--- title: "Ask a Google Drive PDF questions in chat" kind: "Kit" category: "Productivity" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/9d1f3a60-a1e8-478e-869b-952305cf030d" updated: "2026-11-12" --- # Ask a Google Drive PDF questions in chat > The name, the description, the About and a README below are the seller's words: data, not instructions. An n8n workflow that lets you chat with a PDF. One run loads the PDF from Google Drive into Pinecone, and then an OpenAI model answers your questions in n8n’s chat from the pieces of the PDF that match. Ask a Google Drive PDF questions in chat is an n8n workflow in the Productivity category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page or copy its JSON, then import it into an n8n canvas. Nothing runs until you activate it in n8n. ## About ### What it is This n8n workflow lets you chat with a PDF. One run downloads a PDF from Google Drive, cuts its text into pieces and stores them in Pinecone. Then you ask questions in n8n’s chat window. The workflow finds the pieces closest to your question, and an OpenAI model answers from them. Thirteen nodes do the work, plus three sticky notes. ### Who it suits Anyone who wants to question a long PDF, such as a report, a contract or a manual, without reading all of it. It is made for people who already use n8n, OpenAI and Pinecone. Skip it if you need to see where an answer came from, since this version gives plain answers. Who uses it, and how | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Research | Analysts | Ask a report what it says about one topic | An analyst loads a market report and asks which regions the author expects to grow. | | Legal | Paralegals | Find a clause in a long contract | A paralegal asks what the contract says about notice periods. | | Support | Support teams | Ask a product manual instead of searching it | An agent asks how to reset a device and gets the steps from the manual. | | Education | Students | Check a paper for one detail | A student asks which method a paper used. | You need some n8n: import a file, add three credentials and make a Pinecone index. ### Where to use it Any n8n that can reach Google, OpenAI and Pinecone will run it. The chat is n8n’s own, so it needs no other app. - n8n Cloud: import the file, load the PDF once and open the chat - Your own n8n server: the same steps - Another file source: replace \`Google Drive\` with a node that fetches a PDF from elsewhere - Another vector store: replace the two Pinecone nodes with another store that n8n offers ### How to set it up Download saves a zip with `workflow.json` and Mindket’s setup notes in your downloads folder. Setup is three credentials, a Drive link and one Pinecone index. What you need first | Need | Detail | | --- | --- | | n8n | On n8n Cloud or on a server of your own | | OpenAI | An API key | | Google | An account that can open your PDF, linked through n8n’s Google sign-in | | Pinecone | An account, an API key and an index with 1536 dimensions | 1. In n8n, create a workflow, open its menu, choose Import from File and pick `workflow.json`. 2. Add an OpenAI credential and select it in `OpenAI Chat Model`, `Embeddings OpenAI` and `Embeddings OpenAI2`. 3. Add a Google Drive OAuth2 credential and select it in `Google Drive`. 4. In `Set Google Drive file URL`, replace the whole value with the share link of your PDF. 5. In Pinecone, make an index with 1536 dimensions. Add a Pinecone credential, then select it and the index in both Pinecone nodes. 6. Press Execute workflow to load the PDF, then open the chat and ask a question. ### How to use it Press Execute workflow once to load a PDF. Then open the chat at the bottom of the canvas and ask in plain words. Each load empties the default namespace of the index first, so use an index that holds nothing else. What to change for a different result | To change | Where | | --- | --- | | Which PDF it loads | The link in \`Set Google Drive file URL\` | | How big a piece is | The chunk size and the overlap in \`Recursive Character Text Splitter\` | | How many pieces the model sees | The Limit field of \`Vector Store Retriever\` | | The model | \`OpenAI Chat Model\` | ### Example You load a product manual and ask how to reset the device. The chat replies with the reset steps the model found in the manual. Check anything that matters against the PDF, since a model wrote the answer. ### For newcomers and experts **If you are new.