# Pinecone

Managed vector database for production AI applications.

- Repository: https://github.com/Orchestra-Research/AI-Research-SKILLs (14176 stars)
- Category: Data and ML
- License: MIT
- Last updated: 2026-10-11
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill pinecone -g -a claude-code -y`
Codex: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill pinecone -g -a codex -y`
Cursor: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill pinecone -g -a cursor -y`
Gemini CLI: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill pinecone -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add Orchestra-Research/AI-Research-SKILLs`

## 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.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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.create_index(
    name="my-index",
    dimension=1536,
    metric="cosine",
    spec=ServerlessSpec(
        cloud="aws",         # or "gcp", "azure"
        region="us-east-1"
    )
)

# Pod-based (for consistent performance)
from pinecone import PodSpec

pc.create_index(
    name="my-index",
    dimension=1536,
    metric="cosine",
    spec=PodSpec(
        environment="us-east1-gcp",
        pod_type="p1.x1"
    )
)
```

### Upsert vectors

The rest: https://github.com/Orchestra-Research/AI-Research-SKILLs/blob/HEAD/15-rag/pinecone/SKILL.md