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Vector Search: Overview

· 6 min read

Imagine you're at a massive library, but instead of searching for books by their titles, you can find them based on the ideas and themes they contain. That's the magic of vector search! Traditional keyword searches can be limiting, often needing deeper connections between words. Vector search, on the other hand, uses the power of text embeddings to understand the semantic meaning behind the text, making your searches more intelligent and relevant.

Text Embeddings​

Text embeddings are a type of word representation that allows words to be represented as vectors in a continuous vector space. The idea is to capture the semantic meaning of words, sentences, or documents in a way that similar meanings are close to each other in this space.

How Text Embeddings Work​

  1. Vector Representation: Each word or text is converted into a fixed-sized dense vector. These vectors are typically high-dimensional (e.g., 300 dimensions for Word2Vec, 768 for BERT).

  2. Training Process: Embeddings are learned from large corpora of text using neural networks. The training process involves adjusting the vectors so that words with similar meanings have similar vectors.

  3. Contextual Information: Modern embeddings like BERT capture the context of words in a sentence, meaning the same word can have different embeddings depending on its context.

Types of Text Embeddings​

  1. Word Embeddings: These embeddings represent individual words. Examples include:

    • Word2Vec: Uses a shallow neural network to learn word associations from a large text corpus. It has two main models: Continuous Bag of Words (CBOW) and Skip-gram.
    • GloVe (Global Vectors for Word Representation): Combines the advantages of matrix factorization and local context window methods.

  2. Sentence Embeddings: These embeddings represent entire sentences or paragraphs. Examples include:

    • BERT (Bidirectional Encoder Representations from Transformers): Uses a transformer architecture to capture the context of words in both directions (left-to-right and right-to-left).
    • Sentence-BERT: A modification of BERT that uses Siamese and triplet network structures to derive semantically meaningful sentence embeddings.

Detailed Example: BERT Embeddings​

BERT embeddings are particularly powerful because they are context-aware. Here's a step-by-step explanation of how BERT generates embeddings:

  1. Tokenization: The input text is tokenized into subwords using a WordPiece tokenizer. For example, "playing" might be tokenized into "play" and "##ing."

  2. Input Representation: Each token is converted into a vector that includes token embeddings, segment embeddings, and position embeddings.

  3. Transformer Layers: The token vectors are passed through multiple transformer layers. Each layer consists of self-attention mechanisms and feed-forward neural networks. The self-attention mechanism allows the model to focus on different parts of the sentence when encoding each token.

  4. Output Embeddings: The final hidden states of the transformer layers are used as the embeddings for the tokens. For sentence embeddings, embedding the [CLS] token (which is added at the beginning of the sentence) is often used.

Here's a code example to generate BERT embeddings using HuggingFace's Transformers library:

from transformers import BertTokenizer, BertModel
import torch

# Load pre-trained model tokenizer
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')

# Encode text
text = "vector search is amazing!"
encoded_input = tokenizer(text, return_tensors='pt')

# Load pre-trained model
model = BertModel.from_pretrained('bert-base-uncased')

# Get the embeddings
with torch.no_grad():
 outputs = model(**encoded_input)
 embeddings = outputs.last_hidden_state

# Use the [CLS] token embedding as the sentence embedding
sentence_embedding = embeddings[:, 0, :].squeeze().numpy()

Why Use Text Embeddings?​

  1. Semantic Understanding: Embeddings capture the meaning of words and their relationships, enabling more accurate and relevant search results.
  2. Context Awareness: Modern embeddings like BERT understand the context in which words are used, improving the quality of search and NLP applications.
  3. Dimensionality Reduction: Embeddings reduce the dimensionality of text data, making it easier to process and analyze.
  4. Transfer Learning: Pre-trained embeddings can be fine-tuned for specific tasks, saving time and computational resources.

Use Cases​

  1. Semantic Search: Text embeddings enable search engines to understand the context and meaning behind queries, providing more relevant results. For instance, if you search for "best places to eat sushi," a semantic search engine can return results for "top sushi restaurants" even if the exact keywords don't match. Read More...
  2. Recommendation Systems: Embeddings can be used to recommend similar items based on user preferences. For example, in a movie recommendation system, embeddings can capture the themes and genres of movies. If a user likes "Inception," the system might recommend "Interstellar" due to their similar embeddings.
  3. Question-Answering Systems: In QA systems, embeddings help in understanding the context of questions and retrieving the most relevant answers. For example, if you ask "What is the capital of France?" the system can use embeddings to match this question with the answer "Paris" even if the exact wording differs.
  4. Document Clustering and Classification: Embeddings can be used to cluster similar documents together or classify them into categories. For instance, in news aggregation, embeddings can group articles about the same event even if they use different words.
  5. Chatbots and Virtual Assistants: Embeddings enhance the ability of chatbots to understand and respond to user queries more naturally. They help in interpreting the intent behind user messages and generating appropriate responses.
  6. Sentiment Analysis: Embeddings can capture the sentiment of text, allowing for more accurate sentiment analysis. For example, they can help determine whether a product review is positive, negative, or neutral based on the context of the words used.
  7. Translation and Multilingual Applications: Embeddings are used in machine translation to understand and translate text between languages. They help in capturing the meaning of sentences and translating them accurately.
  8. Content Summarization: Embeddings can be used to generate summaries of long documents by identifying the most important sentences. This is useful in applications like news summarization or summarizing research papers.
  9. Plagiarism Detection: By comparing the embeddings of different texts, systems can detect similarities and potential plagiarism even if the wording is different.
  10. Image Captioning: In combination with image embeddings, text embeddings can be used to generate captions for images. This involves understanding the content of an image and generating a descriptive text.