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Letting Chatbots See Your Data: Coding framework LlamaIndex enables data interaction with LLMs

A new coding framework lets you pipe your own data into large language models.

What’s new: LlamaIndex streamlines the coding involved in enabling developers to summarize, reason over, and otherwise manipulate data from documents, databases, and apps using models like GPT-4.

How it works: LlamaIndex is a free Python library that works with any large language model.

  • Connectors convert various file types into text that a language model can read. Over 100 connectors are available for unstructured files like PDFs, raw text, video, and audio; structured sources like Excel or SQL files; or APIs for apps such as Salesforce or Slack.
  • LlamaIndex divides the resulting text into chunks, embeds each chunk, and stores the embeddings in a database. Then users can call the language model to extract keywords, summarize, or answer questions about their data.
  • Users can prompt the language model using a description such as, “Given our internal wiki, write a one-page onboarding document for new hires.” LlamaIndex embeds the query, retrieves the best-matching embedding from the database, and sends both to the language model. Users receive the language model's response; in this case, a one-page onboarding document.

Behind the news: Former Uber research scientist Jerry Liu began building LlamaIndex (originally GPT Index) in late 2022 and co-founded a company around it earlier this year. The company, which recently received $8.5 million in seed funding, plans to launch an enterprise version later this year.

Why it matters: Developing bespoke apps that use a large language model typically requires building custom programs to parse private databases. LlamaIndex offers a more direct route.

We’re thinking: Large language models are exciting new tools for developing AI applications. Libraries like LlamaIndex and LangChain provide glue code that makes building complex applications much easier — early entries in a growing suite of tools that promise to make LLMs even more useful.


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