How to connect Microsoft SQL Server and Pinecone
Create a New Scenario to Connect Microsoft SQL Server and Pinecone
In the workspace, click the “Create New Scenario” button.

Add the First Step
Add the first node – a trigger that will initiate the scenario when it receives the required event. Triggers can be scheduled, called by a Microsoft SQL Server, triggered by another scenario, or executed manually (for testing purposes). In most cases, Microsoft SQL Server or Pinecone will be your first step. To do this, click "Choose an app," find Microsoft SQL Server or Pinecone, and select the appropriate trigger to start the scenario.

Add the Microsoft SQL Server Node
Select the Microsoft SQL Server node from the app selection panel on the right.


Microsoft SQL Server

Configure the Microsoft SQL Server
Click on the Microsoft SQL Server node to configure it. You can modify the Microsoft SQL Server URL and choose between DEV and PROD versions. You can also copy it for use in further automations.
Add the Pinecone Node
Next, click the plus (+) icon on the Microsoft SQL Server node, select Pinecone from the list of available apps, and choose the action you need from the list of nodes within Pinecone.


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Authenticate Pinecone
Now, click the Pinecone node and select the connection option. This can be an OAuth2 connection or an API key, which you can obtain in your Pinecone settings. Authentication allows you to use Pinecone through Latenode.
Configure the Microsoft SQL Server and Pinecone Nodes
Next, configure the nodes by filling in the required parameters according to your logic. Fields marked with a red asterisk (*) are mandatory.
Set Up the Microsoft SQL Server and Pinecone Integration
Use various Latenode nodes to transform data and enhance your integration:
- Branching: Create multiple branches within the scenario to handle complex logic.
- Merging: Combine different node branches into one, passing data through it.
- Plug n Play Nodes: Use nodes that don’t require account credentials.
- Ask AI: Use the GPT-powered option to add AI capabilities to any node.
- Wait: Set waiting times, either for intervals or until specific dates.
- Sub-scenarios (Nodules): Create sub-scenarios that are encapsulated in a single node.
- Iteration: Process arrays of data when needed.
- Code: Write custom code or ask our AI assistant to do it for you.

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Save and Activate the Scenario
After configuring Microsoft SQL Server, Pinecone, and any additional nodes, don’t forget to save the scenario and click "Deploy." Activating the scenario ensures it will run automatically whenever the trigger node receives input or a condition is met. By default, all newly created scenarios are deactivated.
Test the Scenario
Run the scenario by clicking “Run once” and triggering an event to check if the Microsoft SQL Server and Pinecone integration works as expected. Depending on your setup, data should flow between Microsoft SQL Server and Pinecone (or vice versa). Easily troubleshoot the scenario by reviewing the execution history to identify and fix any issues.
Most powerful ways to connect Microsoft SQL Server and Pinecone
Microsoft SQL Server + Pinecone + OpenAI ChatGPT: When a new or updated row appears in Microsoft SQL Server, the data is used to create embeddings in Pinecone. Then, OpenAI ChatGPT summarizes insights based on those embeddings.
Pinecone + Microsoft SQL Server + Google Sheets: When a search is performed in Pinecone, the query and results are logged in Microsoft SQL Server. These logs are then used to update a Google Sheet for visualizing usage trends.
Microsoft SQL Server and Pinecone integration alternatives

About Microsoft SQL Server
Use Microsoft SQL Server in Latenode to automate database tasks. Directly query, update, or insert data in response to triggers. Sync SQL data with other apps; simplify data pipelines for reporting and analytics. Build automated workflows without complex coding to manage databases efficiently and scale operations.
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About Pinecone
Use Pinecone in Latenode to build scalable vector search workflows. Store embeddings from AI models, then use them to find relevant data. Automate document retrieval or personalized recommendations. Connect Pinecone with other apps via Latenode, bypassing complex coding and scaling easily with our pay-as-you-go pricing.
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See how Latenode works
FAQ Microsoft SQL Server and Pinecone
How can I connect my Microsoft SQL Server account to Pinecone using Latenode?
To connect your Microsoft SQL Server account to Pinecone on Latenode, follow these steps:
- Sign in to your Latenode account.
- Navigate to the integrations section.
- Select Microsoft SQL Server and click on "Connect".
- Authenticate your Microsoft SQL Server and Pinecone accounts by providing the necessary permissions.
- Once connected, you can create workflows using both apps.
Can I update Pinecone vectors with new SQL data?
Yes! Latenode simplifies this. Automate updates based on SQL changes, keeping your Pinecone index current without code. Scale effortlessly as data grows.
What types of tasks can I perform by integrating Microsoft SQL Server with Pinecone?
Integrating Microsoft SQL Server with Pinecone allows you to perform various tasks, including:
- Enrich vector embeddings with real-time data from Microsoft SQL Server.
- Automate the indexing of SQL data into Pinecone for semantic search.
- Synchronize product catalogs between SQL and a Pinecone-powered search index.
- Trigger updates to Pinecone vectors when corresponding SQL records are modified.
- Build AI-powered recommendation systems using both SQL data and Pinecone vectors.
How does Latenode handle large Microsoft SQL Server datasets?
Latenode offers scalable data processing, handling large SQL datasets efficiently using batch operations and optimized data transfer methods.
Are there any limitations to the Microsoft SQL Server and Pinecone integration on Latenode?
While the integration is powerful, there are certain limitations to be aware of:
- Complex SQL queries might require optimization for efficient data transfer.
- Rate limits on Pinecone's API can impact the speed of large-scale data indexing.
- Maintaining perfect data consistency requires careful workflow design.