Large language models (LLMs) and vector databases are complementary technologies that work together to enable advanced AI applications, such as semantic search, recommendation systems, and retrieval-augmented generation (RAG).
LLMs like OpenAI’s GPT or Google’s BERT generate high-dimensional vector embeddings for text, capturing semantic meaning beyond keywords. These embeddings represent text as numerical vectors in a shared space, allowing similarity-based comparison.
Vector databases, such as Milvus, Weaviate, or Pinecone, store and index these embeddings efficiently. They are optimized for Approximate Nearest Neighbor (ANN) searches, enabling fast retrieval of semantically similar content even in large-scale datasets.
For example, in a semantic search system, a user’s query is transformed into an embedding by an LLM. The vector database retrieves documents with similar embeddings, ensuring relevant results. In RAG workflows, the database provides context to the LLM, which generates accurate, context-aware responses.
The integration of LLMs and vector databases creates scalable systems capable of handling unstructured data, enhancing user experiences in domains like customer support, e-commerce, and knowledge management. Developers should consider factors like model compatibility, indexing techniques, and latency when combining these technologies.