[ SKILL_DOCUMENTATION ]
# Pinecone Deployment Guide
Production deployment patterns for Pinecone.
## Serverless vs Pod-based
### Serverless (Recommended)
```python
from pinecone import Pinecone, ServerlessSpec
pc = Pinecone(api_key="your-key")
# Create serverless index
pc.create_index(
name="my-index",
dimension=1536,
metric="cosine",
spec=ServerlessSpec(
cloud="aws", # or "gcp", "azure"
region="us-east-1"
)
)
```
**Benefits:**
- Auto-scaling
- Pay per usage
- No infrastructure management
- Cost-effective for variable load
**Use when:**
- Variable traffic
- Cost optimization important
- Don't need consistent latency
### Pod-based
```python
from pinecone import PodSpec
pc.create_index(
name="my-index",
dimension=1536,
metric="cosine",
spec=PodSpec(
environment="us-east1-gcp",
pod_type="p1.x1", # or p1.x2, p1.x4, p1.x8
pods=2, # Number of pods
replicas=2 # High availability
)
)
```
**Benefits:**
- Consistent performance
- Predictable latency
- Higher throughput
- Dedicated resources
**Use when:**
- Production workloads
- Need consistent p95 latency
- High throughput required
## Hybrid search
### Dense + Sparse vectors
```python
# Upsert with both dense and sparse vectors
index.upsert(vectors=[
{
"id": "doc1",
"values": [0.1, 0.2, ...], # Dense (semantic)
"sparse_values": {
"indices": [10, 45, 123], # Token IDs
"values": [0.5, 0.3, 0.8] # TF-IDF/BM25 scores
},
"metadata": {"text": "..."}
}
])
# Hybrid query
results = index.query(
vector=[0.1, 0.2, ...], # Dense query
sparse_vector={
"indices": [10, 45],
"values": [0.5, 0.3]
},
top_k=10,
alpha=0.5 # 0=sparse only, 1=dense only, 0.5=balanced
)
```
**Benefits:**
- Best of both worlds
- Semantic + keyword matching
- Better recall than either alone
## Namespaces for multi-tenancy
```python
# Separate data by user/tenant
index.upsert(
vectors=[{"id": "doc1", "values": [...]}],
namespace="user-123"
)
# Query specific namespace
results = index.query(
vector=[...],
namespace="user-123",
top_k=5
)
# List namespaces
stats = index.describe_index_stats()
print(stats['namespaces'])
```
**Use cases:**
- Multi-tenant SaaS
- User-specific data isolation
- A/B testing (prod/staging namespaces)
## Metadata filtering
### Exact match
```python
results = index.query(
vector=[...],
filter={"category": "tutorial"},
top_k=5
)
```
### Range queries
```python
results = index.query(
vector=[...],
filter={"price": {"$gte": 100, "$lte": 500}},
top_k=5
)
```
### Complex filters
```python
results = index.query(
vector=[...],
filter={
"$and": [
{"category": {"$in": ["tutorial", "guide"]}},
{"difficulty": {"$lte": 3}},
{"published": {"$gte": "2024-01-01"}}
]
},
top_k=5
)
```
## Best practices
1. **Use serverless for development** - Cost-effective
2. **Switch to pods for production** - Consistent performance
3. **Implement namespaces** - Multi-tenancy
4. **Add metadata strategically** - Enable filtering
5. **Use hybrid search** - Better quality
6. **Batch upserts** - 100-200 vectors per batch
7. **Monitor usage** - Check Pinecone dashboard
8. **Set up alerts** - Usage/cost thresholds
9. **Regular backups** - Export important data
10. **Test filters** - Verify performance
## Resources
- **Docs**: https://docs.pinecone.io
- **Console**: https://app.pinecone.io