Retrieval-Augmented Generation (RAG)
RAG is a framework that improves language models by using two key parts: a Retriever and a Generator. This combination is useful for tasks like open-domain question-answering, knowledge-based chatbots, and retrieving accurate real-world information.
Choosing the right RAG pipeline for your specific data and needs can be challenging and time-consuming. Evaluating different RAG modules is essential but often difficult without a clear understanding of which pipeline works best for you.
Introducing AutoRAG
AutoRAG (RAG AutoML Tool) simplifies the process of finding the best RAG pipeline for your data. It automatically assesses various RAG modules using your own evaluation data, ensuring you select the most effective pipeline for your needs.
Key Features of AutoRAG:
- Data Creation: Generate RAG evaluation data from raw documents.
- Optimization: Automatically test different RAG pipelines to find the best fit for your data.
- Deployment: Easily deploy the optimal RAG pipeline using a single YAML file, with support for Flask servers.
How AutoRAG Works
In AutoRAG, each function is represented as a node. The output from one node feeds into the next. The main nodes include retrieval, prompt maker, and generator, with additional nodes to boost performance. AutoRAG optimizes by testing all possible combinations of modules and parameters, selecting the best outcomes based on set strategies.
Each node operates independently, similar to a Markov Chain, where only the previous output is needed to determine the next step.
Generating Data with LLMs
RAG models require data for evaluation, but suitable data is often scarce. Large Language Models (LLMs) can generate synthetic data to address this issue. Here’s how to create compatible data for AutoRAG:
- Parsing: Set up the YAML file to parse raw documents quickly.
- Chunking: Use a single corpus to create initial QA pairs and map the remaining data.
- QA Creation: Ensure each corpus has a corresponding QA dataset.
- QA-Corpus Mapping: Map remaining data to the QA dataset to evaluate RAG performance.
Evaluating Nodes
Some nodes, like query_expansion or prompt_maker, require ground truth values for evaluation. This involves retrieving documents during the evaluation process and assessing these nodes based on the results.
Currently, AutoRAG is in its alpha phase, with many opportunities for future enhancements.
Conclusion
AutoRAG is an automated tool that helps you find the best RAG pipeline for your specific datasets and use cases. It streamlines the evaluation of RAG modules, supporting data creation, optimization, and deployment. By structuring the pipeline into interconnected nodes, AutoRAG efficiently identifies the best configurations. The use of synthetic data from LLMs further enhances evaluation capabilities.
For more information, check out our GitHub Repo. Follow us on Twitter, join our Telegram Channel, and connect with our LinkedIn Group. If you enjoy our work, subscribe to our newsletter and join our 55k+ ML SubReddit.
Transform Your Business with AI
Stay competitive by leveraging AutoRAG to optimize your RAG pipelines. Discover how AI can transform your work processes:
- Identify Automation Opportunities: Find key customer interactions that can benefit from AI.
- Define KPIs: Ensure your AI initiatives have measurable impacts.
- Select an AI Solution: Choose tools that meet your needs and allow for customization.
- Implement Gradually: Start with a pilot project, gather data, and expand wisely.
For AI KPI management advice, contact us at hello@itinai.com. For ongoing insights into AI, follow us on Telegram or Twitter.
Explore how AI can enhance your sales processes and customer engagement at itinai.com.
