Vector Databases: Pinecone, Weaviate & pgvector · 课时

使用 Weaviate 模块

探索并集成 Weaviate 丰富的模块生态系统，实现问答、摘要等功能。

使用 Weaviate 模块 是 CoddyKit 上的免费 Vector Databases: Pinecone, Weaviate & pgvector 课时。这是第 2 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 Modules Introduction

Weaviate's power comes from its flexible architecture, which can be extended using modules. These modules add specialized functionalities directly to your Weaviate instance.

Think of them as plugins that enhance Weaviate's core capabilities. They can handle tasks like generating embeddings, performing Q&A, or even processing images.

Why Use Weaviate Modules?

Modules streamline your data pipeline by integrating advanced AI functionalities directly into your vector database. This means:

Automatic Vectorization: Weaviate can create embeddings for you.
Enhanced Search: Add capabilities like Q&A or summarization to queries.
Simplified Development: Less external code needed for common AI tasks.
Multi-modal Support: Handle various data types like text and images.

Types of Modules

Weaviate offers a rich ecosystem of modules, typically categorized by their function:

Text2Vec: Generate vector embeddings from text (e.g., text2vec-openai, text2vec-huggingface).
Generative: Add Large Language Model (LLM) capabilities for Q&A, summarization (e.g., generative-openai, generative-cohere).
Multi-modal: Process different data types like images (e.g., img2vec-clip).
Rerank: Improve search relevance by reordering results.

Enabling Modules for Use

Before you can use a module, it must be enabled in your Weaviate instance. This is typically done during setup (e.g., via Docker Compose) or when using Weaviate Cloud.

When you initialize your client, you often specify the modules you intend to use. For example, to use text2vec-openai and generative-openai, you'd configure your client accordingly.

Text2Vec: Auto-Vectorization

The text2vec modules are fundamental for automatically creating vector embeddings. When you define a class schema, you specify which vectorizer to use.

Weaviate then takes care of calling the embedding model for you whenever new data is imported, turning your text into searchable vectors.

Code: Schema with Text2Vec Module

Here's how to define a schema that uses the text2vec-openai module to automatically vectorize the description property of a 'Article' class:

import weaviate
import os

# NOTE: Replace with your Weaviate URL and API key
# and OpenAI API key if using text2vec-openai
# client = weaviate.Client(
#   url="YOUR_WEAVIATE_URL",
#   auth_client_secret=weaviate.AuthApiKey(api_key="YOUR_WEAVIATE_API_KEY"),
#   headers={
#     "X-OpenAI-Api-Key": os.environ.get("OPENAI_API_KEY") # Or your key
#   }
# )

# For demonstration, we'll just show the schema
# and assume client is configured.

class_obj = {
    "class": "Article",
    "vectorizer": "text2vec-openai", # Enable vectorization
    "moduleConfig": {
        "text2vec-openai": {
            "model": "ada",
            "modelVersion": "002",
            "type": "text"
        }
    },
    "properties": [
        {
            "name": "title",
            "dataType": ["text"]
        },
        {
            "name": "description",
            "dataType": ["text"]
        }
    ]
}

# client.schema.create_class(class_obj)
print("Schema definition for Article class:")
print(class_obj)

# This code isn't runnable as is without a Weaviate instance and API keys
# but demonstrates the schema structure.

Generative Modules for LLMs

Generative modules (like generative-openai) allow you to integrate Large Language Models (LLMs) directly into your Weaviate queries. This enables powerful features such as:

Q&A: Ask questions about your retrieved data.
Summarization: Get summaries of search results.
Extraction: Pull specific information from context.

You use these modules via the _additional { generate { ... } } GraphQL syntax in your queries.

Code: Query with Generative Module

Here's a conceptual example of how to use a generative module to get an answer to a question based on retrieved data. Assume a 'Question' class exists with relevant data.

import weaviate
import os

# NOTE: Replace with your Weaviate URL and API key
# client = weaviate.Client(
#   url="YOUR_WEAVIATE_URL",
#   auth_client_secret=weaviate.AuthApiKey(api_key="YOUR_WEAVIATE_API_KEY"),
#   headers={
#     "X-OpenAI-Api-Key": os.environ.get("OPENAI_API_KEY") # Or your key
#   }
# )

# For demonstration, we'll just show the query structure.

# query_result = client.query
#     .get("Question", ["question", "answer"])
#     .with_generate(single_prompt="What is the main topic of these questions?")
#     .with_limit(2)
#     .do()

print("Conceptual query using generative module:")
print("client.query.get(\"Question\", [\"question\"]).with_generate(...)")
print("This would ask an LLM to summarize or answer based on results.")

# This code is illustrative and not runnable without a Weaviate instance,
# data, and API keys.

Multi-modal & Advanced Modules

Beyond text, Weaviate supports multi-modal modules like img2vec-clip, which can generate embeddings for images. This allows you to perform similarity searches on visual data.

Other advanced modules include those for reranking search results (e.g., rerank-transformers) to boost relevance, ensuring users see the most pertinent information first.

Module Best Practices

When using Weaviate modules, consider these best practices:

Choose Wisely: Select modules that align with your specific application needs (e.g., OpenAI for general text, Hugging Face for specialized models).
Monitor Costs: Many modules rely on external APIs (like OpenAI), which incur costs. Monitor usage and set limits.
Version Control: Keep track of module versions as they can impact embedding quality or generative output.
Security: Protect your API keys and ensure proper access control to your Weaviate instance.

Module Capabilities Check

Which of the following are primary benefits of using Weaviate modules?

Recap & Next Steps

In this lesson, we explored Weaviate's powerful module ecosystem. We learned that modules extend Weaviate's capabilities for tasks like automatic vectorization (text2vec), generative AI (generative), and multi-modal data handling (img2vec).

By integrating these modules, you can build more sophisticated and efficient AI applications directly on top of your Weaviate instance. Understanding how to enable and configure them is key to unlocking advanced functionalities.

常见问题解答

「使用 Weaviate 模块」课时是免费的吗？

是的 — 「使用 Weaviate 模块」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。

「使用 Weaviate 模块」这节课中我会学到什么？

探索并集成 Weaviate 丰富的模块生态系统，实现问答、摘要等功能。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 2 节课，共 4 节。

「使用 Weaviate 模块」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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