네임스페이스 관리하기
더 나은 구성을 위해 네임스페이스를 사용하여 하나의 Pinecone 색인 안에서 데이터를 분할하는 방법을 이해합니다.
네임스페이스 관리하기은(는) CoddyKit의 무료 Vector Databases: Pinecone, Weaviate & pgvector 강의입니다. 이것은 4개 중 2번째 강의입니다. 아래에서 전체 강의를 무료로 읽을 수 있으며, 내장 코드 에디터와 24/7 AI 튜터와 함께 브라우저에서 직접 실습할 수 있습니다. 이 강의는 Vector Databases: Pinecone, Weaviate & pgvector 학습 경로의 일부이며, 진행 상황이 웹과 CoddyKit 앱에 동기화됩니다. Vector Databases: Pinecone, Weaviate & pgvector 강의에는 총 4개의 강의가 포함되어 있습니다.
이 강의의 일부는 아직 번역되지 않았으며 영어로 표시됩니다.
What are Pinecone Namespaces?
Welcome to managing namespaces in Pinecone! A namespace is like a logical partition within a single Pinecone index.
Think of it as a folder inside a main directory. It allows you to segment your data without needing to create entirely separate indexes.
Why Use Namespaces?
Namespaces are incredibly useful for organizing your vector data, especially in complex applications:
- Multi-tenancy: Isolate data for different users or customers within one index.
- A/B Testing: Store different versions of embeddings for experiments.
- Data Isolation: Keep distinct datasets separate while leveraging the same index infrastructure.
- Categorization: Group vectors by topic, source, or type.
The Default Namespace
Every Pinecone index has a default namespace. If you don't explicitly specify a namespace when upserting or querying, your operations will interact with this default namespace.
It's often represented by an empty string "" or simply by omitting the namespace parameter.
Setting Up for Namespaces
Before we work with namespaces, ensure your Pinecone client is initialized. We'll assume you have your API key and environment configured, typically via environment variables.
Here's a basic setup for demonstration:
from pinecone import Pinecone
import os
def main():
# Replace with your actual API key and environment from environment variables
api_key = os.environ.get("PINECONE_API_KEY", "YOUR_API_KEY")
environment = os.environ.get("PINECONE_ENVIRONMENT", "YOUR_ENVIRONMENT")
if api_key == "YOUR_API_KEY" or environment == "YOUR_ENVIRONMENT":
print("Please set PINECONE_API_KEY and PINECONE_ENVIRONMENT env vars.")
return
pc = Pinecone(api_key=api_key, environment=environment)
print("Pinecone client initialized.")
if __name__ == "__main__":
main()Upserting Data into a Namespace
To add vectors to a specific namespace, you simply include the namespace parameter in your index.upsert() call.
This example upserts a few vectors into a namespace called "product-recommendations".
from pinecone import Pinecone, Index
import os
def main():
api_key = os.environ.get("PINECONE_API_KEY", "YOUR_API_KEY")
environment = os.environ.get("PINECONE_ENVIRONMENT", "YOUR_ENVIRONMENT")
if api_key == "YOUR_API_KEY" or environment == "YOUR_ENVIRONMENT":
print("Please set PINECONE_API_KEY and PINECONE_ENVIRONMENT env vars.")
return
pc = Pinecone(api_key=api_key, environment=environment)
index_name = "my-product-index" # Ensure this index exists
if index_name not in pc.list_indexes():
print(f"Index '{index_name}' does not exist. Please create it first.")
return
index = pc.Index(index_name)
vectors_to_upsert = [
{"id": "item-1", "values": [0.1, 0.2, 0.3]}, # 3 dimensions for example
{"id": "item-2", "values": [0.4, 0.5, 0.6]}
]
# Upsert into a specific namespace
index.upsert(vectors=vectors_to_upsert, namespace="product-recommendations")
print("Vectors upserted into 'product-recommendations' namespace.")
if __name__ == "__main__":
main()Querying a Specific Namespace
When you want to retrieve vectors from a particular namespace, you also specify the namespace parameter in your index.query() call.
