pinecone

# 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": [0.3, 0.4, ...], "metadata": {"category": "B"}} ]) # Query results = index.query( vector=[0.1, 0.2, ...], top_k=5, include_metadata=True ) print(results["matches"]) ``` ## Core operations ### Create index ```python # Serverless (recommended) pc...