Manage Pinecone indexes for upsert, query, and delete operations on dense and sparse vectors.
npx skills add https://github.com/qcmuu/AI-Research-Skills --skill pinecone-qcmuu
Please help me install this Agent Skill. Skill: pinecone Source: https://github.com/qcmuu/AI-Research-Skills/tree/main/15-rag/pinecone Command: npx skills add https://github.com/qcmuu/AI-Research-Skills --skill pinecone-qcmuu
Pinecone helps you store and retrieve embeddings efficiently for production AI systems, without having to manage infrastructure or performance tuning yourself.
Use case example: You have an app where each user’s documents must be searched and filtered; use Pinecone namespaces plus metadata filtering to retrieve the most relevant chunks for RAG responses.
Create a Pinecone client, create a serverless index with your embedding dimension, then upsert vectors with metadata and query using top_k with a namespace.
High-intent search queries and answers about installing and using this skill.
Build low-latency production RAG by using a managed vector database that supports auto-scaling, semantic retrieval, and upsert/query operations to avoid manual infrastructure tuning.
Hybrid search combines semantic dense similarity with keyword sparse relevance. You control blending via the alpha parameter and apply metadata filtering to restrict matches to specific vector attributes.
Partition vector data for multi-tenant retrieval by using namespaces. Namespaces isolate data per tenant or environment so queries only return matches from the specified partition.
To set up a serverless vector database index, create a client and specify the correct embedding dimension and metric. You can then upsert vectors with metadata and query using top_k.
Yes, you can retrieve dense and sparse vectors in the same query by supplying a sparse_vector. This hybrid approach returns match metadata while combining semantic and keyword relevance.
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