Telegram RAG PDF
This workflow receives PDF files via Telegram, automatically processes the document content, and converts it into vectors, which are stored in a Pinecone database to enable vector-based intelligent Q&A. Users can directly upload PDFs and ask questions in Telegram, and the system instantly retrieves relevant information to generate accurate answers, greatly improving the efficiency of information retrieval. It is suitable for scenarios such as corporate knowledge bases, customer support, and educational training, allowing users to efficiently utilize document content.
Workflow Name
Telegram RAG PDF
Key Features and Highlights
This workflow enables receiving PDF documents via Telegram, automatically splitting the document content, converting it into vectors, and storing them in the Pinecone vector database. It supports intelligent question answering based on vector retrieval. Users can directly send PDF files through Telegram and ask questions in natural language. The system instantly retrieves relevant information from the document knowledge base and generates accurate answers.
Core Problems Addressed
Traditional document content is difficult to quickly search and use for intelligent Q&A, especially within chat tools where direct utilization of document information is not feasible. This workflow converts documents into searchable vector formats and leverages large language models to achieve real-time, precise document-based Q&A, significantly improving information utilization efficiency.
Application Scenarios
- Internal corporate knowledge base Q&A: Employees query company manuals, process documents, and other PDF materials via Telegram.
- Customer support: Customers upload manuals or contracts, and the system automatically answers related questions.
- Education and training: Students upload textbook PDFs and ask questions about knowledge points anytime through chat.
- Any scenario requiring intelligent Q&A based on PDF documents.
Main Workflow Steps
- Telegram Trigger: Monitor Telegram messages and detect if they contain PDF documents.
- File Processing: Download the document and forcibly set the file format to application/pdf.
- Text Splitting: Use a recursive character text splitter to divide PDF content into appropriately sized text chunks.
- Vector Embedding: Call the OpenAI Embeddings API to convert text chunks into vectors.
- Vector Storage: Insert the vector data into the Pinecone vector database index.
- Q&A Retrieval: Receive user query messages, retrieve relevant text chunks via vector search, and generate answers using the Groq LLM model.
- Result Feedback: Reply to users with answers via Telegram messages, while also providing information such as the number of pages in the uploaded document.
Involved Systems or Services
- Telegram (message receiving and sending, file downloading)
- OpenAI Embeddings (text vectorization)
- Pinecone (vector database for efficient vector storage and retrieval)
- Groq LLM (large language model for generating answers based on retrieved information)
- n8n (workflow automation platform coordinating execution of each node)
Target Users and Value
- Enterprise IT and knowledge management personnel looking to quickly build intelligent document Q&A bots
- Customer service and support teams aiming to improve response efficiency for document-related inquiries
- Educational and training institutions seeking personalized document-based Q&A assistance
- Any users who want to efficiently leverage PDF document content through chat tools
This workflow automates the complex processes of document handling, vector storage, and intelligent Q&A integration, greatly lowering technical barriers and delivering a convenient and efficient new experience for intelligent document interaction.