Vector Embeddings & Semantic Similarity Search
Convert unstructured text into high-dimensional dense vector embeddings, compute Cosine Similarity, and store vectors in databases.
Vector Spaces & Similarity Metrics
### Dense Vector Embeddings
Embedding models transform text into high-dimensional floating-point vectors (e.g., 1536 dimensions) where semantically similar concepts reside close to one another in vector space. **Cosine Similarity** measures the angle between vectors:
$$ ext{Cosine Similarity} = rac{mathbf{A} cdot mathbf{B}}{|mathbf{A}| |mathbf{B}|}$$
