Preparing the Environment

Basics of Text Search

• Basics of Information Retrieval
• Introduction to vector spaces, bag of words, and TF-IDF

Implementing Basic Text Search

• TF-IDF scoring with sklearn
• Keyword filtering using pandas
• Creating a class for relevance search

Embeddings and Vector Search

• Vector embeddings
• Word2Vec and other approaches for word embeddings
• LSA (Latent Semantic Analysis) for document embeddings
• Implementing vector search with LSA
• BERT embeddings

Combining Text and Vector Search (5 minutes)

Practical Implementation Aspects and Tools (10 minutes)

• Real-world implementation tools:
  • inverted indexes for text search
  • LSH for vector search (using random projections)
• Technologies:
  • Lucene/Elasticsearch for text search
  • FAISS and and other vector databases