AutoRAG: An Automated Tool for Optimizing Generation Pipelines with Increased Recovery

Retrieval-Augmented Generation (RAG) is a framework that improves language models by combining two main components: Retriever and Generator. A RAG pipeline combines the retriever and the generator in an iterative process and is widely used in open domain question and answering, knowledge-based chatbots, and specialized information retrieval tasks where the accuracy and relevance of real-world data are crucial. “Despite the availability of various RAG modules and channels, it is difficult to select which channel is ideal for your own data and use cases.” Additionally, creating and evaluating all the RAG modules is time-consuming and difficult to do, but without it, it is difficult to know which RAG channel is best for one's use case.

AutoRAG (𝐑𝐀𝐆 𝐀𝐮𝐭𝐨𝐌𝐋 𝐓𝐨𝐨𝐥) is a tool for finding an optimal RAG pipeline for “autodata”. It helps to automatically evaluate various RAG modules with self-assessment data and find the best RAG channel for your own use cases. AutoRAG supports:

• Data Creation – Create RAG assessment data with raw documents.
• Optimization: Automatically run experiments to find the best RAG pipeline for the data.
• Implementation: Implement the best RAG pipeline with a single YAML file and also support Flask server.

In optimizing a RAG pipeline, a node represents a specific function, and the result of each node is passed to the next node. The primary nodes for an effective RAG pipeline are the recovery, message creator, and generator, with additional nodes available to improve performance. AutoRAG It achieves optimization by creating all possible combinations of modules and parameters within each node, running the pipeline with each configuration and selecting the optimal result according to predefined strategies. The chosen outcome of the previous node becomes the input for the next one, meaning that each node operates based on the best outcome of its predecessor. Each node works independently of how the input result is produced, similar to a Markov Chain, where only the previous state is required to generate the next state, without knowledge of the entire process or previous steps.

RAG models need data for evaluation, but in most cases there is little or no suitable data available. However, with the advent of large language models (LLM), synthetic data generation has become an effective solution to this challenge. The following guide describes how to use LLM to create data in an AutoRAG-compatible format:

• Analyzing– Configure the YAML file and start parsing. Here, raw documents can be analyzed with just a few lines of code to prepare the data.
• fragmentation: A single corpus is used to create initial QC pairs, after which the remaining corpus is mapped to QC data.
• Creation of quality control: Each corpus needs a corresponding QC dataset if multiple corpora are generated using different chunking methods.
• QA and Corpus Mapping: For multiple corpora, the remaining data in the corpus can be assigned to the quality control data set. To optimize fragmentation, the performance of RAG can be evaluated using various corpus data.

Certain nodes, such as query_expansion either prompt_creatorcannot be evaluated directly. To evaluate these nodes, you need to set ground truth values, such as “expanded query ground truth” or “prompt ground truth.” In this method, documents are retrieved during the evaluation process using designated modules and the query_expansion node is evaluated based on these retrieved documents. A similar approach is applied to the Prompt_maker and Generation nodes, where the Prompt_maker node is evaluated using the results of the Generation node. AutoRAG is currently in its alpha phase with numerous optimization possibilities for future development.

In conclusion, AutoRAG is an automated tool designed to identify the optimal RAG pipeline for specific data sets and use cases. It automates the evaluation of various RAG modules using self-assessment data, offering support for data creation, optimization, and deployment. Additionally, AutoRAG structures the pipeline into interconnected nodes (recovery, message creator, and generator) and evaluates combinations of modules and parameters to find the best configuration. Synthetic data from LLMs improve assessment. Currently in its alpha phase, AutoRAG offers significant potential for further optimization and development in the selection and implementation of RAG pipelines.

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Sajjad Ansari is a final year student of IIT Kharagpur. As a technology enthusiast, he delves into the practical applications of ai with a focus on understanding the impact of ai technologies and their real-world implications. Its goal is to articulate complex ai concepts in a clear and accessible way.