Vector Databases: Pinecone, Weaviate & pgvector · درس

إدارة مساحات الأسماء

افهموا كيفية تقسيم بياناتكم داخل فهرس Pinecone واحد باستخدام مساحات الأسماء لتحسين تنظيمها.

إدارة مساحات الأسماء درس مجاني في Vector Databases: Pinecone, Weaviate & pgvector على CoddyKit. هذا هو الدرس 2 من أصل 4. يمكنك قراءة الدرس كاملاً أدناه مجاناً — ثم تمرن عليه مباشرة في المتصفح باستخدام محرر أكواد مدمج ومدرس ذكاء اصطناعي متاح 24/7. هذا الدرس جزء من مسار التعلم في Vector Databases: Pinecone, Weaviate & pgvector، وتقدمك يتزامن عبر الويب وتطبيق CoddyKit. تتضمن دورة Vector Databases: Pinecone, Weaviate & pgvector 4 دروس في المجموع.

بعض أجزاء هذا الدرس لم تُترجم بعد وتظهر باللغة الإنجليزية.

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!

الأسئلة الشائعة

هل درس «إدارة مساحات الأسماء» مجاني؟

نعم — نص درس «إدارة مساحات الأسماء» كامل متاح مجاناً هنا على الويب. لتمرينه بشكل تفاعلي (محرر أكواد مدمج ومدرس ذكاء اصطناعي متاح 24/7) وفتح باقي دورة Vector Databases: Pinecone, Weaviate & pgvector، انتقل إلى CoddyKit PRO. تتضمن دورة Vector Databases: Pinecone, Weaviate & pgvector 4 دروس في المجموع.

ماذا ستتعلم في «إدارة مساحات الأسماء»؟

افهموا كيفية تقسيم بياناتكم داخل فهرس Pinecone واحد باستخدام مساحات الأسماء لتحسين تنظيمها. تتمرن على Vector Databases: Pinecone, Weaviate & pgvector مع أكواد عملية تشغلها مباشرة في المتصفح، ومدرس ذكاء اصطناعي متاح 24/7 يجيب على أسئلتك أثناء عملك.

هل أحتاج إلى خبرة سابقة لأبدأ Vector Databases: Pinecone, Weaviate & pgvector؟

لا تُشترط خبرة سابقة. Vector Databases: Pinecone, Weaviate & pgvector على CoddyKit منظم للمبتدئين حتى المتقدمين، لذا يمكنك البدء من هنا أو من البداية والتقدم بسرعتك الخاصة. هذا هو الدرس 2 من أصل 4.

كم من الوقت يستغرق درس «إدارة مساحات الأسماء»؟

معظم دروس CoddyKit تستغرق حوالي 5–10 دقائق. كل منها موجز وتفاعلي، لذا تحرز تقدماً مستمراً وتستأنف من حيث توقفت عبر الويب والتطبيق.

هل يمكنني كتابة وتشغيل أكواد في درس Vector Databases: Pinecone, Weaviate & pgvector هذا؟

نعم. كل درس في Vector Databases: Pinecone, Weaviate & pgvector يتضمن محرر أكواد مدمج، لذا تكتب وتشغل أكواداً حقيقية مباشرة في متصفحك وتحصل على تعليقات فورية من الذكاء الاصطناعي — بدون إعداد محلي.

جميع الدروس في هذه الدورة

التصفية باستخدام البيانات الوصفية
إدارة مساحات الأسماء
التحديثات وعمليات الحذف الفورية
البحث الهجين باستخدام متجهات Sparse وDense

← العودة إلى Vector Databases: Pinecone, Weaviate & pgvector