FinBot: Open-Source AI for Parsing and Understanding Financial Statements
Table of Content
What is Finbot?
Finbot is a powerful tool designed to analyze and extract key insights from complex financial documents with ease. Built on cutting-edge AI technologies like LangChain, OpenAI embeddings, and Faiss vector indexing, Finbot enables fast, accurate, and intelligent querying of financial data.
By leveraging Retrieval-Augmented Generation (RAG), it ensures responses are not only context-aware but also highly relevant and factually grounded.
This makes Finbot an essential tool for financial analysts, investors, and decision-makers who need reliable, on-demand insights from reports, filings, and other dense financial materials.
Features
- AI-Driven Chatbot: Utilises OpenAI's models to understand and generate human language.
- LangChain: Integrated for managing prompts, handling document loading, and embedding text.
- Retrieval-Augmented Generation (RAG): Implements RAG to ensure that the responses are contextually relevant and grounded in the source documents.
- Faiss Index: Uses Faiss for efficient similarity search and clustering of dense vectors.
- User-Friendly Interface: Provides a seamless user experience with real-time feedback.
Components
1- Flask Web Application
The backend of Finbot is built using Flask, providing the essential tools to handle HTTP requests, manage file uploads, and serve the web interface.
2- LangChain
A framework designed to build applications that understand and generate human language, providing tools for managing prompts and integrating with various embeddings and language models.
3- OpenAI Embeddings
Converts textual data into high-dimensional vectors that capture semantic meaning and context, crucial for accurately addressing user queries based on the document content.
4- Faiss Index
A library for efficient similarity search and clustering of dense vectors, enabling rapid and precise retrieval of relevant document chunks in response to user queries.
How does it work? (Workflow)
- File Upload: Users upload 10-K or 10-Q financial documents in PDF format through the web interface.
- Document Processing: The PDF is processed to extract and split the document into manageable chunks.
- Embedding Generation: The text chunks are converted into embeddings using OpenAI's models.
- Faiss Indexing: The embeddings are stored in a Faiss index for efficient similarity search.
- Query Handling: Generates an embedding for the user query and retrieves the most relevant document chunks.
- Answer Generation: Uses the retrieved chunks to generate a contextually relevant answer.
Resources & Downloads
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