名前空間の管理
名前空間を使って単一のPineconeインデックス内でデータを分割し、整理する方法を理解します。
「名前空間の管理」はCoddyKit上の無料Vector Databases: Pinecone, Weaviate & pgvectorレッスンです。 これはレッスン2/4です。 下記で完全なレッスンを無料で読むことができます。その後、ブラウザ内の組み込みコードエディタと24時間対応のAIチューターでハンズオン演習できます。 これはVector Databases: Pinecone, Weaviate & pgvector学習パスの一部であり、ウェブとCoddyKitアプリ全体で進捗が同期されます。 Vector Databases: Pinecone, Weaviate & pgvectorコースには全4レッスンが含まれています。
このレッスンの一部はまだ翻訳されておらず、英語で表示されています。
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!
よくある質問
「名前空間の管理」レッスンは無料ですか？
はい。「名前空間の管理」の完全なテキストはこのウェブで無料で読めます。インタラクティブに演習し（組み込みコードエディタと24時間対応のAIチューター）、Vector Databases: Pinecone, Weaviate & pgvectorコースの残りをアンロックするには、CoddyKit PROにアップグレードしてください。 Vector Databases: Pinecone, Weaviate & pgvectorコースには全4レッスンが含まれています。
「名前空間の管理」で何を学びますか？
名前空間を使って単一のPineconeインデックス内でデータを分割し、整理する方法を理解します。 ブラウザで直接実行するハンズオンコードでVector Databases: Pinecone, Weaviate & pgvectorを演習し、24時間対応のAIチューターがレッスンを進める中での質問に答えます。
Vector Databases: Pinecone, Weaviate & pgvectorを始めるのに経験は必要ですか？
事前経験は必要ありません。CoddyKitのVector Databases: Pinecone, Weaviate & pgvectorは初級者から上級者向けに構成されているため、ここから始めるか最初から始めて、自分のペースで進むことができます。 これはレッスン2/4です。
「名前空間の管理」レッスンにはどのくらい時間がかかりますか？
ほとんどのCoddyKitレッスンは約5～10分かかります。各レッスンはコンパクトでインタラクティブなので、着実に進歩し、ウェブとアプリ全体で正確に前回の場所から再開できます。
このVector Databases: Pinecone, Weaviate & pgvectorレッスンでコードを書いて実行できますか？
はい。すべてのVector Databases: Pinecone, Weaviate & pgvectorレッスンに組み込みコードエディタが含まれているため、ブラウザでリアルコードを書いて実行し、即座のAIフィードバックを取得できます。ローカル設定は不要です。