FinanceBench evaluation of Mafin 2.5 (Powered by PageIndex)
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Updated
Oct 20, 2025 - Python
FinanceBench evaluation of Mafin 2.5 (Powered by PageIndex)
Advanced RAG pipelines for medical (HealthBench, MedCaseReasoning, MetaMedQA, PubMedQA) and financial (FinanceBench, Earnings Calls) QA. LangGraph orchestration + BAML structructed generation, Milvus Hybrid search (Dense + BM25 + RRF), three-layer Metadata Enrichment, Contextual AI instruction-following reranker, and DeepEval evaluation.
Multi-agent LangGraph RAG for financial Q&A — 72.7% on FinanceBench under κ=0.932 calibrated judge. RBAC at the vector layer, multi-party HITL on high-stakes answers, self-hosted LLM observability. pip install financebench-rag-agent
Finance-grade tool-calling agent over SEC filings for any US public company: exact XBRL numbers (never LLM arithmetic), every claim cited, abstains instead of fabricating. Validated on FinanceBench (44%→87% coverage, near-zero hallucination) with a 3-layer eval and a domain judge calibrated to human labels (κ=0.76). LangGraph + pgvector.
🔍 Empower efficient retrieval with PageIndex, a reasoning-based system that eliminates the need for vector databases and chunking for human-like results.
FinanceBench financial-report RAG with FAISS, Flask, Ragas, and financial numeric accuracy evaluation
Production RAG pipeline on FinanceBench: hybrid BM25+dense+RRF+Cohere Rerank, Anthropic Contextual Retrieval, conformal abstention (Yadkori 2024), Citations API grounding, triple eval (RAGAS+HHEM+DeepEval). Every eval number is reproducible and CI-gated.
a deliberately challenging benchmark built around real financial filings, where retrieving the right evidence is just as important as generating the answer.
Grounded Q&A over financial filings (10-Ks/10-Qs): routes to the right document, decomposes the question, retrieves, and answers with page citations — or abstains when unsure. Simple-agentic RAG; FinanceBench hardest subset: 0.71 corpus / ~0.79 per-doc vs 0.19 naive.
An end-to-end RAG pipeline that answers questions about SEC 10-K filings with grounded citations instead of hallucinated numbers. Built on FinanceBench: indexing with FAISS + BGE embeddings, generation with Llama-3.3-70B, three-axis evaluation (correctness / faithfulness / page-hit@k), improvement cycles.
RAG pipeline for FinanceBench with retrieval, evaluation, improvement cycles, and chunk-size experiments.
Rigorous evaluation of contextual retrieval techniques on FinanceBench: comparing 5 embedders × 4 chunking strategies with bootstrapped confidence intervals on FinMTEB and FinanceBench.
Five RAG architectures built from scratch in plain Python — Naive, Hybrid, Agentic, Graph & production-grade Advanced — over real 10-K filings, evaluated on FinanceBench. No LangChain, no API keys, 100% local.
A RAG pipeline built and evaluated on the FinanceBench dataset — retrieval, generation, and multi-dimensional evaluation across improvement cycles.
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