Best of RAG — July 2024

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   Article

   Machine Learning News·2y

   Korvus: An All-in-One Open-Source RAG (Retrieval-Augmented Generation) Pipeline Built for Postgres

   Korvus aims to simplify the Retrieval-Augmented Generation (RAG) pipeline by executing the entire process within a Postgres database using PostgresML. This approach eliminates the need for multiple external tools, reduces development complexity, and improves efficiency by leveraging in-database machine learning for tasks like embedding generation and data retrieval. Korvus supports multiple programming languages, facilitating easier integration and maintenance of search applications, although its performance metrics are yet to be quantified.

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2. 2
   Article

   GoPenAI·2y

   Building an Effective RAG Pipeline: A Guide to Integrating Self-RAG, Corrective RAG, and Adaptive RAG

   A comprehensive guide to building an effective Retrieval-Augmented Generation (RAG) pipeline by integrating Self-RAG, Corrective RAG, and Adaptive RAG. This pipeline aims to intelligently handle questions of varying complexity, ensure information accuracy, and generate useful answers. It leverages LangGraph for stateful, multi-agent workflows, and includes methods for routing questions, retrieving documents, evaluating relevance, and grading output quality.

   78

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3. 3
   Article

   Towards AI·2y

   Improving RAG Answer Quality Through Complex Reasoning

   Multi-hop retrieval enhances the capabilities of Retrieval-Augmented Generation (RAG) systems by enabling complex reasoning over multiple pieces of information. This method is especially powerful for advanced question-answering systems. The post demonstrates building a Q&A chatbot for the healthcare domain using Indexify, OpenAI, and DSPy, showcasing how multi-hop retrieval can significantly improve answer quality in complex queries.

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4. 4
   Article

   Towards Data Science·2y

   Improving RAG Answer Quality Through Complex Reasoning

   Explore how multi-hop retrieval can enhance the quality of answers in Retrieval-Augmented Generation (RAG) systems, particularly in complex reasoning tasks. Using DSPy and Indexify, the post demonstrates the construction of a question-answering chatbot for the healthcare domain. The setup includes the installation of necessary packages, data ingestion, and creating multi-hop retrieval logic for efficient question handling. The integration allows for dynamic context generation, deduplication, and chain-of-thought reasoning, showcasing significant improvements in handling complex queries.

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