Vector Search Basics

1. Introduction

Vector search is a method of searching for data using vector representations, typically employed in machine learning and natural language processing. This lesson covers the foundational aspects of vector search, how it differs from traditional search methods, and its applications.

2. Key Concepts

• Vector: A mathematical representation of data points in a multi-dimensional space.
• Embedding: The process of converting raw data into vector form.
• Similarity Search: Finding vectors that are close to a target vector in the embedding space.

3. Vector Representation

To perform vector search, data must be represented as vectors. This can be done using various methods:

1. Word Embeddings: Techniques like Word2Vec and GloVe convert words into vectors.
2. Document Embeddings: Methods like Doc2Vec extend word embeddings to entire documents.
3. Image Embeddings: Utilizing convolutional neural networks (CNNs) to generate vector representations of images.

4. Search Process

The vector search process can be illustrated through the following steps:

graph TD;
    A[Input Query] --> B[Convert to Vector]
    B --> C[Search Vector Database]
    C --> D[Return Similar Vectors]
            

In this flow, user input is transformed into a vector, which is then compared against existing vectors in the database to find the most similar results.

5. Best Practices

• Normalize vectors to ensure uniformity across dimensions.
• Select appropriate similarity measures (e.g., cosine similarity, Euclidean distance).
• Employ dimensionality reduction techniques (e.g., PCA) for efficiency.
• Use optimized libraries and frameworks (e.g., Faiss, Annoy) for large-scale vector search.

6. FAQ