title: Financial Qa Agent
emoji: 🚀
colorFrom: red
colorTo: red
sdk: docker
app_port: 8501
tags:
- streamlit
pinned: false
short_description: Streamlit template space
license: mit
Welcome to Streamlit!
Edit /src/streamlit_app.py to customize this app to your heart's desire. :heart:
If you have any questions, checkout our documentation and community forums.
📊 Financial QA Agent
An AI-powered financial report assistant built with RAG (Retrieval-Augmented Generation).
This app lets you upload financial reports, search them with semantic embeddings, and get concise answers/summaries using an open-source LLM.
🚀 Features
- Cleans financial report text files automatically
- Generates vector embeddings with FAISS for efficient retrieval
- Summarizes answers using
google/gemma-2b(or lightweight models for deployment) - Streamlit UI for easy interaction
- Evaluation pipeline with ROUGE, BLEU, and BERTScore
🛠️ Tech Stack
- Streamlit for UI
- FAISS for vector search
- Sentence-Transformers for embeddings
- Transformers (Gemma/LLMs) for summarization
- Scikit-learn, NLTK, BERTScore for evaluation metrics
📂 Project Structure
├── app.py # Main Streamlit app (entrypoint) ├── Embeddings.py # Embedding + FAISS pipeline ├── Data_Cleaning.py # Data cleaning utility ├── Logger.py # Logging utility ├── evaluation.py # Evaluation pipeline ├── config.json # Configurations ├── eval_dataset.json # Sample evaluation dataset ├── requirements.txt # Dependencies ├── README.md # Project documentation └── .gitignore # Ignore unnecessary files
⚡ Running Locally
pip install -r requirements.txt
streamlit run app.py