# Pinecone
## Prerequisite
1. Register an account for [Pinecone](https://app.pinecone.io/)
2. Click **Create index**
3. Fill in required fields:
* **Index Name**, name of the index to be created. (e.g. "flowise-test")
* **Dimensions**, size of the vectors to be inserted in the index. (e.g. 1536)
4. Click **Create Index**
## Setup
1. Get/Create your **API Key**
2. Add a new **Pinecone** node to canvas and fill in the parameters:
* Pinecone Index
* Pinecone namespace (optional)
3. Create new Pinecone credential -> Fill in **API Key**
4. Add additional nodes to canvas and start the upsert process
* **Document** can be connected with any node under [**Document Loader**](https://docs.flowiseai.com/integrations/langchain/document-loaders) category
* **Embeddings** can be connected with any node under [**Embeddings** ](https://docs.flowiseai.com/integrations/langchain/embeddings)category
5. Verify from [Pinecone dashboard](https://app.pinecone.io) to see if data has been successfully upserted:
6.
## Resources
* LangChain Pinecone vectorstore integrations
* [Python](https://python.langchain.com/v0.2/docs/integrations/providers/pinecone/)
* [NodeJS](https://js.langchain.com/v0.2/docs/integrations/vectorstores/pinecone)
* [Pinecone LangChain integration](https://docs.pinecone.io/integrations/langchain)
* [Pinecone Flowise integration](https://docs.pinecone.io/integrations/flowise)
* [Pinecone official clients](https://docs.pinecone.io/reference/pinecone-clients)
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