From RAGs to riches: A practical guide to making your local AI chatbot smarter

### From RAGs to Riches: Making Your Local AI Chatbot Smarter

**Retrieval Augmented Generation (RAG)** is revolutionizing the way AI models like LLMs (Large Language Models) function by enhancing their relevance and accuracy. Instead of relying solely on pre-trained data, RAG enables these models to draw from an external, updatable database. Here’s a practical guide to implementing RAG to make your AI chatbot more capable and useful:

#### Understanding RAG

RAG integrates an embedding model with a vector database:

1. **Embedding Model**: Converts user prompts into a numeric format.

2. **Vector Database**: Matches these numeric formats with stored information.

3. **LLM Integration**: Combines matched data with the LLM to generate a response.

#### Benefits of RAG

- **Dynamic Updates**: Databases can be updated independently without retraining the model.

- **Contextual Relevance**: LLM responses are more accurate and context-specific.

#### Setting Up RAG with Open WebUI and Ollama

##### Prerequisites:

- **Machine Specs**: Capable of running LLMs like LLama3-8B with at least 6 GB of vRAM. Apple Silicon Macs should have at least 16 GB of memory.

- **Software Setup**: Docker installed and Ollama set up.

##### Deployment Steps:

1. **Deploy Open WebUI Using Docker**:

   ```bash

   docker run -d --network=host -v open-webui:/app/backend/data -e OLLAMA_BASE_URL=http://127.0.0.1:11434 --name open-webui --restart always ghcr.io/open-webui/open-webui:main

   ```

2. **Access the Dashboard**:

   - Visit `http://localhost:8080` to access Open WebUI.

3. **Connect to Ollama**:

   - Ensure Open WebUI connects to the Ollama webserver at `http://127.0.0.1:11434`.

4. **Download a Model**:

   - Use Open WebUI to download and load the desired LLM model, e.g., LLama3-8B.

5. **Upload Documents**:

   - Navigate to the "Workspace" tab and upload documents to the "Documents" section.

6. **Test the Chatbot**:

   - Query the chatbot with questions relevant to the uploaded documents.

##### Integrating RAG:

1. **Tagging Documents**:

   - Tag documents to streamline queries (e.g., “Support” for support documents).

2. **Using Web Search**:

   - Configure Open WebUI to use web search engines like Google PSE for real-time data querying.

##### Practical Example:

- **Ask Questions**: “How do I install Podman on Rocky Linux?”

- **Document Reference**: Prefix the prompt with "#" and select the relevant document.

#### Benefits of This Setup:

- **Enhanced Accuracy**: Responses are more precise as they draw from updated, relevant documents.

- **Flexibility**: Easily switch between documents and tags for comprehensive answers.

- **Real-Time Information**: Incorporate real-time web data to keep responses current.

By following this guide, you can significantly enhance the capabilities of your AI chatbot, making it a powerful tool for specific, context-aware responses. This approach is ideal for enterprise applications where up-to-date and accurate information is crucial.