News Extraction

This workflow can automatically scrape the latest news articles from specified news websites without relying on RSS subscriptions. It regularly extracts article links, publication dates, titles, and body content, and uses the GPT-4 model to generate brief summaries and extract key technical keywords. The organized structured data will be stored in a NocoDB database, facilitating subsequent retrieval and analysis, significantly improving the efficiency of news monitoring and content management, making it suitable for use by businesses, media, and data analysts.

This workflow utilizes AI language models and various data sources to achieve automated deep information retrieval and research report generation. After the user inputs a query, the system generates precise search keywords, conducts web searches using SerpAPI, and combines content analysis with Jina AI, ultimately integrating the results into a structured research report. This process enhances research efficiency, ensures the coherence and accuracy of information extraction, and is applicable in scenarios such as academic research, market research, content creation, and corporate decision-making, helping users quickly obtain high-quality materials.

This workflow integrates an intelligent assistant and vector storage, aiming to achieve smart Q&A after document retrieval and automatically add literature citations to the retrieved content. Users can format the output results as Markdown or HTML, facilitating the generation of professional documents with dynamic citation numbers, thereby enhancing the credibility and traceability of the information. It is suitable for fields such as research, education, and law, addressing issues of missing citations and strange characters in answers, and helping users efficiently generate standardized documents.

This workflow is capable of automatically loading text prompt files from a specified GitHub repository, extracting and replacing variable placeholders, and generating complete prompt content for use by AI models. It features a variable validation mechanism to ensure that all required variables are correctly assigned, preventing errors and improving efficiency. Additionally, by integrating the Ollama chat model and LangChain AI Agent, it achieves full-process automation from text prompts to intelligent responses, making it suitable for various scenarios that require dynamic content generation.

This workflow automatically fetches the latest artificial intelligence news from mainstream news APIs at a scheduled time every day. It then filters, summarizes, and translates the news into Traditional Chinese using advanced AI models. Finally, the organized news summaries are promptly pushed to designated Telegram chat groups or channels, helping users efficiently access cutting-edge AI information. This solution addresses the cumbersome issues of manual searching and translation, ensuring the timeliness and continuity of information, making it suitable for various AI industry professionals and general users.

This workflow automatically extracts the transcription text from a YouTube video by inputting the video ID and performs intelligent summarization. After obtaining the text using the SearchApi, it undergoes multiple steps of splitting and content merging, combined with the OpenAI GPT-4 model to generate a concise summary. This process effectively addresses the challenge of quickly extracting key information from long videos, making it suitable for content creators, educators, and market researchers, significantly improving the efficiency and accuracy of information retrieval.

This workflow can automatically identify and extract license plate numbers from uploaded vehicle images, directly returning clean license plate characters, eliminating the need for manual input by users. By integrating advanced large language models, it significantly improves the efficiency and accuracy of license plate recognition, streamlining the traditional license plate extraction process. It is applicable in various scenarios such as traffic management, parking lots, and logistics monitoring, helping users achieve rapid automated collection of vehicle information, enhance management intelligence, and save time and labor costs.

The Tech Radar workflow automates the management and intelligent querying of enterprise technology radar data by integrating various technologies. It transforms data from Google Sheets into structured text and stores it in vector and relational databases, supporting multidimensional queries. Equipped with an intelligent AI agent, it can accurately respond to user inquiries, enhancing information retrieval efficiency. Additionally, scheduled synchronization updates ensure data timeliness, lowering the information access barrier for non-technical personnel and facilitating technology decision-making and internal communication.