Vector Databases: Pinecone, Weaviate & pgvector · Lección

Gestión de namespaces

Comprenda cómo segmentar sus datos dentro de un único índice de Pinecone mediante namespaces para organizarlos mejor.

Gestión de namespaces es una lección gratuita de Vector Databases: Pinecone, Weaviate & pgvector en CoddyKit. Esta es la lección 2 de 4. Puedes leer la lección completa abajo gratuitamente — luego la practicas en el navegador con un editor de código integrado y un tutor de IA 24/7. Forma parte de la ruta de aprendizaje de Vector Databases: Pinecone, Weaviate & pgvector, y tu progreso se sincroniza en la web y la app de CoddyKit. El curso de Vector Databases: Pinecone, Weaviate & pgvector incluye 4 lecciones en total.

Partes de esta lección aún no han sido traducidas y se muestran en inglés.

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!

Preguntas frecuentes

¿La lección «Gestión de namespaces» es gratis?

Sí — el texto completo de «Gestión de namespaces» es gratis para leer aquí en la web. Para practicarla de forma interactiva (editor de código integrado y tutor de IA 24/7) y desbloquear el resto del curso de Vector Databases: Pinecone, Weaviate & pgvector, actualiza a CoddyKit PRO. El curso de Vector Databases: Pinecone, Weaviate & pgvector incluye 4 lecciones en total.

¿Qué aprenderé en «Gestión de namespaces»?

Comprenda cómo segmentar sus datos dentro de un único índice de Pinecone mediante namespaces para organizarlos mejor. Practicas Vector Databases: Pinecone, Weaviate & pgvector con código real que ejecutas directamente en el navegador, y un tutor de IA 24/7 responde tus preguntas mientras trabajas en la lección.

¿Necesito experiencia previa para empezar Vector Databases: Pinecone, Weaviate & pgvector?

No se requiere experiencia previa. Vector Databases: Pinecone, Weaviate & pgvector en CoddyKit está estructurado para principiantes hasta estudiantes avanzados, así que puedes empezar aquí o desde el inicio y avanzar a tu ritmo. Esta es la lección 2 de 4.

¿Cuánto tiempo toma la lección «Gestión de namespaces»?

La mayoría de las lecciones de CoddyKit toman alrededor de 5–10 minutos. Cada una es compacta e interactiva, así que avanzas constantemente y retomas exactamente por donde dejaste en la web y la app.

¿Puedo escribir y ejecutar código en esta lección de Vector Databases: Pinecone, Weaviate & pgvector?

Sí. Cada lección de Vector Databases: Pinecone, Weaviate & pgvector incluye un editor de código integrado, así que escribes y ejecutas código real directamente en tu navegador y obtienes retroalimentación instantánea de IA — sin configuración local necesaria.

Todas las lecciones de este curso

Filtrado mediante metadatos
Gestión de namespaces
Actualizaciones y eliminaciones en tiempo real
Búsqueda híbrida con vectores sparse-dense

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