The King RAGent is an AI-powered research assistant that leverages vector databases and external APIs to provide comprehensive answers to user queries. This application is built using Streamlit for the frontend and integrates various backend services for document processing and information retrieval.
- PDF Upload and Processing: Upload PDF documents to initialize the vector store.
- AI-Powered Query Handling: Use AI models to process and respond to user queries.
- Web Search Integration: Augment responses with web search results for enhanced accuracy.
- Dry Run Mode: Test the application without making actual API calls or database operations.
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Clone the repository:
git clone https://github.com/yourusername/ragents.git cd ragents -
Install the required packages:
pip install -r requirements.txt
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Set up environment variables:
- Create a
.envfile in the root directory. - Add your API keys and other necessary configurations.
- Create a
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Run the Streamlit app:
streamlit run streamlit_app.py
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Open your browser and navigate to the local URL provided by Streamlit.
The application now supports a dry_run mode, which allows you to test the app without making actual API calls or database operations. This is useful for development and testing purposes.
- Streamlit Interface: Use the "🔧 Dry Run Mode" checkbox in the sidebar to toggle dry run mode on or off.
- Backend Logic: The
dry_runparameter is propagated through the application, affecting the following components:- VectorStore: Skips database loading and returns mock data.
- call_claude_rag: Returns a mock response instead of calling the Claude API.
- assess_confidence: Returns a mock confidence level.
- synthesize_information: Returns a mock synthesis of information.
- call_tavily_web_search: Returns mock search results.
Contributions are welcome! Please fork the repository and submit a pull request for any improvements or bug fixes.
This project is licensed under the MIT License. See the LICENSE file for details.