Master Semantic Search with Vector Databases

Semantic search leverages embeddings and vector databases like ChromaDB, FAISS, and Pinecone to deliver context-aware, accurate search results by capturing the meaning and relationships in data. This article explores embeddings, their generation using models like BERT and OpenAI, and demonstrates building a semantic search application with ChromaDB.


Using Embeddings for Semantic Search with Vector Databases
Introduction
In the era of massive unstructured data, semantic search has emerged as a game-changing technique to improve the way we retrieve information. Unlike traditional keyword-based search, semantic search leverages embeddings—numerical representations of data—to capture the context, meaning, and relationships between data points. This approach enables more accurate and meaningful search results.
Vector databases such as ChromaDB, FAISS, Pinecone, and Weaviate have become key technologies for implementing semantic search. In this article, we'll introduce these tools and provide an end-to-end example of building a semantic search application using ChromaDB.
What are Embeddings?
Embeddings are dense vector representations of data, such as text, images, or other modalities. They are designed to capture semantic meaning, enabling data points with similar meaning to have vectors that are close to each other in the high-dimensional space. For example, the words "dog" and "puppy" will have similar embeddings, while "dog" and "car" will be farther apart.
These embeddings can be generated using pre-trained models like OpenAI’s embeddings, BERT, or sentence-transformers for text, and CLIP for images. By leveraging embeddings, we can compare data points efficiently using operations like cosine similarity or Euclidean distance.
Why Use Vector Databases for Semantic Search?
Vector databases are specifically designed to store and query high-dimensional embeddings for similarity search. They optimize operations like nearest-neighbor search, which is the backbone of semantic search applications. Key features of vector databases include:
  • Efficient indexing and retrieval of high-dimensional data.
  • Support for approximate nearest-neighbor (ANN) search for scalable performance.
  • Integration with popular embedding models and frameworks.
  • Ability to


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