Vector Databases

In the midst of the AI revolution, applications are structuring large amounts of unstructured data using LLMs. Text, images, and sound are transformed into high-dimensional vectors called embeddings, mathematical objects representing semantic attributes of data.

Applications using embeddings enable features like semantic search, hyper-personalization, and chatbots. Nevertheless, managing these embeddings, especially at scale, presents its unique set of challenges. Each embedding record can encapsulate multiple attributes, including a high-dimensional vector. To effectively search and filter such data, high-performance VectorDB's are a necessity.

In the quest for personalized AI experiences, applications aspire to fine-tune LLMs on an individual level using external context data. Context is pivotal in curating superior AI experiences and constructing custom pipelines

However, context utilization in LLMs is not without its limitations

• LLMs are hindered by context length, impacting user experience significantly.

• LLMs do not always employ context efficiently. In fact, existing literature suggests contexts are not uniformly treated by LLMs.

In order to solve these problems, VectorDB steps in to serve as the long-term memory of AI. Many open-source libraries like FAISS, ANNOY provide excellent functionalities with different trade-offs.

Check out Pinecone's great articles on what is a vector database?