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Recent developments in Retrieval Augmented Generation (RAG) pipelines have introduced advanced multimodal capabilities, allowing for the integration of text, images, code, and graphs. The LlamaParse RAG Pipeline addresses the limitations of standard RAG pipelines by handling complex and diverse documents more effectively. Fahd Mirza's tutorial demonstrates how to set up an advanced multimodal RAG pipeline in minutes, making it easier to index complex documents like slide decks and product manuals. Additionally, new tutorials show how to build AI RAG agents with web access using GPT-4o in just 15 lines of Python code and how to deploy serverless RAG apps with DBOS and LlamaIndex in just 9 lines of code. These innovations promise to reduce costs by 50x compared to AWS Lambda and create more resilient AI applications. Furthermore, the RAGAs framework simplifies the evaluation of RAG pipelines.
Evaluating #RAG pipelines can be challenging 😥 But we have frameworks like RAGAs that make is easy to evaluate RAG pipelines in minutes. @ragas_io is a framework designed to evaluate Retrieval Augmented Generation (RAG) pipelines, which integrate external data sources into… https://t.co/XRHrZAjipy
Build a serverless RAG app in just 9 lines of code with @DBOS_Inc and LlamaIndex! Learn how to: 🚀 Deploy AI apps to the cloud with a single command 💰 Reduce costs by 50x compared to AWS Lambda 🔄 Create resilient AI applications with durable execution This tutorial shows you… https://t.co/RX5SQQFIVp
Build a serverless RAG app in just 9 lines of code with DBOS and LlamaIndex! Learn how to: 🚀 Deploy AI apps to the cloud with a single command 💰 Reduce costs by 50x compared to AWS Lambda 🔄 Create resilient AI applications with durable execution This tutorial shows you how… https://t.co/bu3KBQKvOL
