This repo explores the implementation of vector search, an advanced method for information retrieval using numerical representations to enhance large dataset searches.
The code demonstrates how vector search can surpass traditional search methods by leveraging vector similarity to uncover semantic relationships within data. This technique is widely applied in natural language processing and machine learning, driving improvements in personalized recommendations, data analysis, and search engine optimization.
In this implementation, we use the pgvector extension in PostgreSQL to enable vector search and create embeddings with Azure OpenAI's embedding-ada-002 model, showcasing how to integrate these capabilities into your .NET applications for more efficient and intelligent data querying.