# Using AI Runner with RAG (Retrieval-Augmented Generation)

AI Runner provides a Retrieval-Augmented Generation (RAG) system that allows you to enhance your AI's responses with knowledge from your own documents.

> **Note**: For long-term memory and fact retention, see the [Knowledge System](Knowledge-System.md) documentation.

## How RAG Works in AI Runner

AI Runner uses a keyword-based retrieval system to find relevant information in your documents. When you ask a question, the system:

1. Extracts keywords from your query
2. Searches the index for documents containing those keywords
3. Retrieves the most relevant passages
4. Uses those passages to generate a more informed response

## RAG vs Knowledge System

AI Runner provides two complementary systems for enhancing LLM responses:

- **RAG Search** (this page): Retrieves relevant passages from ingested documents (PDFs, text files, etc.) to answer questions based on your document library
- **[Knowledge System](Knowledge-System.md)**: Stores specific facts learned from conversations in a database for long-term memory and personalization

Use RAG for document-based Q&A and the Knowledge System for remembering user-specific facts and preferences.

## Performance Optimizations

RAG search in AI Runner has been optimized for performance:

- **Caching of keyword extraction**: Previously extracted keywords are cached to avoid redundant processing
- **Throttled index refreshing**: The index is only refreshed after a certain time interval (5 minutes) to avoid excessive processing during multiple searches
- **Batched document processing**: Documents are processed in batches for more efficient indexing
- **Progress reporting**: Long-running operations show progress indicators
- **Performance timing**: Key operations are timed to help identify bottlenecks

## Using RAG with Your Documents

To use RAG with your own documents:

1. Add documents in the AI Runner interface
2. The system will automatically index them
3. When chatting with your AI, it will now use these documents to enhance its responses

## Troubleshooting

If RAG search seems slow:

- Reduce the number of documents in your index
- Use smaller documents or break large documents into smaller chunks
- Ensure your documents are properly formatted
- Check the logs for any error messages related to indexing