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9bc6394fae
- Replaced PDFUrlKnowledgeBase with Knowledge class for improved document management. - Updated to use AgentOS for the application interface instead of Playground. - Adjusted README to reflect changes in the interface and knowledge base loading process. - Specified minimum version for the 'agno' package in requirements.txt.
🦙 Local RAG Agent with Llama 3.2
This application implements a Retrieval-Augmented Generation (RAG) system using Llama 3.2 via Ollama, with Qdrant as the vector database. Built with Agno v2.0.
Features
- Fully local RAG implementation
- Powered by Llama 3.2 through Ollama
- Vector search using Qdrant
- Interactive AgentOS interface
- No external API dependencies
- Uses Agno v2.0 Knowledge class for document management
How to get Started?
- Clone the GitHub repository
git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
- Install the required dependencies:
cd awesome-llm-apps/rag_tutorials/local_rag_agent
pip install -r requirements.txt
- Install and start Qdrant vector database locally
docker pull qdrant/qdrant
docker run -p 6333:6333 qdrant/qdrant
- Install Ollama and pull Llama 3.2 for LLM and OpenHermes as the embedder for OllamaEmbedder
ollama pull llama3.2
ollama pull openhermes
- Run the AI RAG Agent
python local_rag_agent.py
- Open your web browser and navigate to the URL provided in the console output (typically
http://localhost:7777) to interact with the RAG agent through the AgentOS interface.
Note
- The knowledge base loads a Thai Recipes PDF on the first run. You can comment out the
knowledge_base.add_content()line after the first run to avoid reloading. - The AgentOS interface provides a web-based UI for interacting with your agent.