Finance Tracker - AI-powered Financial Analysis

Overview

• First Prize Winner at Frosthack 2025
• This project is an AI-powered financial analysis system that processes and analyzes transaction data to provide insights and answer user queries. It integrates LangChain, Fetch.ai, and Pathway for efficient data processing and retrieval.

PS

• https://drive.google.com/file/d/1YEGmv8PXDTB0AETmod5dBxU7puimRLl0/view

video

• https://www.youtube.com/watch?v=DY6-u3Y996Q

Our Result

• https://ai-agent-hackatho.devpost.com/project-gallery

Features

• Parses and extracts financial transactions from documents.
• Stores structured financial data for quick retrieval.
• Provides financial insights based on user queries.
• Implements advanced retrieval-augmented generation (RAG) techniques.
• Uses Fetch.ai and Pathway for efficient data processing.
• Supports visualization of financial data trends.

Project Structure

├── agent_fetchai.py  #fetchAI Agents
├── agents
│   ├── DocToGDrive.py
│   ├── DocumentParsingAgent2.py
│   ├── DocumentParsingAgent.py
│   ├── GetReleventTransactionByDate.py
│   ├── GetReleventTransaction.py
│   ├── GetUserQueryOutput.py
│   ├── IsContextNeeded.py
│   └── __pycache__
├── app.py  
├── Cache
│   ├── 1-0-0
│   └── 1-0-1
├── credentials.json
├── data
│   └── finance_docs_extracted.csv
├── INFO
│   ├── data
│   ├── filtered_transactions.json
│   ├── output.json
│   ├── processed_output.json
│   ├── staticPlots
├── LanggraphApp.py
├── output_chunks
├── pathwayF
│   ├── baseModel.py
│   ├── langchainPathwayClient.py
│   ├── pathwayServer.py
│   ├── pdfToCSVByLLM.py
│   └── __pycache__
├── README.md
├── requirements.txt
└── staticVisualizationAgent.py

App Diagrams

Project Diagram

Fetch-AI Agent Diagram

Installation

1. Set Environment Variables

Before running the application, configure the following environment variables:

export PATHWAY_KEY=""
export LANGSMITH_TRACING=true
export LANGSMITH_ENDPOINT="https://api.smith.langchain.com"
export LANGSMITH_API_KEY=""
export LANGSMITH_PROJECT=""
export GEMINI_API_KEY="--"
export GROQ_API_KEY=""
export TAVILY_API_KEY=""
export DEEPSEEK_API_KEY=""
export HUGGINGFACEHUB_API_TOKEN=""
export GDRIVEFOLDERID=""

2. Set Up credentials.json

Create a credentials.json file in the root directory and populate it with your Google Cloud credentials:

{
  "type": "service_account",
  "project_id": "rag-app-frosthack",
  "private_key_id": "<PRIVATE_KEY_ID>",
  "private_key": "-----BEGIN PRIVATE KEY-----\n<PRIVATE_KEY>\n-----END PRIVATE KEY-----\n",
  "client_email": "<CLIENT_EMAIL>",
  "client_id": "<CLIENT_ID>",
  "auth_uri": "https://accounts.google.com/o/oauth2/auth",
  "token_uri": "https://oauth2.googleapis.com/token",
  "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
  "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/<SERVICE_ACCOUNT>.iam.gserviceaccount.com",
  "universe_domain": "googleapis.com"
}

Note: Replace <PRIVATE_KEY>, <CLIENT_EMAIL>, <CLIENT_ID>, and <SERVICE_ACCOUNT> with your actual credentials.

3. Install Dependencies

Run the following command to install required dependencies:

pip install -r requirements.txt

Running the Application

Follow these steps to start the application:

1. Start the Fetch.AI Agents

   python -m agents.ActivateVectorStore
   python -m agents.DocParserAgent
   python -m agents.getReleventTransaction
   python -m agents.GetUserQueryOutput
   python -m agents.GraphingAgent
   python -m agents.IsContextNeeded

2. Run the Streamlit App

   streamlit run app.py

Relevant Resources

• FetchAi with LangChain
• Pathway with LangChain
• Pathway Adaptive RAG with LangGraph
• Pathway as a Vector Store in LangChain
• Pathway Unstructured to Structured SQL Queries
• Pathway INTERIIT Project
• Google Drive with Additional Files

Contributing

Feel free to open issues and submit pull requests for improvements.