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  1. A toolkit designed to easily connect LLM’s with your external data LlamaIndex
  2. Context LLMs are powerful for knowledge generation and reasoning, pre-trained on publicly available data. To augment LLMs with private data, in- context learning has emerged as a paradigm, where context is inserted into the input prompt. In-context learning takes advantage of LLMs' reasoning capabilities to generate a response, offering a simple and effective way to use them for data augmentation.
  3. To perform LLM’s data augmentation in a performant, efficient, and cheap manner, we need to solve two components: Data Ingestion Data Indexing
  4. 1 2 3 Provides indices over your unstructured and structured data for use with LLM’s. These indices help to abstract away common boilerplate and pain points for in-context learning: Storing context in an easy-to-access format for prompt insertion. Dealing with prompt limitations (e.g. 4096 tokens for Davinci) when context is too big. Dealing with text splitting.
  5. Just Starting Out (Vector Store Indices) Connecting LlamaIndex to an External Data Source of Documents Summarization over Documents Combining information across multiple indices Routing a Query to the Right Index Using Keyword Filters Including Hierarchical Context in your Answer Use cases
  6. How Each Index Works Vector Store Index The vector store index stores each Node and a corresponding embedding in a Vector Store.
  7. Querying
  8. Keyword Table Index
  9. Data Connectors This module contains the data connectors for LlamaIndex. Each connector inherits from a BaseReader class, connects to a data source, and loads Document objects from that data source.
  10. Github repository reader BeautifulSoup web page reader Discord reader Google Docs reader Generic interface for a data document Notion Page reader. LLama Hub Simple directory reader. Youtube Transcript reader. File/audio reader Image loader (text extract) and so on....
  11. Example of usage with Gmail connector This loader seaches your Gmail account and parses the resulting emails into Documents.
  12. Example of usage with Audio connector This loader uses OpenAI's Whisper model to transcribe the text of an audio file or the audio track of a video file.
  13. And many other uses LlamaIndex - Unlock the Full Potential of Your Data with LLMs
  14. gpt- est/index.html