Building a Financial Compliance Intelligent Q&A System: A Practical Guide to RAG-Based Regulatory Document Retrieval

This article introduces a complete implementation of a Retrieval-Augmented Generation (RAG) system for intelligent Q&A on regulatory documents in financial compliance scenarios. The system combines semantic vector retrieval and keyword search, generates context-aware answers using OpenAI's large language model, and adopts a Docker containerization and AWS EKS cloud-native deployment architecture.

• Original Author/Maintainer: Roy16Keane
• Source Platform: GitHub
• Original Title: RAG-regulatory-copilot
• Original Link: https://github.com/Roy16Keane/RAG-regulatory-copilot
• Publication Date: 2026-05-25

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Compliance work in the financial industry faces pressure from processing massive regulatory documents. From the Basel Accords to regulatory guidelines from central banks around the world, compliance personnel need to quickly locate specific clauses, understand regulatory requirements, and ensure business operations comply with the latest regulations. Traditional manual retrieval methods are inefficient, and general search engines struggle to understand industry-specific terminology and context in finance.

Retrieval-Augmented Generation (RAG) technology provides a feasible solution to this pain point. By storing document vectors and combining with the generation capabilities of large language models, the system can understand natural language queries, accurately retrieve relevant content from massive documents, and generate structured answers.

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This project adopts an end-to-end design approach, combining semantic search and keyword retrieval to build a complete financial compliance Q&A system.

The system uses a microservice architecture and includes the following core components:

Retrieval Layer

• Qdrant Vector Database: Stores semantic embedding vectors of documents and supports efficient similarity search
• OpenSearch Keyword Index: Provides traditional keyword-based retrieval capabilities to complement the limitations of pure vector retrieval
• Hybrid Search Strategy: Combines vector similarity and keyword matching to improve retrieval recall and precision

Generation Layer

• OpenAI API Integration: Uses GPT models to generate natural language answers based on retrieved context
• FastAPI Inference Service: Provides high-performance RESTful API interfaces to handle query requests and coordinate retrieval and generation processes

User Interaction Layer

• Streamlit Frontend Interface: A clean and intuitive web interface that supports natural language queries and result display
• Real-Time Response: After users input questions, the system immediately returns retrieval results and generated answers.

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The system uses OpenAI's embedding model to convert regulatory documents into high-dimensional vectors. This representation can capture the semantic information of text, so that content with similar semantics but different wording is close in the vector space. For example, "capital adequacy ratio" and "CAR ratio" are mapped to similar vector regions.

As a vector database, Qdrant supports efficient Approximate Nearest Neighbor (ANN) search, which can find the most relevant content from hundreds of thousands of document fragments in milliseconds.

Although pure vector retrieval can understand semantics, it may not be precise enough when dealing with specific terms, numbers, or proper nouns. The system introduces OpenSearch as a supplement to ensure retrieval quality in these exact matching scenarios through keyword indexing.

The fusion strategy for hybrid search usually uses weighted scoring: vector similarity scores and keyword matching scores are combined with certain weights, and the most relevant results are returned after final sorting.

The project uses Docker for containerization, packaging the API service and frontend UI into separate images. This design brings multiple benefits:

• Environmental Consistency: Development, testing, and production environments use exactly the same container images
• Rapid Scaling: Kubernetes can automatically adjust the number of Pods based on load
• Service Isolation: API and UI are deployed independently without interfering with each other

The deployment process uses AWS EKS (Elastic Kubernetes Service) as the orchestration platform, and images are stored in Amazon ECR (Elastic Container Registry). The frontend interface is exposed via a LoadBalancer service, allowing users to access it directly through a public URL.

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