quant-mind: a knowledge extraction and retrieval framework for transforming financial research into a semantic knowledge graph

What it solves

QuantMind addresses the problem of information overload in quantitative finance, where hundreds of research papers, news articles, and reports are published daily. It automates the process of extracting structured knowledge from these unstructured sources, reducing the time and effort required for research teams to identify alpha-generating insights.

How it works

The system uses a decoupled two-stage architecture:

1. Knowledge Extraction: It pulls content from sources like arXiv, news feeds, and blogs using an intelligent parser to extract text, tables, and figures. A tagger categorizes the content, and an agent orchestrates the pipeline to ensure quality and deduplication.
2. Intelligent Retrieval: Extracted knowledge is converted into high-dimensional vectors (embeddings) and stored in a semantic knowledge graph. Users can then retrieve insights using natural language queries through patterns like RAG (Retrieval-Augmented Generation) and DeepResearch for complex multi-hop reasoning.

Who it’s for

QuantMind is designed for institutional investors, hedge funds, and quantitative research teams who need to process large volumes of financial research at scale.

Highlights

• Multi-source ingestion: Supports PDFs, web pages, and APIs (e.g., arXiv).
• Semantic Knowledge Graph: Transforms unstructured data into a queryable, structured format.
• Domain-specific LLMs: Utilizes LLMs fine-tuned for the finance domain.
• Flexible retrieval: Offers multiple scenarios including RAG and multi-hop reasoning for deep research.