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I would argue the level of abstraction it provides lowers the barrier to entry for most average programmers, myself included. LllamaIndex was my entrance to pro
by byteknight 3y ago
I would argue the level of abstraction it provides lowers the barrier to entry for most average programmers, myself included. LllamaIndex was my entrance to programatically utilizing LLMs. I have since moved to LangChain, with some documents loaded via LlamaIndex, but it has been a blast.
- ramoz 3y agoRight on— I should’ve and do recognize the utility of open source abstractions; esp with AI/ML.
- ibains 3y agoYes but this is just ETL - LlamaIndex and LangChain are re-inventing it - why use them when you have robust technology already? 1. You ETL your documents into a vector database - you run this pipeline everyday to keep it up to date. You can run scalable, robust pipelines on Spark for this. 2. You have a streaming inference pipeline that has components that make API calls (agents) and between them transform data. This is Spark streaming. Prophecy is working with large enterprises to implement generative AI use cases, but they don’t talk so much on HN. Here’s our talk from Data+AI Summit: Build a Generative AI App on Enterprise Data in 13 Minutes https://www.youtube.com/watch?v=1exLfT-b-GM https://www.youtube.com/watch?v=1exLfT-b-GM Here’s a blog/demo https://www.prophecy.io/blog/prophecy-generative-ai-platform-applications-on-enterprise-data-built-in-hours https://www.prophecy.io/blog/prophecy-generative-ai-platform...
- bread90 3y agoCool! Lets say I have thousands of documents that I want questions and answers for. Would your solution work for this? I wouldn’t know which documents to send with the prompts though as I want info on the aggregate (like trends and most mentioned phrases or words).