Open Deep Research - AI-Powered Autonomous Research Workflow

This workflow automates the retrieval of the latest academic papers from Hugging Face, utilizing the advanced GPT-4 model for in-depth analysis and structured extraction of paper abstracts. Ultimately, it intelligently stores key information in a Notion database. It effectively addresses the tediousness of manually searching for papers, avoids redundant information storage, and provides efficient management of academic resources. This is suitable for researchers, academic institutions, and AI practitioners to continuously track the latest research developments, enhancing the efficiency and quality of literature organization.

The DSP Agent is an intelligent learning assistant specifically designed for students in the field of signal processing. It receives text and voice messages through Telegram and utilizes advanced AI models to provide instant knowledge queries, calculation assistance, and personalized learning tracking. This tool helps students quickly understand complex concepts, offers dynamic problem analysis and learning suggestions, addressing the issues of insufficient interactivity and lack of personalized tutoring in traditional learning. It enhances learning efficiency and experience.

This workflow implements a Retrieval-Augmented Generation (RAG) function through real-time data updates, automatically retrieving the latest content from the Notion knowledge base. It performs text chunking and vectorization, storing the results in the Supabase vector database. By integrating OpenAI's GPT-4 model, it provides contextually relevant intelligent Q&A, significantly enhancing the efficiency and accuracy of knowledge base utilization. This is applicable in scenarios such as enterprise knowledge management, customer support, and education and training, ensuring that users receive the most up-to-date information.

This workflow implements a session-based A/B split testing, which can randomly assign different prompts (baseline and alternative) to users in order to evaluate the effectiveness of language model responses. By integrating a database to record sessions and allocation paths, and combining it with the GPT-4o-mini model, it ensures continuous management of conversation memory, enhancing the scientific rigor and accuracy of the tests. It is suitable for AI product development, chatbot optimization, and multi-version effectiveness verification, helping users quickly validate prompt strategies and optimize interaction experiences.

This workflow enables real-time synchronization of data from the Airtable database to Obsidian notes. Users simply need to select the relevant text in Obsidian and send a request. An intelligent AI agent will understand the query intent and invoke the OpenAI model to retrieve the required data. Ultimately, the results will be automatically inserted into the notes, streamlining the process of data retrieval and knowledge management, thereby enhancing work efficiency and user experience. It is suitable for professionals and team collaboration users who need to quickly access structured data.

This workflow builds a multi-agent AI analysis system that integrates real-time data from CoinMarketCap, providing comprehensive insights into the cryptocurrency market. Users can quickly obtain analysis results for cryptocurrency prices, exchange holdings, and decentralized trading data through Telegram. The system can handle complex queries and automatically generate reports on market sentiment and trading data, assisting investors and researchers in making precise decisions, thereby enhancing information retrieval efficiency and streamlining operational processes.

This workflow automatically processes CSV files exported from Screaming Frog to generate an `llms.txt` file that meets AI training standards. It supports multilingual environments and features intelligent URL filtering and optional AI text classification, ensuring that the extracted content is of high quality and highly relevant. Users simply need to upload the file to obtain structured data, facilitating AI model training and website content optimization, significantly enhancing work efficiency and the accuracy of data processing. The final file can be easily downloaded or directly saved to cloud storage.

This workflow builds an intelligent movie recommendation chatbot that utilizes Retrieval-Augmented Generation (RAG) technology, combining the Qdrant vector database and OpenAI language model to provide personalized movie recommendations for users. By importing rich IMDb data, it generates text vectors and conducts efficient similarity searches, allowing for a deep understanding of users' movie preferences, optimizing recommendation results, and enhancing user interaction experience. It is particularly suitable for online film platforms and movie review communities.