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 producti
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The vector database for production AI applications.
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pip install pinecone-client
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"])
# 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" ) )
# 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)
# 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": {"$eq": "tutorial"}} ) # Namespace query results = index.query( vector=[0.1, 0.2, ...], top_k=5, namespace="production" ) # Access results for match in results["matches"]: print(f"ID: {match['id']}") print(f"Score: {match['score']}") print(f"Metadata: {match['metadata']}")
# Exact match filter = {"category": "tutorial"} # Comparison filter = {"price": {"$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"]}}
# 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'])
# 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 )
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})
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)
# 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 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)
| 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:
Free tier:
MIT
mkdir -p ~/.hermes/skills/mlops/pinecone && curl -o ~/.hermes/skills/mlops/pinecone/SKILL.md https://raw.githubusercontent.com/NousResearch/hermes-agent/main/optional-skills/mlops/pinecone/SKILL.md
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