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
Vouch — 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. 92% coverage with zero hallucinations on FinanceBench, 97% of narrative answers grounded, behind a paired per-case regression gate. LangGraph + pgvector on Cloud Run.
Audit-grade financial RAG engine over SEC 10-K filings featuring table-aware chunking, hybrid BM25 + dense search, and deterministic cell verification via sandboxed Python AST.
Measuring RAG Groundedness on Financial Filings
FinanceBench financial-report RAG with FAISS, Flask, Ragas, and financial numeric accuracy evaluation
Enterprise Multimodal Document Intelligence, Hybrid RAG (Qdrant + BM25 RRF), Cross-Encoder Reranking & QLoRA/vLLM Engine
Page-level retrieval on FinanceBench's SEC filings: a company and period filter and statement routing take held-out Hit@10 from 0.14 to 0.71. BM25 vs MiniLM, load test, browser port.
Chat with your documents: page-cited answers and refusals, fully local on one GPU. 61.8% on FinanceBench (GPT-4-Turbo RAG baseline: 19%).
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.
Local RAG over SEC financial reports, evaluated on FinanceBench: 75.3% top-10 page retrieval, LLM-as-judge answer grading, all experiments measured.
A filings analyst that cites the page behind every figure, scored on FinanceBench. Retrieval measured; model runs next.
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.
Does a table-aware PDF parser retrieve better? Measured on FinanceBench: 53,901 pages, 150 questions, and a difference that did not survive a paired test.
RAG for financial QA on FinanceBench: FAISS + bge embeddings + Llama-3.3-70B, evaluated on correctness, RAGAS faithfulness and hit-rate
Research agent that answers analyst questions from SEC filings using the XBRL API, filing search and a calculator. Graded on FinanceBench and on a contamination-free benchmark of post-cutoff filings, with stage-level error analysis and an MCP server.
RAG assistant for SEC filings that cites its pages. Evaluated on 150 FinanceBench questions and stress-tested on amended filings, wrong-company pages and prompt injection, across three models. LangChain, LangGraph, Chroma, FastAPI, MCP, Google ADK.
SEC dosyaları üzerinde ölçülmüş finans RAG hattı: e5 + belge yönlendirme + reranker + Gemini. Ön kayıtlı deneyler, kilitli test, olumsuz sonuçlar dahil.
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