Welcome to LlamaIndex 🦙 !

LlamaIndex (GPT Index) is a project that provides a central interface to connect your LLM's with external data.

• Github: https://github.com/jerryjliu/llama_index

• PyPi:

  • LlamaIndex: https://pypi.org/project/llama-index/.

  • GPT Index (duplicate): https://pypi.org/project/gpt-index/.

• Twitter: https://twitter.com/gpt_index

• Discord https://discord.gg/dGcwcsnxhU

Ecosystem

• 🏡 LlamaHub: https://llamahub.ai

• 🧪 LlamaLab: https://github.com/run-llama/llama-lab

🚀 Overview

Context

• LLMs are a phenomenonal piece of technology for knowledge generation and reasoning. They are pre-trained on large amounts of publicly available data.

• How do we best augment LLMs with our own private data?

• One paradigm that has emerged is in-context learning (the other is finetuning), where we insert context into the input prompt. That way, we take advantage of the LLM's reasoning capabilities to generate a response.

To perform LLM's data augmentation in a performant, efficient, and cheap manner, we need to solve two components:

• Data Ingestion

• Data Indexing

Proposed Solution

That's where the LlamaIndex comes in. LlamaIndex is a simple, flexible interface between your external data and LLMs. It provides the following tools in an easy-to-use fashion:

• Offers data connectors to your existing data sources and data formats (API's, PDF's, docs, SQL, etc.)

• 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.

• Provides users an interface to query the index (feed in an input prompt) and obtain a knowledge-augmented output.

• Offers you a comprehensive toolset trading off cost and performance.