Vector Databases: Pinecone, Weaviate & pgvector · Ders

Weaviate Modüllerini Kullanma

Soru-cevap, özetleme ve daha fazlası gibi işlevler için Weaviate'ın kapsamlı modül ekosistemini keşfedin ve entegre edin.

Weaviate Modüllerini Kullanma, CoddyKit'te ücretsiz bir Vector Databases: Pinecone, Weaviate & pgvector dersidir. Bu, 4 dersinin 2. dersidir. Aşağıdan dersin tamamını ücretsiz okuyabilir, sonra tarayıcıda yerleşik kod editörü ve 7/24 yapay zeka koçu ile uygulamalı olarak pratik yapabilirsin. Bu, Vector Databases: Pinecone, Weaviate & pgvector öğrenme yolunun bir parçasıdır ve ilerlemeniz web ve CoddyKit uygulaması arasında senkronize olur. Vector Databases: Pinecone, Weaviate & pgvector kursu toplamda 4 dersten oluşur.

Bu dersin bazı bölümleri henüz çevrilmemiş olup İngilizce olarak gösterilmektedir.

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.

Sıkça Sorulan Sorular

“Weaviate Modüllerini Kullanma” dersi ücretsiz mi?

Evet — “Weaviate Modüllerini Kullanma” dersin tüm metni burada web'de ücretsiz olarak okunabilir. Etkileşimli olarak pratik yapmak (yerleşik kod editörü ve 7/24 yapay zeka koçu) ve Vector Databases: Pinecone, Weaviate & pgvector kursunun geri kalanını açmak için CoddyKit PRO'ya yükselt. Vector Databases: Pinecone, Weaviate & pgvector kursu toplamda 4 dersten oluşur.

“Weaviate Modüllerini Kullanma” dersinde ne öğreneceğim?

Soru-cevap, özetleme ve daha fazlası gibi işlevler için Weaviate'ın kapsamlı modül ekosistemini keşfedin ve entegre edin. Vector Databases: Pinecone, Weaviate & pgvector ile uygulamalı kodu tarayıcıda doğrudan çalıştırarak pratik yaparsın ve 7/24 yapay zeka koçu dersi çalışırken sorularını yanıtlar.

Vector Databases: Pinecone, Weaviate & pgvector öğrenmeye başlamak için deneyim gerekli mi?

Önceden deneyim gerekmez. CoddyKit'te Vector Databases: Pinecone, Weaviate & pgvector, başlangıçtan ileri seviyeye kadar yapılandırıldığı için buradan başlayabilir veya başından başlayıp kendi hızında ilerleme yapabilirsin. Bu, 4 dersinin 2. dersidir.

“Weaviate Modüllerini Kullanma” dersi ne kadar sürer?

Çoğu CoddyKit dersi yaklaşık 5–10 dakika sürer. Her biri kısa ve etkileşimli olduğu için sabit ilerleme yaparsın ve web ile uygulama arasında tam olarak bıraktığın yerden devam edebilirsin.

Bu Vector Databases: Pinecone, Weaviate & pgvector dersinde kod yazıp çalıştırabilir miyim?

Evet. Her Vector Databases: Pinecone, Weaviate & pgvector dersi yerleşik bir kod editörü içerir, bu sayede tarayıcıda gerçek kod yazıp çalıştırabilir ve anlık yapay zeka geri bildirimi alırsın — yerel kurulum gerekli değildir.

Bu kursun tüm dersleri

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