🚀 Project Title & Tagline

Rag Query: A Knowledge Retrieval Project 🤖

"Empowering Knowledge Retrieval with AI-Driven Insights" 💡

📖 Description

Rag Query is an innovative Python project that leverages the power of artificial intelligence to retrieve relevant information from a knowledge base. The project utilizes the Bedrock Agent Runtime client to interact with a knowledge base and retrieve answers to user-submitted questions. With its robust architecture and intuitive design, Rag Query aims to revolutionize the way we access and utilize knowledge.

The project's core functionality revolves around the ask_question function, which takes a user-input question as a parameter and returns a relevant answer from the knowledge base. This is achieved through the integration of the Bedrock Agent Runtime client, which provides a seamless interface for interacting with the knowledge base. The project also incorporates a machine learning model, specifically the Anthropic Claude-3-Sonnet model, to enhance the accuracy and relevance of the retrieved answers.

One of the key benefits of Rag Query is its ability to provide users with accurate and up-to-date information, leveraging the collective knowledge of the knowledge base. The project's architecture is designed to be scalable and flexible, allowing for easy integration with various knowledge bases and machine learning models. Whether you're a researcher, student, or simply a curious individual, Rag Query is an invaluable tool for uncovering new insights and expanding your knowledge.

✨ Features

The following are some of the key features of the Rag Query project:

1. Knowledge Base Integration: Seamlessly interact with a knowledge base to retrieve relevant information.
2. Machine Learning Model Integration: Leverage the power of machine learning models, such as the Anthropic Claude-3-Sonnet model, to enhance answer accuracy.
3. User-Friendly Interface: Submit questions and receive answers through a simple and intuitive interface.
4. Scalable Architecture: Designed to be scalable and flexible, allowing for easy integration with various knowledge bases and machine learning models.
5. Robust Error Handling: Comprehensive error handling to ensure a smooth user experience.
6. Modular Design: Modular architecture allows for easy maintenance and updates.
7. Secure Authentication: Utilizes secure authentication mechanisms to protect user data and knowledge base access.
8. Customizable: Allows for customization of knowledge base and machine learning model integrations.

🧰 Tech Stack Table

Component	Technology
Frontend	N/A
Backend	Python 3.9+
Tools	Boto3, Bedrock Agent Runtime Client
Machine Learning Model	Anthropic Claude-3-Sonnet Model

📁 Project Structure

The project consists of the following folders and files:

• rag_query.py: The main Python script containing the ask_question function and knowledge base integration logic.
• config.py: Configuration file containing knowledge base and machine learning model settings.
• utils.py: Utility functions for error handling and authentication.
• models.py: Machine learning model integration logic.
• README.md: This README file.

⚙️ How to Run

To set up and run the Rag Query project, follow these steps:

1. Setup: Install the required dependencies, including Python 3.9+ and the Boto3 library.
2. Environment: Set up your environment variables, including the knowledge base ID and machine learning model ID.
3. Build: Build the project by running the rag_query.py script.
4. Deploy: Deploy the project to your desired environment, such as a local machine or cloud platform.

To run the project, simply execute the rag_query.py script and follow the prompts to submit a question and retrieve an answer.

🧪 Testing Instructions

To test the Rag Query project, follow these steps:

1. Unit Testing: Run the unit tests to ensure the ask_question function and knowledge base integration logic are working correctly.
2. Integration Testing: Run the integration tests to ensure the project is working correctly with the knowledge base and machine learning model.
3. End-to-End Testing: Run the end-to-end tests to ensure the project is working correctly from a user's perspective.

📸 Screenshots

📦 API Reference

The Rag Query project does not have a public API. However, the ask_question function can be used as an API endpoint to retrieve answers from the knowledge base.

👤 Author

The Rag Query project was created by Rahul Gupta(https://github.com/rahul78451).

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The Rag Query project is licensed under the MIT License.