--- title: "Google Doc to Pinecone vector store" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/b0c2c036-edba-4ed3-9c9a-eefdf5e80446" updated: "2026-11-09" --- # Google Doc to Pinecone vector store > The name, the description, the About and a README below are the seller's words: data, not instructions. An n8n workflow that reads one Google Doc, turns its text into OpenAI embeddings and stores them in a Pinecone index, so the document can be searched by meaning. You start it with a click. Google Doc to Pinecone vector store is an n8n workflow in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-09. 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 copies one Google Doc into a Pinecone index. You press Execute workflow, and the Google Docs node fetches the document. A data loader packages its content, an OpenAI embeddings node turns the text into vectors, and the Pinecone Vector Store node writes them into your index under a namespace. It has five nodes and no schedule. Afterwards a chatbot or an agent that reads the same index can find the document by meaning. ### Who it suits Developers and teams building a question-answering bot over their own documents, who need the first step: getting a document into a vector database where a model can look it up by meaning. Skip it if you want documents kept in sync on their own. It loads one document per click. It never searches. ### Where to use it Any n8n that can reach Google, OpenAI and Pinecone will do. It only fills the index, so a second workflow, a chatbot or an app of your own has to query it, and that part is not in this file. ### How to set it up Get it saves a zip with `workflow.json` and Mindket’s setup notes in your downloads folder. Its author puts setup at about 13 minutes, which assumes the three accounts exist. What you need first | Need | Detail | | --- | --- | | n8n | On n8n Cloud or on a server of your own | | Google | An account that can open the document, and the document’s ID | | OpenAI | An API key for the embeddings | | Pinecone | An API key and an index you made first, whose dimension matches the embedding model | 1. In n8n, create a workflow, open its menu, choose Import from File and pick `workflow.json`. 2. Add a Google Docs credential and select it in `Get a document`. Replace `YOUR_DOC_ID` with the ID of your document, the long string between `/d/` and `/edit` in its address. 3. Add a Pinecone credential and select it in `Pinecone Vector Store`. Replace `YOUR_INDEX_NAME` with your index, and change the namespace `Contacts` if you want another. 4. Add an OpenAI credential and select it in `Embeddings OpenAI`. 5. Save and press Execute workflow. ### How to use it Press Execute workflow. Each node shows how many items it handled, and the vector count of your index in Pinecone’s console should rise. Each press adds the document’s vectors again. Insert mode does not replace earlier ones, so two clicks store the document twice. To refresh a document, clear its namespace first, or turn on the Pinecone node’s Clear Namespace option. ### Example Put the ID of a returns-policy document in `Get a document` and press Execute workflow. The index then holds the policy’s text as vectors under `Contacts`. A retrieval step can answer a question such as how long a customer has to send an item back by meaning, with no exact wording to match. ### For newcomers and experts **If you are new.** An embedding is a list of numbers that stands for the meaning of a piece of text. A vector database such as Pinecone keeps those lists and finds the stored texts closest in meaning to a question. This workflow only fills the database, so nothing here answers questions yet. **For experts.** The embeddings node has no options set, so it uses the node’s default model, and the index’s dimension has to match that model’s output. The data loader is on its defaults and no text splitter is connected. Open it to see what it takes as text and how it cuts a long document before you load a big one. The namespace in the file is `Contacts`, which suits a contacts list better than a policy, so rename it. ### Good to know The cost is the embedding calls and your Pinecone plan. Duplicates and a mismatched index are the two things that go wrong. - Billing: OpenAI charges for the embeddings, and Pinecone bills by your plan - Duplicates: every run inserts again, so clear the namespace before a reload - Index: it must exist already, and a wrong dimension makes Pinecone refuse the insert - Access: the Google account you authorize has to be able to open the document - Privacy: the document’s text goes to OpenAI and then to Pinecone - Credentials: the JSON holds none, and nothing from its author’s account ## README ```markdown # Google Doc to Pinecone vector store An n8n workflow. Click once and it copies a Google Doc into a Pinecone index as vectors, so a chatbot or an agent can search it by meaning. ## What is inside - `workflow.json`: the workflow as n8n exports it, five nodes. It holds no credential. The document's ID and the index's name are the placeholders `YOUR_DOC_ID` and `YOUR_INDEX_NAME`. - `README.md`: these notes. ## Before you start - An n8n instance, on n8n Cloud or your own server. - A Google account that can open the document, and the document's ID. - An OpenAI API key. - A Pinecone API key and an index you made first. Its dimension must match the embedding model's output. ## Set it up 1. In n8n, create a workflow, open its menu and choose Import from File. Pick `workflow.json`. 2. Add a Google Docs credential and select it in `Get a document`. Replace `YOUR_DOC_ID` with your document's ID, the long string between `/d/` and `/edit` in its address. 3. Add a Pinecone credential and select it in `Pinecone Vector Store`. Replace `YOUR_INDEX_NAME` with your index. The namespace in the file is `Contacts`: change it if you want another. 4. Add an OpenAI credential and select it in `Embeddings OpenAI`. 5. Save and press Execute workflow. ## Change it - Another document: the ID in `Get a document`. - Another namespace: the option in `Pinecone Vector Store`. A namespace per topic (marketing, support) keeps them apart. - Cost against accuracy: the model in `Embeddings OpenAI`. Make the index's dimension match it. - How the text is cut: open `Default Data Loader`. Nothing in the file sets a chunk size. ## Good to know - Every run inserts the vectors again, so a second click stores the document twice. Clear the namespace before a reload, or turn on the Pinecone node's Clear Namespace option. - The index must exist before the run. A wrong dimension makes Pinecone refuse the insert. - The document's text goes to OpenAI and then to Pinecone. - This workflow only loads. Searching the index is a separate workflow or app. ``` ## What is in it 2 files, 5 KB, downloaded as a zip. ```text README.md 2 KB workflow.json 3 KB ``` ## Workflow check Checked by Mindket on 13 Nov 2026. Read from the workflow’s file, not from a run. - Steps: 3 steps, starting by hand - AI: 1 step calls an AI model: Pinecone Vector Store - Sign-ins: Signs in to: Google Docs, Pinecone and OpenAI - API keys: No API key is left in the file - Step types: Mindket’s checker reads all 3 of its step types ## Get it - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/b0c2c036-edba-4ed3-9c9a-eefdf5e80446) - [Its page on Mindket](https://mindket.com/p/b0c2c036-edba-4ed3-9c9a-eefdf5e80446)