Day 2: Working with Embeddings & Vector Indexing

Introduction

In our Day 1 discussion, we explored the fundamentals of AI Vector Search, understanding how it differs from traditional keyword-based search and how Oracle AI Vector Search enhances information retrieval. Today, we take a deeper dive into embeddings and vector indexing—the backbone of semantic search and AI-driven information retrieval.

Understanding Vector Embeddings

What Are Vector Embeddings?

Vector embeddings are mathematical representations of data—whether it be text, images, or audio—where similar items are mapped closer together in a multi-dimensional space. Unlike traditional search, which relies on keyword matching, vector embeddings encode semantic meaning, allowing AI-powered searches to retrieve more relevant results based on similarity.

How Do Vector Embeddings Store Meaning?

Vector embeddings work by capturing relationships between words, sentences, or even images based on their contextual meaning. Consider the following example:

• "King" - "Man" + "Woman" ≈ "Queen"

This equation showcases how embeddings capture the semantic relationships between words by mapping them in a multi-dimensional space, enabling context-aware search.

Generating Embeddings Using AI Models

Embedding Generation Process

1. Tokenization – Breaking down text into meaningful chunks (tokens).

2. Embedding Model Processing – An AI model transforms tokens into dense vector representations.

3. Vector Storage – Embeddings are stored for indexing and retrieval.

AI Models for Generating Embeddings

Several AI models specialize in generating embeddings, such as:

• Word2Vec – One of the first successful word embedding models.

• GloVe – Trained on word co-occurrence to enhance contextual understanding.

• BERT (Bidirectional Encoder Representations from Transformers) – Captures bidirectional context in language.

• Oracle AI Services – Oracle’s built-in AI capabilities for vector search and retrieval.

Hands-on: Generating Embeddings in Oracle AI Vector Search

Oracle provides built-in tools to generate embeddings. Below is an example of generating embeddings using Oracle’s AI services:

from oracle_ai_sdk import EmbeddingModel

model = EmbeddingModel("oracle-ai-vector-search")
text = "AI-powered search enhances user experience"
embedding = model.generate_embedding(text)
print(embedding)

This snippet converts the input text into a vector embedding, which can then be stored and indexed for semantic search applications.

Creating and Managing Vector Indexes in Oracle AI Vector Search

What Is Vector Indexing?

Vector indexing is the process of organizing and storing vector embeddings in a way that allows for fast and efficient retrieval. Instead of scanning the entire dataset, vector indexes help locate similar items quickly by reducing the number of comparisons needed.

Types of Vector Indexing Methods

• Flat Index – Compares every vector (brute-force approach, but highly accurate).

• Hierarchical Navigable Small World (HNSW) Index – Optimized for speed and scalability.

• Product Quantization (PQ) – Reduces memory usage by compressing embeddings.

Creating a Vector Index in Oracle

Using Oracle AI Vector Search, you can create a vector index as follows:

CREATE VECTOR INDEX ai_search_index
ON embeddings_table (embedding_column)
USING HNSW;

This command creates an HNSW-based vector index, making searches much more efficient.

Querying Vector Indexes

Once indexed, we can perform vector similarity searches like this:

SELECT * FROM embeddings_table
ORDER BY ai_vector_search(embedding_column, input_vector)
LIMIT 10;

This retrieves the top 10 most similar results based on the input vector.

Real-World Applications of Embeddings & Vector Indexing

1. E-Commerce & Recommendation Systems – AI-driven product suggestions based on user preferences.

2. Chatbots & Virtual Assistants – Context-aware conversational AI.

3. Healthcare & Medical Diagnosis – AI-assisted clinical decision-making.

4. Cybersecurity & Fraud Detection – Identifying anomalies in network traffic and transactions.

Conclusion

Vector embeddings and indexing are fundamental to modern AI search systems, enabling fast, context-aware, and intelligent search capabilities. By leveraging Oracle AI Vector Search, businesses can implement highly scalable and efficient search experiences.

What’s Next?

In Day 3, we will explore Building & Optimizing Vector Search Pipelines, covering how to structure an end-to-end AI-powered search workflow. Stay tuned! 🚀

🔗 Read the full series here: [https://oracle-cloud-infrastructure-demystified.hashnode.dev/mastering-oracle-ai-vector-search-a-7-day-learning-journey]