GPT Cache: An Overview

GPT Cache is a system that enhances the performance and efficiency of language models by incorporating caching mechanisms. It aims to optimize the retrieval process of relevant information by storing precomputed embeddings and their corresponding similar vectors. This document provides an overview of the key components and their functionalities within the GPT Cache system.

1. LLM Adapter: Connecting the LLM Model to the Backend -> The LLM Adapter acts as an intermediary between the LLM (Language Model) and the backend systems. It facilitates communication and integration by establishing a connection, allowing the LLM model to access and retrieve data from the backend as needed.
2. Embedding Generator: Generating Query Embeddings -> The Embedding Generator is responsible for generating embeddings of user queries. These embeddings represent the semantic information contained within the query, capturing its essence in a numerical representation. By converting queries into embeddings, the system can efficiently compare and evaluate their similarity with stored vectors.
3. Similarity Evaluator: Assessing Vector Similarity -> The Similarity Evaluator plays a crucial role in the GPT Cache system. It assesses the similarity between the query embedding and the stored vectors in the cache. By employing various similarity metrics, such as cosine similarity, it determines the degree of resemblance between vectors, aiding in identifying the most relevant matches.
4. Cache Storage: Storing Vectors and Similar Vectors -> The Cache Storage component serves as the repository for vectors and their corresponding similar vectors. It stores these key-value pairs, arranging them in descending order based on their distance or similarity. By organizing vectors in this manner, the system can quickly retrieve the most relevant and similar vectors when processing user queries.
5. Cache Hit: Checking for Vector Existence in the -> Cache During the query processing phase, the Cache Hit functionality is employed to determine whether a given vector already exists in the cache storage. By checking for vector existence, the system can efficiently identify and retrieve previously stored vectors, thereby potentially avoiding the need for redundant computations.
6. LLM: Responding with Relevant Paragraphs -> The LLM (Language Model) forms the core component of the GPT Cache system. It receives the relevant paragraph, which is typically extracted from a larger document corpus, and generates a response based on the query and the provided context. The LLM leverages its language understanding capabilities to provide accurate and contextually appropriate responses, enhancing the overall user experience.

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