Vector Databases: Pinecone, Weaviate & pgvector · Lektion

Weaviate-GraphQL-Abfragen

Beherrschen Sie die Abfrage Ihrer Weaviate-Daten über die leistungsfähige GraphQL-API für semantische Suche und Datenabruf.

Weaviate-GraphQL-Abfragen ist eine kostenlose Vector Databases: Pinecone, Weaviate & pgvector-Lektion auf CoddyKit. Dies ist Lektion 3 von 4. Du kannst die komplette Lektion unten kostenlos lesen – dann übst du sie direkt im Browser mit einem integrierten Code-Editor und einem KI-Tutor rund um die Uhr. Sie ist Teil des Vector Databases: Pinecone, Weaviate & pgvector-Lernpfads, und dein Fortschritt wird über Web und CoddyKit-App synchronisiert. Der Vector Databases: Pinecone, Weaviate & pgvector-Kurs umfasst insgesamt 4 Lektionen.

Teile dieser Lektion wurden noch nicht übersetzt und werden auf Englisch angezeigt.

Weaviate's GraphQL Power

Welcome! In this lesson, you'll master querying your Weaviate data. Weaviate uses GraphQL, a powerful query language, for flexible and efficient data retrieval.

GraphQL allows you to request exactly the data you need, nothing more, nothing less. This is especially useful for complex searches, including vector similarity.

Basic Data Retrieval: Get

The foundation of querying in Weaviate is the Get operation. It allows you to fetch data objects from a specific class defined in your schema.

Specify the class name (e.g., Article).
Select the properties you want to retrieve (e.g., title, content).
Weaviate will return all objects of that class with the specified properties.

Get All Objects Example

Let's see a basic Get query in action. This Python code connects to your Weaviate instance and fetches the title and content of all Article objects.

(Ensure you have a Weaviate instance running locally at http://localhost:8080 and an 'Article' class with some data.)

import weaviate

# Connect to your Weaviate instance
# For local: http://localhost:8080
client = weaviate.Client(url="http://localhost:8080")

# Perform a basic Get query for 'Article' objects
try:
    response = client.query.get("Article", ["title", "content"]).do()
    print("--- Retrieved Articles ---")
    for article in response["data"]["Get"]["Article"]:
        print(f"Title: {article['title']}")
except Exception as e:
    print(f"Error during query: {e}")

Filtering Data with 'where'

Often, you don't want all objects, but specific ones. The where filter allows you to add conditions to your queries, narrowing down the results.

You can filter by property values (text, number, boolean, date).
Use operators like Equal, Like, GreaterThan, etc.
Combine multiple conditions with _and or _or clauses.

Filter by Property Value

This example shows how to use the where filter to find articles written by a specific author. We'll look for articles where the author property matches 'Jane Doe'.

import weaviate

client = weaviate.Client(url="http://localhost:8080")

# Define the 'where' filter
where_filter = {
    "path": ["author"],
    "operator": "Equal",
    "valueText": "Jane Doe"
}

# Perform the Get query with the filter
try:
    response = client.query.get("Article", ["title", "author"])
                          .with_where(where_filter).do()
    print("--- Articles by Jane Doe ---")
    for article in response["data"]["Get"]["Article"]:
        print(f"Title: {article['title']}, Author: {article['author']}")
except Exception as e:
    print(f"Error during query: {e}")

Retrieving Vector Embeddings

Weaviate stores a vector embedding for each object, representing its semantic meaning. You can retrieve this vector directly as part of your GraphQL query.

To do this, you use the _additional { vector } clause. This is useful for debugging, understanding your data, or performing custom operations outside Weaviate.

Get Vector Embedding

Here's how to fetch the vector embedding along with other properties. We'll get the title and the vector for the first article found.

import weaviate

client = weaviate.Client(url="http://localhost:8080")

# Query for a title and its vector
try:
    response = client.query.get("Article", ["title"])
                          .with_additional("vector")
                          .with_limit(1).do()
    print("--- Article Title and Vector ---")
    if response["data"]["Get"]["Article"]:
        article = response["data"]["Get"]["Article"][0]
        print(f"Title: {article['title']}")
        print(f"Vector (first 5 elements): {article['_additional']['vector'][:5]}...")
    else:
        print("No articles found.")
except Exception as e:
    print(f"Error during query: {e}")

Powerful Semantic Search

One of Weaviate's core strengths is semantic search. Instead of keyword matching, it finds data objects based on their meaning, even if the exact words aren't present.

