Embeddings & Semantic Search

1. Introduction

In the realm of Retrieval and Knowledge-Driven AI, embeddings and semantic search play critical roles in enhancing information retrieval systems. This lesson explores the foundations of embeddings, their applications, and how they facilitate semantic search.

2. What are Embeddings?

Embeddings are dense vector representations of items (words, sentences, or documents) in a continuous vector space. They capture semantic meanings and relationships, enabling machines to understand and process human language effectively.

2.1 Common Types of Embeddings

• Word Embeddings (e.g., Word2Vec, GloVe)
• Sentence Embeddings (e.g., Sentence-BERT)
• Document Embeddings

2.2 Example: Generating Word Embeddings

import gensim
from gensim.models import Word2Vec

# Sample sentences for training
sentences = [['this', 'is', 'a', 'sample'], ['word', 'embeddings', 'are', 'useful']]

# Create and train the model
model = Word2Vec(sentences, vector_size=100, window=5, min_count=1, workers=4)

# Retrieve the embedding for a word
word_vector = model.wv['sample']
print(word_vector)
        

3. Understanding Semantic Search

Semantic search improves search accuracy by understanding the intent and contextual meaning of search queries, rather than relying solely on keyword matching.

3.1 How Semantic Search Works

1. User inputs a query.
2. The system converts the query into an embedding.
3. The system retrieves relevant documents by comparing their embeddings with the query's embedding.
4. Results are ranked based on semantic similarity.

graph TD;
    A[User Query] --> B[Convert to Embedding]
    B --> C[Retrieve Documents]
    C --> D[Rank by Similarity]
    D --> E[Return Results]
        

4. Best Practices

• Utilize pre-trained models for embeddings when possible to save time.
• Regularly update and retrain embeddings as the dataset grows.
• Implement a feedback loop to refine search results based on user interactions.

5. FAQ