Get Ready for GPT-4 with GPTCache & Milvus, Save Big on Multimodal AI

OpenAI's ChatGPT, powered by GPT-3.5, has revolutionized natural language processing (NLP) and sparked interest in large language models (LLMs). As the adoption of LLMs grows across various industries, so does the need for more advanced AI models that can process multimodal data. The tech world is buzzing with anticipation for GPT-4, which promises to be even more powerful by enabling visual inputs. To prepare for this upcoming revolution, Zilliz has introduced GPTCache integrated with Milvus - a game-changing solution that can help businesses save big on multimodal AI. Multimodal AI refers to integrating multiple modes of perception and communication, such as speech, vision, language, and gesture, to create more intelligent and effective AI systems. This approach allows AI models to better understand and interpret human interactions and environments and generate more accurate and nuanced responses. Multimodal AI has applications in various fields, including healthcare, education, entertainment, and transportation. GPTCache is a project developed to optimize response time and reduce expenses for API calls associated with large models. It enables the system to search for potential answers in the cache first before sending a request to a large model. GPTCache speeds up the entire process and helps reduce the costs of running large models. Semantic cache stores and retrieves knowledge representations of concepts. It is designed to store and retrieve semantic information or knowledge in a structured way. Thus, an AI system can better understand and respond to queries or requests. The idea behind a semantic cache is to provide faster access to relevant information by providing precomputed answers to commonly asked questions or queries, which can help improve the performance and efficiency of AI applications. One of the cornerstones of a semantic cache such as GPTCache is the vector database. Specifically, the embedding generator of GPTCache converts data to embeddings for vector storage and semantic search. Storing vectors in a vector database, such as Milvus, not only supports storage for a large data scale but also helps speed up and improve the performance of similarity search. This allows for more efficient retrieval of potential answers from the cache. The Milvus ecosystem provides helpful tools for database monitoring, data migration, and data size estimation. For more straightforward implementation and maintenance of Milvus, there is a cloud-native service Zilliz Cloud. The combination of Milvus with GPTCache offers a powerful solution for enhancing the functionality and performance of multimodal AI applications. Temperature in machine learning has become a valuable tool to balance randomness and coherence and align with the user's or application's specific needs and preferences. The temperature in GPTCache mainly retains the general concept of temperature in machine learning. It is achieved through 3 options in the workflow: 1. Select after evaluation 2. Call model without cache 3. Edit result from cache GPTCache and Milvus represent an exciting and innovative approach to building intelligent multimodal systems. The following examples showcase how GPTCache and Milvus have been implemented in multimodal situations: 1. Text-to-Image: Image Generation 2. Image-to-Text: Image Captioning 3. Audio-to-Text: Speech Transcription With its support for unstructured data, Milvus is an ideal solution for building and scaling multimodal applications. Furthermore, adding more features in GPTCache, such as session management, context awareness, and server support, further enhances the capabilities of multimodal AI. With these advancements, multimodal AI models have more potential uses and scenarios.