Vector Databases: Pinecone, Weaviate & pgvector · Ders

Ad Alanlarını Yönetme

Daha iyi bir düzen sağlamak için ad alanlarını kullanarak verilerinizi tek bir Pinecone dizini içinde nasıl bölümlere ayıracağınızı anlayın.

Ad Alanlarını Yönetme, CoddyKit'te ücretsiz bir Vector Databases: Pinecone, Weaviate & pgvector dersidir. Bu, 4 dersinin 2. dersidir. Aşağıdan dersin tamamını ücretsiz okuyabilir, sonra tarayıcıda yerleşik kod editörü ve 7/24 yapay zeka koçu ile uygulamalı olarak pratik yapabilirsin. Bu, Vector Databases: Pinecone, Weaviate & pgvector öğrenme yolunun bir parçasıdır ve ilerlemeniz web ve CoddyKit uygulaması arasında senkronize olur. Vector Databases: Pinecone, Weaviate & pgvector kursu toplamda 4 dersten oluşur.

Bu dersin bazı bölümleri henüz çevrilmemiş olup İngilizce olarak gösterilmektedir.

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!

Sıkça Sorulan Sorular

“Ad Alanlarını Yönetme” dersi ücretsiz mi?

Evet — “Ad Alanlarını Yönetme” dersin tüm metni burada web'de ücretsiz olarak okunabilir. Etkileşimli olarak pratik yapmak (yerleşik kod editörü ve 7/24 yapay zeka koçu) ve Vector Databases: Pinecone, Weaviate & pgvector kursunun geri kalanını açmak için CoddyKit PRO'ya yükselt. Vector Databases: Pinecone, Weaviate & pgvector kursu toplamda 4 dersten oluşur.

“Ad Alanlarını Yönetme” dersinde ne öğreneceğim?

Daha iyi bir düzen sağlamak için ad alanlarını kullanarak verilerinizi tek bir Pinecone dizini içinde nasıl bölümlere ayıracağınızı anlayın. Vector Databases: Pinecone, Weaviate & pgvector ile uygulamalı kodu tarayıcıda doğrudan çalıştırarak pratik yaparsın ve 7/24 yapay zeka koçu dersi çalışırken sorularını yanıtlar.

Vector Databases: Pinecone, Weaviate & pgvector öğrenmeye başlamak için deneyim gerekli mi?

Önceden deneyim gerekmez. CoddyKit'te Vector Databases: Pinecone, Weaviate & pgvector, başlangıçtan ileri seviyeye kadar yapılandırıldığı için buradan başlayabilir veya başından başlayıp kendi hızında ilerleme yapabilirsin. Bu, 4 dersinin 2. dersidir.

“Ad Alanlarını Yönetme” dersi ne kadar sürer?

Çoğu CoddyKit dersi yaklaşık 5–10 dakika sürer. Her biri kısa ve etkileşimli olduğu için sabit ilerleme yaparsın ve web ile uygulama arasında tam olarak bıraktığın yerden devam edebilirsin.

Bu Vector Databases: Pinecone, Weaviate & pgvector dersinde kod yazıp çalıştırabilir miyim?

Evet. Her Vector Databases: Pinecone, Weaviate & pgvector dersi yerleşik bir kod editörü içerir, bu sayede tarayıcıda gerçek kod yazıp çalıştırabilir ve anlık yapay zeka geri bildirimi alırsın — yerel kurulum gerekli değildir.

Bu kursun tüm dersleri

Üst Verilerle Filtreleme
Ad Alanlarını Yönetme
Gerçek Zamanlı Güncellemeler ve Silmeler
Seyrek-Yoğun Vektörlerle Karma Arama

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