Finbot - AI-Driven Corporate Financial Analysis
Finbot - AI-Driven Corporate Financial Analysis
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Unlocking Corporate Financial Insights Using AI
Unlocking Corporate Financial Insights Using AI
Experience Finbot in action: Try the Deployed Chatbot
Finbot is a tool for analysing and extracting information from complex financial documents. By leveraging LangChain, OpenAI embeddings, and Faiss indexing, Finbot provides users with a robust solution for querying financial data. The integration of Retrieval-Augmented Generation ensures that the answers are both relevant and contextually accurate, making Finbot an invaluable asset for financial analysts, investors, and other stakeholders.
Project Objectives
Objective: To assist users in extracting pertinent financial information and responding to specific queries based on the content of financial documents.
Scope: The project focuses on processing 10-K and 10-Q financial documents, providing precise and contextually relevant answers.
Methodology
Data Understanding: Utilized financial documents in PDF format, specifically 10-K and 10-Q reports.
Data Preparation and Cleaning: Extracted and split documents into manageable chunks for analysis.
Embedding Generation: Converted text chunks into high-dimensional vectors using OpenAI's embedding model.
Faiss Indexing: Stored embeddings in a Faiss index for efficient similarity search.
Query Handling: Generated embeddings for user queries and retrieved the most relevant document chunks.
Answer Generation: Used retrieved chunks to generate contextually relevant answers using OpenAI's language model.
Results
The project successfully developed a chatbot that:
Processes: 10-K and 10-Q financial documents to extract key information.
Responds: To user queries with accurate and contextually relevant answers.
Leverages: Advanced NLP and machine learning techniques to streamline financial document analysis.
Key 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): Ensures 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.
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