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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
When RAG and agents fall in love
| Date | Stars |
|---|---|
| 2026-07-31 | 332 |
| 2026-08-04 | 332 |
| 2026-08-06 | 332 |
Today
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growth rate 0.00%/day
# The King RAGent 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. ## Features - **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. ## Installation 1. Clone the repository: ```bash git clone https://github.com/yourusername/ragents.git cd ragents ``` 2. Install the required packages: ```bash pip install -r requirements.txt ``` 3. Set up environment variables: - Create a `.env` file in the root directory. - Add your API keys and other necessary configurations. ## Usage 1. Run the Streamlit app: ```bash streamlit run streamlit_app.py ``` 2. Open your browser and navigate to the local URL provided by Streamlit. ## Dry Run Mode 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. ### How to Enable Dry Run Mode - **Streamlit Interface**: Use the "🔧 Dry Run Mode" checkbox in the sidebar to toggle dry run mode on or off. - **Backend Logic**: The `dry_run` parameter 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. ## Contributing Contributions are welcome! Please fork the repository and submit a pull request for any improvements or bug fixes. ## License This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:d110ed50f4d88662, llm:Repository name and description: 'RAGent' and 'When RAG and agents fall in love' — implies combination of Retrieval-Augmented Generation (RAG) and agents (agent frameworks). Language: Python.
matched fp:d110ed50f4d88662, llm:Repository name and description: 'RAGent' and 'When RAG and agents fall in love' — implies combination of Retrieval-Augmented Generation (RAG) and agents (agent frameworks). Language: Python.
matched fp:d110ed50f4d88662, llm:Repository name and description: 'RAGent' and 'When RAG and agents fall in love' — implies combination of Retrieval-Augmented Generation (RAG) and agents (agent frameworks). Language: Python.