dockerized app

.dockerignore

@@ -0,0 +1,67 @@
+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+*.egg-info/
+dist/
+build/
+*.egg
+
+# Virtual environments
+.venv/
+venv/
+env/
+ENV/
+
+# IDE
+.vscode/
+.idea/
+*.swp
+*.swo
+*~
+
+# Jupyter
+.ipynb_checkpoints/
+*.ipynb
+
+# Environment files
+.env.local
+.env.*.local
+
+# Git
+.git/
+.gitignore
+
+# Temporary files
+*.log
+*.sqlite
+*.db
+temp_data/
+qdrant_db/
+merchant_cache.json
+
+# Data files (upload via UI instead)
+demo_data/
+data/
+
+# Logs
+logs/
+*.log
+
+# Docker
+Dockerfile
+docker-compose.yml
+.dockerignore
+
+# Documentation
+README.md
+ARCHITECTURE.md
+*.md
+
+# macOS
+.DS_Store
+
+# Notebooks (testing only)
+notebooks/

Dockerfile

@@ -0,0 +1,41 @@
+# Use Python 3.12 slim image
+FROM python:3.12-slim
+
+# Set working directory
+WORKDIR /app
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y \
+    build-essential \
+    curl \
+    && rm -rf /var/lib/apt/lists/*
+
+
+ENV STREAMLIT_BROWSER_GATHER_USAGE_STATS=false
+
+# Copy requirements first for better caching
+COPY requirements.txt .
+
+# Install Python dependencies
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy application code
+COPY . .
+
+# Create directories for data persistence
+RUN mkdir -p /app/data /app/logs
+
+# Expose Streamlit default port
+EXPOSE 8501
+
+# Health check
+HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \
+    CMD curl --fail http://localhost:8501/_stcore/health || exit 1
+
+# Set environment variables
+ENV PYTHONUNBUFFERED=1
+ENV STREAMLIT_SERVER_PORT=8501
+ENV STREAMLIT_SERVER_ADDRESS=0.0.0.0
+
+# Run Streamlit app
+CMD ["streamlit", "run", "app.py", "--server.port=8501", "--server.address=0.0.0.0", "--server.maxUploadSize=200", "--server.maxMessageSize=200", "--server.runOnSave=true", "--server.enableXsrfProtection=false", "--server.enableCORS=false"]

README.md

@@ -1,10 +1,346 @@
+# MoneyRAG - Personal Finance Transaction Analysis
+
+AI-powered financial transaction analysis using RAG (Retrieval-Augmented Generation) with Model Context Protocol (MCP) integration.
+
+## Features
+
+- **Smart CSV Ingestion**: Automatically maps any CSV format to standardized transaction schema using LLM
+- **Multi-Provider Support**: Works with Google Gemini and OpenAI models
+- **Merchant Enrichment**: Automatically enriches transactions with web-searched merchant information
+- **Dual Storage**: SQLite for structured queries + Qdrant for semantic search
+- **MCP Integration**: Leverages Model Context Protocol for tool-based agent interactions
+- **Interactive UI**: Streamlit-based web interface for chat-based analysis
+- **Dockerized**: Complete containerized deployment ready for production
+
 ## Architecture
 
