# Connectors: bringing your drive and tools in
Connectors let ChatGPT read from your work tools (Google Drive, SharePoint, OneDrive, GitHub, and more) so that answers come from your own documents instead of the model's general training. You ask a question in plain language, ChatGPT searches the connected source, retrieves the relevant passages, and answers with citations back to the original files.
This is RAG, but you are not building the pipelinepipelineAll active sales opportunities across the stages of the sales process, together with their combined potential value and probability of closing.View full definition →. OpenAI hosts the indexing, retrieval, and permission checks. You just authorize the connection.
A Connector is an authorized bridge between your ChatGPT account and an external data source. When you connect Google Drive, you are granting ChatGPT permission (via OAuth) to search and read files on your behalf.
Two modes matter:
Crucially, the model does not get a copy of your entire Drive dumped into the context windowcontext windowThe context window is the maximum amount of text (measured in tokens) a language model can process at once, including both the input prompt and the generated output.View full definition →. It runs a search, gets back the most relevant chunks, and reasons over those. Your foundations covered why: the context windowcontext windowThe context window is the maximum amount of text (measured in tokens) a language model can process at once, including both the input prompt and the generated output.View full definition → is finite, so retrieval beats stuffing.
See the official overview at help.openai.com for the current list of supported sources, which expands regularly.
Say your team keeps everything in Google Drive: a Pricing Policy 2026.gdoc, a folder of signed customer contracts, and a Security Questionnaire Responses.xlsx.
You connect Google Drive once (Settings, then Connectors, then authorize). Now in a normal chat:
> "What's our standard discount cap for multi-year deals, and which contracts have exceptions? Cite the docs."
ChatGPT will:
1. Search your connected Drive for relevant files (the pricing doc, the contracts folder).
2. Retrieve the matching passages.
3. Synthesize an answer like: "Standard cap is 15% for 2 or 3 year terms (Pricing Policy 2026, section 4). Two contracts exceed it: Acme (20%) and Globex (18%)."
4. Show citations linking back to the source files.
That last step is the point. You can click through and verify. An answer with no traceable source is a liability; a cited answer from your own contract is something you can act on.
This is the part professionals get wrong, so be precise.
Permissions are inherited, not granted. When you connect Google Drive, ChatGPT acts as *you*. It can only see files your Google account can already see. If a folder is shared with you, it is reachable. If it is not, the connector cannot magic it into view. This matters most in Workspace and Enterprise setups where document permissions are tightly scoped.
Scope can be narrowed. Connectors and the admin controls let you limit what is connected. An organization can restrict which connectors are even available, and you can often scope a connection to specific folders rather than an entire drive.
Indexed content lives in OpenAI's systems for retrieval. For synced connectors, the content is processed and stored so it can be searched. Read your plan's data terms. On Team, Enterprise, and Edu, business data is not used to train OpenAI's models by default. See openai.com/enterprise-privacy for the current commitments.
Deleting the connection removes access. Revoke the OAuth grant in ChatGPT (or in Google's security settings) and the bridge is gone.
A quick mental rule: a Connector never *elevates* your access. It only *exposes* what you already have, to a model that can read fast.
You now have three ways to get external data into ChatGPT, and they are not interchangeable.
| Mechanism | Best for | Who controls it |
|---|---|---|
| File upload | One-off analysis of a specific file | You, per chat |
| Connectors | Standing access to a tool's content (Drive, SharePoint) | You / your admin |
| Actions (in Custom GPTs) | Calling an APIAPIApplication Programming Interface: a standardised interface that lets applications communicate and exchange data without knowing each other's internal workings.View full definition → to *do* something or fetch live data | The GPT builder |
The distinction that matters: Connectors read your knowledge sources; Actions call APIs. A Connector answers "what does our policy say?" An Action does "create the Jira ticket" or "look up this order's live status." We covered Actions in the previous lesson; Connectors are the read-heavy counterpart.
