Penyematan Vektor dan Pencarian Kemiripan
Pahami konsep penyematan vektor, cara membuatnya, serta cara pencarian kemiripan memungkinkan pengambilan dokumen yang relevan.
Penyematan Vektor dan Pencarian Kemiripan adalah pelajaran LLM Apps in Production (RAG + Vector DB + Caching) gratis di CoddyKit. Ini adalah pelajaran 2 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 LLM Apps in Production (RAG + Vector DB + Caching), dan progresmu tersinkronisasi di web dan aplikasi CoddyKit. Kursus LLM Apps in Production (RAG + Vector DB + Caching) mencakup 4 pelajaran total.
Bagian dari pelajaran ini belum diterjemahkan dan ditampilkan dalam bahasa Inggris.
Vectors for Meaning
Welcome! In the world of Large Language Models (LLMs), understanding text isn't just about words. It's about meaning.
Computers naturally work with numbers, not human language. How do we bridge this gap to help LLMs understand the meaning of text?
What are Vector Embeddings?
Vector embeddings are numerical representations of text (or images, audio, etc.). Think of them as a list of numbers that capture the 'essence' or 'meaning' of a piece of information.
- Each piece of text (a word, sentence, or document) gets its own unique vector.
- These vectors are usually long lists of floating-point numbers (e.g.,
[0.123, -0.456, 0.789, ...]).
The Magic Behind Embeddings
How are these magical numbers created? Special machine learning models, often called embedding models, are trained to convert text into these vectors.
These models learn to map similar meanings to vectors that are numerically 'close' to each other in a high-dimensional space.
Semantic Similarity Explained
The core idea is that if two pieces of text have similar meanings, their vector embeddings will be close together.
For example, the embedding for "cat" would be closer to "kitten" than to "car". This 'closeness' is what allows computers to understand semantic similarity.
Generating Embeddings (Concept)
In a real RAG system, you'd use an API from a provider like OpenAI, Cohere, or an open-source model to generate embeddings for your text data.
You feed the text, and the API returns the vector. It's that simple from a usage perspective!
Code: Mock Embedding Generation
Here's a Python example showing how you might conceptually interact with an embedding function. In reality, get_embedding would make an API call.
def get_embedding(text):
"""
Simulates an embedding model. Returns a dummy vector.
"""
if "hello" in text.lower():
return [0.1, 0.2, 0.3]
elif "goodbye" in text.lower():
return [0.8, 0.7, 0.6]
else:
return [0.0, 0.0, 0.0]
if __name__ == "__main__":
text1 = "Hello, CoddyKit!"
text2 = "Time to say goodbye."
text3 = "Another sentence."
print(f"Vector for '{text1}': {get_embedding(text1)}")
print(f"Vector for '{text2}': {get_embedding(text2)}")
print(f"Vector for '{text3}': {get_embedding(text3)}")Why Similarity Search?
Once you have all your documents (or chunks of documents) converted into embeddings, how do you find the most relevant ones when a user asks a question?
This is where similarity search comes in. It's the process of finding embeddings that are 'closest' to a given query embedding.
How Similarity Search Works
Similarity search mathematically measures the 'distance' or 'angle' between vectors. Common methods include:
- Cosine Similarity: Measures the angle between two vectors. A smaller angle (closer to 1) means higher similarity.
- Euclidean Distance: Measures the straight-line distance between two points (vectors). A smaller distance means higher similarity.
These calculations quickly identify the most semantically similar documents.
Embeddings + Search = RAG Power
In RAG, when a user asks a question:
- The question is converted into an embedding.
- Similarity search is performed against a database of document embeddings.
- The top N most similar document chunks are retrieved.
These retrieved chunks then provide context to the LLM, making its answers more accurate and grounded.
Quick Check on Embeddings
Vector embeddings are crucial for RAG. Let's test your understanding.
Recap: Embeddings & Search
Great job! You've learned about the foundational concepts of vector embeddings and similarity search.
- Vector embeddings turn text into numbers, capturing meaning.
- Embedding models create these vectors.
- Similarity search uses mathematical distance to find the most relevant vectors (and thus documents) to a query.
These techniques are at the heart of how RAG systems find and provide relevant context to LLMs.
Belajar LLM Apps in Production (RAG + Vector DB + Caching) 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 “Penyematan Vektor dan Pencarian Kemiripan” gratis?
Ya — teks lengkap “Penyematan Vektor dan Pencarian Kemiripan” gratis dibaca di sini di web. Untuk praktiknya secara interaktif (editor kode bawaan dan tutor AI 24/7) dan buka sisa kursus LLM Apps in Production (RAG + Vector DB + Caching), upgrade ke CoddyKit PRO. Kursus LLM Apps in Production (RAG + Vector DB + Caching) mencakup 4 pelajaran total.
Apa yang akan aku pelajari di “Penyematan Vektor dan Pencarian Kemiripan”?
Pahami konsep penyematan vektor, cara membuatnya, serta cara pencarian kemiripan memungkinkan pengambilan dokumen yang relevan. Kamu berlatih LLM Apps in Production (RAG + Vector DB + Caching) 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 LLM Apps in Production (RAG + Vector DB + Caching)?
Tidak diperlukan pengalaman sebelumnya. LLM Apps in Production (RAG + Vector DB + Caching) 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 2 dari 4.
Berapa lama pelajaran “Penyematan Vektor dan Pencarian Kemiripan” 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 LLM Apps in Production (RAG + Vector DB + Caching) ini?
Ya. Setiap pelajaran LLM Apps in Production (RAG + Vector DB + Caching) 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
- Pentingnya Basis Data Vektor
- Penyematan Vektor dan Pencarian Kemiripan
- Mengintegrasikan dengan Basis Data Vektor
- Pengindeksan, Penyaringan, dan Pencarian Hibrida