-<<<<<<< Updated upstream
-See [Architecture v1](ARCHITECTURE.md) for detailed system design.
-=======
-# Tutorial on getting transaction history
+```mermaid
+%%{init: {'theme': 'base', 'themeVariables': { 'primaryColor': '#fff', 'primaryBorderColor': '#333', 'primaryTextColor': '#333', 'lineColor': '#666' }}}%%
+
+graph TD
+    %% --- Top Layer: Entry Point ---
+    subgraph UI["💻 User Interface"]
+        Streamlit["🌐 Streamlit Web App<br/><i>Interactive Dashboard</i>"]
+    end
+
+    %% --- Middle Layer: Split Processes ---
+    
+    %% Left Column: Ingestion (The Write Path)
+    subgraph Ingestion["📥 Data Pipeline (Write)"]
+        direction TB
+        CSV["📄 CSV Upload<br/><i>Raw Data</i>"]
+        Mapper["🧠 LLM Mapper<br/><i>Schema Norm.</i>"]
+        Enrich["🔍 Web Enrich<br/><i>DuckDuckGo</i>"]
+        
+        CSV --> Mapper
+        Mapper --> Enrich
+    end
+
+    %% Right Column: Intelligence (The Read Path)
+    subgraph Agent["🤖 AI Orchestration (Read)"]
+        direction TB
+        Brain["🧩 LangGraph Agent<br/><i>Controller</i>"]
+        LLM["✨ LLM Model<br/><i>Gemini / GPT-4</i>"]
+        Brain <-->|Inference| LLM
+    end
+
+    subgraph MCP["🔧 MCP Tool Server"]
+        direction LR
+        SQL_Tool["⚡ SQL Tool<br/><i>Structured</i>"]
+        Vector_Tool["🎯 Vector Tool<br/><i>Semantic</i>"]
+    end
+
+    %% --- Bottom Layer: Persistence ---
+    subgraph Storage["💾 Storage Layer"]
+        direction LR
+        SQLite[("🗄️ SQLite")]
+        Qdrant[("🔮 Qdrant")]
+    end
+
+    %% --- Connections & Logic ---
+    
+    %% 1. User Actions
+    Streamlit -->|1. Upload| CSV
+    Streamlit -->|3. Query| Brain
+
+    %% 2. Ingestion to Storage flow
+    Enrich -->|2. Store| SQLite
+    Enrich -->|2. Embed| Qdrant
+
+    %% 3. Agent to Tools flow
+    Brain -->|4. Route| SQL_Tool
+    Brain -->|4. Route| Vector_Tool
+    
+    %% 4. Tools to Storage flow (Vertical alignment matches)
+    SQL_Tool <-->|5. Read/Write| SQLite
+    Vector_Tool <-->|5. Search| Qdrant
+    
+    %% 5. Return Path
+    Brain -.->|6. Response| Streamlit
+
+    %% --- Styling ---
+    classDef ui fill:#E3F2FD,stroke:#1565C0,stroke-width:2px,color:#0D47A1,rx:10,ry:10
+    classDef ingest fill:#E8F5E9,stroke:#2E7D32,stroke-width:2px,color:#1B5E20,rx:5,ry:5
+    classDef agent fill:#F3E5F5,stroke:#7B1FA2,stroke-width:2px,color:#4A148C,rx:5,ry:5
+    classDef mcp fill:#FFF3E0,stroke:#EF6C00,stroke-width:2px,color:#E65100,rx:5,ry:5
+    classDef storage fill:#ECEFF1,stroke:#455A64,stroke-width:2px,color:#263238,rx:5,ry:5
+
+    class Streamlit ui
+    class CSV,Mapper,Enrich ingest
+    class Brain,LLM agent
+    class SQL_Tool,Vector_Tool mcp
+    class SQLite,Qdrant storage
+
+    %% Curve the lines for better readability
+    linkStyle default interpolate basis
+```
+
+## Quick Start
+
+### Option 1: Docker (Recommended)
+
+1. **Run the application**
+   ```bash
+   ./docker-run.sh
+   ```
+   Choose option 1 to build and run.
+
+2. **Access the app**
+   Open browser: http://localhost:8501
+
+3. **Enter your API key**
+   - Select your LLM provider (Google or OpenAI)
+   - Choose your model
+   - Enter your API key in the sidebar
+   - Click "Authenticate"
+
+**Note:** No `.env` file is required! API keys are entered through the web interface.
+
+### Option 2: Local Development
+
+1. **Install dependencies**
+   ```bash
+   python -m venv .venv
+   source .venv/bin/activate  # On Windows: .venv\Scripts\activate
+   pip install -r requirements.txt
+   ```
+
+2. **Run Streamlit app**
+   ```bash
+   streamlit run app.py
+   ```
+
+3. **Access the app**
+   Open browser: http://localhost:8501
+
+## Usage
+
+1. **Authenticate**: Enter your API key in the sidebar
+2. **Upload CSV**: Upload one or more transaction CSV files
+3. **Ask Questions**: Chat with your financial data:
+   - "How much did I spend on restaurants last month?"
+   - "What are my largest expenses?"
+   - "Show me all transactions over $100"
+   - "Analyze my spending patterns"
+
+## CSV Format Support
+
+MoneyRAG automatically handles different CSV formats including:
+- **Chase Bank**: Negative values for spending
+- **Discover**: Positive values for spending
+- **Custom formats**: LLM-based column mapping
