Telegram RAG PDF
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.
Workflow Name
Telegram RAG PDF
Key Features and Highlights
This workflow enables receiving PDF documents via Telegram, automatically splitting and converting the document content into vector embeddings, and storing them in the Pinecone database. It supports intelligent question answering based on vector retrieval. The highlight lies in the seamless integration of Telegram file reception, OpenAI embedding generation, text splitting, Pinecone vector storage, and context-aware Q&A powered by the Groq large language model, achieving a fully automated closed loop from “document to knowledge base to Q&A.”
Core Problems Addressed
Traditional document content retrieval is inefficient, making it difficult to quickly locate information. This workflow vectorizes PDF document content and combines it with natural language Q&A, enabling users to conveniently query document information directly within the Telegram chat interface, significantly improving the speed and accuracy of knowledge acquisition.
Application Scenarios
- Internal enterprise document management and rapid retrieval
- Automated customer support answering questions based on product manuals or instructions
- Intelligent querying of educational and training materials
- Any scenario requiring intelligent document content Q&A through a chat interface
Main Process Steps
- Use a Telegram trigger to monitor messages and detect incoming document files.
- Retrieve the uploaded PDF file from Telegram and modify its metadata to ensure correct formatting.
- Load the file’s binary data using the default data loader.
- Split the document into manageable text chunks using a recursive character splitter.
- Generate vector embeddings for each text chunk using OpenAI.
- Insert the vector data into the Pinecone vector database for efficient retrieval.
- Upon receiving a user query, retrieve relevant content chunks from Pinecone via vector search.
- Use the Groq Chat large language model to generate answers based on the retrieved context.
- Reply to the user with the intelligent Q&A results through Telegram messages, completing the interaction.
Involved Systems or Services
- Telegram (message reception and file retrieval)
- OpenAI (text embedding generation)
- Pinecone (vector database storage and retrieval)
- Groq (large language model for Q&A generation)
- n8n (workflow automation platform)
Target Users and Value
This workflow is ideal for enterprise users, technical teams, customer service personnel, and educational institutions that require intelligent document content Q&A via instant messaging tools. It greatly simplifies the construction of document knowledge bases and the implementation of natural language Q&A, enhancing information retrieval efficiency and user experience while reducing manual organization and response costs.