Financial reports such as 10-K, 10-Q, and annual filings contain valuable business insights, but they are often lengthy, complex, and difficult to analyze manually.
This project leverages Retrieval-Augmented Generation to build an AI-powered financial document question-answering system. The application allows users to upload financial PDF reports and ask natural-language questions about revenue, net income, operating expenses, earnings per share, risk factors, business performance, and other key financial metrics.
The system converts financial PDFs into structured text, splits the content into meaningful chunks, stores embeddings in a FAISS vector database, retrieves the most relevant sections, and generates context-aware answers using a Groq-powered Llama model.
This project demonstrates an end-to-end Generative AI workflow, including document ingestion, PDF parsing, text chunking, vector embeddings, semantic search, retrieval-based question answering, and an interactive Gradio user interface.
Processes financial reports such as 10-Q, 10-K, and annual reports.
Converts complex PDF documents into structured Markdown text using Docling.
Uses RAG architecture to retrieve relevant financial context before generating answers.
Ensures responses are grounded in the uploaded document instead of relying only on the language model.
Creates vector embeddings using HuggingFace Sentence Transformers.
Stores and retrieves relevant document chunks using FAISS vector database.
Uses Groq API with Llama model for fast and efficient response generation.
Answers financial questions in a clear and structured format.
Provides a simple web-based interface for uploading financial documents.
Allows users to ask questions directly after document processing.
Supports questions related to:
Revenue performance Net income Earnings per share Operating expenses Product vs service sales Year-over-year comparison Risk factors Business performance summary
LangChain Groq API Llama Model Retrieval-Augmented Generation
FAISS HuggingFace Sentence Transformers all-MiniLM-L6-v2
Docling PDF to Markdown Conversion
Gradio
Python Jupyter Notebook
The user uploads a financial PDF report through the Gradio interface.
The uploaded PDF is converted into Markdown text using Docling.
The converted Markdown content is split into smaller sections using LangChain text splitters.
Each text chunk is converted into vector embeddings using HuggingFace Sentence Transformers.
The generated embeddings are stored in a FAISS vector database for efficient similarity search.
When a user asks a question, the system retrieves the most relevant chunks from the financial document.
The retrieved context is passed to the Groq Llama model to generate a document-grounded answer.
The final answer is displayed through the Gradio interface.
What was the company’s total revenue?
How does the current quarter revenue compare with the previous year?
What was the net income for the reporting period?
What were the earnings per share?
How much revenue came from product sales versus service sales?
What were the main operating expense categories?
What are the key risk factors mentioned in the report?
Summarize the company’s financial performance.
The system reduces the time required to manually search through lengthy financial filings.
The RAG pipeline ensures that answers are generated based on the uploaded financial document.
FAISS enables fast semantic search across large financial reports.
This solution can support financial analysts, investors, business teams, and researchers in quickly extracting insights from financial documents.
Generative AI Retrieval-Augmented Generation LangChain Vector Databases FAISS HuggingFace Embeddings Groq API LLM Application Development Financial Document Analysis PDF Processing Semantic Search Gradio App Development Python Prompt Engineering
Financial reports are information-rich but time-consuming to analyze. This application helps users quickly extract meaningful insights from financial documents using natural-language questions.
By combining semantic search with LLM-powered answer generation, the solution improves accessibility, speeds up financial analysis, and supports better decision-making for business and investment research.
This project is built for educational and portfolio purposes only. It does not provide financial advice, investment recommendations, or professional financial analysis.