# Build a retrieval-based chatbot with Telegram and OpenAI: n8n workflow

> Started by an incoming Telegram message, and works across Google Drive, Telegram and in-memory vectors.

Source: https://millionrounds.com/workflow/build-a-retrieval-based-chatbot-with-telegram-and-openai (Millionrounds; every figure on the page links to its filing or article)

[Automation library](https://millionrounds.com/workflows) · [Documents and files](https://millionrounds.com/workflows?category=Documents+and+files#library)

# Build a retrieval-based chatbot with Telegram and OpenAI

Started by an incoming Telegram message, and works across Google Drive, Telegram and in-memory vectors. It puts OpenAI behind a retrieval step, so answers are drawn from documents you supply rather than from the model alone. 13 steps, 1 branch: a working afternoon to understand properly.

Medium Calls a model: OpenAI Retrieval

[Download free](https://millionrounds.com/login?next=%2Fapp%2Fworkflow%2Fbuild-a-retrieval-based-chatbot-with-telegram-and-openai)

**13 steps** Medium: a working afternoon

**1** Branch: places the flow splits

**0** Loops or waits

**3** Services it connects to

## The flow

Starts it 2 Model 6 Logic 2 Service 3

The steps in the order they run, from what starts it.

- 1 **Embeddings OpenAI** Embeddings Open Ai *Model*
- 2 **Default Data Loader** Document Default Data Loader *Model*
- 3 **OpenAI Chat Model** Lm Chat Open Ai *Model*
- 4 **Telegram Trigger** Telegram trigger *Starts it*
- 5 **Upload your PDF document here** Form trigger *Starts it*
- 6 **Insert Data to Store** Vector Store In Memory *Model*
- 7 **Query Data Tool** Vector Store In Memory *Model*
- 8 **Telegram document query agent** Agent *Model*
- 9 **Is text message?** If *Logic*
- 10 **Code** Code *Logic*
- 11 **Send respond to user** Telegram *Service*
- 12 **Un-supported message type** Telegram *Service*
- 13 **Backup document(s) to Google Drive** Google Drive *Service*

## How it's built

- **Starts on:** An incoming Telegram message
- **Steps:** 13, with 1 branch
- **Size:** Medium: a working afternoon
- **Connects to:** Google Drive, Telegram, in-memory vectors
- **Model:** OpenAI
- **Retrieval:** Answers are drawn from documents you supply, not from the model alone

## Questions

What does it need to run?

An n8n account, plus your own Google Drive, Telegram and in-memory vectors accounts, and a key for OpenAI. You connect your own accounts after importing it; none are included.

What starts it?

It starts on an incoming Telegram message.

Does it use AI?

Yes. It calls OpenAI. Its answers draw on documents you give it, not on the model alone. Model calls are billed by the provider, not by n8n.

How long does it take to set up?

Plan an afternoon. 13 steps is enough that you should read each one before running it.

How do I import it?

Copy the workflow, open a new workflow in n8n and paste. The steps appear on the canvas, ready for your own accounts.

**Before you run it**

Nothing here has been tested by us. It is somebody's working automation, read and described from its own steps. Treat the connections it wants as a list of accounts it will touch, and read the 13 steps before you point it at anything that matters.

## Run it in n8n

- **Create your n8n account** Free to start. n8n is where the workflow runs. [Create an n8n account ↗](https://millionrounds.com/n8n)
- **Download the workflow** Free with a Millionrounds account. [Download free](https://millionrounds.com/login?next=%2Fapp%2Fworkflow%2Fbuild-a-retrieval-based-chatbot-with-telegram-and-openai)
- **Paste it into n8n** Open a new workflow in n8n and paste. Then connect your own accounts.

Stored with the author's traces removed: canvas notes, instance identifiers, saved credential names and any live document they had left behind.

## Connects to

Each opens the other workflows that use it.

[Google Drive](https://millionrounds.com/workflows?service=Google+Drive#library)[Telegram](https://millionrounds.com/workflows?service=Telegram#library)[in-memory vectors](https://millionrounds.com/workflows?service=in-memory+vectors#library)

## Workflows like it

Also documents and files, with the same services where possible, closest in size first.

- [**Automate multi-language text translation with OpenAI** 13 steps · calls a model · Google Drive](https://millionrounds.com/workflow/automate-multi-language-text-translation-with-openai)
- [**Automate n8n workflow documentation with Gemini and Telegram** 13 steps · calls a model · Telegram](https://millionrounds.com/workflow/automate-n8n-workflow-documentation-with-gemini-and-telegram)
- [**Build a PDF Q&A system with LlamaIndex and OpenAI** 13 steps · calls a model · Google Drive, Pinecone](https://millionrounds.com/workflow/build-a-pdf-q-and-a-system-with-llamaindex-and-openai)
- [**Build a document QA system with RAG and Milvus** 13 steps · calls a model · Google Drive](https://millionrounds.com/workflow/build-a-document-qa-system-with-rag-and-milvus)
- [**Document-based AI chatbot with RAG and OpenAI** 13 steps · calls a model · Google Drive, Supabase vectors](https://millionrounds.com/workflow/document-based-ai-chatbot-with-rag-and-openai)
- [**Google Drive AI Document Classifier with DeepSeek** 13 steps · calls a model · Google Drive](https://millionrounds.com/workflow/google-drive-ai-document-classifier-with-deepseek)