+
+Required information (can have any column names):
+- Date
+- Merchant/Description
+- Amount
+- Category (optional)
+
+## Project Structure
+
+```
+money_rag/
+├── app.py                 # Streamlit UI
+├── money_rag.py          # Core RAG engine
+├── mcp_server.py         # MCP server with tools
+├── requirements.txt      # Python dependencies
+├── Dockerfile           # Container definition
+├── docker-compose.yml   # Docker orchestration
+├── docker-run.sh        # Helper script
+└── utils/
+    └── data_ingestion.py # Data processing utilities
+```
+
+## Docker Commands
+
+### Using the Helper Script
+```bash
+./docker-run.sh
+```
+Interactive menu with options:
+- Build and run
+- Start/stop
+- View logs
+- Clean up
+
+### Manual Docker Compose Commands
+
+**Build and start:**
+```bash
+docker-compose up -d --build
+```
+
+**Stop:**
+```bash
+docker-compose down
+```
+
+**View logs:**
+```bash
+docker-compose logs -f
+```
+
+**Rebuild after code changes:**
+```bash
+docker-compose build --no-cache
+docker-compose up -d
+```
+
+## Configuration
+
+### API Keys
+
+**API keys are entered through the Streamlit UI** - no environment variables needed!
+
+For standalone MCP server or development, optionally create a `.env` file:
+```bash
+cp .env.example .env
+# Edit with your keys (optional)
+```
+
+### Supported Models
+
+**Google (via Google AI Studio):**
+- gemini-2.0-flash-exp
+- gemini-1.5-flash
+- gemini-1.5-pro
+
+**OpenAI:**
+- gpt-4o
+- gpt-4o-mini
+
+### Data Storage
+
+- **SQLite**: Structured transaction data (session-based)
+- **Qdrant**: Vector embeddings for semantic search (session-based)
+- **Temporary**: All data is ephemeral per session
+
+## Troubleshooting
+
+### Container won't start
+```bash
+docker-compose logs
+```
+
+### Check container health
+```bash
+docker ps
+docker inspect money-rag-app | grep Health
+```
+
+### Reset everything
+```bash
+docker-compose down -v
+docker rmi money_rag-money-rag
+./docker-run.sh  # Choose option 1
+```
+
+### MCP Server Issues
+The MCP server runs as a subprocess. If you see connection errors:
+1. Check logs: `docker-compose logs -f`
+2. Verify mcp_server.py exists: `docker exec money-rag-app ls -la`
+
+### Permission Issues
+```bash
+chmod +x docker-run.sh
+sudo chown -R $USER:$USER data logs
+```
+
+## Production Deployment
+
+### Using Docker Hub
+
+1. **Tag and push:**
+   ```bash
+   docker tag money-rag:latest your-username/money-rag:latest
+   docker push your-username/money-rag:latest
+   ```
+
+2. **Pull and run on server:**
+   ```bash
+   docker pull your-username/money-rag:latest
+   docker run -d -p 8501:8501 your-username/money-rag:latest
+   ```
+
+### Cloud Platforms
+
+**Google Cloud Run:**
+```bash
+gcloud builds submit --tag gcr.io/PROJECT-ID/money-rag
+gcloud run deploy money-rag \
+  --image gcr.io/PROJECT-ID/money-rag \
+  --platform managed \
+  --allow-unauthenticated
+```
+
+**AWS ECS / Azure Container Instances:**
+- Build and push to respective container registries
+- Deploy using platform-specific CLI tools
+
+## Security Notes
+
+⚠️ **Important:**
+- API keys are entered via UI and stored only in session state (not persisted)
+- Keys are cleared when browser session ends
+- Transaction data is session-based and ephemeral
+- No sensitive data stored in environment variables or files
+- For production, implement secure session management and authentication
+
+## Development
+
+### Hot Reload
+Mount code as volume in docker-compose.yml:
+```yaml
+volumes:
+  - ./app.py:/app/app.py
+  - ./money_rag.py:/app/money_rag.py
+  - ./mcp_server.py:/app/mcp_server.py
+```
+
+### Testing
+```bash
+# Run unit tests (if available)
+pytest tests/
+
+# Test CSV ingestion
+python -c "from money_rag import MoneyRAG; ..."
+```
+
+## Technologies
+
+- **LangChain**: Agent orchestration and tool integration
+- **LangGraph**: Conversational agent with memory
+- **MCP**: Model Context Protocol via langchain-mcp-adapters
+- **Qdrant**: Vector database for semantic search
+- **SQLite**: Relational database for structured queries
+- **Streamlit**: Web UI framework
+- **FastMCP**: MCP server implementation
+- **DuckDuckGo**: Web search for merchant enrichment
+
+## License
+
+MIT
+
+## Contributing
 
