# Chat with PDF and CSV: n8n workflow template

> Started by someone opening a chat with it, and works across in-memory vectors. It puts Gemini behind a retrieval step, so answers are drawn from documents...

Source: https://millionrounds.com/workflow/chat-with-pdf-and-csv (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)

# Chat with PDF and CSV

Started by someone opening a chat with it, and works across in-memory vectors. It puts Gemini behind a retrieval step, so answers are drawn from documents you supply rather than from the model alone. 11 steps: a working afternoon to understand properly.

Medium Calls a model: Gemini Retrieval

[Download free](https://millionrounds.com/login?next=%2Fapp%2Fworkflow%2Fchat-with-pdf-and-csv)

**11 steps** Medium: a working afternoon

**0** Branches: places the flow splits

**0** Loops or waits

**1** Service it connects to

## The flow

Starts it 2 Model 8 Logic 1

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

- 1 **Document Upload Form** Form trigger *Starts it*
- 2 **Chatbot Trigger** Chat trigger *Starts it*
- 3 **Chat Memory** Memory Buffer Window *Model*
- 4 **Embeddings Google Gemini** Embeddings Google Gemini *Model*
- 5 **Google Gemini Chat Model** Lm Chat Google Gemini *Model*
- 6 **Token Splitter** Text Splitter Recursive Character Text Splitter *Model*
- 7 **Add Metadata** Set *Logic*
- 8 **Knowledge Base Agent** Agent *Model*
- 9 **Vector Store Insert** Vector Store In Memory *Model*
- 10 **Vector Store Retrieve** Vector Store In Memory *Model*
- 11 **Document Loader** Document Default Data Loader *Model*

## How it's built

- **Starts on:** Someone opening a chat with it
- **Steps:** 11
- **Size:** Medium: a working afternoon
- **Connects to:** in-memory vectors
- **Model:** Gemini
- **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 in-memory vectors account, and a key for Gemini. You connect your own accounts after importing it; none are included.

What starts it?

It starts on someone opening a chat with it.

Does it use AI?

Yes. It calls Gemini. 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. 11 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 11 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%2Fchat-with-pdf-and-csv)
- **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.

[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.

- [**Document Q&A chatbot with Google Drive and Telegram** 11 steps · calls a model · Google Drive, Telegram](https://millionrounds.com/workflow/document-q-and-a-chatbot-with-google-drive-and-telegram)
- [**RAG OCR for PDFs with OpenAI and Google Drive** 11 steps · calls a model · Google Drive, in-memory vectors](https://millionrounds.com/workflow/rag-ocr-para-pdfs-with-openai-and-google-drive)
- [**AI-powered company documents Q&A assistant with Google Drive** 12 steps · calls a model · Google Drive, in-memory vectors](https://millionrounds.com/workflow/ai-powered-company-documents-q-and-a-assistant-with-google-d)
- [**Build a retrieval-based chatbot with Telegram and OpenAI** 13 steps · calls a model · Google Drive, Telegram](https://millionrounds.com/workflow/build-a-retrieval-based-chatbot-with-telegram-and-openai)
- [**Build a document-based chat assistant with OpenAI RAG** 8 steps · calls a model · in-memory vectors](https://millionrounds.com/workflow/build-a-document-based-chat-assistant-with-openai-rag)
- [**Answer product queries with WhatsApp and OpenAI** 18 steps · calls a model · WhatsApp, in-memory vectors](https://millionrounds.com/workflow/answer-product-queries-with-whatsapp-and-openai)