From 43926fb527869b82b6dfc61afd3c04f31ffb9f6f Mon Sep 17 00:00:00 2001 From: Deshraj Yadav Date: Wed, 20 Dec 2023 14:54:02 +0530 Subject: [PATCH] Update introduction in README and docs (#1036) --- README.md | 15 +++------------ docs/get-started/introduction.mdx | 2 +- 2 files changed, 4 insertions(+), 13 deletions(-) diff --git a/README.md b/README.md index a1541006..ec5e2645 100644 --- a/README.md +++ b/README.md @@ -37,7 +37,7 @@ ## What is Embedchain? -Embedchain is a production ready Open Source RAG framework - load, index, retrieve, and sync any unstructured data. +Embedchain is an Open Source RAG Framework that makes it easy to create and deploy AI apps. At its core, Embedchain follows the design principle of being *"Conventional but Configurable"* to serve both software engineers and machine learning engineers. Embedchain streamlines the creation of RAG applications, offering a seamless process for managing various types of unstructured data. It efficiently segments data into manageable chunks, generates relevant embeddings, and stores them in a vector database for optimized retrieval. With a suite of diverse APIs, it enables users to extract contextual information, find precise answers, or engage in interactive chat conversations, all tailored to their own data. @@ -48,15 +48,6 @@ Embedchain streamlines the creation of RAG applications, offering a seamless pro pip install embedchain ``` -### REST API -You can also run Embedchain as a REST API server using the following command: - -```bash -docker run --name embedchain -p 8080:8080 embedchain/rest-api:latest -``` - -Then, navigate to http://127.0.0.1:8080/docs to interact with the API. - ## 🔍 Usage and Demo @@ -90,14 +81,14 @@ You can also try it in your browser with Google Colab: ## 📖 Documentation Comprehensive guides and API documentation are available to help you get the most out of Embedchain: -- [Getting Started](https://docs.embedchain.ai/get-started/quickstart) - [Introduction](https://docs.embedchain.ai/get-started/introduction#what-is-embedchain) +- [Getting Started](https://docs.embedchain.ai/get-started/quickstart) - [Examples](https://docs.embedchain.ai/examples) - [Supported data types](https://docs.embedchain.ai/components/data-sources/overview) ## 🔗 Join the Community -Connect with fellow developers and users by joining our [Slack Workspace](https://join.slack.com/t/embedchain/shared_invite/zt-22uwz3c46-Zg7cIh5rOBteT_xe1jwLDw). Dive into discussions, ask questions, and share your experiences. +Connect with fellow developers and users by joining our [Slack Workspace](https://join.slack.com/t/embedchain/shared_invite/zt-22uwz3c46-Zg7cIh5rOBteT_xe1jwLDw) or [Discord Community](https://discord.gg/CUU9FPhRNt). Dive into discussions, ask questions, and share your experiences. ## 🤝 Schedule a 1-on-1 Session diff --git a/docs/get-started/introduction.mdx b/docs/get-started/introduction.mdx index e5424538..c6c22ce0 100644 --- a/docs/get-started/introduction.mdx +++ b/docs/get-started/introduction.mdx @@ -4,7 +4,7 @@ title: 📚 Introduction ## What is Embedchain? -Embedchain is a production ready Open Source RAG framework - load, index, retrieve, and sync any unstructured data. +Embedchain is an Open Source RAG Framework that makes it easy to create and deploy AI apps. At its core, Embedchain follows the design principle of being *"Conventional but Configurable"* to serve both software engineers and machine learning engineers. Embedchain streamlines the creation of RAG applications, offering a seamless process for managing various types of unstructured data. It efficiently segments data into manageable chunks, generates relevant embeddings, and stores them in a vector database for optimized retrieval. With a suite of diverse APIs, it enables users to extract contextual information, find precise answers, or engage in interactive chat conversations, all tailored to their own data.