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Explanation

A Vector Database stores data as numerical vectors (embeddings) and enables similarity search based on meaning rather than exact keywords.

Code:

// Text -> Embedding Model -> Vector

Explanation

Embeddings are generated from text, images, or other data using AI models. Similar content produces similar vectors.

Code:

float[] vector = embeddingModel.
embed("What is Java?");

Explanation

Vectors are stored in a Vector Database such as Qdrant, ChromaDB, Milvus, or Pinecone.

Code:

vectorStore.add(List.of(new Document(text)));

Explanation

When a user asks a question, the query is converted into a vector and compared with stored vectors to find the most relevant documents.

Code:

List<Document> docs = vectorStore.
similaritySearch(question);

Explanation

Vector Databases are commonly used in RAG applications to provide context from company documents, PDFs, websites, and knowledge bases.

Code:

// Query -> Similarity Search -> 
Relevant Documents