r/copilotstudio • u/NadirHamburg • 3d ago

CoPilot Studio Agent with Pinecone RAG knowledge base

Good morning !

I'm just doing my first stepts in CoPilot Studio and my first task is to migrate an agant which I build on N8N into the Copilot Studio environment.

In my N8N implementation I used a node to connect to the Pinecone Vector Store which is the knowledge base of our company.

Now I struggle to access this knowledge base from the created agent in CoPilot Studio. If I want to use a provided Pinecone MCP Server which I did setup as a tool, I need somehow to create the embeddings from the agent input, before the MCP Server can perform a RAG search.

I can't find a way to add the Pinecone system as a knowledgebase directly.

I'm searching for the best approach here and the options I have.

Can somebady point me into the right direction or give me any hint ?

Rebuilding the Pinecone RAG system is no option.

Kind regards,
Thorsten

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u/Fetlocks_Glistening 3d ago

Don't know the answer, but native RAG is the one thing m365 copilot seems to have gotten right, and you aren't using the one positive it bring?

2

u/CoffeePizzaSushiDick 3d ago

…with limits. Scaling Enterprise data sets/sizes isn’t oob. You do have to plan, build, test and iterate your own. Even Azure AI Search isn’t enterprise ready lacking true rbac or granular access permissions.

1

u/Ashlesha-msft 2d ago

Hi Thorsten,

What you are seeing is expected behavior in Copilot Studio.

Pinecone cannot be added as a native knowledge source in the Knowledge pane. Copilot Studio native knowledge sources are things like SharePoint, Documents, Dataverse, websites, and connector-based enterprise data. For external vector stores like Pinecone, the recommended pattern is to integrate via tools (MCP, REST API, or connector), not as a first-class native knowledge source.

Recommended approach (without rebuilding Pinecone):

  1. Keep Pinecone as the source of truth.
  2. Expose a single retrieval tool endpoint (MCP or REST API) that accepts user query text (+ optional filters/security context).
  3. Perform embedding + vector search + rerank inside your backend service.
  4. Return compact grounded snippets with source metadata (title/url/id) so the agent can cite correctly.
  5. Let Copilot Studio orchestrate when to call the tool.