FinancialDatasets
title: financial-datasets radar_quadrant: Languages & Frameworks radar_ring: Assess radar_position: outer
financial-datasets is an open-source Python library for generating structured question-and-answer datasets from financial documents using an LLM. It accepts raw text, PDF URLs, or SEC filings (10-K, 10-Q by ticker and year) and produces JSON arrays of question, answer, and context triples suitable for RAG pipeline evaluation or fine-tuning. The library is developed by Virat Tyagi, has approximately 430 GitHub stars, and was last updated in May 2026.
Three generation modes are provided: generate_from_texts for arbitrary string lists, generate_from_pdf for any accessible PDF URL, and generate_from_10K for SEC EDGAR filings by ticker and year. The generator wraps the OpenAI API (GPT-4 Turbo in the documented examples); no model-agnostic path is exposed in the public interface.
Placed in Languages & Frameworks / Assess / outer.
The underlying pattern — using an LLM to synthesise QA pairs from domain documents as a RAG evaluation corpus — is broadly applicable to any document-heavy RAG pipeline. financial-datasets provides a working implementation of that pattern scoped to financial documents.
Outer position reflects three constraints that limit near-term applicability. First, the library is domain-specific: it provides no generalisation beyond financial texts, and the generate_from_10K convenience method is only relevant to finance use cases. Second, the generator is GPT-4 dependent with no documented path to substitute another model, creating a per-run API cost and a vendor lock-in. Third, at 430 stars the community is small and the abstraction layer is thin — comparable results are achievable with a direct LangChain or LlamaIndex pipeline without the additional dependency.
The blip is worth tracking for anyone building financial RAG pipelines; the SEC 10-K integration (ticker + year → structured QA corpus) eliminates meaningful boilerplate. For general RAG evaluation dataset generation the pattern deserves its own technique article rather than this specific library.
Trial gate: QA dataset generated from at least one real 10-K or financial PDF, integrated into a RAG evaluation harness with measurable retrieval quality improvement over a baseline.
