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Vector Databases: Pinecone, Weaviate & pgvector · Pelajaran

Menyiapkan Ekstensi pgvector

Instal dan konfigurasikan ekstensi pgvector untuk menambahkan tipe data dan fungsi vektor ke PostgreSQL.

Menyiapkan Ekstensi pgvector adalah pelajaran Vector Databases: Pinecone, Weaviate & pgvector gratis di CoddyKit. Ini adalah pelajaran 1 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.

Meet pgvector: Vector Power

Welcome to pgvector! This powerful extension brings vector database capabilities directly to your PostgreSQL database.

Why is this cool? It means you can store, index, and query vector embeddings alongside your traditional data, all within the familiar PostgreSQL environment. No need for a separate vector database for simple cases!

Why Use pgvector?

pgvector is perfect for adding semantic search, recommendation systems, and AI features to applications that already use PostgreSQL.

  • Simplicity: Use your existing database skills.
  • Integration: Vectors live with your other data.
  • Cost-Effective: Often cheaper than dedicated vector databases for smaller scales.

Prerequisites: PostgreSQL

Before we can install pgvector, you need to have PostgreSQL installed and running on your system. This lesson assumes you have a working PostgreSQL instance and administrative access.

If you don't have PostgreSQL, you'll need to install it first. Many operating systems offer packages for easy installation.

Connecting to PostgreSQL

To manage your PostgreSQL database and install extensions, you'll typically use the psql command-line tool. Open your terminal or command prompt and connect to your database.

You might need to specify the user and database. For example, connecting as the default 'postgres' user to the default database:

psql -U postgres

Enabling the pgvector Extension

Once you're connected to your PostgreSQL database via psql, enabling an extension is straightforward. PostgreSQL has a built-in mechanism for this.

The key command you'll use is CREATE EXTENSION. This command tells PostgreSQL to load and activate the functionality provided by the specified extension.

The CREATE EXTENSION Command

To enable pgvector, simply run the following command. Make sure you are connected to the specific database where you want to use pgvector.

Note: You might need superuser privileges to create extensions.

CREATE EXTENSION vector;

Verifying Installation

After running CREATE EXTENSION vector;, it's good practice to verify that the extension was successfully installed and is active in your database.

PostgreSQL provides a command within psql to list all installed extensions. This helps confirm pgvector is ready to use.

Listing Active Extensions

Inside psql, use the \dx command to list all installed extensions. You should see 'vector' in the list if the installation was successful.

After confirming, you can exit psql by typing \q.

\dx

Introducing the Vector Type

With pgvector installed, you now have access to a new data type: vector. This type allows you to store arrays of floating-point numbers, which are perfect for embeddings.

When defining a column, you'll specify the vector type and its dimension (e.g., vector(1536) for OpenAI's embeddings).

Quick Check

You've just learned how to enable the pgvector extension. Let's test your understanding!

Recap: pgvector Setup

Great job! In this lesson, you've successfully learned how to set up pgvector.

  • We understood what pgvector is and its benefits.
  • We practiced connecting to PostgreSQL using psql.
  • You now know how to enable the extension with CREATE EXTENSION vector;.
  • Finally, you can verify its installation using \dx.

Now your PostgreSQL database is ready to store and work with vector embeddings!

Pertanyaan yang Sering Diajukan

Apakah pelajaran “Menyiapkan Ekstensi pgvector” gratis?

Ya — teks lengkap “Menyiapkan Ekstensi pgvector” 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 “Menyiapkan Ekstensi pgvector”?

Instal dan konfigurasikan ekstensi pgvector untuk menambahkan tipe data dan fungsi vektor ke PostgreSQL. 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 1 dari 4.

Berapa lama pelajaran “Menyiapkan Ekstensi pgvector” 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

  1. Menyiapkan Ekstensi pgvector
  2. Menyimpan Vektor di PostgreSQL
  3. Menjalankan Kueri Kemiripan
  4. Memilih Metrik Jarak di pgvector
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