pineconelisted
Install: claude install-skill Zephyrex21/claude-skills-vetted
# 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