pinecone Skill
Managed vector database for production AI applications. Fully managed, auto-scaling, with hybrid search (dense + sparse), metadata filtering, and namespaces. Low latency (<100ms p95). Use for production RAG, recommendation systems, or semantic search at scale. Best for serverless, managed infrastructure. Published by NousResearch in hermes-agent.
What is pinecone Skill?
Managed vector database for production AI applications. Fully managed, auto-scaling, with hybrid search (dense + sparse), metadata filtering, and namespaces. Low latency (<100ms p95). Use for production RAG, recommendation systems, or semantic search at scale. Best for serverless, managed infrastructure. Published by NousResearch in hermes-agent. This profile combines repository metadata with install, compatibility, and usage signals so developers can quickly decide whether it fits their agent workflow before opening the source repository.
Automated repository signals based on public metadata such as recency, license, installation evidence, and adoption. These are not a security audit or endorsement.
Key capabilities
- Includes SKILL.md support
- Reusable instructions support
- Database workflows
- Deployment
- Research
- Database workflows use cases
- Deployment use cases
Technical details
- Install or run with Copy skill directory
When to use pinecone Skill
- Use it for database workflows.
- Use it for deployment.
- Use it for research.
Built with
Editorial notes
Source
- Creator: NousResearch
- Repository: NousResearch/hermes-agent
- Skill file: optional-skills/mlops/pinecone/SKILL.md
What it does
Managed vector database for production AI applications. Fully managed, auto-scaling, with hybrid search (dense + sparse), metadata filtering, and namespaces. Low latency (<100ms p95). Use for production RAG, recommendation systems, or semantic search at scale. Best for serverless, managed infrastructure.
Skill instructions
Pinecone - Managed Vector Database The vector database for production AI applications. When to use Pinecone Use when: - Need managed, serverless vector database - Production RAG applications - Auto-scaling required - Low latency critical (<100ms) - Don't want to manage infrastructure - Need hybrid search (dense + sparse vectors) Metrics: - Fully managed SaaS - Auto-scales to billions of vectors - p95 latency <100ms - 99.9% uptime SLA Use alternatives instead: - Chroma: Self-hosted, open-source - FAISS: Offline, pure similarity search - Weaviate: Self-hosted with more features Quick start Installation bash pip install pinecone-client Basic usage python from pinecone import Pinecone, ServerlessSpec Initialize pc = Pinecone(apikey="your-api-key") Create index pc.createindex( name="my-index", dimension=1536, Must match embedding dimension metric="cosine", or "euclidean", "dotproduct" spec=ServerlessSpec(cloud="aws", region="us-east-1") ) Connect to index index = pc.Index("my-index") Upsert
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Frequently asked questions
What is pinecone?
pinecone is a open-source AI agent skill with Copy skill directory. Managed vector database for production AI applications. Fully managed, auto-scaling, with hybrid search (dense + sparse), metadata filtering, and namespaces. Low latency (<100ms p95).
Who is pinecone best for?
pinecone is best for reusing agent instructions, scripts, and references, database workflows, deployment workflows, research workflows.
How do I install pinecone?
Install or run pinecone using Copy skill directory. Check pinecone for the latest setup command.
Is pinecone actively maintained?
pinecone may need a closer maintenance check before production use.
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