Partition vectors using namespaces, indexes, and metadata in a vector database. 🚀
Namespaces:
What are namespaces?
Namespaces allow you to organize vectors within a single index.
Think of them as separate containers or partitions for your data.
Why use namespaces?
Speed: Queries can be filtered by namespace, which speeds up search operations.
Multitenancy: If you need to isolate data for different customers or users, namespaces are essential.
Indexes:
An index is like a big book where you store your vectors.
Each index can have multiple namespaces.
For example:
Index: “Fruit Basket”
Namespace 1: “Sweet Fruits” (contains apples, grapes)
Namespace 2: “Sour Fruits” (contains oranges, unripe bananas)
Metadata:
Metadata adds extra information to your vectors.
Imagine each fruit having tags:
Apple: [“sweet”, “red”, “crunchy”]
Orange: [“sour”, “orange”, “juicy”]
You can use metadata to:
Weight different features (e.g., prioritize titles over content).
Filter vectors based on specific tags (e.g., search for “sweet” fruits).
Example Use Case: Semantic Search Engine
Let’s say you’re building a semantic search engine for articles.
Each article has:
Title
Content
Tags: Keywords, Meta Description
How to structure it:
Namespace 1: “Titles”
Namespace 2: “Content”
Namespace 3: “Tags”
Use metadata to store the type of data (e.g., “title,” “content,” “tag”).
Querying with Metadata and Namespaces:
If a user searches for “apple”:
Query the “Titles” namespace for articles with titles containing “apple.”
Query the “Tags” namespace for articles tagged with “apple.”
If a user wants “sweet apples”:
Combine queries from both namespaces.
Use metadata to filter by “sweet.”
Summary:
Namespaces organize vectors.
Indexes hold namespaces.
Metadata adds context and filters.
Remember, vector databases are like organized fruit baskets—each fruit has a place, and you can find the right one quickly! 🍎📚
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