Pinecone Tool

Introduction

The Pinecone Tool enables your AI agent to perform fast and scalable vector similarity searches by integrating directly with Pinecone’s managed vector database. With configurable options such as index URL, namespaces, filters, and embedding models, agents can efficiently store, organize, and retrieve high-dimensional embeddings for tasks like semantic search, personalization, and recommendation. This tool provides flexible control over embedding configurations and retrieval limits, ensuring that your AI agents can deliver contextually relevant results with speed and precision.

Get started

How to use it

To use the Pinecone Tool:

Adding the Pinecone tools

1. Locate the Pinecone element under the Tools section of the palette.
2. Drag and drop the File element into the Drop a tool to here section.
   

Configure Form Properties

To properly integrate and configure the Google Web Search, set the following fields:

• Personalized Name: A custom name to display in the node viewer.
• Pinecone Index URL: A fully managed vector database designed for fast, scalable similarity search and retrieval.Example: https://your-index-name.svc.pinecone.io
• Pinecone Configuration: Select an AI configuration for the Pinecone vector database
• Namespace: Optional namespace for logically grouping vectors.
• Result Limit: Maximum number of matching vectors to retrieve from Pinecone.
• Filters: Define metadata filters to refine vector search results based on specific conditions.
• Pinecone API Version: Pinecone API version header to use for requests.
• Embedding Configuration: Select an AI configuration for embedding
• Embedding Model: Model name for text embedding
• Embedding Dimension: Vector embedding dimension
• Embedding Base URL: Base URL for the embedding model