data-analysis-tool

Data Analysis Tool using Amazon Bedrock

Build a streamlined Streamlit application to analize your CSV data. This guide walks you through the process of setting up and running the application.

Features

• Streamlit: For a smooth web application interface.
• Langchain: Integrated for advanced functionalities.
• AWS Services: Harness the power of Amazon's cloud services.
  • Amazon Bedrock: A fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon with a single API, along with a broad set of capabilities you need to build generative AI applications, simplifying development while maintaining privacy and security

Getting Started

1. Setting Up a Virtual Environment

Use virtualenv to create an isolated Python environment:

1. Install virtualenv:

   pip install virtualenv

2. Navigate to the directory where you cloned the repository.

3. Initialize the virtual environment:

   virtualenv da-env

4. Activate the environment:

   source da-env/bin/activate 

3. Installing Dependencies

With your virtual environment active, install the necessary packages:

pip install -r requirements.txt

This command installs all dependencies from the requirements.txt file into your da-env environment.

4. Usage

To launch the application:

1. Ensure the dependencies are installed and your .env file has the Aurora PostgreSQL DB details.

2. Install the pgvector extension on your Aurora PostgreSQL DB cluster:

   CREATE EXTENSION vector;

3. Launch the application using Streamlit:

   streamlit run app.py

4. Your default web browser will open, showcasing the application interface.

5. Follow the on-screen instructions to load your CSV data (sample data)