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pinecone

Upsert, query, fetch, and delete vectors in Pinecone indexes via Robomotion CLI sessions.

2|1|Updated Mar 13, 2026
Search Tags:#embeddings#indexes#vector#namespaces#pinecone#similarity-search#robomotion
One-click install
npx skills add https://github.com/robomotionio/robomotion-skills --skill pinecone-robomotionio
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: pinecone
Source: https://github.com/robomotionio/robomotion-skills/tree/main/skills/pinecone
Command: npx skills add https://github.com/robomotionio/robomotion-skills --skill pinecone-robomotionio

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Pinecone vector databases require a consistent method to store, search, and manage embeddings across projects. The CLI integration simplifies connecting to Pinecone, performing upserts, queries, and index management from Robomotion.

Core Features & Use Cases

  • Upsert vectors into Pinecone indexes and manage namespaces
  • Query for similar vectors with optional metadata filtering
  • Create, describe, list, delete indexes and fetch vectors by ID
  • Use Case: Seamlessly manage embeddings for product recommendations or semantic search across datasets.

Quick Start

Install the pinecone integration, connect with a session, and upsert a set of vectors into a Pinecone index.

Frequently Asked Questions about pinecone

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I manage Pinecone embeddings and indexes from an automation workflow?▼

You can manage Pinecone embeddings by connecting to Pinecone from Robomotion to upsert, query, fetch, and delete vectors. The integration also supports creating, describing, listing, and deleting indexes and namespaces across your projects.

What do I need to connect to Pinecone and upsert vectors from Robomotion?▼

To connect to Pinecone and upsert vectors, you need the robomotion CLI installed, a configured Pinecone API key, and a session-based workflow executed with the --session and --session-id flags.

Can I filter Pinecone similarity search results by metadata when querying vectors?▼

Yes, you can query Pinecone for similar vectors with optional metadata filtering. This allows you to narrow down similarity search results based on specific metadata associated with the stored embeddings.

What is the best way to automate semantic search across datasets using vector databases?▼

Automating semantic search involves storing embeddings in a vector database and querying for similar vectors. This integration enables seamless similarity search across datasets for use cases like product recommendations.

Does this integration support creating and deleting Pinecone namespaces?▼

Yes, the integration supports full namespace management. You can upsert vectors into specific namespaces within Pinecone indexes and manage them directly from your Robomotion workflows.

How do I fetch specific vectors by ID from a Pinecone index?▼

You can fetch vectors by ID using the integration's fetch functionality. This allows you to retrieve specific embeddings directly from your Pinecone indexes alongside querying and upserting capabilities.

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