> For the complete documentation index, see [llms.txt](https://docs.dat-hub.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.dat-hub.com/integrations/destinations/pinecone.md). # Pinecone ### Overview The Pinecone destination allows you to load and store vector embeddings in a Pinecone index. Pinecone is a vector database designed for high-performance vector similarity search and is commonly used for applications like semantic search, recommendation systems, and other machine learning tasks involving embeddings. This connector streamlines the process of creating a Pinecone index and loading your vector data into it. Once configured, you can set up connections to extract data from various sources, generate embeddings, and seamlessly store them in your Pinecone index for efficient similarity searches and retrieval. ### Configuration Options **Name**: This field represents the name you want to assign to the actor instance responsible for managing the Pinecone destination. Choose a descriptive and unique name to easily identify this instance. **Pinecone Index**: Enter the name of the Pinecone index you want to use for storing your vector embeddings. Please note that the specified index should already exist and have the correct dimensions set to match your embedding vectors. If the index doesn't exist or has mismatched dimensions, the connector will not be able to load the data. **Pinecone Environment**: Specify the Pinecone environment you want to use for this index. Pinecone offers different environments (e.g., cloud regions) to choose from based on your location and performance requirements. To know more about the cloud environment where you want the index to be hosted environment click [here](https://docs.pinecone.io/guides/indexes/understanding-indexes#pod-environments). **Pinecone API Key**: Provide a valid Pinecone API key that has permissions to access and manage the specified environment and index. You can generate or retrieve an API key from your Pinecone account dashboard. **Embedding Dimensions**: Enter the number of dimensions for the vector embeddings you plan to store in the Pinecone index. This value should match the dimensions of the embeddings generated by your model or algorithm. Once you've filled in the required fields, you can use the "Test and Save" button to validate the provided information and create or update the Pinecone destination in your dat configuration. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://docs.dat-hub.com/integrations/destinations/pinecone.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.