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pinecone

Manage vector indexes, upserts, and hybrid queries with the Pinecone API.

Updated Apr 18, 2026
Search Tags:#serverless#semantic search#embeddings#rag#vector database#hybrid search#pinecone
One-click install
npx skills add https://github.com/azaanaliraza/operarius --skill pinecone-azaanaliraza
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: pinecone
Source: https://github.com/azaanaliraza/operarius/tree/main/src-tauri/bin/hermes/optional-skills/mlops/pinecone
Command: npx skills add https://github.com/azaanaliraza/operarius --skill pinecone-azaanaliraza

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pinecone-client, and includes references (resource) components.

What problem does it solve? Setting up and operating a production vector database for RAG and semantic search requires managing infrastructure, scaling, and low-latency queries, which is complex to build and maintain yourself. ## Core Features & Use Cases - Managed Index Operations: Create serverless or pod-based indexes, upsert vectors in batches, and query with metadata filtering and namespaces. - Hybrid Search: Combine dense and sparse vectors with an alpha weighting parameter for semantic plus keyword retrieval. - Framework Integration: Connect Pinecone to LangChain and LlamaIndex as a vector store for RAG pipelines. - Use Case: Build a production RAG chatbot that stores document embeddings in a serverless index, partitions tenants with namespaces, and retrieves the top 5 filtered matches per query at under 100ms p95 latency. ## Quick Start Use the pinecone skill to create a serverless index and upsert my document embeddings for a RAG application.

Frequently Asked Questions about pinecone

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

FAQPage Schema
How do I create a Pinecone index in Python?▼

Install pinecone-client, initialize Pinecone with your API key, then call pc.create_index with a name, dimension matching your embedding model, metric such as cosine, and a ServerlessSpec specifying cloud and region. Connect with pc.Index(name) afterward.

How do I query Pinecone with metadata filtering?▼

Pass a filter dictionary to index.query alongside your vector and top_k. Filters support exact matches, comparison operators like $gte and $lte, logical $and/$or combinations, and $in for list membership.

Pinecone vs Chroma vs FAISS for vector search?▼

Pinecone is a fully managed serverless service with auto-scaling and a 99.9% uptime SLA, suited for production workloads. Chroma is self-hosted and open-source, while FAISS is an offline library for pure similarity search without a server.

Does Pinecone support hybrid search with sparse vectors?▼

Yes, Pinecone supports hybrid search by upserting both dense values and sparse_values with token indices and weights. Query with both vector and sparse_vector, and set the alpha parameter to balance sparse versus dense scoring.

When should I use pod-based instead of serverless Pinecone indexes?▼

Use pod-based indexes when you need consistent p95 latency, predictable throughput, and dedicated resources for production workloads. Serverless is better for variable traffic, cost optimization, and development since it auto-scales and bills per usage.

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