Tuana (@tuanacelik) on X (original) (raw)

DevRel & AI Engineering at

@prior_labs

from Istanbul ☀️ in Amsterdam 🚲 Posts about AI/ML and occasionally other random tidbits.

• 
  We’re well and truly in the 50 shades of AI agents era.. Numerous ways to build them, numerous workflow patterns and applications. It’s exciting (also sometimes worrying, but let’s not focus on that for now). At
  @weaviate_io
  - we’ve been slowly releasing our very own agent
• 
  I’m incredibly happy to announce that our
  @Haystack_AI
  short course "Building AI Applications with Haystack" with
  @AndrewYNg
  and
  @DeepLearningAI
  is out now. Learn how to build AI applications with a step by step course, starting with the basics of the building blocks, and go
• 
  You can build quite sophisticated pipelines with LLMs to extract structured outputs. Our new cookbook on
  @huggingface
  by
  @theanakin87
  shows you how to build one with
  @Haystack_AI
  and NuExtract
  @numind_ai
  : a small Language Model fine-tuned for structured data extraction. 1️⃣
• 
  What about doing search or QA on videos? Using Whisper with the WhisperTranscriber component in Haystack, we can setup an indexing pipeline that transcribes, cleans and chunks videos from YouTube into our vector database of choice 📺 For example, the script below indexes
• 
  New projects for
  @Haystack_AI
  and the AI community coming soon! Thank you to
  @AndrewYNg
  and
  @DeepLearningAI
  for the hospitality last week. And I’m looking forward to sharing our work with everyone 🎸
• 
  Advanced RAG with ✨query expansion✨ Query expansion is a technique which is most beneficial in cases where you want to rely on keyword based search, but also want to improve/increase the number of relevant documents you're able to retrieve with a simple keyword query (it
• 
  A life update: after a short but wonderful time at
  @weaviate_io
  , last week, I joined the
  @llama_index
  team 🦙 Honestly, life is weird sometimes.. I don't have anything but positive things to say about the amazing team at Weaviate. I'm gonna miss them sooo so much. So first, a
• 
  3 days ago
  @qdrant_engine
  announced a new baseline for hybrid search: BM42 ​ My understanding is that the work stems from the observation that typical search algorithms such as TF-IDF and BM25 (which evolved from TF-IDF) are powerful, but not the best suited for RAG applications.
• 
  We often talk about RAG in terms of simple question-answering, but that's not necessarily the case. What your RAG pipeline does depends on what you instruct the LLM to do, and how good the result is (apart from the LLM itself), depends on the quality and relevance of the context
• 
  Today's weekend hack. I hadn't used
  @weaviate_io
  embedded before, and I've been meaning to build a Haystack RAG pipeline with Weaviate + some custom components I've built. So here we are. Easy to run in Colab, uses the ReadmeDocsFetcher, with an end result: Generative QA for the