Transform Your Telegram Chats into a Knowledge Base with AI
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Automate the process of turning PDF documents sent via Telegram into an intelligent, searchable knowledge base powered by AI. This workflow enables instant answers to your questions based on the content of your uploaded documents.
About This Workflow
This n8n workflow seamlessly integrates Telegram with advanced AI capabilities to create a powerful document-based question-and-answer system. Users can simply send PDF files through Telegram. The workflow automatically processes these PDFs, extracts their content, splits it into manageable chunks, and stores them in a vector database (Pinecone). When a user asks a question via Telegram, the system retrieves relevant information from the vector store and uses a sophisticated AI model (Groq Llama 3.1) to generate accurate and contextually relevant answers. This transforms your Telegram chats into an interactive, intelligent knowledge base accessible on demand.
Key Features
- Telegram Integration: Trigger workflows by sending documents and messages directly via Telegram.
- PDF Processing: Automatically extract and process content from uploaded PDF files.
- Intelligent Chunking: Splits large documents into smaller, optimized chunks for AI processing.
- Vector Database Storage: Leverages Pinecone for efficient storage and retrieval of document embeddings.
- AI-Powered Q&A: Utilizes advanced language models for accurate and context-aware responses.
- Automated Knowledge Base Creation: Effortlessly build and maintain a searchable repository of your documents.
How To Use
- Set up Telegram Trigger: Configure the
Telegram Triggernode with your bot's API token to receive incoming messages and documents. - Document Handling: Use an
IFnode (Check If is a document) to differentiate between text messages and document uploads. - File Retrieval: For documents, use the
Telegram get Filenode to download the file associated with the Telegram message. - MIME Type Correction: Employ the
Change to application/pdfcode node to ensure the file's MIME type is correctly set toapplication/pdffor subsequent processing. - Data Loading & Embedding: The
Default Data LoaderandEmbeddings OpenAInodes process the PDF content and generate embeddings. - Text Splitting: The
Recursive Character Text Splitternode divides the document text into manageable chunks. - Vector Store Insertion: The
Pinecone Vector Store1node stores these embeddings and document chunks in your Pinecone index (ensure your Pinecone index is named 'telegram'). - Q&A Chain Setup: For text messages, the
Question and Answer Chainnode prepares the query. It takes the user's message text and instructs the system to search the retriever. - Vector Store Retrieval: The
Vector Store Retrievernode fetches relevant document chunks from your vector store based on the user's query. - AI Model for Answers: The
Groq Chat Model(using Llama 3.1) takes the retrieved information and the user's question to formulate a comprehensive answer. - Telegram Response: Finally, the
Telegram Responsenode sends the AI-generated answer back to the user via Telegram. - Error Handling: The
Stop and Errornode is included for basic error management.
Apps Used
Workflow JSON
{
"id": "23e005c9-311e-4e74-a62d-a341c138f9f1",
"name": "Transform Your Telegram Chats into a Knowledge Base with AI",
"nodes": 17,
"category": "DevOps",
"status": "active",
"version": "1.0.0"
}Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.
Get This Workflow
ID: 23e005c9-311e...
About the Author
AI_Workflow_Bot
LLM Specialist
Building complex chains with OpenAI, Claude, and LangChain.
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