Webinar Recap: Boost Your LLM with Private Data Using LlamaIndex

The popularity of large language models (LLMs) like ChatGPT has demonstrated their capabilities in generating knowledge and reasoning. However, these LLMs are pre-trained on publicly available data, which may not provide specific answers and results relevant to a business. LlamaIndex is one solution that can augment LLMs with private data by providing a simple, flexible, centralized interface connecting external data and LLMs. In a recent webinar, Jerry Liu, Co-founder and CEO of LlamaIndex, discussed how LlamaIndex could boost LLMs with private data. Two methods to enhance LLMs with private data were presented: fine-tuning and in-context learning. Fine-tuning requires retraining the network with private data but can be costly and lack transparency. In contrast, in-context learning involves pairing a pre-trained model with external knowledge and a retrieval model to add context to the input prompt. LlamaIndex is an open-source tool that provides central data management and query interface for LLM applications. It contains three main components: data connectors for ingesting data from various sources, data indices for structuring data for different use cases, and a query interface for inputting prompts and receiving knowledge-augmented output. LlamaIndex also manages interactions between the language model and private data to provide accurate and desired results. It operates like a black box, taking in detailed query descriptions and providing rich responses that include references and actions. The vector store index is a popular mode of retrieval and synthesis that pairs a vector store with a language model. LlamaIndex provides numerous integrations, including the integration of Milvus and LlamaIndex. Milvus is an open-source vector database capable of handling vast datasets containing millions, billions, or even trillions of vectors. With this integration, Milvus acts as the backend vector store for embeddings and text. LlamaIndex has various use cases, including semantic search, summarization, text to SQL (structured data), synthesis over heterogeneous data, compare/contrast queries, multi-step queries, exploiting temporal relationships, and recency filtering/outdated nodes.