-- For discover card: https://www.youtube.com/watch?v=SuR3EItxw30
-- For chase card: 
->>>>>>> Stashed changes
+Contributions welcome! Please open an issue or submit a pull request.

app.py

@@ -34,6 +34,112 @@ This app helps you analyze your personal finances using AI.
 Upload your bank/credit card CSV statements to chat with your data semantically.
 """)
 
+# Guides Section
+col1, col2 = st.columns(2)
+
+with col1:
+    with st.expander("📚 How to get API keys"):
+        st.markdown("**Google Gemini API:**")
+        st.markdown("🔗 [Get API key from Google AI Studio](https://aistudio.google.com/app/apikey)")
+        st.markdown("")
+        st.markdown("**OpenAI API:**")
+        st.markdown("🔗 [Get API key from OpenAI Platform](https://platform.openai.com/api-keys)")
+
+with col2:
+    with st.expander("📥 How to download transaction history"):
+        st.markdown("**Chase Credit Card:**")
+        st.video("https://www.youtube.com/watch?v=gtAFaP9Lts8")
+        st.markdown("")
+        st.markdown("**Discover Credit Card:**")
+        st.video("https://www.youtube.com/watch?v=cry6-H5b0PQ")
+
+# Architecture Diagram
+with st.expander("🏗️ How MoneyRAG Works"):
+    st.markdown("""
+    ```mermaid
+    %%{init: {'theme': 'base', 'themeVariables': { 'primaryColor': '#fff', 'primaryBorderColor': '#333', 'primaryTextColor': '#333', 'lineColor': '#666' }}}%%
+
+    graph TD
+        %% --- Top Layer: Entry Point ---
+        subgraph UI["💻 User Interface"]
+            Streamlit["🌐 Streamlit Web App<br/><i>Interactive Dashboard</i>"]
+        end
+
+        %% --- Middle Layer: Split Processes ---
+        
+        %% Left Column: Ingestion (The Write Path)
+        subgraph Ingestion["📥 Data Pipeline (Write)"]
+            direction TB
+            CSV["📄 CSV Upload<br/><i>Raw Data</i>"]
+            Mapper["🧠 LLM Mapper<br/><i>Schema Norm.</i>"]
+            Enrich["🔍 Web Enrich<br/><i>DuckDuckGo</i>"]
+            
+            CSV --> Mapper
+            Mapper --> Enrich
+        end
+
+        %% Right Column: Intelligence (The Read Path)
+        subgraph Agent["🤖 AI Orchestration (Read)"]
+            direction TB
+            Brain["🧩 LangGraph Agent<br/><i>Controller</i>"]
+            LLM["✨ LLM Model<br/><i>Gemini / GPT-4</i>"]
+            Brain <-->|Inference| LLM
+        end
+
+        subgraph MCP["🔧 MCP Tool Server"]
+            direction LR
+            SQL_Tool["⚡ SQL Tool<br/><i>Structured</i>"]
+            Vector_Tool["🎯 Vector Tool<br/><i>Semantic</i>"]
+        end
+
+        %% --- Bottom Layer: Persistence ---
+        subgraph Storage["💾 Storage Layer"]
+            direction LR
+            SQLite[("🗄️ SQLite")]
+            Qdrant[("🔮 Qdrant")]
+        end
+
+        %% --- Connections & Logic ---
+        
+        %% 1. User Actions
+        Streamlit -->|1. Upload| CSV
+        Streamlit -->|3. Query| Brain
+
+        %% 2. Ingestion to Storage flow
+        Enrich -->|2. Store| SQLite
+        Enrich -->|2. Embed| Qdrant
+
+        %% 3. Agent to Tools flow
+        Brain -->|4. Route| SQL_Tool
+        Brain -->|4. Route| Vector_Tool
+        
+        %% 4. Tools to Storage flow (Vertical alignment matches)
+        SQL_Tool <-->|5. Read/Write| SQLite
+        Vector_Tool <-->|5. Search| Qdrant
+        
+        %% 5. Return Path
+        Brain -.->|6. Response| Streamlit
+
+        %% --- Styling ---
+        classDef ui fill:#E3F2FD,stroke:#1565C0,stroke-width:2px,color:#0D47A1,rx:10,ry:10
+        classDef ingest fill:#E8F5E9,stroke:#2E7D32,stroke-width:2px,color:#1B5E20,rx:5,ry:5
+        classDef agent fill:#F3E5F5,stroke:#7B1FA2,stroke-width:2px,color:#4A148C,rx:5,ry:5
+        classDef mcp fill:#FFF3E0,stroke:#EF6C00,stroke-width:2px,color:#E65100,rx:5,ry:5
+        classDef storage fill:#ECEFF1,stroke:#455A64,stroke-width:2px,color:#263238,rx:5,ry:5
+
+        class Streamlit ui
+        class CSV,Mapper,Enrich ingest
+        class Brain,LLM agent
+        class SQL_Tool,Vector_Tool mcp
+        class SQLite,Qdrant storage
+
+        %% Curve the lines for better readability
+        linkStyle default interpolate basis
+    ```
+    """)
+
+st.divider()
+
 if "rag" in st.session_state:
     uploaded_files = st.file_uploader("Upload CSV transactions", accept_multiple_files=True, type=['csv'])
     

