Vector stores
As generative AI applications continue to push the boundaries of what’s possible in tech, vector stores have emerged as a crucial component, streamlining and optimizing the search and retrieval of relevant data. In our previous discussions, we’ve delved into the advantages of vector DBs over traditional databases, unraveling the concepts of vectors, embeddings, vector search strategies, approximate nearest neighbors (ANNs), and similarity measures. In this section, we aim to provide an integrative understanding of these concepts within the realm of vector DBs and libraries.
The image illustrates a workflow for transforming different types of data—Audio, Text, and Videos—into vector embeddings.
- Audio: An audio input is processed through an “Audio Embedding model,” resulting in “Audio vector embeddings.”
- Text: Textual data undergoes processing in a “Text Embedding model,” leading to “...
