Chia Jeng Yang’s Post

📢 ICYMI: DataStax and unstructured.io have partnered to make enterprise data RAG-ready for AI! This delivers: ⚡ Lightning-fast data ingestion and conversion of documents and data sets into vector data 🔎 Embeddings that can be quickly written to Astra DB for highly relevant GenAI similarity searches ⏩ Simple and elegant pipelines for updating your GenAI data in real time See this powerful partnership in action: https://dtsx.io/4bwxsFY #GenAI #VectorSearch #DataStax

📊 𝐇𝐨𝐰 𝐀𝐫𝐞 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐃𝐚𝐭𝐚 𝐅𝐫𝐚𝐦𝐞 𝐋𝐢𝐛𝐫𝐚𝐫𝐢𝐞𝐬 𝐄𝐦𝐩𝐨𝐰𝐞𝐫𝐢𝐧𝐠 𝐀𝐈 𝐭𝐨 𝐑𝐞𝐯𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐢𝐳𝐞 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬? Explore how modern data frame libraries, specifically Polars, are elevating the capabilities of AI in data analysis, as demonstrated in a recent deep dive into integrating these technologies: 🚀 𝐇𝐢𝐠𝐡-𝐒𝐩𝐞𝐞𝐝 𝐃𝐚𝐭𝐚 𝐌𝐚𝐧𝐢𝐩𝐮𝐥𝐚𝐭𝐢𝐨𝐧: Polars, built on Rust, offers a blazingly fast alternative to traditional data frame libraries like NumPy. Its core advantage lies in its speed and efficiency, making it ideal for handling large datasets without the overhead of external dependencies. 🤖 𝐒𝐞𝐚𝐦𝐥𝐞𝐬𝐬 𝐀𝐈 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧: By combining Polars with local AI models such as Lama 3, users can perform complex data analyses more efficiently. This integration allows for the utilization of AI to interpret and manipulate data dynamically, enhancing both the speed and accuracy of outputs. 🌐 𝐅𝐥𝐞𝐱𝐢𝐛𝐥𝐞 𝐃𝐚𝐭𝐚 𝐀𝐜𝐜𝐞𝐬𝐬: Polars supports a wide range of data sources, from local files to cloud storage solutions like AWS S3, and databases including PostgreSQL and MySQL. This versatility ensures that data can be easily and efficiently processed regardless of its origin. 🔄 𝐈𝐧𝐭𝐮𝐢𝐭𝐢𝐯𝐞 𝐚𝐧𝐝 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐞𝐝 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠: The library optimizes data operations internally without user intervention, thanks to its smart execution planner. This feature ensures that data queries are executed in the most efficient manner possible, leveraging the underlying hardware effectively. 🧠 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐚𝐥 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐃𝐞𝐦𝐨𝐧𝐬𝐭𝐫𝐚𝐭𝐞𝐝: The integration is showcased through the creation of a dynamic data frame that feeds directly into an AI model for financial analysis. This practical example illustrates how seamlessly Polars and AI can work together to predict financial outcomes based on historical data. This innovative approach not only streamlines data processes but also opens up new possibilities for businesses to leverage their data more effectively, driving insights that were previously too complex or time-consuming to uncover. 🔗 Are you utilizing any advanced data manipulation tools in your work? How do you see AI-enhanced data frameworks changing the landscape of your industry? #DataScience #AI #MachineLearning #DataAnalysis #TechnologyInnovation

Anthropic open-sources 'Model Context Protocol' for connecting AI models to datasets and tools: Current Challenge: ------------------ One decision many enterprises have to make when implementing AI use cases revolves around connecting their data sources to the models they’re using.  Different frameworks like LangChain exist to integrate databases, but developers must write code whenever they connect models to a new data source. Possible Solution: ----------------- Anthropic released its Model Context Protocol (MCP) as an open-source tool to provide users with a standard way of connecting data sources to AI use cases. In a blog post, Anthropic said the protocol will serve as a “universal, open standard” to connect AI systems to data sources. The idea is that MCP allows models like Claude to query databases directly. standard in data integration. How MCP works: ----------------- “The architecture is straightforward: developers can either expose their data through MCP servers or build AI applications (MCP clients) that connect to these servers,” Anthropic said in its post. There are three components to an MCP connection: Hosts – LLM applications such as Claude Desktop that initiate connections; Clients – Systems that maintain 1:1 connections with servers, inside the host application; Servers – Systems that provide context, tools, and prompts to clients. Core components include a protocol layer to handle message framing, request/response linking, and high-level communication patterns and a transport layer to deal with communication between client and server. Read more details here: https://lnkd.in/eDd9DZUg #GenAI #Anthropic #ModelContextProtocol

One from the vault today. It's only become clearer in the last year that while foundation model performance continues to increase (and cost continues to decrease) the competitive advantage for a lot of firms remains firmly rooted in the data and knowledge they hold today. Building solutions (like AI agents) which take advantage of this data remains complex, with a vast gulf between compelling prototype and a trusted-at-scale enterprise solution. One of the main ways to cross this gulf is by optimising your knowledge to interact with AI (think about it like pouring refined fuel into your supercar rather than some suspect stuff). There are many ways to do this, but using a graph database as a RAG backend is one (sometimes!). Here's a blog that dives into how to do that, why to do that and the common pitfalls: https://lnkd.in/d_iJi73X PS. it's not a magic bullet - check the blogs for the reasons when NOT to use graph to refine your knowledge if you're interested!

