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Stock & Finance Query Assistant or Agent

This repository contains a Dockerized Streamlit application for answering stock, finance, and cryptocurrency-related queries. It leverages a vector database (ChromaDB) for data retrieval and integrates with Google Generative AI embeddings. The project also includes a CI/CD pipeline using GitHub Actions for automated Docker image building, testing, and deployment.

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Project Structure

• Streamlit Application (app.py): Provides a user interface for querying financial information.
• Vector Database (build_vector_db and load_vector_db functions): Retrieves financial data and supports querying.
• CI/CD Pipeline (.github/workflows/ci-cd.yaml): Automated pipeline for building, testing, and deploying Docker images.
• Dockerfile: Defines the container environment to run the Streamlit app.

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Features

1. User Querying with Streamlit: An interactive user interface for querying insights on stocks, finance, and cryptocurrency.
2. ChromaDB Vector Database:
   • build_vector_db creates and updates the vector database with financial data.
   • load_vector_db loads the existing vector database for queries.
3. CI/CD with GitHub Actions:
   • On every push or pull request to specified branches, the workflow builds and tests the Docker image.
   • After successful tests, the image is pushed to DockerHub.
4. Dockerized Environment: A consistent environment built on a slim Python 3.9 base image.

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Setup

Prerequisites

• Docker: Ensure Docker is installed.
• Python 3.9
• DockerHub Account: For storing the Docker image.
• Google API Key: Required for Google Generative AI embeddings.

Installation

1. Clone the repository:

   git clone https://github.com/bishweashwarsukla/project_agent.git
   cd project_agent

2. Set Up Environment Variables:

   • Create a .env file to store API keys and URLs for financial data sources.
   • Example .env:

     GOOGLE_API_KEY=<your_google_api_key>
     moneycontrol = https://www.moneycontrol.com/stocks/marketstats/index.php
     moneycontrol_2 = https://www.moneycontrol.com/stocksmarketsindia/
     economic_times = https://economictimes.indiatimes.com/markets
     economic_times_2 = https://economictimes.indiatimes.com/markets/live-coverage
     yahoo_fin = https://finance.yahoo.com/topic/stock-market-news/
     yahoo_fin_2 = https://finance.yahoo.com/markets/
     

3. Install Python Dependencies:

   pip install -r requirements.txt

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Usage

Running Locally

1. Start the Streamlit Application:

   streamlit run app.py

2. Create the Knowledge Base: Click “Update Knowledge Base” in the Streamlit app to update the vector database or u can use existing database if it exists.

Running with Docker

if you want you can pull the image using docker pull kanukollugvt/flasktest-app

1. Build the Docker Image:

   docker build -t project_agent .

2. Run the Docker Container:

   docker run -p 8501:8501 kanukollugvt/flasktest-app

3. Open http://localhost:8501 in a browser to access the app.

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CI/CD Pipeline

GitHub Actions automatically builds, tests, and publishes the Docker image upon every push to prod, bish_dev, or kgvt_dev branches.

• Workflow Triggers:
  • Push to prod, bish_dev, kgvt_dev
  • Pull Requests to prod

Workflow File .github/workflows/ci-cd.yaml

The workflow includes:

• Building the Docker image.
• Running unit tests with pytest.
• Pushing the image to DockerHub on successful tests.

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Dockerfile

The Dockerfile creates a containerized environment:

• Base Image: python:3.9-slim
• Working Directory: /project_agent
• Requirements Installation: Installs packages listed in requirements.txt.
• Port Exposure: Exposes port 8501 for Streamlit.
• Startup Command: CMD ["streamlit", "run", "app.py"]

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Example Diagram

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Contributing

1. Fork the repository.
2. Create your branch (git checkout -b feature/YourFeature).
3. Commit your changes (git commit -am 'Add new feature').
4. Push to the branch (git push origin feature/YourFeature).
5. Open a pull request.

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License

This project is licensed under the MIT License.

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Contact

For queries, feel free to reach out at [email protected] or [email protected].

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This README.md serves as a comprehensive guide for understanding, running, and contributing to the project. Let me know if there’s anything more you’d like to add!

project_agent

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How to setup environment ? steps

1. python - m venv
2. kill terminal and restart it again
3. pip install --upgrade pip setuptools
4. source .venv activate
5. pip3 install -r requirements.txt