This ensures your search is confined to that segment of your index.
from pinecone import Pinecone, Index
import os
def main():
api_key = os.environ.get("PINECONE_API_KEY", "YOUR_API_KEY")
environment = os.environ.get("PINECONE_ENVIRONMENT", "YOUR_ENVIRONMENT")
if api_key == "YOUR_API_KEY" or environment == "YOUR_ENVIRONMENT":
print("Please set PINECONE_API_KEY and PINECONE_ENVIRONMENT env vars.")
return
pc = Pinecone(api_key=api_key, environment=environment)
index_name = "my-product-index"
if index_name not in pc.list_indexes():
print(f"Index '{index_name}' does not exist. Please create it first.")
return
index = pc.Index(index_name)
query_vector = [0.15, 0.25, 0.35] # Example query vector
# Query within the 'product-recommendations' namespace
results = index.query(
vector=query_vector,
top_k=2,
namespace="product-recommendations",
include_values=False
)
print("Query results from 'product-recommendations' namespace:")
for match in results.matches:
print(f"ID: {match.id}, Score: {match.score}")
if __name__ == "__main__":
main()Understanding Query Scope
It's crucial to remember that queries in Pinecone are namespace-specific by default. If you query without specifying a namespace, it will only search the default namespace.
There's no direct way to query across all namespaces with a single API call; you'd need to query each namespace individually if you wanted to combine results.
Listing Namespaces in an Index
To see which namespaces currently exist in your index and their statistics, you can use the index.describe_index_stats() method.
This returns a summary including a map of namespaces and their vector counts.
from pinecone import Pinecone, Index
import os
def main():
api_key = os.environ.get("PINECONE_API_KEY", "YOUR_API_KEY")
environment = os.environ.get("PINECONE_ENVIRONMENT", "YOUR_ENVIRONMENT")
if api_key == "YOUR_API_KEY" or environment == "YOUR_ENVIRONMENT":
print("Please set PINECONE_API_KEY and PINECONE_ENVIRONMENT env vars.")
return
pc = Pinecone(api_key=api_key, environment=environment)
index_name = "my-product-index"
if index_name not in pc.list_indexes():
print(f"Index '{index_name}' does not exist. Please create it first.")
return
index = pc.Index(index_name)
# Get index statistics
index_stats = index.describe_index_stats()
print("Namespaces in index:")
for ns, stats in index_stats.namespaces.items():
print(f"- Namespace: '{ns}', Vector Count: {stats.vector_count}")
if __name__ == "__main__":
main()Deleting Vectors from a Namespace
You can delete specific vectors from a namespace by providing their IDs along with the namespace parameter to index.delete().
This example deletes a specific item from the "product-recommendations" namespace.
from pinecone import Pinecone, Index
import os
def main():
api_key = os.environ.get("PINECONE_API_KEY", "YOUR_API_KEY")
environment = os.environ.get("PINECONE_ENVIRONMENT", "YOUR_ENVIRONMENT")
if api_key == "YOUR_API_KEY" or environment == "YOUR_ENVIRONMENT":
print("Please set PINECONE_API_KEY and PINECONE_ENVIRONMENT env vars.")
return
pc = Pinecone(api_key=api_key, environment=environment)
index_name = "my-product-index"
if index_name not in pc.list_indexes():
print(f"Index '{index_name}' does not exist. Please create it first.")
return
index = pc.Index(index_name)
# Delete a specific vector by ID within a namespace
index.delete(ids=["item-1"], namespace="product-recommendations")
print("Vector 'item-1' deleted from 'product-recommendations' namespace.")
if __name__ == "__main__":
main()Deleting an Entire Namespace
To remove all vectors within a specific namespace, effectively deleting the namespace itself, you can use index.delete() with the namespace parameter and set delete_all=True.
