Intelligent Document Q&A and Vector Database Management Workflow

The API architecture crawling and extraction workflow is an intelligent automation tool that efficiently searches, crawls, and extracts API documentation for specified services. By integrating search engines, web crawlers, and large language models, this workflow not only accurately identifies API operations but also structures the information for storage in Google Sheets. Additionally, it generates customized API architecture JSON files for centralized management and sharing, significantly enhancing development and integration efficiency, and helping users quickly obtain and organize API information.

This workflow automates the process of web data scraping, extracting and formatting content, generating high-quality text vector embeddings, and storing them in a vector database, forming a complete data processing loop. By combining efficient data crawling, intelligent content extraction, and vector retrieval technologies, users can quickly build vector datasets suitable for training large language models, enhancing data quality and processing efficiency, and making it applicable to various scenarios such as machine learning, intelligent search, and knowledge management.

This workflow transforms a Telegram bot into an intelligent document assistant. Users can upload PDF documents via Telegram, and the system automatically parses them to generate semantic vectors, which are stored in a Supabase database for easy intelligent retrieval and Q&A. The bot utilizes a powerful language model to answer complex questions in real-time, supporting rich HTML format output and automatically splitting long replies to ensure clear information presentation. Additionally, it integrates a weather query feature to enhance user experience, making it suitable for personal knowledge management, corporate assistance, educational tutoring, and customer support scenarios.

This workflow automatically extracts new documents, generates intelligent summaries, stores vectors, and produces various formats of documents such as study notes, briefings, and timelines by monitoring a local folder. It supports multiple file formats including PDF, DOCX, and plain text. By integrating advanced AI language models and vector databases, it enhances content understanding and retrieval capabilities, significantly reducing the time required for traditional document organization. This workflow is suitable for scenarios such as academic research, training, content creation, and corporate knowledge management, greatly improving the efficiency of information extraction and utilization.

This workflow primarily implements the automatic downloading of documents from Google Drive, utilizing OpenAI for text processing and vector generation, which are then stored in the Pinecone vector database. Users can quickly ask questions in natural language through a chat interface, and the system will return relevant answers based on vector retrieval. This solution effectively addresses the inefficiencies and inaccuracies of traditional document retrieval, making it widely applicable in scenarios such as corporate knowledge bases, legal, research, and customer service, thereby enhancing the convenience and accuracy of information retrieval.

This workflow is designed to automate the comparison of different large language models by real-time invoking independent responses from multiple models based on user chat input. It records the results and contextual information into Google Sheets for easy subsequent evaluation and comparison. It supports memory isolation management to ensure accurate context transmission while providing user-friendly templates to facilitate the participation of non-technical personnel in model performance evaluation, thereby enhancing the team's decision-making efficiency and testing accuracy.

This workflow builds an intelligent AI chat agent that allows users to converse with it in natural language to query and analyze website data from Google Search Console in real time. Leveraging OpenAI's intelligent conversational understanding and the historical memory storage of a Postgres database, users can easily obtain accurate data reports without needing to understand API details. Additionally, the agent can proactively guide users, optimizing the data querying process and enhancing user experience, while supporting multi-turn conversations to simplify data analysis and decision-making processes.