Upstash Vector Db Skills

What it does

This skill enables AI agents to interact with Upstash Vector, a serverless vector database designed for embedding storage and retrieval. It allows agents to perform semantic searches, manage vector collections, and store high-dimensional data efficiently within a Next.js environment.

When to use it

• Storing and retrieving embeddings for RAG (Retrieval-Augmented Generation) applications.
• Performing similarity searches on unstructured text or image data.
• Building recommendation systems that rely on vector proximity.
• Managing metadata alongside vector data for filtered queries.

Key capabilities

• Connect to Upstash Vector instances via standard SDKs.
• Create and manage named collections for different data types.
• Insert, update, and delete vectors with associated metadata.
• Execute hybrid search combining keyword and vector similarity.
• Retrieve top-k results based on cosine or dot product similarity.

Example prompts

• "Store a new document embedding into the 'support_tickets' collection with metadata including user_id and timestamp."
• "Search for similar customer feedback entries related to 'billing issues' and return the top 5 matches with their scores."
• "Update an existing vector record in the 'product_catalog' collection with new embedding data and refresh its metadata fields."

Tips & gotchas

Ensure your Upstash Vector instance is properly provisioned and credentials are securely stored before initializing the connection. Be mindful of token limits when inserting large batches of vectors to avoid rate throttling or request failures.