pinecone

What problem does it solve?

This Skill solves the complex problem of building and scaling AI applications like RAG or semantic search, which require a robust, low-latency vector database. It eliminates the burden of infrastructure management, allowing you to focus on your AI logic.

Core Features & Use Cases

• Fully Managed & Serverless: Deploy and scale your vector database automatically, without managing any underlying infrastructure, from small projects to billions of vectors.
• Low Latency: Achieve sub-100ms p95 latency for queries, critical for real-time AI applications and responsive user experiences.
• Hybrid Search: Combine dense (semantic) and sparse (keyword) vectors for superior search relevance and recall.
• Metadata Filtering & Namespaces: Precisely filter search results based on rich metadata and isolate data for multi-tenancy or A/B testing using namespaces.
• Use Case: Power a production RAG chatbot that needs to retrieve relevant documents from a vast corpus with sub-100ms response times, scaling automatically with user demand and ensuring data isolation for each user.

Quick Start

Initialize Pinecone with your API key, create a serverless index named "my-index" with 1536 dimensions, then upsert two example vectors with metadata.