pinecone

name	pinecone
description	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.
version	1.0.0
author	Orchestra Research
license	MIT
dependencies	["pinecone-client"]
platforms	["linux","macos","windows"]
metadata	{"sonic":{"tags":["RAG","Pinecone","Vector Database","Managed Service","Serverless","Hybrid Search","Production","Auto-Scaling","Low Latency","Recommendations"]}}

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

pip install pinecone-client

Basic usage

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": [, , ...], : {: }}
])


results = index.query(
    vector=[, , ...],
    top_k=,
    include_metadata=
)

(results[])