Sublime

An inspiration engine for ideas

AllPeopleCollectionsArticlesAudioBooksFilesHighlightsImagesLinksNotesTextTweetsVideosSocial

LangChain

LangChain excels at chaining LLMs together using agents to delegate actions to models. Its use cases emphasize prompt optimization and context-aware information retrieval/generation; however, with its Pythonic highly modular interface and its huge collection of tools, it is the number-one tool to implement complex business logic.

Ben Auffarth • Generative AI with LangChain: Build large language model (LLM) apps with Python, ChatGPT, and other LLMs

LangGraph

LangChain langchain.com

LangChain simplifies the development of sophisticated LLM applications by providing reusable components and pre-assembled chains.

Ben Auffarth • Generative AI with LangChain: Build large language model (LLM) apps with Python, ChatGPT, and other LLMs

LangChainHub is a central repository for sharing artifacts like prompts, chains, and agents used in LangChain. Inspired by the Hugging Face Hub, it aims to be a one-stop resource for discovering high-quality building blocks to compose complex LLM apps.

Ben Auffarth • Generative AI with LangChain: Build large language model (LLM) apps with Python, ChatGPT, and other LLMs

LangChain supports a map reduce approach for processing documents using LLMs, which allows for efficient processing and analysis of documents. A chain can be applied to each document individually and then we combine the outputs into a single document.

Ben Auffarth • Generative AI with LangChain: Build large language model (LLM) apps with Python, ChatGPT, and other LLMs

Autonomous Agents & Agent Simulations

LangChain blog.langchain.dev

LangChain is an open-source Python framework for building LLM-powered applications. It provides developers with modular, easy-to-use components for connecting language models with external data sources and services.

Ben Auffarth • Generative AI with LangChain: Build large language model (LLM) apps with Python, ChatGPT, and other LLMs

TL;DR

LLMLingua utilizes a compact, well-trained language model (e.g., GPT2-small, LLaMA-7B) to identify and remove non-essential tokens in prompts. This approach enables efficient inference with large language models (LLMs), achieving up to 20x compression with minimal performance loss.

LLMLingua: Compressing Prompts for Accelerated Inference of La

microsoft • GitHub - microsoft/LLMLingua: To speed up LLMs' inference and enhance LLM's perceive of key information, compress the prompt and KV-Cache, which achieves up to 20x compression with minimal performance loss.