# 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)
Pinecone Node
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**](/integrations/langchain/document-loaders.md) category
Document loaders and text splitters for LlamaIndex are not yet available, but using one of the ones available under LangChain will still allow querying with LlamaIndex as normal.
\- \*\*Embeddings\*\* can be connected with any node under \[\*\*Embeddings\*\* ]\(../embeddings/)category
5. Verify on [Pinecone dashboard](https://app.pinecone.io) that data has been successfully upserted:
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# Agent Instructions: 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:
```
GET https://docs.flowiseai.com/integrations/llamaindex/vector-stores/pinecone.md?ask=
```
The question should be specific, self-contained, and written in natural language.
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.