Pinecone

The vector database for machine learning applications. Through Langdock’s integration, you can access and manage Pinecone directly from your conversations.

Authentication: API Key Category: AI & Search Availability: All workspace plans

Available Actions

Search Namespace — pinecone.searchNamespace

Searches the database for the most relevant information based on the query provided.

• Requires Confirmation: No

• Parameters:

  • query (VECTOR, Required): Vector query for similarity search

• Output: Returns search results with matching vectors, metadata, and similarity scores

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Common Use Cases

• Data Management Manage and organize your Pinecone data

• Automation Automate workflows with Pinecone

• Reporting Generate insights and reports

• Integration Connect Pinecone with other tools

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Best Practices

1

Getting Started

• Enable the Pinecone integration in your workspace settings

• Authenticate using API Key

• Test the connection with a simple read operation

• Explore available actions for your use case

2

Important Considerations

• Ensure proper authentication credentials

• Respect rate limits and API quotas

• Review data privacy settings

• Test operations in a safe environment first

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Troubleshooting

Issue	Solution
Authentication failed	Verify your API Key credentials
Rate limit exceeded	Reduce request frequency
Data not found	Check permissions and data availability
Connection timeout	Verify network connectivity

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Support

For additional help with the Pinecone integration, contact [email protected]