The integration of Retrieval Augmented Generation (RAG) with large language models (LLMs) is gaining traction as a method to enhance custom chatbot development. Users can now build a custom chatbot within hours by adopting an end-to-end LLM Ops system. A new repository, 'llamaindex_aws_ingestion', has been launched that allows the setup of a production ETL pipeline for RAG/LLM applications, enabling the indexing of thousands of documents in seconds, significantly faster than on a personal laptop. This full architecture bundles LlamaIndex with other popular tools. However, the process of productionizing RAG, which includes deploying core components like data ingestion into a backend architecture, is more complex than building RAG in a notebook. Creating a ChatGPT-like tool with a custom knowledge base is challenging, requiring a semantic understanding of the query and a full-scale search engine for retrieval. Open-source LLMs, like OpenLLM offered by BentoML, can be hosted in production with LlamaIndex, providing a cost-effective alternative to big commercial models. LlamaIndex has also made it easier to use advanced retrieval models like ColBERT in an end-to-end RAG pipeline with a single line of code, with the brand-new 'RAGatouille' by @bclavie. Educational resources are also expanding, with new courses on advanced retrieval for RAG by Chroma and practical workshops demonstrating RAG in production. Additionally, the multimodal capabilities of RAG are being explored in the field of computer vision, and part-2 learnings from building RAG application on @nextjs have been released.
👀 What is Multimodal RAG (Retrieval Augmented Generation)? @QuantumMarks, ML Engineer at @Voxel51 breaks it down for you. Check out the full conversation of how large language models (#LLMs) are transforming computer vision here: https://t.co/MZG1nLO9NY https://t.co/cqjcTnWPmO
Made a video on this showcasing how to create llm apps using the RAG framework! https://t.co/AhtVoqQfeu https://t.co/EMZqvYdHNV
RAGatouille (@bclavie) is an awesome way to easily use ColBERT, a more advanced retrieval model compared to dense embedding-based retrieval techniques. In turn we’ve made it easy to use ColBERT in an e2e @llama_index RAG pipeline in one line of code ⚡️, with our brand-new… https://t.co/LENrAl6A08 https://t.co/6reMeIRxzD
See what RAG actually looks like in production! Press ▶️ on our workshop with @Llama_Index and learn how to build an open-source retrieval augmented generation application. @PatrickMcFadin @yi_ding #DataStax https://t.co/oNj99zAC1K
New short course on advanced retrieval for RAG (retrieval augmented generation)! RAG fetches relevant documents to give context to an LLM. In Advanced Retrieval for AI with Chroma, taught by @trychroma founder @atroyn, you’ll learn: (i) Query expansion using an LLM to rewrite… https://t.co/7MHX4HT09V
We just released part-2 learnings from building RAG application on @nextjs code, docs, blog, forum and youtube videos. Checkout here 👇 https://t.co/X9jvgwVvXu
Easily host any open-source LLM in production with OpenLLM from @bentomlai! Big commercial models can get pricey and might not be the best fit for your use case. But you can build a retrieval-augmented generation application using LlamaIndex on top of any model out there using… https://t.co/JHYfgyTb8U
Dive into the synergy of OpenLLM and @llama_index in our latest blog post! Leverage the power of open-source LLMs and learn to build RAG systems that understand and respond accurately to your custom dataset. 🔗 https://t.co/hRFO7lK80Z
RAG with LLMS seems quite simple but is quite difficult to pull off. Creating a ChatGPT-like tool with a custom knowledge base requires multiple non-trivial components: you need a semantic understanding of the query and a full-scale search engine for the “retrieval.” We at… https://t.co/c5jzcpEN6K
Scaling @llama_index to Thousands of Documents 📈 Productionizing RAG involves deploying core components like data ingestion into a backend architecture - this can be way more complex than building RAG in a notebook. Our new `llamaindex_aws_ingestion` repo is a perfect… https://t.co/6j0r5elYOe https://t.co/vfQYxfds0K
Today we’re launching a repo that lets you setup a production ETL pipeline for your RAG/LLM app 💫 Index thousands of documents in seconds ⚡️ (and orders of magnitude faster than running on your laptop). It’s a full architecture which bundles LlamaIndex with other popular… https://t.co/lIXcCZB6bb
Transforming LLMs: The Power of Retrieval-Augmented Generation #AI #AItechnology #artificialintelligence #Challenges #currentinformation #domainspecific #dynamicexternaldatasources #dynamicmethodology #externaldatabases #llm #machinelearning https://t.co/JodN2uBofC https://t.co/BJX7h3JEqJ
RAG (Retrieval Augmented Generation) You can experiment with the details endlessly or adopt an end-to-end LLM Ops system and build a custom chatbot in hours https://t.co/rEkstLxEJE