The atmospheric river flooded San Francisco, so we flooded our codebase with a downpour of new updates. 🌊 ⛈️ 🌊 ⛈️ 🌊 ⛈️ 💨 Underscore expressions now support more chalk.functions for working with arrays and Dataframes, mathematical operations, encoding, formatting datetime, and strings. 😶🌫️ You can now choose whether to cache nulls or default values in the online store with the cache_nulls and cache_defaults parameters. Customers with Redis or DynamoDB online stores can also select to evict null/default feature values for any null/default feature value that would have existed in the online store. 🗺️ You can now define Chalk features as map types, for example user_preferences: dict[str, bool] 🎣 In addition, you can now retrieve Map document types from DynamoDB data sources as either dicts or strings. As always, more detail and much more linked in the full changelog in comments. From the Chalk team, have a wonderful holiday! 🦃
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Full episode here: https://lnkd.in/dUTmhE5k In 5mins of Postgres E76, we optimize subqueries by understanding the Postgres planner better. We show correlated vs. uncorrelated subqueries, as well as scalar subqueries vs. tabular subqueries.
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“Zombodb : Today I Learned (TIL)” Life is rarely either or And don’t fall for the false dichotomy! Today I learned that you don’t have to pit Postgresql vs ElasticSearch — when it comes to storing and searching JSON objects that is because they are different animals! https://zombodb.com
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I spent last week exploring the internals of DragonflyDB - a drop-in replacement of Redis, and it does a staggering 6.43 million ops/second on a single machine 🤯 DragonflyDB is a multi-threaded implementation of Redis with a bunch of insane optimizations. Some of the architectural design decisions and internals of data structure implementation just blew my mind. To understand it better, I went through their blogs, docs, and even some fragments of source code. I compiled all my learnings in a series of videos and trust me you will absolutely love going through it. the videos will change how you look at data structures :) subscribe to my channel, and the video drops every Friday evening. youtube.com/c/ArpitBhayani ps: Until then watch other System Design and Database Internals videos on my channel; they are all no-fluff. #AsliEngineering #DatabaseInternals #SystemDesign
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As an engineer who is curious about how the "world" actually functions, sometimes I can't resist looking into the internals. Even if Postgres is just an API endpoint provided by a managed service, it's still worthwhile to explore topics like this one. 👇 https://lnkd.in/eCD6D9yG
Bruce Momjian: Inside PostgreSQL Shared Memory
https://www.youtube.com/
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Agentic RAG with Claude 3.5 Sonnet, MongoDB, and LlamaIndex 🤖 This tutorial by Richmond Alake is a fantastic beginner’s walkthrough on building an agentic knowledge assistant over a pre-existing RAG pipeline. Take advantage of tool selection, task decomposition, and reasoning through adding a function calling agentic layer. It’s simple and doesn’t require too many lines of code! Video: https://lnkd.in/gxNDsS9s Notebook: https://lnkd.in/gDcNsb9j
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Data Science Leader | 28,300+ followers | Transforming businesses with AI / Data Science / Machine Learning / LLMs
Agentic RAG with Claude 3.5 Sonnet, MongoDB, and LlamaIndex 🤖 This tutorial by Richmond Alake is a fantastic beginner’s walkthrough on building an agentic knowledge assistant over a pre-existing RAG pipeline
Agentic RAG with Claude 3.5 Sonnet, MongoDB, and LlamaIndex 🤖 This tutorial by Richmond Alake is a fantastic beginner’s walkthrough on building an agentic knowledge assistant over a pre-existing RAG pipeline. Take advantage of tool selection, task decomposition, and reasoning through adding a function calling agentic layer. It’s simple and doesn’t require too many lines of code! Video: https://lnkd.in/gxNDsS9s Notebook: https://lnkd.in/gDcNsb9j
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Enterprise Data Science Specialist @Mastech Digital | NLP | NER | Deep Learning | Gen AI | MLops
Thanks for sharing this excellent tutorial on building an agentic knowledge assistant! 🤖 Richmond Alake’s walkthrough on integrating Claude 3.5 Sonnet with MongoDB and LlamaIndex is a must-watch for anyone looking to enhance their RAG pipeline. Here's a breakdown of what you'll gain from this tutorial: Tool Selection: Learn how to choose the right tools for your specific needs, ensuring you get the most out of your setup. 🛠️ Task Decomposition: Understand how to break down complex tasks into manageable components, improving efficiency and clarity. 📊 Reasoning & Function Calling: Discover how to add an agentic layer to your pipeline for improved function calling and reasoning capabilities. 🧠🔄 The tutorial is designed to be straightforward and beginner-friendly, requiring minimal code to get started. It’s a great resource for anyone looking to dive into agentic RAG systems and build more intelligent knowledge assistants. #AI #GenerativeAI #RAG #MachineLearning #TechTutorial #Claude3.5 #MongoDB #LlamaIndex #KnowledgeAssistant #DataScience
Agentic RAG with Claude 3.5 Sonnet, MongoDB, and LlamaIndex 🤖 This tutorial by Richmond Alake is a fantastic beginner’s walkthrough on building an agentic knowledge assistant over a pre-existing RAG pipeline. Take advantage of tool selection, task decomposition, and reasoning through adding a function calling agentic layer. It’s simple and doesn’t require too many lines of code! Video: https://lnkd.in/gxNDsS9s Notebook: https://lnkd.in/gDcNsb9j
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Shoutout to Ken Krugler, one of our recent StarTree All-Stars. 🏆 𝗤: 𝗪𝗵𝗮𝘁’𝘀 𝘆𝗼𝘂𝗿 𝗳𝗮𝘃𝗼𝗿𝗶𝘁𝗲 𝗣𝗶𝗻𝗼𝘁 𝗳𝗲𝗮𝘁𝘂𝗿𝗲 𝗮𝗻𝗱 𝘄𝗵𝘆? A: Batch generation of pre-indexed segments with metadata push. We can efficiently build segments from historical data using a Flink workflow, store the results in S3 or HDFS, and efficiently update tables. 𝗤: 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗮 𝗰𝗼𝗺𝗺𝘂𝗻𝗶𝘁𝘆 𝗽𝗿𝗼𝗷𝗲𝗰𝘁 𝗼𝗿 𝗰𝗼𝗻𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻 𝘆𝗼𝘂’𝗿𝗲 𝗽𝗿𝗼𝘂𝗱 𝗼𝗳? A: The talk I gave about comparing Pinot and Elasticsearch. (Editor's Note: You can find it here: https://lnkd.in/dj43iJt7) Through his data consulting company, Scale Unlimited, Ken helps companies around the world design and develop solutions for big data processing and search-based analytics problems using Apache Pinot, Flink, and other technologies. Ken is a member of The Apache Software Foundation and an active contributor to the Pinot community. Learn more about him:
Pinot vs Elasticsearch, a Tale of Two PoCs
https://www.youtube.com/
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Scaling Postgres 344 is released! In this episode, we discuss new releases, collation speed, ZFS performance, insert benchmarking and pglz vs. lz4 performance: https://lnkd.in/eyi8b5an
Performance & Hard Things | Scaling Postgres 344
scalingpostgres.com
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We've all made decisions in our more youthful days that we've come to regret. If choosing a relational database for a critical workload is one of your regrets, with MongoDB Relational Migrator it's never too late to make amends. Learn about Nationwide Building Society's journey in this great new case study. https://lnkd.in/gkPCDUZM
https://docs.chalk.ai/docs/changelog#november-25-2024