LEANN/benchmarks/financebench

FinanceBench Benchmark for LEANN-RAG

FinanceBench is a benchmark for evaluating retrieval-augmented generation (RAG) systems on financial document question-answering tasks.

Dataset

• Source: PatronusAI/financebench
• Questions: 150 financial Q&A examples
• Documents: 368 PDF files (10-K, 10-Q, 8-K, earnings reports)
• Companies: Major public companies (3M, Apple, Microsoft, Amazon, etc.)
• Paper: FinanceBench: A New Benchmark for Financial Question Answering

Structure

benchmarks/financebench/
├── setup_financebench.py        # Downloads PDFs and builds index
├── evaluate_financebench.py     # Intelligent evaluation script
├── data/
│   ├── financebench_merged.jsonl     # Q&A dataset
│   ├── pdfs/                         # Downloaded financial documents
│   └── index/                        # LEANN indexes
│       └── financebench_full_hnsw.leann
└── README.md

Usage

1. Setup (Download & Build Index)

cd benchmarks/financebench
python setup_financebench.py

This will:

• Download the 150 Q&A examples
• Download all 368 PDF documents (parallel processing)
• Build a LEANN index from 53K+ text chunks
• Verify setup with test query

2. Evaluation

# Basic retrieval evaluation
python evaluate_financebench.py --index data/index/financebench_full_hnsw.leann

# Include QA evaluation with OpenAI
export OPENAI_API_KEY="your-key"
python evaluate_financebench.py --index data/index/financebench_full_hnsw.leann --qa-samples 20

Evaluation Methods

Retrieval Evaluation

Uses intelligent matching with three strategies:

1. Exact text overlap - Direct substring matches
2. Number matching - Key financial figures ($1,577, 1.2B, etc.)
3. Semantic similarity - Word overlap with 20% threshold

QA Evaluation

LLM-based answer evaluation using GPT-4o:

• Handles numerical rounding and equivalent representations
• Considers fractions, percentages, and decimal equivalents
• Evaluates semantic meaning rather than exact text match

Expected Results

Previous runs show:

• Question Coverage: ~65-75% (questions with relevant docs retrieved)
• Index Size: 53,985 chunks from 368 PDFs
• Search Time: ~0.1-0.2s per query
• Build Time: ~5-10 minutes with sentence-transformers/all-mpnet-base-v2

Options

# Use different backends
python setup_financebench.py --backend diskann
python evaluate_financebench.py --index data/index/financebench_full_diskann.leann

# Use different embedding models
python setup_financebench.py --embedding-model facebook/contriever