From 61d429e4a10d54b3f7582655fe184a7f10d2b5c8 Mon Sep 17 00:00:00 2001 From: Lux Date: Fri, 29 Mar 2024 15:16:35 +0800 Subject: [PATCH] add news about first release (#66) * add news about first release * Update README.md * fix some typos * opt the link words * change the docker compose link --- README.md | 15 ++++++++------- 1 file changed, 8 insertions(+), 7 deletions(-) diff --git a/README.md b/README.md index 5fab0a9..ff8c678 100644 --- a/README.md +++ b/README.md @@ -1,10 +1,10 @@ -:construction: This repository is currently under construction. Stay tuned – it's coming soon! - # LinguFlow +🎉🚀🌍 **LinguFlow** is now live for the world to see! `Hello, World!` + ## What is LinguFlow -LinguFlow, a low-code tool designed for LLM application development, simplifies the building, debugging, and deployment process for developers. It utilizes a DAG-based message flow for business logic, requiring only minimal familiarity with LinguFlow blocks to effectively use. +LinguFlow, a low-code tool designed for LLM application development, simplifies the building, debugging, and deployment process for developers. It utilizes a [DAG (Directed Acyclic Graph)](https://en.wikipedia.org/wiki/Directed_acyclic_graph)-based message flow for business logic, requiring only minimal familiarity with LinguFlow blocks to effectively use. ### Why we need LinguFlow? @@ -14,9 +14,9 @@ When attempting to apply LLM to real-world business scenarios, the limitations o - The inability to restrict the conversation to business-relevant topics only. - Challenges in handling complex business processes. -LinguFlow is needed precisely to overcome these challenges, offering a platform that enables the structured building of LLM applications tailored to specific business needs and enhancing their accuracy over time. The most classic approach to deploying applications with LLM (Large Language Models) is through the construction of a [DAG (Directed Acyclic Graph)](https://en.wikipedia.org/wiki/Directed_acyclic_graph). +LinguFlow is needed precisely to overcome these challenges, offering a platform that enables the structured building of LLM applications tailored to specific business needs and enhancing their accuracy over time. The most classic approach to deploying applications with LLM (Large Language Models) is through the construction of a DAG. -### the features with LinguFlow +### Features with LinguFlow Thus, the features of applications developed with LinguFlow include: @@ -33,11 +33,12 @@ In essence, LinguFlow's design and implementation method offer a structured and ### Localhost (docker) -You can run LinguFlow on your local machine using [docker](https://docs.docker.com/get-docker/) compose. This setup is perfect for developing, testing LinguFlow applications, and diagnosing integration issues. +You can run LinguFlow on your local machine using [docker compose](https://docs.docker.com/compose/install/). This setup is perfect for developing, testing LinguFlow applications, and diagnosing integration issues. ```sh # Clone the LinguFlow repository git clone git@github.com:pingcap/LinguFlow.git + # Navigate into the LinguFlow directory cd LinguFlow @@ -63,4 +64,4 @@ LinguFlow Server, which includes the API and Web UI, is open-source and can be s ## License -This repository is MIT licensed, except for the ee/ folder. See [LICENSE](LICENSE) for more details. \ No newline at end of file +This repository is MIT licensed, except for the ee/ folder. See [LICENSE](LICENSE) for more details.