Upsert vectors and query a managed Pinecone index for semantic retrieval.
npx skills add https://github.com/devMoez/titan --skill pinecone-devmoez
Please help me install this Agent Skill. Skill: pinecone Source: https://github.com/devMoez/titan/tree/main/optional-skills/mlops/pinecone Command: npx skills add https://github.com/devMoez/titan --skill pinecone-devmoez
Pinecone eliminates the operational burden of hosting and scaling a vector database for production retrieval tasks, so you can deliver fast semantic (and hybrid) search with reliable performance.
Use cases:
Use the pinecone skill to connect to your Pinecone API key, create a serverless index for your embedding dimension, upsert your vectors with metadata, and run a similarity (optionally hybrid) query to retrieve the top matching items.
High-intent search queries and answers about installing and using this skill.
A serverless vector database like Pinecone provides managed infrastructure for production semantic retrieval, eliminating self-hosted vector store operations. It supports RAG, recommendation systems, and hybrid search across dense and sparse embeddings with low-latency queries.
Yes, Pinecone supports hybrid search by combining dense and sparse embeddings for better recall. It uses optional hybrid query parameters including sparse vectors and alpha mixing to blend semantic retrieval with token-based sparse signals.
Pinecone supports namespace isolation and metadata filtering to partition tenant or user data. This enables structured filtering for running multi-tenant RAG and personalized recommendation pipelines cleanly within a single vector database.
You need a configured Pinecone index with defined embedding dimension and metric, a Pinecone API key, and vectors with metadata. You then upsert vectors and call the query APIs with optional hybrid parameters to retrieve top matching items.
Choose a serverless vector database like Pinecone when you need production semantic retrieval with reliable latency and uptime without operational overhead. It replaces self-hosted vector stores for scaling RAG, recommendation systems, and hybrid search workflows.
qcmuuManage Pinecone indexes for upsert, query, and delete operations on dense and sparse vectors.
dawsonblockCreate serverless Pinecone indexes, upsert embeddings, and run vector queries.
Tommy-ywManage Pinecone vector databases for storing and querying vectors.
Orchestra-ResearchManage a fully automated vector database with hybrid search and metadata filtering for AI applications.
