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56 lines
1.8 KiB
Markdown
56 lines
1.8 KiB
Markdown
## 🦙 Local RAG Agent with Llama 3.2
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### 🎓 FREE Step-by-Step Tutorial
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**👉 [Click here to follow our complete step-by-step tutorial](https://www.theunwindai.com/p/build-a-local-rag-agent) and learn how to build this from scratch with detailed code walkthroughs, explanations, and best practices.**
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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.
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### Features
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- Fully local RAG implementation
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- Powered by Llama 3.2 through Ollama
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- Vector search using Qdrant
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- Interactive AgentOS interface
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- No external API dependencies
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- Uses Agno v2.0 Knowledge class for document management
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### How to get Started?
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1. Clone the GitHub repository
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```bash
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git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
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```
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2. Install the required dependencies:
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```bash
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cd awesome-llm-apps/rag_tutorials/local_rag_agent
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pip install -r requirements.txt
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```
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3. Install and start [Qdrant](https://qdrant.tech/) vector database locally
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```bash
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docker pull qdrant/qdrant
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docker run -p 6333:6333 qdrant/qdrant
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```
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4. Install [Ollama](https://ollama.com/download) and pull Llama 3.2 for LLM and OpenHermes as the embedder for OllamaEmbedder
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```bash
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ollama pull llama3.2
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ollama pull openhermes
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```
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5. Run the AI RAG Agent
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```bash
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python local_rag_agent.py
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```
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6. 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.
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### Note
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- 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.
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- The AgentOS interface provides a web-based UI for interacting with your agent.
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