Simply Explained! - Vector Databases With the rapid rise of GenAI over the past few years, you've likely come across the term vector databases. But what precisely are they, and why are they becoming so crucial in this AI-driven era? Let's break it down. What is a Vector Database? A vector database is designed to handle and search data represented as numerical vectors. These vectors often capture complex data patterns, such as images, text embeddings, or audio, enabling advanced search capabilities like similarity matching. Mechanism of Vector Databases: Instead of storing traditional rows and columns, vector databases store high-dimensional vectors. These vectors are generated by machine learning models that translate input data into numerical forms. They leverage algorithms like approximate nearest neighbor (ANN) search to find similar vectors efficiently. Comparison with Traditional Databases Traditional databases excel in structured data storage and exact-match queries (e.g., SQL queries). However, they fall short in handling unstructured data and fuzzy searches. Vector databases, on the other hand, are tailored for AI-driven applications, enabling context-aware and similarity-based querying. Importance of Vector Databases As AI adoption grows, the demand for handling unstructured data (like text, images, and videos) is skyrocketing. Vector databases empower businesses with the ability to: - Implement semantic search. - Enhance recommendation systems. - Enable context-aware interactions, such as in chatbots. Real-World Applications of Vector Databases Some of the use-cases of vector databases are the following: Search Engines: Delivering results based on context and intent, not just keywords. Recommendation Systems: Providing suggestions based on user behavior and preferences. Image/Video Search: Finding similar media based on visual content rather than metadata.

The Benefits of Using Structured Output from OpenAI over JSON In today’s data-driven world, structured data is crucial for efficient data handling, especially when integrating AI into workflows and applications. While JSON has become a de facto standard for representing structured data, OpenAI’s new structured output feature offers distinct advantages for those working with complex AI outputs. 1. Enhanced Clarity and Precision JSON is known for its flexibility but often leaves room for ambiguity. OpenAI's structured output leverages specific schemas that dictate exactly how the data should be structured and labeled. This built-in validation ensures that the response strictly follows the specified structure, reducing the likelihood of errors or misinterpretations. 2. Minimized Post-Processing With JSON outputs, developers frequently need to parse, validate, and sometimes reformat the data before it’s usable in the application. Structured outputs eliminate this step by providing results in a format that aligns directly with the defined schema. 3. Reduced Errors in Complex Scenarios JSON’s flexibility can sometimes backfire when handling complex data that needs strict structuring, such as nested data or multi-step responses. The structured output feature enables developers to outline complex, hierarchical schemas that JSON alone can’t easily enforce. 4. Improved Readability for Developers OpenAI’s structured output provides a predefined and organized format that’s easier for developers to read and understand, as it removes unnecessary clutter and adheres strictly to the schema. JSON, while straightforward, can become difficult to parse in complex datasets, leading to errors in data handling. Structured output makes it clear exactly what data can be expected, enhancing readability and helping developers quickly understand how to integrate responses into their applications. 5. Streamlined Error Handling and Debugging Error handling in JSON responses often requires additional validation to catch null values, mismatched types, or unexpected structures. OpenAI’s structured output minimizes these issues by providing a controlled environment where outputs are consistently formatted. 6. Scalability for Larger Applications For organizations that use AI across multiple departments or large-scale applications, consistency is key. Structured output provides a reliable format that different teams and services can depend on, making it easier to scale applications and maintain interoperability between systems. Conclusion While JSON will always have its place, Open AI’s structured output represents a significant improvement for handling complex data in AI-driven applications. With its emphasis on clarity, precision, and minimized need for post-processing, structured output is a valuable tool for developers aiming to integrate AI more seamlessly into their workflows.

Roe AI (YC W24) is an AI-powered data warehouse that simplifies data analytics on texts, documents, websites, videos, and more. A whopping 80% of data today is unstructured. Business contracts, credit card transactions, call center transcripts, company landing pages, and industry diagrams — these types of data hold immense potential to inform 360-degree business decisions, yet existing data warehouses often fall short of harnessing their power. Roe AI, founded by Richard Meng and Jason Wang, is a new data warehouse that specializes in the kind of unstructured data that other data warehouses can't handle. The product has built-in multimodal AI that makes analysis quick and easy with natural language instead of long Python scripts (for example, imagine performing a complex customer segmentation with a short prompt). Richard, ex-Snowflake and LinkedIn, formerly led the charge on Snowflake’s Gen AI initiatives. Jason, ex-Retool and Meta, laid the groundwork for supporting countless enterprise Retool applications. Together, they're on a mission to empower data teams to unlock the full potential of every byte of data at their disposal. Say hello to Roe AI – the universe for all kinds of data in your enterprise. Congrats Richard and Jason on the launch!

