mirror of
https://github.com/Shubhamsaboo/awesome-llm-apps.git
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47 lines
1.1 KiB
Markdown
47 lines
1.1 KiB
Markdown
## 🦙 Local RAG Agent with Llama 3.2
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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.
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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 playground interface
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- No external API dependencies
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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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4. 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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5. Open your web browser and navigate to the URL provided in the console output to interact with the RAG agent through the playground interface.
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