ENGLISH IS BASE COMPUTER LANGUAGE

单词

2024/11/3

• Retrieval Augmented Generation
• An agent is a piece of software that semi-autonomously performs tasks by combining LLMs with other tools.
• Use Cases - 使用案例
• can be roughly grouped into four categories
• Structured Data Extraction
• This is useful for extracting structured data from unstructured sources like PDFs, websites, and more, and is key to automating workflows.
• Here’s what to expect:
• whether via remote API calls or running locally on your machine.
• Retrieval-Augmented Generation (RAG) is a key technique for getting your data into an LLM, and a component of more sophisticated agentic systems. We’ll show you how to build a full-featured RAG pipeline that can answer questions about your data. This includes:
  • Loading & Ingestion: Getting your data from wherever it lives, whether that’s unstructured text, PDFs, databases, or APIs to other applications. LlamaIndex has hundreds of connectors to every data source over at LlamaHub.
  • Indexing and Embedding: Once you’ve got your data there are an infinite number of ways to structure access to that data to ensure your applications is always working with the most relevant data. LlamaIndex has a huge number of these strategies built-in and can help you select the best ones.
  • Storing: You will probably find it more efficient to store your data in indexed form, or pre-processed summaries provided by an LLM, often in a specialized database known as a Vector Store (see below). You can also store your indexes, metadata and more.
  • Querying: Every indexing strategy has a corresponding querying strategy and there are lots of ways to improve the relevance, speed and accuracy of what you retrieve and what the LLM does with it before returning it to you, including turning it into structured responses such as an API.