Automated Research Report Generation with OpenAI, Wikipedia, Google Search, and Gmail/Telegram
This workflow is designed to automate the generation of research reports based on user-defined topics, integrating various information sources such as OpenAI, Wikipedia, news APIs, Google Search, and Google Scholar. Through intelligent analysis and integration, it produces structured PDF reports that include an introduction to the topic, key findings, and academic insights, which are automatically sent to designated users via Gmail and Telegram. Additionally, all data is recorded in Google Sheets for easy management and tracking, significantly enhancing research efficiency and the accuracy of information integration.
Tags
Workflow Name
Automated Research Report Generation with OpenAI, Wikipedia, Google Search, and Gmail/Telegram
Key Features and Highlights
This workflow automates the generation of research reports based on user-input topics by integrating multiple information sources, including OpenAI GPT models, Wikipedia, News API, Google Search, and Google Scholar. It produces a structured PDF report that is automatically delivered to designated users via email (Gmail) and instant messaging (Telegram). The report comprehensively covers topic introduction, summary, key findings, news highlights, academic insights, and Wikipedia background, ensuring authoritative and well-rounded content.
Highlights include:
- Automated query optimization generating five diverse and relevant search keywords to broaden research scope;
- Multi-channel data acquisition (news, Wikipedia, web pages, academic papers) to ensure rich and credible information;
- Intelligent aggregation and content generation using OpenAI GPT models, guaranteeing logical clarity and depth;
- Professional-style PDF report creation featuring cover page, table of contents, and formatted layout;
- Seamless automatic distribution via Gmail and Telegram for convenient and rapid sharing;
- Data logging into Google Sheets for easy management and tracking.
Core Problems Addressed
Traditional research report writing is time-consuming and labor-intensive, with dispersed information sources that are difficult to consolidate. This workflow automates information gathering, content generation, formatting, and distribution, significantly improving research efficiency, reducing manual intervention, and ensuring accurate, comprehensive reports that enable users to quickly obtain high-quality research outputs.
Application Scenarios
- Academic researchers rapidly summarizing the latest developments and literature on a specific scholarly topic;
- Corporate market research and competitive analysis with real-time access to industry news and trends;
- Consulting professionals assisting in client report preparation;
- Media personnel or content creators conducting in-depth topic exploration and material preparation;
- Educational institutions producing specialized teaching materials or course content;
- Any scenario requiring fast generation of authoritative research reports.
Main Workflow Steps
- Input Validation: Receive and validate the user’s research topic to ensure input effectiveness;
- Query Optimization: Use OpenAI model to generate five relevant and multi-dimensional search keywords to expand research perspectives;
- Multi-Channel Data Acquisition:
- Fetch latest news via News API;
- Retrieve foundational information from Wikipedia;
- Obtain web data through Google Custom Search;
- Search academic papers via SerpApi’s Google Scholar integration;
- AI Research Assistant: Perform comprehensive analysis based on collected data to generate structured report content, including introduction, summary, key findings, news highlights, academic insights, and Wikipedia synopsis;
- Data Aggregation and Organization: Merge multiple data entries into a complete JSON structure representing the full report content;
- PDF Report Generation: Render structured content into HTML and convert it into a professional PDF file using PDFShift API;
- Report Distribution: Automatically send the PDF report to specified recipients via Gmail and Telegram;
- Research Metadata Storage: Store topic, search keywords, sources, and timestamps in Google Sheets for record-keeping and retrieval.
Involved Systems and Services
- OpenAI GPT-4o-mini: Natural language processing and content generation;
- Wikipedia API: Foundational knowledge retrieval;
- NewsAPI: Latest news fetching;
- Google Custom Search API: Web information search;
- SerpApi (Google Scholar Search): Academic literature retrieval;
- PDFShift API: HTML to PDF conversion service;
- Gmail: Email delivery;
- Telegram: Instant messaging delivery;
- Google Sheets: Research metadata storage and management;
- Google Drive: File storage and management.
Target Users and Value Proposition
- Researchers and scholars: Quickly obtain multi-dimensional, authoritative research materials and analytical reports;
- Market analysts and consultants: Efficiently generate industry updates and competitive intelligence reports;
- Content creators and media professionals: Rapidly build comprehensive background and latest news summaries;
- Educators: Support in producing course materials and lecture notes;
- Business decision-makers: Timely access to the latest trends in relevant fields to support informed decision-making.