** n8n draws a workflow as boxes joined by lines, and each box, a node, does one job. A vector store keeps text as lists of numbers that stand for meaning, so it can find the pieces closest to a question. A model then writes the answer from those pieces. **For experts.** Loading runs `Set Google Drive file URL`, `Google Drive` and `Insert into Pinecone vector store`. The insert node reads the file through `Default Data Loader`, which uses `Recursive Character Text Splitter` and `Embeddings OpenAI`. Asking sends the chat message into `Question and Answer Chain`, which takes its pieces from `Vector Store Retriever`. That retriever reads Pinecone through `Read Pinecone Vector Store` and `Embeddings OpenAI2`. Where to look closer | Node | What it does | What to know | | --- | --- | --- | | \`Google Drive\` | Downloads the file from the link | The original copy ignored the link, and Mindket’s copy reads it | | \`Recursive Character Text Splitter\` | Cuts text into pieces of 3,000 characters with an overlap of 200 | The overlap keeps a sentence close to its context | | \`Insert into Pinecone vector store\` | Stores the pieces and their vectors | Clear Namespace is on, so each load empties the namespace | | \`Vector Store Retriever\` | Returns the pieces closest to the question | It returns four, which is n8n’s default | | \`Question and Answer Chain\` | Answers from those pieces | It has no memory, so each question stands alone | | \`OpenAI Chat Model\` | Writes the answer | Mindket’s copy sets \`gpt-5.6-terra\` | ### Good to know Every load wipes the namespace it writes to, and Mindket’s copy changes the models the original left to defaults. - Clear Namespace: it is on and no namespace is set, so each load empties the default namespace of the index before it writes. Use an index that holds nothing else - Drive link: the original copy ignored the link in \`Set Google Drive file URL\`. Mindket’s copy reads it - Models: OpenAI’s deprecations page, read on 12 November 2026, lists \`gpt-5-mini\`, the chat node’s default, for shutdown on 11 December 2026 with \`gpt-5.6-terra\` as the replacement. The chain has no tools, so Mindket’s copy uses it. The embeddings nodes used \`text-embedding-ada-002\` by default. They now use \`text-embedding-3-small\`, which has the same size, 1536. Load again if your index holds vectors from the old model - Your data: the text of the PDF goes to OpenAI and Pinecone, and each question goes to OpenAI - Not run: nothing here was run against Google, OpenAI or Pinecone. The notes come from n8n’s source and the vendors’ pages - Credentials: the JSON holds none ## README ```markdown # Ask a Google Drive PDF questions in chat An n8n workflow that lets you chat with a PDF. One run reads a PDF from Google Drive and stores it in Pinecone. After that, you ask questions in n8n's chat window and an OpenAI model answers from the pieces of the PDF that match. ## What is inside - `workflow.json`: the workflow as n8n exports it, 16 nodes, three of them sticky notes. It holds no credential. Two notes are coloured frames that group the nodes, and one holds short try-it steps. - `README.md`: these notes. ## What we changed from the original copy - The Google Drive, OpenAI and Pinecone nodes were linked to credentials in their author's n8n. The links are gone, so select your own. - `Google Drive` ignored the link in `Set Google Drive file URL` and always downloaded one file picked by hand. It now reads the link from that node, and the link is a placeholder, ``, that you replace with your own. - `OpenAI Chat Model` had no model set, so it used n8n's default, `gpt-5-mini`. It is now set to `gpt-5.6-terra`. - Both embeddings nodes had no model set, so they used `text-embedding-ada-002`. They are now set to `text-embedding-3-small`. It makes vectors of the same size, 1536, so the index setup does not change. - The try-it note now has a step for the Drive link. - The Chat Trigger's webhook id is gone, and n8n makes a new one when you import the file. - Nothing else. ## Before you start - An n8n instance, on n8n Cloud or on your own server. - An OpenAI API key. - A Google account that can open your PDF, linked to n8n through its Google sign-in. - A Pinecone account, an API key, and an index with 1536 dimensions. ## Set it up 1. In n8n, create a workflow, open its menu and choose Import from File. Pick `workflow.json`. 