Build search and recommendation features with Pinecone, a managed database for numerical representations of content. Covers data loading, combined meaning and keyword search, filtering by attributes, and separating data by user.
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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 production RAG, recommendation systems, or semantic search at scale. Best for serverless, managed infrastructure.
安装前可以查看交给 AI 代理的指令内容。
The vector database for production AI applications.
Use when:
Metrics:
Use alternatives instead:
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:
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