By integrating diverse intelligent tools and data sources, this workflow automates and streamlines research report generation, greatly enhancing information consolidation efficiency and content quality, delivering substantial practical value.
Chat with GitHub OpenAPI Specification using RAG (Pinecone and OpenAI)
This workflow utilizes RAG technology, combined with the Pinecone vector database and OpenAI intelligent models, to build an intelligent Q&A chatbot for the GitHub API. It can real-time scrape and index GitHub's API documentation, quickly answering users' technical queries through vector search and semantic understanding, significantly improving the efficiency and accuracy of developers in obtaining interface information. It is suitable for scenarios such as technical support, documentation maintenance, and training.
💥🛠️ Build a Web Search Chatbot with GPT-4o and MCP Brave Search
This workflow builds an intelligent chatbot that combines the GPT-4o language model with MCP Brave Search, enabling it to process user chat messages in real-time and perform web searches. The chatbot not only generates high-quality intelligent responses but also supports short-term memory, enhancing the coherence of conversations and the user experience. It is suitable for various scenarios such as automated customer service, knowledge retrieval, and information inquiry, helping users quickly obtain the information they need and improving interaction efficiency.
N8N Español - NocodeBot
This workflow creates a multilingual No-Code tool query bot. When users input the tool name in Telegram, the bot automatically retrieves detailed information from a remote database and translates it into the user's native language, subsequently sending it as a multimedia message. Through this process, users can easily access introductions to No-Code tools, overcoming language barriers and achieving instant information retrieval. This greatly enhances the convenience and user-friendliness of inquiries, making it suitable for technical support and educational training in multilingual environments.
Integrating AI with Open-Meteo API for Enhanced Weather Forecasting
This workflow combines AI language models with the Open-Meteo weather forecast API to provide intelligent weather inquiry and forecasting services. Users can simply enter the city name and their requirements through a chat interface, and the AI will automatically obtain the geographic coordinates and retrieve weather information, generating accurate weather forecast responses. This process significantly simplifies the traditional weather inquiry operations, enhances interaction efficiency, and is suitable for various scenarios such as smart customer service, travel planning, and education and training, meeting users' needs for real-time weather information.
n8n DeepResearcher
This in-depth research workflow helps users efficiently conduct research on complex topics through automated searches and content scraping, combined with advanced language models. After the user inputs the research topic, the system generates multiple search queries and filters relevant information, supporting dynamic adjustments to the depth and breadth of the research. Ultimately, the gathered information is compiled into a detailed report and automatically uploaded to a cloud management platform, achieving systematic organization and sharing of materials, significantly enhancing research efficiency and quality.
Text Fact-Checking Assistance Workflow
This workflow is designed to automate fact-checking in text by utilizing natural language processing technology to split the input text into sentences and verify the authenticity of each one. By invoking a locally running customized language model, it efficiently identifies false information, reduces the workload of manual proofreading, and enhances the accuracy and efficiency of content review. It is suitable for fields such as media, research, and content creation, helping users ensure the authenticity and authority of information, and enabling rapid fact screening and error correction.
Intelligent Web Query and Semantic Re-Ranking Flow
This workflow automatically generates optimized web search queries through intelligent semantic analysis and multi-chain thinking, and calls the Brave Search API to obtain relevant results. It is capable of deeply reordering search results and extracting information based on the user's true intent, filtering out the top 10 most relevant high-value links to help users quickly locate the answers they need. It supports Webhook triggers and is applicable in various scenarios such as scientific research, market research, and corporate decision-making, significantly enhancing the relevance and effectiveness of information retrieval.
n8n Research AI Agent Intelligent Assistant Workflow
This workflow provides real-time consultation and assistance through intelligent dialogue and multi-tool collaboration, aiming to enhance users' learning and usage efficiency on the automation platform. It intelligently receives user inquiries, analyzes issues, and automatically retrieves relevant tools and content to generate clear, actionable responses. This helps solve users' challenges in understanding functions and operational guidance, making it suitable for beginners, advanced users, corporate support teams, and training scenarios.