Build your own LLM Apps with LangChain & GPT-Index

LangChain: https://github.com/hwchase17/langchain

Large language models (LLMs) are emerging as a transformative technology, enabling developers to build applications that they previously could not. But using these LLMs in isolation is often not enough to create a truly powerful app - the real power comes when you can combine them with other sources of computation or knowledge.

This library is aimed at assisting in the development of those types of applications. Common examples of these types of applications include:

GPT Index: https://github.com/jerryjliu/gpt_index

Context
LLMs are a phenomenonal piece of technology for knowledge generation and reasoning.
A big limitation of LLMs is context size (e.g. Davinci's limit is 4096 tokens. Large, but not infinite).
The ability to feed "knowledge" to LLMs is restricted to this limited prompt size and model weights.
Proposed Solution
At its core, GPT Index contains a toolkit of index data structures designed to easily connect LLM's with your external data. GPT Index helps to provide the following advantages:

Remove concerns over prompt size limitations.
Abstract common usage patterns to reduce boilerplate code in your LLM app.
Provide data connectors to your common data sources (Google Docs, Slack, etc.).
Provide cost transparency + tools that reduce cost while increasing performance.

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