FinMetaMind: A Tech Blueprint on NLQ Systems for Financial Knowledge Search
Lalit Pant, Shivang Nagar
TL;DR
This paper presents a blueprint for building natural language query (NLQ) systems tailored to financial knowledge search, integrating transformer-based embeddings, vector search, and hybrid retrieval within a two-service architecture: offline indexing and online retrieval. It details a four-pipeline offline indexing workflow (Frontier, Extract, AI Enrichment, Index) implemented on ECS, leveraging embedding models and OpenSearch with HNSW/IVF for fast, semantically aware indexing and keyword tagging. The online retrieval service combines keyword, semantic, and hybrid search with re-ranking, demonstrating that hybrid retrieval often yields the best relevance across financial queries. Real-world applications span text-to-SQL, retrieval-augmented generation, content recommendations, ML feature stores, and metadata governance, with future directions including advanced embeddings, knowledge graphs, multimodal inputs, and Agentic RAG for dynamic orchestration.
Abstract
Natural Language Query (NLQ) allows users to search and interact with information systems using plain, human language instead of structured query syntax. This paper presents a technical blueprint on the design of a modern NLQ system tailored to financial knowledge search. The introduction of NLQ not only enhances the precision and recall of the knowledge search compared to traditional methods, but also facilitates deeper insights by efficiently linking disparate financial objects, events, and relationships. Using core constructs from natural language processing, search engineering, and vector data models, the proposed system aims to address key challenges in discovering, relevance ranking, data freshness, and entity recognition intrinsic to financial data retrieval. In this work, we detail the unique requirements of NLQ for financial datasets and documents, outline the architectural components for offline indexing and online retrieval, and discuss the real-world use cases of enhanced knowledge search in financial services. We delve into the theoretical underpinnings and experimental evidence supporting our proposed architecture, ultimately providing a comprehensive analysis on the subject matter. We also provide a detailed elaboration of our experimental methodology, the data used, the results and future optimizations in this study.