Google has introduced LangExtract, an open-source Python library designed to help developers extract structured information from unstructured text using large language models such as the Gemini models. The library simplifies the process of converting free-form text into structured data, offering features like controlled generation, text chunking, parallel processing, and integration with various LLMs.
This article is part 4 of a crash course on the Model Context Protocol (MCP). It focuses on resources and prompts, explaining their mechanics, distinctions, and implementation, and how they differ from tools. It covers resource types, discovery mechanisms, and application-controlled access patterns.
Keboola MCP Server enables AI-powered data pipeline creation and management. It allows users to build, ship, and govern data workflows using natural language and AI assistants, integrating with tools like Claude and Cursor. It's free to use, with costs based on standard Keboola usage.
The article discusses how Visa leverages retrieval-augmented generation (RAG) and deep learning to enhance operations. It describes Visa's 'Secure ChatGPT,' which offers a multi-model interface for secure internal use, and how RAG improves policy-related data retrieval. The article also explores Visa's data infrastructure and AI's role in fraud prevention.
This article describes a workflow using Large Language Models (LLMs) to automate the process of normalising spreadsheet data, making it tidy and machine-readable for easier analysis and insights.
Google has enhanced Google Sheets with an AI-powered upgrade using its Gemini technology. This update allows users to automatically convert spreadsheets into charts, identify trends, and create advanced visualizations like heatmaps. Users can interact with the Gemini feature directly through a chat interface within Sheets.
An article on building an AI agent to interact with Apache Airflow using PydanticAI and Gemini 2.0, providing a structured and reliable method for managing DAGs through natural language queries.
- Agent interacts with Apache Airflow via the Airflow REST API.
- Agent can understand natural language queries about workflows, fetch real-time status updates, and return structured data.
- Sample DAGs are implemented for demonstration purposes.
- Agent interacts with Apache Airflow via the Airflow REST API.
- Agent can understand natural language queries about workflows, fetch real-time status updates, and return structured data.
- Sample DAGs are implemented for demonstration purposes.
- standardization, governance, simplified troubleshooting, and reusability in ML application development.
- integrations with vector databases and LLM providers to support new applications -
provides tutorials on integrating
- integrations with vector databases and LLM providers to support new applications -
provides tutorials on integrating
Intro to Streamlit
- Simple and complex Streamlit example
- Data and state management in Streamlit apps
- Data widgets for Streamlit apps
- Deploying Streamlit apps
- Simple and complex Streamlit example
- Data and state management in Streamlit apps
- Data widgets for Streamlit apps
- Deploying Streamlit apps
