A newer version of the Gradio SDK is available: 6.20.0
metadata
title: MiniCPM Financial RAG
sdk: gradio
sdk_version: 5.34.0
python_version: '3.11'
app_file: app.py
pinned: false
tags:
- track:backyard
- sponsor:openbmb
- sponsor:modal
- achievement:offbrand
- achievement:fieldnotes
💰 MiniCPM Financial RAG
📊 Financial Document Intelligence Powered by Retrieval-Augmented Generation
Real-world Problem
Financial reports, insurance documents, annual reports, SEC filings, balance sheets, and investment documents often contain hundreds of pages of complex information.
Finding specific financial insights manually is time-consuming and error-prone.
MiniCPM Financial RAG enables users to upload financial PDF documents and ask questions in natural language. The system retrieves the most relevant information from the document and generates accurate, context-aware answers using MiniCPM models.
Build Small Hackathon Submission
Space: https://huggingface.co/spaces/build-small-hackathon/MiniCPM_Financial_RAG
Demo: https://youtu.be/0z1i5ESbgYk
Social: https://x.com/gajanand2004/status/2066422082725163265
Article: https://huggingface.co/blog/build-small-hackathon/minicpm-financial-rag
👥 Team
| Name | Hugging Face Username |
|---|---|
| Gajanan Deshmukh | Gaju19 |
RAG architecture
🤖 Models Used
| Task | Model | Parameters | Purpose |
|---|---|---|---|
| Financial Question Answering | openbmb/MiniCPM-2B-128K | 2B | Financial reasoning and answer generation |
| Embedding Generation | openbmb/MiniCPM-Embedding-Light | Lightweight | Semantic retrieval and vector search |
🧠 Why MiniCPM?
| Model | Benefits |
|---|---|
| MiniCPM-2B-128K | Lightweight, fast inference, long-context understanding |
| MiniCPM-Embedding-Light | Efficient embeddings with strong retrieval performance |
🚀 Features
| Feature | Description |
|---|---|
| 📄 PDF Upload | Upload financial reports and PDF documents |
| ✂️ Smart Chunking | Automatically split documents into meaningful chunks |
| 🔎 Semantic Search | Retrieve the most relevant financial information |
| 🧠 Financial Question Answering | Ask questions in natural language |
| 📚 Retrieval-Augmented Generation | Generate context-grounded answers |
| ⚡ GPU Acceleration | Fast inference using Modal GPU infrastructure |
| 📊 Financial Analysis | Analyze revenue, expenses, assets, and liabilities |
| 🎯 High Accuracy Retrieval | FAISS-based vector similarity search |
🎯 Example Questions
- What is the company's total revenue?
- What is the net income for this period?
- What are the major risk factors?
- Summarize the financial outlook.
- What liabilities are reported?
- What is the operating cash flow?
📚 Knowledge Pipeline
| Stage | Purpose |
|---|---|
| PDF Parsing | Extract text from PDF documents |
| Text Chunking | Break large documents into manageable sections |
| Embedding Generation | Convert text chunks into vector representations |
| FAISS Storage | Store vectors efficiently for retrieval |
| Similarity Search | Retrieve the most relevant document chunks |
| LLM Generation | Generate grounded answers from retrieved context |
⚙️ Tech Stack
| Layer | Technology |
|---|---|
| Frontend | Gradio |
| Backend | Modal |
| LLM | MiniCPM-2B-128K |
| Embeddings | MiniCPM-Embedding-Light |
| Vector Database | FAISS |
| Framework | LangChain |
| PDF Processing | PyPDFLoader |
| Deep Learning | PyTorch |
| Deployment | Hugging Face Spaces |
📈 Monitoring
The application is continuously monitored to ensure reliability and performance.
| Component | Monitoring Method |
|---|---|
| Hugging Face Space | Build Logs & Runtime Logs |
| Modal Backend | Endpoint Monitoring |
| Retrieval Pipeline | Context Validation |
| Vector Search | Similarity Search Accuracy |
| Question Answering | Response Validation |
| System Health | Runtime Monitoring |
🌐 Deployment Architecture
Hugging Face Spaces
│
▼
Gradio Frontend
│
▼
Modal Backend
│
┌──────┴──────┐
▼ ▼
MiniCPM QA FAISS Retrieval
📂 Project Structure
MiniCPM_Financial_RAG/
├── app.py
│
├── requirements.txt
├── Architecture.png
└── README.md
💻 Installation
Create Virtual Environment
python -m venv venv
Activate Environment
Windows
venv\Scripts\activate
Linux / Mac
source venv/bin/activate
Install Dependencies
pip install -r requirements.txt
🚀 Run Application
python app.py
Application URL:
http://localhost:8000
🎯 Target Users
| User Type | Use Case |
|---|---|
| Financial Analysts | Analyze reports and statements |
| Investors | Extract investment insights |
| Accountants | Review financial data quickly |
| Auditors | Validate financial information |
| Researchers | Analyze large financial documents |
| Students | Learn financial concepts interactively |
📊 Benefits
| Benefit | Description |
|---|---|
| Faster Analysis | Reduce manual document review time |
| Accurate Retrieval | Retrieve the most relevant financial information |
| Context-Aware Answers | Grounded responses from document content |
| Scalable Architecture | Handles large financial reports efficiently |
| Cost Effective | Uses lightweight MiniCPM models |