Persistent Vector Storage for LlamaIndex

This article discusses the challenges and solutions in building applications using large language models (LLMs) such as OpenAI's ChatGPT. The three main challenges are high costs, lack of up-to-date information, and need for domain-specific knowledge. Two proposed frameworks to address these issues are fine-tuning and caching + injection. LlamaIndex is a powerful tool that can abstract much of the latter framework. The article introduces LlamaIndex as a "black box around your Data and an LLM" and explains its four main indexing patterns: list, vector store, tree, and keyword indices. It then demonstrates how to create and save a persistent vector index using LlamaIndex with both local and cloud vector databases (Milvus Lite and Zilliz). In summary, the article provides an overview of LlamaIndex, its applications in LLM-based applications, and offers guidance on creating and managing persistent vector store indices for real-world use cases.