Show HN: RAGBuilder – Hyperparameter tuning on various RAG parameters
RagBuilder is a toolkit for creating efficient Retrieval-Augmented Generation setups, focusing on hyperparameter tuning, offering pre-defined templates, and providing an intuitive interface for data processing and optimization.
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RagBuilder is a toolkit designed to facilitate the creation of efficient Retrieval-Augmented Generation (RAG) setups for data processing. Its primary function is to assist users in hyperparameter tuning for various RAG parameters, such as chunking strategies and sizes, to optimize performance for specific datasets. The toolkit offers several pre-defined RAG templates that have demonstrated strong results across a variety of datasets. Key features include the use of Bayesian optimization for hyperparameter tuning, options for generating synthetic test datasets, and an intuitive user interface that guides users through the setup process.
Installation of RagBuilder can be accomplished via a script for Mac or Windows, or by using Docker to pull the latest image. Users must create a project in the RagBuilder UI, describe their use case, select RAG options, and confirm their selections to view results. Additionally, users need to configure environment variables in a `.env` file for authentication and setup, including keys for various APIs such as OpenAI, Mistral, and Pinecone. The repository provides detailed instructions for installation and setup, enabling users to effectively utilize RagBuilder for their data retrieval and generation tasks.
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