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‎.github/workflows/main.yml‎

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name: Docker Image CI-CD
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on:
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push:
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branches: [ "master" ]
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pull_request:
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branches: [ "master" ]
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jobs:
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build:
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runs-on: ubuntu-latest
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steps:
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- name: Checkout code
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uses: actions/checkout@v3
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- name: Set up Python
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uses: actions/setup-python@v4
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with:
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python-version: '3.x'
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- name: Set up Docker Buildx
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uses: docker/setup-buildx-action@v2
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- name: Cache Docker layers
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uses: actions/cache@v3
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with:
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path: /tmp/.buildx-cache
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key: ${{ runner.os }}-buildx-${{ github.sha }}
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restore-keys: |
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${{ runner.os }}-buildx-
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- name: Build Docker image
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run: docker build -t true-wealth-ai .
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- name: Test the application (Run tests inside container)
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run: docker run --rm true-wealth-ai pytest tests/

‎.gitignore‎

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‎Dockerfile‎

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# Use an official Python runtime as a parent image
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FROM python:3.11-slim
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# Set the working directory
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WORKDIR /main
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# Copy the current directory contents into the container
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COPY . /main
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# Install the dependencies
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RUN pip install --no-cache-dir -r requirements.txt
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# Expose Streamlit default port
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EXPOSE 8501
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# Correct command to run Streamlit app
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CMD ["streamlit", "run", "streamlit_app.py", "--server.port=8501", "--server.address=0.0.0.0"]

‎LICENSE‎

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MIT License
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Copyright (c) 2025 Md Emon Hasan
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.