docker-compose.yml

@@ -0,0 +1,28 @@
+version: '3.8'
+
+services:
+  money-rag:
+    build:
+      context: .
+      dockerfile: Dockerfile
+    container_name: money-rag-app
+    ports:
+      - "8501:8501"
+    environment:
+      - PYTHONUNBUFFERED=1
+      - STREAMLIT_SERVER_PORT=8501
+      - STREAMLIT_SERVER_ADDRESS=0.0.0.0
+    # Note: API keys are entered through the Streamlit UI
+    # No environment variables required
+    volumes:
+      # Mount data directory for persistence (optional)
+      - ./data:/app/data
+      # Mount logs directory
+      - ./logs:/app/logs
+    restart: unless-stopped
+    healthcheck:
+      test: ["CMD", "curl", "-f", "http://localhost:8501/_stcore/health"]
+      interval: 30s
+      timeout: 10s
+      retries: 3
+      start_period: 10s

docker-run.sh

@@ -0,0 +1,65 @@
+#!/bin/bash
+
+# MoneyRAG Docker Run Script
+# This script helps you run the application easily
+
+set -e
+
+echo "🐳 MoneyRAG Docker Setup"
+echo "========================"
+echo "ℹ️  Note: API keys are entered through the web UI"
+echo ""
+
+# Check if Docker is running
+if ! docker info > /dev/null 2>&1; then
+    echo "❌ Docker is not running. Please start Docker Desktop."
+    exit 1
+fi
+
+# Create data and logs directories
+mkdir -p data logs
+
+echo ""
+echo "Choose an option:"
+echo "1) Build and run (first time or after code changes)"
+echo "2) Run existing container"
+echo "3) Stop container"
+echo "4) View logs"
+echo "5) Clean up (remove containers and images)"
+echo ""
+read -p "Enter choice [1-5]: " choice
+
+case $choice in
+    1)
+        echo "🔨 Building Docker image..."
+        docker-compose build
+        echo "🚀 Starting container..."
+        docker-compose up -d
+        echo "✅ Application is running at http://localhost:8501"
+        echo "📋 View logs with: docker-compose logs -f"
+        ;;
+    2)
+        echo "🚀 Starting container..."
+        docker-compose up -d
+        echo "✅ Application is running at http://localhost:8501"
+        ;;
+    3)
+        echo "🛑 Stopping container..."
+        docker-compose down
+        echo "✅ Container stopped"
+        ;;
+    4)
+        echo "📋 Showing logs (Ctrl+C to exit)..."
+        docker-compose logs -f
+        ;;
+    5)
+        echo "🧹 Cleaning up..."
+        docker-compose down -v
+        docker rmi money_rag-money-rag 2>/dev/null || true
+        echo "✅ Cleanup complete"
+        ;;
+    *)
+        echo "❌ Invalid choice"
+        exit 1
+        ;;
+esac

requirements.txt

@@ -39,4 +39,5 @@ requests>=2.32.5
 tenacity>=9.1.2
 
 
-streamlit
+streamlit>=1.53.0
+ddgs>=9.10.0