DeepSeek RAG Agents UI

A Project-Based RAG AI implementation using DeepSeek R1 and ChromaDB, built with Streamlit for an interactive chatbot UI.

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🚀 Features

• Retrieval-Augmented Generation (RAG) for context-aware responses.
• DeepSeek R1 as the LLM for generating answers.
• ChromaDB as the vector store for efficient document retrieval.
• Nomic Embed Text for embedding knowledge into a searchable format.
• Streamlit UI for easy interaction with the AI chatbot.

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📂 Project Structure

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├── deepseek-rag-agents-ui.py  # Main Streamlit App
├── istqb-ctfl.pdf             # Sample knowledge document
└── README.md                  # Project Documentation

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🛠️ Installation & Setup

1️⃣ Clone the Repository

git clone https://github.com/your-username/project-based-rag-ai.git
cd project-based-rag-ai

2️⃣ Install Dependencies

Make sure you have Python 3.8+ installed.

• Install Python
• Install Ollama

3️⃣ Start Ollama (Locally) and other Dependencies

Ensure you have Ollama installed and running:

#Install Ollama

Download DeepSeek R1:

ollama pull deepseek-r1:latest

Download Nomic Embed models:

ollama pull nomic-embed-text:latest

Install required packages:

pip install "praisonaiagents[knowledge]" ollama streamlit

Now Set your OpenAI API key just the requirement for SDK:

export OPENAI_BASE_URL=http://localhost:11434/v1
export OPENAI_API_KEY=fake-key

4️⃣ Run the Application

streamlit run deepseek-rag-agents-ui.py

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🏗️ How It Works

1. Loads Knowledge – Uses sample.pdf For Example istqb-ctfl.pdf for retrieval-based answering.
2. Embeds Data – Utilizes Nomic Embed Text for vectorized search.
3. Retrieves Relevant Info – Searches ChromaDB for the most relevant content.
4. Generates Responses – Feeds retrieved data into DeepSeek R1 for contextual answers.

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📌 Example Usage

1. Run the app and open the Streamlit UI.
2. Ask a question related to the uploaded document.
3. Get AI-generated responses based on retrieved knowledge!

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🔥 Future Enhancements

• Will Add support for multiple document uploads.
• Will Improve UI/UX for better user experience.
• Will Deploy as a cloud-hosted RAG AI service.

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📜 License

MIT License - Feel free to use and modify this project.

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🤝 Contributing

Pull requests are welcome! If you'd like to improve the project, feel free to fork and submit changes.

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📞 Contact

For questions, feel free to reach out via LinkedIn or create an issue in this repository.

Happy coding! 🚀