{"repo":"Mattral/RAG-Multimodal-Financial-Doc-Analysis-and-Recall","free":true,"listed":false,"github":"https://github.com/Mattral/RAG-Multimodal-Financial-Doc-Analysis-and-Recall","clone":"git clone https://github.com/Mattral/RAG-Multimodal-Financial-Doc-Analysis-and-Recall.git","description":"Production-grade multimodal RAG for financial document intelligence. Chart understanding · hybrid retrieval · numeric guardrails · multi-tenancy · full observability.","language":"Python","stars":78,"topics":["async-processing","document-intelligence","enterprise-ai","financial-ai","llama-index","llm","machine-learning","multimodal","observability","production-system"],"license":"MIT","category":"ai-agents","readme_excerpt":"RAG Financial Multimodal — Enterprise v2.0 Production-grade multimodal RAG for financial document intelligence. Chart understanding · hybrid retrieval · numeric guardrails · multi-tenancy · full observability. --- System Architecture Ingestion (top) and query (bottom) pipelines. Every component is pluggable — switch provider by changing one config value. --- Why This System Financial documents are mixed-media: narrative text, tables, charts, footnotes, cross-references. Standard RAG pipelines fail on charts and hallucinate numbers. Problem Solution --- --- Charts contain the most important data but RAG ignores them GPT-4o / Gemini / Qwen2-VL vision extraction — every chart yields exact axis values Exact figures like $23.35B or TSLA miss semantic search Hybrid RRF: dense embeddings + BM25 keyword fused with Reciprocal Rank Fusion LLMs fabricate financial numbers Numeric grounding guardrail — every stated number verified against source context PII in analyst queries leaks to APIs Presidio + CUSIP/ISIN/account number redaction before any external call One broken vendor = full outage Fallback chains — primary → secondary → local for every model-facing layer --- Retrieval Quality Hybrid RRF achieves 89% Recall@5 and 84% Precision@5 on our 22-sample financial QA benchmark — 25% better recall than dense-only and 41% better than BM25-only. --- Quickstart Or use the CLI: --- Multi-Provider Support Every model-facing layer (text generation, vision, embeddings, vector store) is independ","default_branch":null,"files":null,"tree":[],"storefront":"/r/Mattral","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/Mattral/RAG-Multimodal-Financial-Doc-Analysis-and-Recall/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}