Telegram PDF Q&A with Gemini + Pinecone (n8n Workflow)
Telegram PDF Q&A with Gemini + Pinecone (n8n Workflow)
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Telegram PDF Q&A with Gemini + Pinecone (n8n Workflow)
Regular price
£47.99
Regular price
£47.99
Sale price
Unit price
/
per
Turn your Telegram PDF uploads into instant Q&A—powered by Gemini + Pinecone (n8n workflow)
This n8n workflow lets you upload a PDF in Telegram and then ask follow-up questions directly in chat. It indexes your PDF content in Pinecone using Google Gemini embeddings and generates answers with Google Gemini via retrieval-augmented generation formatted for Telegram HTML.
What this workflow does
- Listens on Telegram: Triggers whenever a new message is received by your Telegram bot.
- Indexes PDFs automatically: If the incoming message includes a document, it downloads the file from Telegram and forces the binary metadata to application/pdf.
- Creates searchable vectors: Splits the PDF text into overlapping chunks, generates embeddings with Google Gemini, and inserts them into a Pinecone index.
- Confirms indexing: Sends a Telegram message indicating how many PDF pages were saved to Pinecone.
- Answers questions with context: If the message is text, it retrieves relevant context from Pinecone and uses the Gemini chat model to generate an answer constrained to that retrieved content.
- Responds in Telegram: Sends the generated response back using Telegram HTML parse mode formatting.
Use cases
- Support & documentation Q&A: Upload product manuals or policies to Telegram, then answer questions without leaving chat.
- Internal knowledge lookup: Index team PDFs (SOPs, contracts, reports) and retrieve exact answers through Pinecone-powered retrieval.
- SaaS operator workflows: Create a lightweight “chat with your PDFs” experience for demos, onboarding, or internal review.
Technical details
- Telegram integration: Telegram Trigger + Telegram nodes for receiving PDFs and sending HTML-formatted answers.
- Embeddings & chat: Google Gemini (PaLM) API credential used for both embeddings generation and Gemini chat responses.
- Vector database: Pinecone index (e.g., named telegram) to store and retrieve embeddings for retrieval-augmented generation.
- Workflow control: Uses nodes including if, code, limit, sticky note, stop, and error.