This is achieved using the nearText operator. You provide 'concepts' (text) and Weaviate uses them to find the most semantically similar objects in your database.

Find Similar Articles

Let's perform a semantic search to find articles related to 'machine learning applications'. Notice how we use with_near_text and provide our query concepts.

import weaviate

client = weaviate.Client(url="http://localhost:8080")

# Define the concepts for semantic search
concepts = ["machine learning applications"]

# Perform a nearText query
try:
    response = client.query.get("Article", ["title", "content"])
                          .with_near_text({"concepts": concepts})
                          .with_limit(3).do()
    print("--- Articles similar to 'machine learning applications' ---")
    for article in response["data"]["Get"]["Article"]:
        print(f"Title: {article['title']}")
except Exception as e:
    print(f"Error during query: {e}")

GraphQL Query Challenge

You've learned about various ways to query data in Weaviate using GraphQL.

Which of the following GraphQL query operations is specifically used to find data objects that are semantically similar to a given text concept?

Weaviate Queries Recap

Great job! You've mastered the essentials of Weaviate's powerful GraphQL API:

The Get operation retrieves data objects and their properties.
The where filter allows you to apply precise conditions to your searches.
You can explicitly fetch an object's vector embedding using _additional { vector }.
The nearText operator powers semantic search, finding items based on meaning.

These tools enable you to efficiently retrieve and explore your vector data in Weaviate!

Häufig gestellte Fragen

Ist die Lektion „Weaviate-GraphQL-Abfragen“ kostenlos?

Ja — der vollständige Text von „Weaviate-GraphQL-Abfragen“ ist hier im Web kostenlos zu lesen. Um sie interaktiv zu üben (integrierter Code-Editor und 24/7 KI-Tutor) und den Rest des Vector Databases: Pinecone, Weaviate & pgvector-Kurses freizuschalten, upgrade auf CoddyKit PRO. Der Vector Databases: Pinecone, Weaviate & pgvector-Kurs umfasst insgesamt 4 Lektionen.

Was lerne ich in „Weaviate-GraphQL-Abfragen“?

Beherrschen Sie die Abfrage Ihrer Weaviate-Daten über die leistungsfähige GraphQL-API für semantische Suche und Datenabruf. Du übst Vector Databases: Pinecone, Weaviate & pgvector mit praktischem Code, den du direkt im Browser ausführst, und ein 24/7 KI-Tutor beantwortet deine Fragen während du die Lektion bearbeitest.

Brauche ich Erfahrung, um Vector Databases: Pinecone, Weaviate & pgvector zu starten?

Keine Vorkenntnisse erforderlich. Vector Databases: Pinecone, Weaviate & pgvector auf CoddyKit ist für Anfänger bis fortgeschrittene Lernende strukturiert, sodass du hier starten oder von Anfang an beginnen und in deinem eigenen Tempo voranschreiten kannst. Dies ist Lektion 3 von 4.

Wie lange dauert die Lektion „Weaviate-GraphQL-Abfragen“?

Die meisten CoddyKit-Lektionen dauern etwa 5–10 Minuten. Jede ist kompakt und interaktiv, sodass du stetig Fortschritte machst und genau dort weitermachst, wo du aufgehört hast – im Web und in der App.

Kann ich in dieser Vector Databases: Pinecone, Weaviate & pgvector-Lektion Code schreiben und ausführen?

Ja. Jede Vector Databases: Pinecone, Weaviate & pgvector-Lektion enthält einen integrierten Code-Editor, sodass du echten Code direkt in deinem Browser schreibst und ausführst und sofort KI-Feedback erhältst — ohne lokale Einrichtung erforderlich.

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