> For the complete documentation index, see [llms.txt](https://docs.stackai.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.stackai.com/workflow-builder/apps/pinecone.md).

# Pinecone

The **Pinecone Node** allows you to search a Pinecone vector database for vectors that are most similar to a given text input. It returns a list of similar vectors along with their metadata.

#### Required Inputs for the Pinecone Node

To use the Pinecone node, you need to provide the following input parameters:

1. **Query** (string, required): The text you want to search for similar vectors. For example, "AI marketing trends".
2. **Number of Results (top\_k)** (integer, required): How many similar vectors you want to retrieve. The default is 5.
3. **Index Name** (string, required): The name of the Pinecone index you want to query.
4. **Namespace** (string, optional): An optional namespace within the Pinecone index to scope your query.

#### Output

* The node outputs a field called **Results**, which contains the similar vectors found in the database, along with their metadata.

**Example Usage**

* If you want to find the top 5 most similar vectors to the phrase "StackAI product launch" in your "company-updates" index, you would set:
  * Query: "StackAI product launch"
  * Number of Results: 5
  * Index Name: "company-updates"
  * Namespace: (leave blank or specify if needed)

The Pinecone node will then return the most relevant vectors, which you can use for recommendations, search, or further processing in your workflow.


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# Agent Instructions
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## 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.stackai.com/workflow-builder/apps/pinecone.md?ask=<question>&goal=<endgoal>
```

`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.

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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.