https://github.com/abhay-kanwasi/ai_powered_financial_analyst
LangChain with Groq's LLM to analyze financial reports from PDF files. It has been optimized for simplicity and efficiency you can ask questions about provided pdf
https://github.com/abhay-kanwasi/ai_powered_financial_analyst
aisolution chat-application finance-analytics groq python streamlit
Last synced: 5 months ago
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LangChain with Groq's LLM to analyze financial reports from PDF files. It has been optimized for simplicity and efficiency you can ask questions about provided pdf
- Host: GitHub
- URL: https://github.com/abhay-kanwasi/ai_powered_financial_analyst
- Owner: Abhay-Kanwasi
- Created: 2025-04-09T17:36:54.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2025-04-09T18:11:08.000Z (8 months ago)
- Last Synced: 2025-04-09T18:48:12.999Z (8 months ago)
- Topics: aisolution, chat-application, finance-analytics, groq, python, streamlit
- Language: Python
- Homepage: https://abhay-kanwasi-ai-powered-financial-analyst-app-5midtu.streamlit.app/
- Size: 125 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: Readme.md
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README
# AI-Powered Financial Analyst
A powerful AI-driven tool that analyzes financial reports from PDF files using LangChain with Groq's LLM. This application extracts text from financial PDFs and provides intelligent analysis through a conversational interface, making complex financial data more accessible and understandable.
## 📋 Table of Contents
- [Overview](#overview)
- [Features](#features)
- [System Requirements](#system-requirements)
- [Installation](#installation)
- [Usage](#usage)
- [Project Structure](#project-structure)
- [How It Works](#how-it-works)
- [Troubleshooting](#troubleshooting)
- [Contributing](#contributing)
- [License](#license)
## 🔍 Overview
The AI-Powered Financial Analyst is designed to help users quickly extract insights from financial reports. By leveraging the power of Large Language Models (specifically Groq's LLama-3), the application can understand complex financial documents and answer questions about them in natural language.
## ✨ Features
- **Efficient PDF Text Extraction**: Quickly extracts text from PDF financial reports
- **Advanced Financial Analysis**: Powered by Groq's LLama-3 model (70B parameters)
- **Conversational AI Agent**: Interact with your financial data through natural language
- **Memory Capabilities**: The AI remembers context from your conversation
- **User-Friendly Interface**: Streamlit web interface for easy interaction
- **PDF Upload**: Easily upload PDF files through the web interface
- **Chat History**: View and track your conversation with the AI analyst
- **Error Handling**: Clear messages for non-text PDFs and extraction issues
## 💻 System Requirements
- Python 3.8 or higher
- Internet connection (for API calls to Groq)
- 100MB+ of free disk space
- Modern web browser (for Streamlit interface)
## 🔧 Installation
1. **Clone the repository**
```bash
git clone https://github.com/yourusername/AI_powered_financial_analyst.git
cd AI_powered_financial_analyst
```
2. **Create a virtual environment (recommended)**
```bash
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
```
3. **Install dependencies**
```bash
pip install -r requirements.txt
```
4. **Set up your API key**
Create a `.env` file in the root directory with your Groq API key:
```
GROQ_API_KEY=your_groq_api_key_here
```
You can obtain a Groq API key by signing up at [https://console.groq.com/](https://console.groq.com/)
## 🚀 Usage
1. Launch the Streamlit app:
```bash
streamlit run app.py
```
2. Your default web browser will open automatically with the application
3. Use the sidebar to upload a financial PDF report
4. Once the PDF is processed, you can ask questions in the chat interface
5. Your conversation history will be displayed in the main area
## 📁 Project Structure
```
.
├── app.py # Streamlit web interface
├── config.py # Configuration settings
├── requirements.txt # Project dependencies
└── src/ # Source code
├── agent.py # LangChain agent implementation
├── extraction.py # PDF text extraction functions
└── models.py # LLM model configuration
```
## ⚙️ How It Works
1. **PDF Processing**: The application extracts text from the uploaded PDF using PyMuPDF (fitz)
2. **LLM Integration**: The extracted text is processed by Groq's LLama-3 model via LangChain
3. **Agent Creation**: A conversational agent is created with the financial report context
4. **User Interaction**: Users can ask questions about the report in natural language
5. **Response Generation**: The AI analyzes the report and generates informative responses
## ❓ Troubleshooting
- **API Key Issues**: Ensure your Groq API key is correctly set in the `.env` file
- **PDF Extraction Errors**: Make sure your PDF is text-based and not scanned/image-based
- **Model Responses**: If responses are incomplete, the PDF might be too large; try with a smaller document
- **Memory Issues**: For very large reports, the system might need more memory; consider splitting the PDF
## 🤝 Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
## 📄 License
This project is licensed under the MIT License - see the LICENSE file for details.