pinecone
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.
npx skills add Orchestra-Research/AI-Research-SKILLs --skill pinecone --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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(api_key="your-api-key") # Create index pc.create_index( 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 vectors index.upsert(vectors=[ {"id": "vec1", "values": [0.1, 0.2, ...], "metadata": {"category": "A"}}, {"id": "vec2", "values": [
- When to use Pinecone
- Quick start
- Installation
- Basic usage
- Core operations
- Create index
- Upsert vectors
- Query vectors
- Metadata filtering
- Namespaces
- Hybrid search (dense + sparse)
- LangChain integration
- LlamaIndex integration
- Index management
pip install pinecone-client
What does the pinecone skill do?
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.
How do I install it?
Run `npx skills add Orchestra-Research/AI-Research-SKILLs --skill pinecone --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From Orchestra-Research/AI-Research-SKILLs, a repository with 11,391 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.