Large Language Models (LLMs) have revolutionized natural language understanding and processing. We are witnessing an era of unprecedented improvements in areas of accuracy, speed and .
In the realm of LLMs, a vector (also called an embedding) represents the numerical encoding of a word, phrase, or token. These vectors are multi-dimensional and capture semantic relationships between words. If all elements of the language are encoded in vectors then a mechanism is needed to search fast within the huge list of vectors. One of the key enablers to do this and generate contextually relevant and coherent text are vector indexes.
How does Vector Indexing work ?
In a traditional index - we would usually have a list or tree of elements to be searched in the index and we perform an exact match on the elements with the target to be found, in order to search faster. But vectors are highly multi-dimensional and multi-faceted hence a traditional methods wont work.
In order to compare two vectors - we do an approximation of how "far" are the vectors from the target to be found. This comparison can be done using metrics like Cosine similarity, Dot product, or Euclidean distance. These measures, in essence, identify similarities and differences among vectors, guiding the retrieval process efficiently. The vector indexes encapsulate semantic information, with closer vectors indicating greater semantic similarity.
These are approximations just like in real life.
Attention Mechanism: LLMs employ attention mechanisms that dynamically weigh the importance of different vector positions during the generation process. Vector indexing plays a vital role in attention mechanisms by facilitating the selective focus on relevant portions of input text.
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