Let’s explore the concept of namespaces in Pinecone’s vector database! 🌟🔍
Namespaces in Pinecone: Organizing Vectors with Style 📁
What Are Namespaces?
Namespaces allow you to partition the vectors in an index.
Each namespace acts like a separate container for related vectors.
Queries and other operations are then limited to one specific namespace.
Think of it as organizing your vector data into different labeled folders.
Why Use Namespaces?
Optimized Search:
By dividing your vectors into namespaces, you can focus searches on specific subsets.
For example, you might want one namespace for articles by content and another for articles by title.
Contextual Filtering:
Metadata or context-specific vectors can reside in different namespaces.
This helps you filter and retrieve relevant information efficiently.
Example Use Case :
Coffee Shop Locator Bot ☕🤖:
Imagine you’re building a chatbot that finds nearby coffee shops.
You have two namespaces:
Namespace 1 (“ns1”): Contains vectors for coffee shop locations based on ratings and ambiance.
Namespace 2 (“ns2”): Contains vectors for coffee shop locations based on cuisine type (e.g., Italian, French).
When a user queries for “cozy coffee shops,” you search in “ns1.”
When they ask for “Italian cafes,” you search in “ns2.”
Creating Namespaces:
Namespaces are created implicitly when you upsert records into them.
For example, if you insert vectors with a namespace of “test-1,” Pinecone creates that namespace for you.
Querying a Namespace:
To target a specific namespace during a query, pass the namespace parameter.
If you don’t specify a namespace, Pinecone uses the default (empty string) namespace.
Example query:
# Search in "ns1" for cozy coffee shops
index.query(namespace="ns1", vector=[0.3, 0.3, 0.3, 0.3], top_k=3, include_values=True)
Operations Across All Namespaces:
Most vector operations apply to a single namespace.
However, there’s one exception: your imagination! 🌈✨
Remember, namespaces help you keep your vectors organized and your searches efficient. Happy vector partitioning!
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