Vector Databases: Pinecone, Weaviate & pgvector · 课时

定义 Weaviate 模式

在 Weaviate 中设计和管理数据模式，为向量对象定义类和属性。

定义 Weaviate 模式 是 CoddyKit 上的免费 Vector Databases: Pinecone, Weaviate & pgvector 课时。这是第 1 节课，共 4 节。你可以在下方免费阅读本课时的完整内容 — 然后在浏览器中使用内置代码编辑器和全天候 AI 导师进行实践。这是 Vector Databases: Pinecone, Weaviate & pgvector 学习路径的一部分，你的进度在网页和 CoddyKit 应用中同步。 Vector Databases: Pinecone, Weaviate & pgvector 课程共包含 4 节课。

本课时的部分内容尚未翻译，以英文显示。

Weaviate Schema: Your Data Map

Welcome to Weaviate! To store and search data effectively, Weaviate needs to understand its structure. This is where the schema comes in.

Think of a schema as a blueprint for your data. It defines the types of data you'll store and how they relate.

Why Schema Matters

A well-defined schema is crucial for:

Data Consistency: Ensuring all objects conform to expected types.
Efficient Indexing: Weaviate uses the schema to build efficient search indexes.
Vectorization: Guiding how Weaviate generates and stores vector embeddings for your data.
Querying: Enabling powerful semantic and filtered searches.

Classes: Main Data Types

In Weaviate, the primary building blocks of your schema are classes. A class is like a table in a relational database or a collection in a NoSQL database.

Each class represents a distinct type of object you want to store, such as an "Article", "Product", or "User".

Your First Weaviate Class

Let's define a simple class named Article. This class will represent articles in our database.

In Weaviate, a class definition includes its name and an optional description. We'll add properties later.

import weaviate

if __name__ == "__main__":
    # Define a basic class schema
    article_class_schema = {
        "class": "Article",
        "description": "A class for storing articles"
    }
    print("--- Defined Article Class Schema ---")
    print(f"Class Name: {article_class_schema['class']}")
    print(f"Description: {article_class_schema['description']}")
    # In a real app, you'd add this to Weaviate:
    # client.schema.create_class(article_class_schema)

Properties: Data Fields

Within each class, you define properties. These are the individual fields or attributes that describe an object of that class.

For an Article class, properties might include title, content, author, or publicationDate.

Essential Property Types

Weaviate supports various data types for properties. Some common ones include:

text: For strings of text (e.g., names, descriptions).
int: For whole numbers.
number: For floating-point numbers.
boolean: For true/false values.
date: For date and time values.
text[]: For arrays of text strings.

Each property must have a defined data type.

Class with Properties

Now, let's enhance our Article class by adding some properties. We'll include title, content, and wordCount.

Notice how each property has a name and a dataType.

import weaviate

if __name__ == "__main__":
    # Define a class schema with properties
    article_class_with_props = {
        "class": "Article",
        "description": "A class for storing articles with properties",
        "properties": [
            {
                "name": "title",
                "dataType": ["text"],
                "description": "The title of the article"
            },
            {
                "name": "content",
                "dataType": ["text"],
                "description": "The main content of the article"
            },
            {
                "name": "wordCount",
                "dataType": ["int"],
                "description": "The number of words in the article"
            }
        ]
    }
    print("--- Defined Article Class with Properties ---")
    print(f"Class Name: {article_class_with_props['class']}")
    for prop in article_class_with_props['properties']:
        print(f"  Property: {prop['name']}, Type: {prop['dataType'][0]}")
    # client.schema.create_class(article_class_with_props)

Vectorization & Indexing

Weaviate automatically handles vectorizing your data and building a vector index. By default, it uses a text2vec model for text properties.

You can configure the vectorizer (e.g., which model to use) and the vector index (e.g., HNSW parameters) directly within your class definition.

import weaviate

if __name__ == "__main__":
    # Example of a class with vectorizer configuration
    product_class_schema = {
        "class": "Product",
        "description": "A class for e-commerce products",
        "vectorizer": "text2vec-openai", # Or "text2vec-huggingface", etc.
        "vectorIndexConfig": {
            "efConstruction": 128,
            "maxConnections": 64,
            "ef": -1 # default for queries
        },
        "properties": [
            {"name": "name", "dataType": ["text"]},
            {"name": "description", "dataType": ["text"]}
        ]
    }
    print("--- Defined Product Class with Vector Config ---")
    print(f"Class Name: {product_class_schema['class']}")
    print(f"Vectorizer: {product_class_schema['vectorizer']}")
    print(f"Vector Index Config (efConstruction): {product_class_schema['vectorIndexConfig']['efConstruction']}")
    # client.schema.create_class(product_class_schema)

Adding a New Property

What if you need to add a new property to an existing class? Weaviate allows you to update your schema by adding new properties.

You cannot change existing property types or delete properties directly without re-creating the class (and potentially losing data).

import weaviate

if __name__ == "__main__":
    # Imagine 'Article' class already exists
    # This defines the NEW property to add
    new_property = {
        "name": "category",
        "dataType": ["text"],
        "description": "The category of the article"
    }
    print("--- New Property Definition ---")
    print(f"Name: {new_property['name']}, Type: {new_property['dataType'][0]}")
    # In a real app, you'd add this to Weaviate:
    # client.schema.property.create(
    #     class_name="Article",
    #     property=new_property
    # )
    print("\nTo add this, you'd call client.schema.property.create(class_name, property_object)")

Schema Quick Check

Which of the following statements about Weaviate schema definition are TRUE?

Recap: Schema Essentials

You've learned the fundamentals of Weaviate schema definition!

Classes are your main data types.
Properties define the attributes within each class.
Weaviate uses the schema for indexing and vectorization.
You can add new properties to existing classes to evolve your schema.

A well-structured schema is key to powerful semantic search in Weaviate.

常见问题解答

「定义 Weaviate 模式」课时是免费的吗？

是的 — 「定义 Weaviate 模式」的完整文本可在网页上免费阅读。要进行交互式练习（内置代码编辑器和全天候 AI 导师）并解锁 Vector Databases: Pinecone, Weaviate & pgvector 课程的其余内容，请升级到 CoddyKit PRO。 Vector Databases: Pinecone, Weaviate & pgvector 课程共包含 4 节课。

「定义 Weaviate 模式」这节课中我会学到什么？

在 Weaviate 中设计和管理数据模式，为向量对象定义类和属性。你通过在浏览器中直接运行的动手代码来练习 Vector Databases: Pinecone, Weaviate & pgvector，全天候 AI 导师会在你学习这节课的过程中回答你的问题。

学习 Vector Databases: Pinecone, Weaviate & pgvector 需要有经验吗？

无需任何先前经验。CoddyKit 上的 Vector Databases: Pinecone, Weaviate & pgvector 课程适合初学者到高级学习者，你可以从这里开始或从头开始，按照自己的节奏学习。这是第 1 节课，共 4 节。

「定义 Weaviate 模式」课时需要多长时间？

大多数 CoddyKit 课程大约需要 5–10 分钟。每节课都很精短且互动，所以你能稳步进步，并在网页和应用中从离开的地方继续。

我能在这节 Vector Databases: Pinecone, Weaviate & pgvector 课中编写并运行代码吗？

能。每节 Vector Databases: Pinecone, Weaviate & pgvector 课都包含内置代码编辑器，你可以在浏览器中直接编写并运行真实代码，并获得即时 AI 反馈 — 无需本地设置。

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