Kognitos’ Post

Unlocking the Power of Document Loaders in Langchain's RAG Framework. Dive into the seamless integration of external data sources for enriched conversational AI experiences! #RAG #ai #langchain

LangSmith is an "LLMOps" tool for managing the lifecycle of your LLM-powered applications. It is primarily intended to be used with LangChain, but you can make it work with other tools too. Viktor Nawrath, my colleague at profiq, prepared a short video tutorial to show you how: https://lnkd.in/eZ2ZFxJK #ai #langchain #llm #gpt

https://lnkd.in/dH-5gXgS Octopus: Powerful integration of multiple LLMs

Are you in the process of integrating large language models (LLM) into your applications? If so, you’ll understand the importance of monitoring and refining your AI interactions to ensure they are production-ready. In our latest video, Viktor dives deep into how you can leverage LangSmith for tracing and enhancing your AI applications. Watch here 👉 https://lnkd.in/enGWXpNY This is for anyone aiming to streamline their development process and achieve better, more reliable outcomes with their LLM integrations.

For week seven of CUNY Tech Prep's AI/Data Science course, I learned about data retrieval and semantic search with LangChain's tools! Learned the ins and outs of segmenting documents for efficient processing and the power of semantic search to navigate vast datasets. I also discovered the art of overcoming its limitations with novel retrieval algorithms. Huge thanks to DeepLearning.AI and Harrison Chase for this course! #DataScience #SemanticSearch #AI #deeplearningai

Hi AI ML engineer and aspirants, Please check his video to expand your knowledge. where he mentioned realtime deployment of a Large language model As API.

🚀 Handling large document sets for question answering can be a challenging task. When working with data or documents, we often struggle to determine the optimal number of chunks to select to provide a comprehensive answer. Invariably, there are instances where we need to analyze a substantial number of documents to address a query adequately. For example, consider a query like "Please let me know all the documents where the contract year is greater than 1950" from a stack of, say, 1000 documents. 🔍 In this regard, the retrieval QA chains of "MapReduce" and "Refine" offered by Langchain appear highly intriguing. According to the documentation, these methods can be advantageous when seeking a comprehensive answer that requires distilling key elements from a large corpus of relevant context. 🌟 Instead of merely selecting the top K chunks, these techniques provide an opportunity to synthesize insights from all pertinent sources, thereby enhancing the depth and quality of the final answer. 📚 Harrison Chase's insightful share, "Langchain: Chat with Your Data" on the Deeplearning.ai platform, may be well worth an hour of your time. #GenAI #QuestionAnswering #Langchain #MapReduce #Refine #aiproductmanagement

Your guide to using #LangChainAI with Astra DB starts here! 📍 Press ▶️ and learn about the key concepts behind AI applications — and how LangChain and DataStax make it easy to get started with RAG. https://ow.ly/xQVe30sCof7

The code generation with LLMs continues to mature. Claude 3.5 sonnet is now the leader in that use case and the 'Artifacts' feature in preview is really really impressive https://lnkd.in/eZ-at8gs

Let's chat about #AI, reliability, and solutions for #Kubernetes operations with Marino Wijay. 🤩 Get to know Marino and his insights through this interview with Christopher Privitere. https://lnkd.in/eEA7ciUt #KubeCon #FiresideChat