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

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"])

```

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

```python

# Single upsert

index.upsert(vectors=[

{

"id": "doc1",

"values": [0.1, 0.2, ...], # 1536 dimensions

"metadata": {

"text": "Document content",

"category": "tutorial",

"timestamp": "2025-01-01"

}

}

])

# Batch upsert (recommended)

vectors = [

{"id": f"vec{i}", "values": embedding, "metadata": metadata}

for i, (embedding, metadata) in enumerate(zip(embeddings, metadatas))

]

index.upsert(vectors=vectors, batch_size=100)

```

Query vectors

```python

# Basic query

results = index.query(

vector=[0.1, 0.2, ...],

top_k=10,

include_metadata=True,

include_values=False

)

# With metadata filtering

results = index.query(

vector=[0.1, 0.2, ...],

top_k=5,

filter={"category": {"$eq": "tutorial"}}

)

# Namespace query

results = index.query(

vector=[0.1, 0.2, ...],

top_k=5,

namespace="production"

)

# Access results

for match in results["matches"]:

print(f"ID: {match['id']}")

print(f"Score: {match['score']}")

print(f"Metadata: {match['metadata']}")

```

Metadata filtering

```python

# Exact match

filter = {"category": "tutorial"}

# Comparison

filter = {"price": {"$gte": 100}} # $gt, $gte, $lt, $lte, $ne

# Logical operators

filter = {

"$and": [

{"category": "tutorial"},

{"difficulty": {"$lte": 3}}

]

} # Also: $or

# In operator

filter = {"tags": {"$in": ["python", "ml"]}}

```

```python

# Partition data by namespace

index.upsert(

vectors=[{"id": "vec1", "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'])

```

```python

# Upsert with sparse vectors

index.upsert(vectors=[

{

"id": "doc1",

"values": [0.1, 0.2, ...], # Dense vector

"sparse_values": {

"indices": [10, 45, 123], # Token IDs

"values": [0.5, 0.3, 0.8] # TF-IDF scores

},

"metadata": {"text": "..."}

}

])

# Hybrid query

results = index.query(

vector=[0.1, 0.2, ...],

sparse_vector={

"indices": [10, 45],

"values": [0.5, 0.3]

},

top_k=5,

alpha=0.5 # 0=sparse, 1=dense, 0.5=hybrid

)

```

```python

from langchain_pinecone import PineconeVectorStore

from langchain_openai import OpenAIEmbeddings

# Create vector store

vectorstore = PineconeVectorStore.from_documents(

documents=docs,

embedding=OpenAIEmbeddings(),

index_name="my-index"

)

# Query

results = vectorstore.similarity_search("query", k=5)

# With metadata filter

results = vectorstore.similarity_search(

"query",

k=5,

filter={"category": "tutorial"}

)

# As retriever

retriever = vectorstore.as_retriever(search_kwargs={"k": 10})

```

```python

from llama_index.vector_stores.pinecone import PineconeVectorStore

# Connect to Pinecone

pc = Pinecone(api_key="your-key")

pinecone_index = pc.Index("my-index")

# Create vector store

vector_store = PineconeVectorStore(pinecone_index=pinecone_index)

# Use in LlamaIndex

from llama_index.core import StorageContext, VectorStoreIndex

storage_context = StorageContext.from_defaults(vector_store=vector_store)

index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)

```

```python

# List indices

indexes = pc.list_indexes()

# Describe index

index_info = pc.describe_index("my-index")

print(index_info)

# Get index stats

stats = index.describe_index_stats()

print(f"Total vectors: {stats['total_vector_count']}")

print(f"Namespaces: {stats['namespaces']}")

# Delete index

pc.delete_index("my-index")

```

```python

# Delete by ID

index.delete(ids=["vec1", "vec2"])

# Delete by filter

index.delete(filter={"category": "old"})

# Delete all in namespace

index.delete(delete_all=True, namespace="test")

# Delete entire index

index.delete(delete_all=True)

```

1. Use serverless - Auto-scaling, cost-effective
2. Batch upserts - More efficient (100-200 per batch)
3. Add metadata - Enable filtering
4. Use namespaces - Isolate data by user/tenant
5. Monitor usage - Check Pinecone dashboard
6. Optimize filters - Index frequently filtered fields
7. Test with free tier - 1 index, 100K vectors free
8. Use hybrid search - Better quality
9. Set appropriate dimensions - Match embedding model
10. Regular backups - Export important data

| Operation | Latency | Notes |

|-----------|---------|-------|

| Upsert | ~50-100ms | Per batch |

| Query (p50) | ~50ms | Depends on index size |

| Query (p95) | ~100ms | SLA target |

| Metadata filter | ~+10-20ms | Additional overhead |

Serverless:

• $0.096 per million read units
• $0.06 per million write units
• $0.06 per GB storage/month

Free tier:

• 1 serverless index
• 100K vectors (1536 dimensions)
• Great for prototyping

• Website: https://www.pinecone.io
• Docs: https://docs.pinecone.io
• Console: https://app.pinecone.io
• Pricing: https://www.pinecone.io/pricing