Kueri GraphQL Weaviate
Kuasai pembuatan kueri untuk data Weaviate menggunakan API GraphQL yang andal demi pencarian semantik dan pengambilan data.
Kueri GraphQL Weaviate adalah pelajaran Vector Databases: Pinecone, Weaviate & pgvector gratis di CoddyKit. Ini adalah pelajaran 3 dari 4. Kamu bisa membaca pelajaran lengkapnya di bawah secara gratis — lalu praktikkan langsung di browser dengan editor kode bawaan dan tutor AI 24/7. Ini adalah bagian dari jalur belajar Vector Databases: Pinecone, Weaviate & pgvector, dan progresmu tersinkronisasi di web dan aplikasi CoddyKit. Kursus Vector Databases: Pinecone, Weaviate & pgvector mencakup 4 pelajaran total.
Bagian dari pelajaran ini belum diterjemahkan dan ditampilkan dalam bahasa Inggris.
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
_andor_orclauses.
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
Getoperation retrieves data objects and their properties. - The
wherefilter allows you to apply precise conditions to your searches. - You can explicitly fetch an object's vector embedding using
_additional { vector }. - The
nearTextoperator powers semantic search, finding items based on meaning.
These tools enable you to efficiently retrieve and explore your vector data in Weaviate!
Belajar Vector Databases: Pinecone, Weaviate & pgvector dengan tutor AI — gratis
Tulis dan jalankan kode asli di browser kamu, dapatkan bantuan instan dari tutor AI 24/7, dan lanjutkan di mana kamu tinggalkan di web atau aplikasi.
- Kursus
- 12
- Pelajaran
- 48
Pertanyaan yang Sering Diajukan
Apakah pelajaran “Kueri GraphQL Weaviate” gratis?
Ya — teks lengkap “Kueri GraphQL Weaviate” gratis dibaca di sini di web. Untuk praktiknya secara interaktif (editor kode bawaan dan tutor AI 24/7) dan buka sisa kursus Vector Databases: Pinecone, Weaviate & pgvector, upgrade ke CoddyKit PRO. Kursus Vector Databases: Pinecone, Weaviate & pgvector mencakup 4 pelajaran total.
Apa yang akan aku pelajari di “Kueri GraphQL Weaviate”?
Kuasai pembuatan kueri untuk data Weaviate menggunakan API GraphQL yang andal demi pencarian semantik dan pengambilan data. Kamu berlatih Vector Databases: Pinecone, Weaviate & pgvector dengan kode praktik yang langsung kamu jalankan di browser, dan tutor AI 24/7 menjawab pertanyaanmu saat kamu mengerjakan pelajaran ini.
Apakah aku perlu pengalaman untuk memulai Vector Databases: Pinecone, Weaviate & pgvector?
Tidak diperlukan pengalaman sebelumnya. Vector Databases: Pinecone, Weaviate & pgvector di CoddyKit dirancang untuk pemula hingga pelajar tingkat lanjut, jadi kamu bisa memulai di sini atau dari awal dan belajar sesuai kecepatan kamu sendiri. Ini adalah pelajaran 3 dari 4.
Berapa lama pelajaran “Kueri GraphQL Weaviate” memakan waktu?
Sebagian besar pelajaran CoddyKit memakan waktu sekitar 5–10 menit. Setiap pelajaran ringkas dan interaktif, jadi kamu membuat kemajuan stabil dan melanjutkan dari tempat kamu tinggalkan di web dan aplikasi.
Bisakah aku menulis dan menjalankan kode dalam pelajaran Vector Databases: Pinecone, Weaviate & pgvector ini?
Ya. Setiap pelajaran Vector Databases: Pinecone, Weaviate & pgvector menyertakan editor kode bawaan, jadi kamu menulis dan menjalankan kode nyata langsung di browser dan mendapatkan umpan balik AI instan — tidak diperlukan penyiapan lokal.
Semua pelajaran dalam kursus ini
- Mendefinisikan Skema Weaviate
- Mengimpor Objek Data
- Kueri GraphQL Weaviate
- Modul Vektorisasi dan Embedding Otomatis