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[Docs] Update docs and readme (#1309)

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Deshraj Yadav
2024-03-10 00:23:21 -08:00
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docs/get-started/introduction.mdx
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## What is Embedchain?
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 is an Open Source Framework that makes it easy to create and deploy personalized 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 personalized LLM 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.
## Who is Embedchain for?
Embedchain is designed for a diverse range of users, from AI professionals like Data Scientists and Machine Learning Engineers to those just starting their AI journey, including college students, independent developers, and hobbyists. Essentially, it's for anyone with an interest in AI, regardless of their expertise level.
Our APIs are user-friendly yet adaptable, enabling beginners to effortlessly create LLM-powered applications with as few as 4 lines of code. At the same time, we offer extensive customization options for every aspect of the RAG pipeline. This includes the choice of LLMs, vector databases, loaders and chunkers, retrieval strategies, re-ranking, and more.
Our APIs are user-friendly yet adaptable, enabling beginners to effortlessly create LLM-powered applications with as few as 4 lines of code. At the same time, we offer extensive customization options for every aspect of building a personalized AI application. This includes the choice of LLMs, vector databases, loaders and chunkers, retrieval strategies, re-ranking, and more.
Our platform's clear and well-structured abstraction layers ensure that users can tailor the system to meet their specific needs, whether they're crafting a simple project or a complex, nuanced AI application.
## Why Use Embedchain?
Developing a robust and efficient RAG (Retrieval-Augmented Generation) pipeline for production use presents numerous complexities, such as:
Developing a personalized AI application for production use presents numerous complexities, such as:
- Integrating and indexing data from diverse sources.
- Determining optimal data chunking methods for each source.
@@ -48,11 +48,11 @@ When a user asks a question, whether for chatting, searching, or querying, Embed
2. **Document Retrieval**: These embeddings are then used to find related documents in the database.
3. **Answer Generation**: The related documents are used by the LLM to craft a precise answer.
With Embedchain, you don’t have to worry about the complexities of building a RAG pipeline. It offers an easy-to-use interface for developing applications with any kind of data.
With Embedchain, you don’t have to worry about the complexities of building a personalized AI application. It offers an easy-to-use interface for developing applications with any kind of data.
## Getting started
Checkout our [quickstart guide](/get-started/quickstart) to start your first RAG application.
Checkout our [quickstart guide](/get-started/quickstart) to start your first AI application.
## Support

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---
title: '⚡ Quickstart'
description: '💡 Create a RAG app on your own data in a minute'
description: '💡 Create an AI app on your own data in a minute'
---
## Installation