pinecone

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": [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

# 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

# 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

# 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

# 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"]}}

Namespaces

# 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'])

Hybrid search (dense + sparse)

# 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
)

LangChain integration

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})

LlamaIndex integration

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)

Index management

# 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")

Delete vectors

# 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)

Best practices

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

Performance

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

Pricing (as of 2025)

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

Resources

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

Agent operating procedure

Check the environment. Confirm the corpus, embedding model and vector store versions, and where the index will live.
Pin down the inputs. Confirm formats, identifiers and parameters from the data or the user. Ask rather than guess any value that changes the result.
Run a small version first. Index a small subset and test retrieval on known question-passage pairs.
Execute the full task using the instructions and references above.
Validate the result. Measure recall@k on a labeled set; answers cite retrieved passages only.
Report. State what was run (versions, commands, parameters), what was checked, and what is still uncertain.

If this happens	Do this
Retrieval returns irrelevant chunks	Revisit chunking, add BM25/hybrid search, or re-rank before changing the generator.
A function, flag or endpoint in these instructions is missing in the installed version	Check the installed version's own documentation (`help()`, `--help`, official docs), adapt, and tell the user. Never invent an API.
A required input, identifier or parameter is ambiguous	Ask the user, or state the assumption explicitly before running.

Integrity rules

Never fabricate results, parameters, identifiers, citations or statistics. If something cannot be run or verified, say so plainly.
Answers must come from retrieved text; say when the corpus does not contain the answer.
Treat version-specific details here as possibly outdated: confirm them against the official documentation for the installed version.
Ask before actions that cost money, consume shared GPUs or cloud quota, touch personal or patient data, or cannot be undone.

Related skills

faiss: Facebook's library for efficient similarity search and clustering of dense vectors.
qdrant-vector-search: High-performance vector similarity search engine for RAG and semantic search.
chroma: Open-source embedding database for AI applications.

About this skill

When to use Pinecone

Quick start

Installation

Basic usage

Core operations

Create index

Upsert vectors

Query vectors

Metadata filtering

Namespaces

Hybrid search (dense + sparse)

LangChain integration

LlamaIndex integration

Index management

Delete vectors

Best practices

Performance

Pricing (as of 2025)

Resources

Agent operating procedure

Related skills