AI Intelligent Assistant Integrated Hacker News Data Query Workflow

This workflow efficiently extracts key information from PDF files. Users only need to set extraction instructions to download the PDF from Google Drive and convert it to Base64 format. Subsequently, the system simultaneously invokes two AI models, Claude 3.5 Sonnet and Gemini 2.0 Flash, for content analysis, allowing for a comparison of their extraction effectiveness and response speed. This process simplifies traditional PDF data extraction methods and is suitable for the automated processing of documents such as financial records and contracts, enhancing enterprise efficiency and intelligence levels.

This workflow utilizes the advanced Google Gemini AI model to automatically process and analyze the content of YouTube videos or playlists. Users simply need to input a link to receive an intelligent summary and in-depth analysis of the video transcription text, saving them time from watching. It supports multi-video processing, intelligent Q&A, and context preservation, enhancing the user experience. Additionally, it incorporates a vector database for rapid retrieval, making video content more structured and easier to query, suitable for various scenarios such as education, content creation, and enterprise knowledge management.

This workflow combines intelligent AI agents with the OpenAI GPT-4 model to achieve automatic web content scraping and processing. After the user inputs a chat message, the system automatically generates HTTP request parameters, retrieves web content from a specified URL, performs deep cleaning of the HTML, and finally outputs it in Markdown format. It supports both complete and simplified scraping modes, intelligently handles request errors, and provides feedback and suggestions. This workflow is suitable for content monitoring, information collection, and AI question-answering systems, enhancing information retrieval efficiency and reducing manual intervention.

This workflow automatically scrapes the latest content from specified news websites, extracting the publication time, title, and body of the news articles. It then uses AI technology to generate summaries and key technical keywords for each news item, ultimately storing the organized data in a database. This process enables efficient monitoring and analysis of news sources without RSS feeds, making it suitable for various scenarios such as media monitoring, market research, and content management, significantly enhancing the efficiency and accuracy of information retrieval.

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.