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Update introduction in README and docs (#1036)

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Deshraj Yadav
2023-12-20 14:54:02 +05:30
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README.md
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@@ -37,7 +37,7 @@
## What is Embedchain? ## 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. 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 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 ## 🔍 Usage and Demo
<!-- Demo GIF or Image --> <!-- Demo GIF or Image -->
@@ -90,14 +81,14 @@ You can also try it in your browser with Google Colab:
## 📖 Documentation ## 📖 Documentation
Comprehensive guides and API documentation are available to help you get the most out of Embedchain: 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) - [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) - [Examples](https://docs.embedchain.ai/examples)
- [Supported data types](https://docs.embedchain.ai/components/data-sources/overview) - [Supported data types](https://docs.embedchain.ai/components/data-sources/overview)
## 🔗 Join the Community ## 🔗 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 ## 🤝 Schedule a 1-on-1 Session

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docs/get-started/introduction.mdx
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@@ -4,7 +4,7 @@ title: 📚 Introduction
## What is Embedchain? ## 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. 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.