Connectors get more powerful when scoped.
In a Project, you can attach connected sources alongside project files and custom instructions. Every chat inside that Project then answers against the same grounded context. A "Q4 RFP Responses" Project with your Drive connected means every drafted answer pulls from the real, current source material without re-uploading anything.
In a Custom GPT, a builder can wire a connector so that everyone using the GPT searches a shared, authorized knowledge base. Picture an internal "Sales Answer Bot" GPT connected to the team's SharePoint. Each user still only sees what their own permissions allow, but the GPT's behavior and source are standardized.
This is the difference between a clever prompt and a deployable internal tool: the source of truth is fixed, and the answers stay current as the underlying files change.
Connectors are not just for one-shot Q&A. They feed ChatGPT's more autonomous modes.
When you run deep research or the ChatGPT agent, connected sources become part of the agent's toolset. A research run can pull from your internal Drive *and* the web in the same task. For example: "Compare our current pricing policy against the three competitor pages I'll paste, and flag where we're undercut." It reads your internal doc via the connector and the competitor pages via browsing, then reasons across both.
You can also pair this with scheduled tasks to get a recurring grounded briefing, for example a Monday summary of changes in a tracked Drive folder.
Knowledge check
1. What is the fundamental purpose of a Connector in ChatGPT?
2. The lesson calls Connectors 'RAG, but you are not building the pipeline.' What does this mean in practice?
3. Why doesn't ChatGPT simply dump your entire Drive into the context window when you ask a question?
4. Select ALL correct statements about how a Connector operates.
Select all the correct answers.
5. In the 'answer from internal docs' example, why is the citation step described as 'the point'? Select ALL that apply.
Select all the correct answers.
ChatGPT Connectors are the no-code path. When you need this behavior inside *your own* application, you move to the OpenAI APIAPIApplication Programming Interface: a standardised interface that lets applications communicate and exchange data without knowing each other's internal workings.View full definition →, where the equivalent capability is file search over a vector storevector storeA vector database stores data as high-dimensional numeric vectors (embeddings) and retrieves items by similarity rather than exact matches, powering semantic search and AI applications.View full definition → using the Responses APIAPIApplication Programming Interface: a standardised interface that lets applications communicate and exchange data without knowing each other's internal workings.View full definition →.
The pattern: upload your documents into a vector storevector storeA vector database stores data as high-dimensional numeric vectors (embeddings) and retrieves items by similarity rather than exact matches, powering semantic search and AI applications.View full definition → once, then attach that store as a tool. The model retrieves and cites automatically, just like a Connector does in the UI, but now it is in your product.
from openai import OpenAI
client = OpenAI()
# 1. Create a vector store and add your internal docs.
store = client.vector_stores.create(name="internal-policy-docs")
client.vector_stores.files.upload_and_poll(
vector_store_id=store.id,
file=open("Pricing_Policy_2026.pdf", "rb"),
)
# 2. Ask a grounded question; the model searches the store and cites sources.
response = client.responses.create(
model="gpt-4.1",
input="What's our discount cap for multi-year deals?",
tools=[{"type": "file_search", "vector_store_ids": [store.id]}],
)
print(response.output_text)That file_search tool is the same idea as a Connector: retrieval is hosted, you just point at the data. The full reference lives at platform.openai.com/docs.
The trade-off is responsibility. In the APIAPIApplication Programming Interface: a standardised interface that lets applications communicate and exchange data without knowing each other's internal workings.View full definition →, you enforce who can query which vector storevector storeA vector database stores data as high-dimensional numeric vectors (embeddings) and retrieves items by similarity rather than exact matches, powering semantic search and AI applications.View full definition →. There is no inherited Google permission protecting a document; if a user can hit your endpoint, they can search what you attached. So in your own app you must build the access control that the ChatGPT connector gave you for free.
A few things to know before you rely on this in production:
Use this to pick the right tool:
These actions are compiled in the role's Playbook.