‎README.md‎

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# **💼 TrueWealth AI: Your Smart Path to Financial Freedom**
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## **Project Overview**
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**TrueWealth AI** is an advanced, AI-powered financial advisor that uses cutting-edge technologies in machine learning, natural language processing (NLP), and document retrieval. By leveraging advanced LLMs, real-time data access from financial tools like Yahoo Finance, DuckDuckGo, and dynamic document retrieval systems like LangChain and ChromaDB, it provides personalized financial advice. The system is designed to simulate a real-world financial advisor, offering clear, insightful, and actionable recommendations.
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### 🧱 Project Structure
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```
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TrueWealth AI/
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├── streamlit_app.py # Streamlit-based user interface for financial advisor chatbot
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├── notebook/
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│ └── experiment.ipynb # Jupyter notebook for prototyping, experimentation, and testing
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├── agents/
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│ ├── __init__.py # Package initializer for agents module
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│ ├── nodes.py # LangGraph nodes: agent planner, tool-caller, executor logic
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│ ├── state.py # Agent state and memory management during graph execution
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│ └── workflow.py # LangGraph planner + executor orchestration for agent workflow
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├── data/
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│ └── The Intelligent Investor - BENJAMIN GRAHAM.pdf # Sample financial PDF for RAG ingestion
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├── finance_db/
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│ └── (SQLite/Postgres DB files) # Local or remote finance database to store processed data
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├── ingestion/
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│ └── pdf_loader.py # Document ingestion module: PDF parsing, metadata extraction, RAG prep
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├── utils/
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│ ├── __init__.py # Package initializer for utility functions
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│ └── memory.py # Short-term memory store (e.g., Chroma, JSON memory, etc.)
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├── logs/
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│ └── advisor.log # Logging outputs for all conversations, errors, or tool executions
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├── tests/
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│ ├── __init__.py # Package initializer for test suite
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│ └── test_app.py # Unit tests for core application components (agents, tools, API, etc.)
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├── retrieval/
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│ ├── __init__.py # Package initializer for retrieval modules
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│ ├── retrievers.py # Top-K retrievers using vector similarity (e.g., Chroma, FAISS)
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│ ├── splitter.py # PDF/document chunking logic (e.g., RecursiveTextSplitter)
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│ └── vectorstore.py # Vectorstore logic using HuggingFace embeddings + ChromaDB
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├── logger.py # Logger configuration for debug/info/error handling across modules
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├── config.py # Centralized config: API keys, constants, paths, env vars (via dotenv)
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├── main.py # run file for muduler package
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├── setup.py # Package metadata and install dependencies (for pip install)
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├── fastapi_app.py # FastAPI app serving REST endpoints for backend API integration
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├── app.png
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├── .gitignore # Files/folders ignoredetc.)
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├── Dockerfile # Docker image definition for containerizing the full app
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├── .github/
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│ └── workflows/
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│ └── ci.yml # GitHub Actions workflow for CI/CD: linting, tests, deploy
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├── requirements.txt # Dependency list for pip install (FastAPI, LangChain, etc.)
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├── LICENSE
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├── README.md # Project overview, setup instructions, features, architecture diagram
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```
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### **Tech Stack:**
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* **LLMs (Large Language Models)**: **Groq**
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* **Data Retrieval**: **LangChain**, **ChromaDB**, **Sentence Transformers**
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* **Document Parsing**: **PyPDFLoader**, **DocumentReaderAgent**
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* **Search Tools**: **Yahoo Finance API**, **DuckDuckGo API**
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* **Memory Management**: **LangGraph Memory Integration**
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* **Agent Technique**: **ToolRouter Agent**, **Planner Agent**
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* **Backend**: **FastAPI** (for RESTful APIs)
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* **Frontend**: **Streamlit** (interactive UI)
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* **Deployment**: **Docker**, **CI/CD** (GitHub Actions/Jenkins)
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---
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## **Features & Functionalities**
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| ✅ Step | 🧠 Feature | ⚙️ Tech Stack / Tool Used |
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| ------ | ------------------------------------ | --------------------------------------------------------------- |
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| 1️⃣ | 🧠 **LLM-based Query Understanding** | **Groq** |
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| 2️⃣ | ✨ **Tone Personalization** | **Prompt Engineering** + **Persona Templates** |
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| 3️⃣ | 📚 **RAG-based Answering** | **LangChain** + **ChromaDB** + **Sentence Transformers** |
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| 4️⃣ | 🔍 **Retrieval Agent** | **RetrieverAgent** + **Vector Store Tools** |
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| 5️⃣ | 🧠 **Answer Generator Agent** | **GeneratorAgent** (LLM-based factual + human-style) |
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| 6️⃣ | 🧾 **Document QA Agent** | **DocumentReaderAgent** + **QA Chain** |
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| 7️⃣ | 🔁 **ToolRouter Agent** | **Conditional Logic** + **Tool Selection** |
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| 8️⃣ | 🧠 **Planner Agent** | **LangGraph Planner Node** |
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| 9️⃣ | 🔄 **Intelligent Tool Routing** | **Retry Logic** + **Fallback Tool** + **Score-based Selection** |
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| 🔟 | 🧠 **Short Conversational Memory** | **LangGraph Memory Integration (short-term)** |
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---
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## **Backend with FastAPI**
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FastAPI serves as the backend for managing API requests, handling the communication between the UI and the core financial advisory logic.
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### **Other Features**:
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* **Fast API Endpoints**: API routes are built to handle financial queries, document retrieval requests, and personalized recommendations.
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* **Asynchronous Processing**: Supports asynchronous task handling for document retrieval and large LLM query processing.
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* **Integration with LangChain and RAG Pipeline**: FastAPI integrates directly with LangChain and RAG to provide on-demand data fetching and query answering.
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---
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## **User Interface with Streamlit**
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Streamlit is used for the front-end, where users can interact with the financial advisor chatbot in a conversational format.
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### **UI Features**:
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* **Real-Time Interaction**: The Streamlit interface allows users to input queries and receive answers instantly.
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* **Tone Personalization**: Users can choose how formal or informal they want the assistant to sound.
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* **Live Data Display**: Financial data (such as stock prices, trends, and company news) is displayed dynamically.
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---
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## **Deployment Process with Docker**
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### **Dockerization**:
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1. **Service Containerization**: The entire TrueWealth AI system is broken down into microservices, each housed within its own Docker container.
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2. **Docker Compose**: Used to coordinate and manage multi-container services for local development and testing.
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### **Deployment Steps**:
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1. **Build Docker Image**:
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```bash
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docker build -t truewealth-ai .
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```
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2. **Run Application in Docker**:
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```bash
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docker-compose up --build
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```
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---
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## **CI/CD Pipeline**
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Continuous Integration and Continuous Deployment (CI/CD) is implemented using **GitHub Actions** to automate testing, building, and deploying the project.
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### **CI/CD Features**:
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* **Automated Testing**: Every code push triggers automated unit and integration tests.
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* **Automated Deployment**: Successful builds are automatically deployed to production or staging environments.
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---
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## **Future Enhancements**
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* **Multilingual Support**: Future iterations will include multilingual capabilities to cater to a global client base.
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* **Advanced Financial Analytics**: Integration with more sophisticated financial forecasting models and real-time market analysis.
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* **Long-Term Memory**: Extended memory for better long-term personalized financial advice.
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---
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### **Personal Information**
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* **Developer:** Md Emon Hasan
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* **GitHub:** [Md-Emon-Hasan](https://github.com/Md-Emon-Hasan)
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* **LinkedIn:** [Md Emon Hasan](https://www.linkedin.com/in/md-emon-hasan)
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* **Email:** [[email protected]](mailto:[email protected])
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* **WhatsApp:** [+8801834363533](https://wa.me/8801834363533)
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---

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