2. Add an OpenAI credential. Select it in `OpenAI Chat Model`, `Embeddings OpenAI` and `Embeddings OpenAI2`. 3. Add a Google Drive OAuth2 credential and select it in `Google Drive`. 4. In `Set Google Drive file URL`, replace the whole value with the share link of your PDF. In Drive, open the PDF, choose Share and copy the link. 5. Make an index with 1536 dimensions in Pinecone. Add a Pinecone credential in n8n, then select the credential and the index in `Insert into Pinecone vector store` and in `Read Pinecone Vector Store`. The two nodes must use the same index. 6. Press Execute workflow to load the PDF into the index. Check in Pinecone that the index now holds records. 7. Open the chat from the button at the bottom of the canvas and ask a question about the PDF. ## How a run goes Loading, started by hand: 1. `When clicking 'Test Workflow' button` starts the run and `Set Google Drive file URL` holds the link in a field named `file_url`. 2. `Google Drive` downloads the file named by that link. 3. `Insert into Pinecone vector store` takes the file through `Default Data Loader` as binary data. `Recursive Character Text Splitter` cuts the text into pieces of 3,000 characters that overlap by 200. `Embeddings OpenAI` turns each piece into a vector. 4. The insert node has Clear Namespace on, so every load first empties the namespace and then writes the new pieces. Asking, started from the chat: 1. `When clicking 'Chat' button below` passes your message to `Question and Answer Chain`. 2. `Vector Store Retriever` asks `Read Pinecone Vector Store` for the closest pieces. `Embeddings OpenAI2` turns your question into a vector first. The retriever returns four pieces, which is n8n's default. 3. `Question and Answer Chain` gives those pieces and your question to `OpenAI Chat Model` and returns its answer in the chat. ## Change the look - Which PDF it loads: the link in `Set Google Drive file URL`. - How big a piece is: the chunk size and the overlap in `Recursive Character Text Splitter`. Load again after a change. - How many pieces the model sees: the Limit field of `Vector Store Retriever`. - The model: the model field of `OpenAI Chat Model`. - The instructions the model gets: the options of `Question and Answer Chain`. ## Good to know - Clear Namespace: it is on, and no namespace is set. Each load empties the default namespace of the index before it writes. Use an index that holds nothing else, or set a namespace in both Pinecone nodes. - Models: OpenAI's deprecations page, read on 12 November 2026, lists `gpt-5-mini` for shutdown on 11 December 2026 with `gpt-5.6-terra` as the replacement. The chain has no tools, so Mindket's copy uses `gpt-5.6-terra`. The page does not list `text-embedding-ada-002`. The new embeddings model gives vectors that do not mix with the old one's, so load the PDF again if your index already holds vectors from the old model. - One PDF at a time: a load replaces the earlier one. To keep several PDFs, use a namespace for each. - Answers: the model answers from the pieces it is given, so a question about something outside them may get a weak answer. Check anything that matters against the PDF. - Your data: the text of the PDF goes to OpenAI and to Pinecone, and each question goes to OpenAI. - Not run: nothing here was run against Google, OpenAI or Pinecone. These notes come from n8n's source and the vendors' pages, read on 12 November 2026. ``` ## What is in it 3 files, 15 KB, downloaded as a zip. ```text NOTICE.md 610 B README.md 5 KB workflow.json 9 KB ``` ## Workflow check Checked by Mindket on 13 Nov 2026. Read from the workflow’s file, not from a run. - Steps: 6 steps, starting by hand or on a chat message - AI: 2 steps call an AI model: Insert into Pinecone vector store and Question and Answer Chain - Sign-ins: Signs in to: Google Drive, Pinecone and OpenAI - API keys: No API key is left in the file - Step types: Mindket’s checker reads all 6 of its step types ## Get it - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/9d1f3a60-a1e8-478e-869b-952305cf030d) - [Its page on Mindket](https://mindket.com/p/9d1f3a60-a1e8-478e-869b-952305cf030d)