guidelines, and ensure that business operations meet various requirements. Traditional document retrieval methods often rely on keyword matching, which struggles to handle natural language queries and cannot understand semantic relationships between documents.

The emergence of RAG (Retrieval-Augmented Generation) technology provides a new approach to solving this pain point. By combining the generative capabilities of large language models with the retrieval capabilities of professional document libraries, RAG systems can understand users' natural language questions, retrieve accurate information from relevant documents, and generate structured answers.

The RAG Regulatory Copilot project is an end-to-end solution built specifically for this application scenario. It not only implements core RAG functions but also provides a complete cloud-native deployment architecture, offering a reference implementation example for enterprise-level applications.

The project's architecture design embodies best practices for modern AI applications, adopting a microservices architecture and cloud-native technology stack. The overall architecture can be divided into the following layers:

The project selects AWS as the cloud platform, using Amazon ECR (Elastic Container Registry) to store container images and AWS EKS (Elastic Kubernetes Service) as the container orchestration platform. This choice ensures the system's high availability, elastic scalability, and production-level stability.

The system adopts a hybrid search strategy, combining vector retrieval and keyword retrieval technologies:

• Qdrant Vector Database: Responsible for storing semantic embedding vectors of documents and supporting similarity-based semantic retrieval. When a user asks a question, the system converts the query into a vector and searches Qdrant for semantically similar document fragments.

• OpenSearch Search Engine: Provides traditional full-text retrieval capabilities, supporting keyword-based exact matching. This is particularly important for queries containing specific terms, numbers, or proper nouns.

This "dual-engine" design allows the system to both understand the semantic intent of user questions and handle professional term queries that require exact matching.

• RAG API Service: An inference service built on the FastAPI framework, responsible for processing query requests, coordinating retrieval processes, and calling large language models to generate answers. The choice of FastAPI ensures high performance and asynchronous processing capabilities of the API.

• RAG UI Service: A user interface built on Streamlit, providing an intuitive interaction method. As a popular data application framework in the Python ecosystem, Streamlit can quickly build beautiful web interfaces, especially suitable for demonstration and prototype development of AI applications.

The core workflow of the system follows the typical RAG pattern:

1. Query Understanding: Receive the user's natural language question
2. Hybrid Retrieval: Perform vector similarity search in Qdrant and keyword search in OpenSearch simultaneously
3. Result Fusion: Fuse and sort the results from both retrieval methods, selecting the most relevant document fragments
4. Context Construction: Organize the retrieved document fragments as context together with the original question into a prompt
5. Answer Generation: Call the OpenAI API to generate accurate and coherent answers based on the provided context

The project uses OpenAI's embedding model to convert documents and queries into high-dimensional vectors. These vectors capture the semantic information of the text, making semantically similar content closer in the vector space. This representation breaks through the limitations of traditional keyword matching and can understand synonyms, near-synonyms, and conceptual associations.