Telegram RAG pdf

For Telegram, this automated workflow processes PDF documents by fetching files, splitting them into manageable chunks, and storing them in a Pinecone vector database. It enables users to interact with the database through chat, retrieving relevant information and providing accurate responses. The system ensures efficient data handling and enhances user experience by confirming successful operations, making it ideal for streamlined communication and data management.

7/8/2025
20 nodes
Complex
manualcomplextelegramtriggerlangchainstopanderrorsticky notetelegramadvancedcommunicationbotlogicconditional
Categories:
Communication & MessagingComplex WorkflowManual Triggered
Integrations:
TelegramTriggerLangChainStopAndErrorSticky NoteTelegram

Target Audience

This workflow is ideal for:
- Developers looking to integrate Telegram with AI capabilities.
- Data Scientists who need to process and analyze documents sent via Telegram.
- Businesses seeking to automate document handling and retrieval processes.
- Educators who want to create interactive chatbots for student engagement.
- Researchers needing a streamlined way to interact with a database through chat.

Problem Solved

This workflow addresses the challenge of efficiently managing and retrieving document-based information sent through Telegram. It automates the process of:
- Receiving documents from users.
- Converting them into a usable format.
- Storing the data in a vector database for quick retrieval.
- Responding to user queries based on the stored data, thereby enhancing the interaction experience and ensuring accurate information delivery.

Workflow Steps

  • Trigger: The workflow begins with a Telegram Trigger that listens for incoming messages.
    2. Document Check: It checks if the incoming message contains a document.
    3. File Retrieval: If a document is detected, it retrieves the file using Telegram get File.
    4. File Processing: The file is processed to ensure it is in PDF format using the Change to application/pdf node.
    5. Data Loading: The processed document is then loaded into a Pinecone vector store for storage.
    6. Embedding Generation: The document is embedded using OpenAI to facilitate efficient querying.
    7. Query Handling: When users send queries, the system retrieves relevant information from the vector store using Vector Store Retriever.
    8. Response Generation: The information is processed using a language model to generate a coherent response.
    9. Response Delivery: The response is sent back to the user via Telegram Response.
    10. Completion Notification: Users are notified about the successful storage of their documents with the total pages saved.

    • Customization Guide

    Users can customize this workflow by:
    - Modifying Telegram Credentials: Update the Telegram API credentials to connect with your bot.
    - Changing the Vector Store Settings: Adjust the Pinecone vector store configurations to match your data storage needs.
    - Adjusting Chunk Sizes: Modify the chunk size and overlap settings in the Recursive Character Text Splitter to optimize data processing based on document lengths.
    - Customizing Responses: Change the text in the Telegram Response nodes to tailor the messages sent back to users.
    - Adding Additional Nodes: Integrate more nodes for additional functionalities like logging, error handling, or further processing of the data.