𝐀 𝐧𝐞𝐱𝐭-𝐠𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧 𝐝𝐚𝐭𝐚 𝐰𝐚𝐫𝐞𝐡𝐨𝐮𝐬𝐞 𝐟𝐨𝐫 𝐮𝐧𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐝 𝐚𝐧𝐝 𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐝 𝐝𝐚𝐭𝐚 𝐩𝐨𝐰𝐞𝐫𝐞𝐝 𝐛𝐲 𝐆𝐞𝐧𝐀𝐈 What if you can process any data in this world? Roe uses AI to process unstructured data such as natural language, documents, images, and structured tabular data.  Supports various unstructured data types - natural languages, documents, websites, images, videos, etc. Connect with data sources from S3, Snowflake, Databricks and many more. Roe team came from Snowflake. Why a new data warehouse? 🔍 Only 20% of data is structured; what about the remaining 80%? 📄 Unstructured data includes transcripts, images, documents, videos, audio. 📊 Over 85% of data teams are leveraging only the structured 20%. 🚫 Relying on this partial view for business decisions is flawed. 🛠️ Processing unstructured data demands significant AI capabilities. 💻 Current AI infrastructures like GCP, Databricks, AWS require extensive MLOps knowledge and ~18 weeks of engineering time. 🔄 Existing solutions (Snowflake, Redshift, Databricks) cater to structured data (numbers, text enums, JSON) but not to images, documents, videos, audio, and more! Congrats to the team!

Building Your Own Database Agent: A Journey into AI and Databases In today's data-driven world, creating an AI agent that can interact with databases is a powerful skill. Data Logic Solution can help you succeed in this digital age. Here’s a glimpse into building your own database agent using Azure OpenAI, making AI more accessible and efficient: Your First AI Agent Using Azure OpenAI: Get started with creating your AI agent.  The Road to Generative AI: Understand the journey and evolution of generative AI technologies.  Key Differentiators: Discover what sets your AI agent apart in functionality and efficiency.  Customizing LLMs: Learn ways to apply prompting on large language models to fit specific use case. Which may be  Resource Intensive  Flexible (Future Model Updates)  Optimal 🔧 Practical Examples: Interacting with your own CSV Data: Learn how to handle and process CSV data using AI agent.  Connecting to SQL Databases: Establish connections and interact with SQL databases seamlessly.  Using Azure OpenAI Function Calling Feature: Leverage the function calling feature for enhanced interactions.  Leveraging Assistants API for SQL Databases: Utilize the Assistants API to optimize database interactions. 🛠️ Customization and Optimization: Customizing large language models (LLMs) to meet specific requirements is crucial. Whether you need high resource-intensive models or more flexible and optimal ones for future updates, this course covers it all. 💡 Why It Matters: Building an AI agent capable of interacting with databases opens endless possibilities for automation, data management, and advanced analytics. Whether you are dealing with simple CSV files or complex SQL databases, the ability to customize and optimize your AI models ensures that you stay ahead in the technology curve. Embark on this journey and transform the way you manage and interact with your data! To know more, visit our website: https://lnkd.in/dGTNPGyc #AI #MachineLearning #DataScience #AzureOpenAI #DatabaseManagement #SQL #CSV #GenerativeAI #TechInnovation #AIModels 

OpenAI’s Secret Sauce: How Vector Stores Power Code Interpreter & File Searching 🍔🤖 Ever wondered what makes OpenAI’s tools feel like a mind-reading assistant? It’s all about vector store architecture—the unsung hero that fuels the intelligence of Code Interpreter and File Searching. Let’s dive in: Vector Stores: The MVP of AI Assistance Imagine having a librarian who doesn’t just look for keywords but understands the meaning behind your request. That’s exactly how vector stores work. Instead of sorting information alphabetically or by tags, they organize data by relationships and context. The Process: How It Works Chunk It Up: Large files are split into smaller, digestible pieces called chunks. Embed the Meaning: Each chunk is transformed into a vector—a mathematical representation of its meaning. Find Your Match: When you ask a question, it’s converted into a vector and matched against the stored chunks to find the most relevant information. How Vector Stores Power OpenAI Tools Code Interpreter 🛠️ This tool is your data analytics superhero. It processes structured data like Excel files, CSVs, and code. By leveraging vector stores, it references meaningful patterns in your data to provide accurate insights, generate solutions, or even create code. File Searching 🔍 File Searching shines when it comes to unstructured data. PDFs, text files, and documents are its playground. It uses vector stores to pull out relevant sections quickly, saving you from endless scrolling. While it doesn’t handle structured data, that’s where Code Interpreter steps in—together, they cover all bases. The Dynamic Duo Think of Code Interpreter and File Searching as Batman and Robin: Code Interpreter tackles structured data with precision. File Searching unearths insights from messy, unstructured files. Each fills in where the other leaves off, creating a seamless, complementary experience. Why It Matters Whether you’re analyzing complex datasets or digging for nuggets of information buried in documents, vector stores ensure accuracy, speed, and context-aware answers. It’s not just about AI tools—it’s about smart AI that works the way you do. 🧠✨