Be careful: This action is irreversible and deletes all data in that namespace!
from pinecone import Pinecone, Index
import os
def main():
api_key = os.environ.get("PINECONE_API_KEY", "YOUR_API_KEY")
environment = os.environ.get("PINECONE_ENVIRONMENT", "YOUR_ENVIRONMENT")
if api_key == "YOUR_API_KEY" or environment == "YOUR_ENVIRONMENT":
print("Please set PINECONE_API_KEY and PINECONE_ENVIRONMENT env vars.")
return
pc = Pinecone(api_key=api_key, environment=environment)
index_name = "my-product-index"
if index_name not in pc.list_indexes():
print(f"Index '{index_name}' does not exist. Please create it first.")
return
index = pc.Index(index_name)
# Delete all vectors within a specific namespace
# This effectively removes the 'test-namespace' and all its contents
index.delete(namespace="test-namespace", delete_all=True)
print("All vectors in 'test-namespace' deleted. Namespace effectively removed.")
if __name__ == "__main__":
main()Namespace Knowledge Check
You have an index named "my-app-index". You upsert vectors into "user-1" and "user-2" namespaces. If you then call index.query(vector=my_vec, top_k=5) without specifying a namespace, what will happen?
Recap: Organize with Namespaces
Great job! You've learned about the power of Pinecone namespaces.
- Namespaces help segment data within a single index.
- They're perfect for multi-tenancy and A/B testing.
- Operations (upsert, query, delete) are namespace-specific.
- The default namespace is used if none is specified.
- You can list existing namespaces and delete entire namespaces.
Using namespaces effectively keeps your vector data organized and manageable!
AI 튜터와 함께 Vector Databases: Pinecone, Weaviate & pgvector을(를) 배우세요 — 무료
브라우저에서 실제 코드를 작성하고 실행하며, 24/7 AI 튜터로부터 즉각적인 도움을 받고, 웹이나 앱에서 중단한 부분부터 계속 학습하세요.
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“네임스페이스 관리하기” 강의는 무료인가요?
네 — “네임스페이스 관리하기” 전체 내용을 이 웹사이트에서 무료로 읽을 수 있습니다. 인터랙티브하게 실습하려면(내장 코드 에디터와 24/7 AI 튜터), CoddyKit PRO로 업그레이드하면 Vector Databases: Pinecone, Weaviate & pgvector 강의 전체를 잠금 해제할 수 있습니다. Vector Databases: Pinecone, Weaviate & pgvector 강의에는 총 4개의 강의가 포함되어 있습니다.
“네임스페이스 관리하기”에서 뭘 배우나요?
더 나은 구성을 위해 네임스페이스를 사용하여 하나의 Pinecone 색인 안에서 데이터를 분할하는 방법을 이해합니다. 브라우저에서 직접 실행하는 실습 코드로 Vector Databases: Pinecone, Weaviate & pgvector을(를) 배우며, 24/7 AI 튜터가 강의를 진행하면서 질문에 답변해줍니다.
Vector Databases: Pinecone, Weaviate & pgvector을(를) 시작하는 데 경험이 필요한가요?
사전 경험은 필요하지 않습니다. CoddyKit의 Vector Databases: Pinecone, Weaviate & pgvector은(는) 초급자부터 고급 학습자까지를 위해 구성되어 있으므로, 여기서 시작하거나 처음부터 시작할 수 있으며 자신의 속도대로 진행할 수 있습니다. 이것은 4개 중 2번째 강의입니다.
“네임스페이스 관리하기” 강의는 얼마나 걸리나요?
대부분의 CoddyKit 강의는 약 5~10분이 소요됩니다. 각 강의는 간결하고 인터랙티브하여 꾸준한 진행이 가능하며, 웹과 앱에서 중단한 부분부터 바로 시작할 수 있습니다.
이 Vector Databases: Pinecone, Weaviate & pgvector 강의에서 코드를 작성하고 실행할 수 있나요?
네. 모든 Vector Databases: Pinecone, Weaviate & pgvector 강의에는 내장 코드 에디터가 포함되어 있으므로, 브라우저에서 바로 실제 코드를 작성하고 실행한 후 즉시 AI 피드백을 받을 수 있습니다 — 로컬 설정이 필요 없습니다.