Agent Milvus Tool

This workflow builds an intelligent question-and-answer system based on company documents stored in Google Drive, utilizing a vector database and large language models to achieve rapid information retrieval and natural language interaction. By automatically synchronizing document updates, employees can obtain concise and accurate answers related to policies and processes in real time, thereby enhancing knowledge management efficiency, optimizing the self-service experience, and addressing the issues of traditional document fragmentation and retrieval difficulties. It is applicable to various scenarios, including internal knowledge bases, HR policy inquiries, and intelligent retrieval of legal compliance documents.

This workflow intelligently captures posts tagged with #damus on the Nostr social platform, utilizes AI models to analyze discussion topics, and automatically generates detailed topic reports. It distributes these reports through multiple channels, including Gmail and Telegram. This effectively addresses the cumbersome process of manually filtering information, helping community operation teams, product managers, and content creators quickly obtain valuable insights, enhance information retrieval efficiency, and achieve intelligent management and dissemination of data.

This workflow utilizes the Google Gemini 1.5 AI model to automatically analyze YouTube videos, generating diverse content such as summaries, verbatim transcriptions, timestamps, and scene descriptions. Users can dynamically adjust the prompts based on their needs to achieve precise information extraction. The processing results can be saved to Google Drive and sent via email for easy access and sharing. This tool significantly enhances the efficiency of obtaining video content, making it suitable for content creators, marketers, educational institutions, and general viewers, saving time and improving information utilization.

This workflow combines real-time web scraping with AI chatbot technology, enabling it to automatically retrieve the latest web content based on user queries and generate accurate responses. Users can obtain precise information quickly by asking questions in natural language, without the need for manual searches, significantly enhancing the efficiency of information retrieval and the interaction experience. It is suitable for users who require real-time information, such as corporate customer service representatives, market analysts, and researchers, helping them make decisions and respond more efficiently.

This workflow automatically scrapes popular posts from specified Reddit communities, utilizing AI for content analysis and sentiment assessment to help users identify business-related opportunities and pain points. It can generate innovative business proposals tailored to specific issues and structurally store the analysis results in Google Sheets for easier management and tracking. Additionally, the classification and saving function for email drafts effectively supports follow-up, enabling entrepreneurs and market research teams to quickly gain insights into market dynamics and enhance decision-making efficiency.

This workflow integrates AI technology and automation tools to achieve intelligent monitoring and summary pushing of thematic information. It regularly retrieves the latest articles from multiple RSS sources, uses AI for relevance classification and content extraction, generates structured summaries in Slack format, and promptly pushes them to designated channels. This enables users to efficiently stay updated on the latest developments in their areas of interest, addressing issues of information overload and inconvenient sharing, thereby enhancing team collaboration and information processing efficiency.

This workflow is designed to automate the testing and analysis of the performance of multiple large language models locally. By dynamically retrieving the list of models and standardizing system prompts, users can easily compare the output performance of different models on specific tasks. The workflow records request and response times, conducts multi-dimensional text analysis, and structures the results for storage in Google Sheets, facilitating subsequent management and comparison. Additionally, it supports flexible parameter configuration to meet diverse testing needs, enhancing the efficiency and scientific rigor of model evaluation.

This workflow receives PDF files via Telegram, automatically splits them, and converts the content into vectors stored in the Pinecone database, supporting vector-based intelligent Q&A. Users can conveniently query document information in the chat window, significantly improving the speed and accuracy of knowledge acquisition. It is suitable for scenarios such as enterprise document management, customer support, and education and training, greatly enhancing information retrieval efficiency and user experience.