[docs]: Revamp embedchain docs (#799)

docs/get-started/introduction.mdx

@@ -0,0 +1,58 @@
+---
+title: 📚 Introduction
+description: '📝 Embedchain is a framework to easily create LLM powered apps on your data.'
+---
+
+## 🤔 What is Embedchain?
+
+Embedchain abstracts the entire process of loading data, chunking it, creating embeddings, and storing it in a vector database.
+
+You can add data from different data sources using the `.add()` method. Then, simply use the `.query()` method to find answers from the added datasets.
+
+If you want to create a Naval Ravikant bot with a YouTube video, a book in PDF format, two blog posts, and a question and answer pair, all you need to do is add the respective links. Embedchain will take care of the rest, creating a bot for you.
+
+```python
+from embedchain import App
+
+naval_bot = App()
+# Add online data
+naval_bot.add("https://www.youtube.com/watch?v=3qHkcs3kG44")
+naval_bot.add("https://navalmanack.s3.amazonaws.com/Eric-Jorgenson_The-Almanack-of-Naval-Ravikant_Final.pdf")
+naval_bot.add("https://nav.al/feedback")
+naval_bot.add("https://nav.al/agi")
+naval_bot.add("The Meanings of Life", 'text', metadata={'chapter': 'philosphy'})
+
+# Add local resources
+naval_bot.add(("Who is Naval Ravikant?", "Naval Ravikant is an Indian-American entrepreneur and investor."))
+
+naval_bot.query("What unique capacity does Naval argue humans possess when it comes to understanding explanations or concepts?")
+# Answer: Naval argues that humans possess the unique capacity to understand explanations or concepts to the maximum extent possible in this physical reality.
+
+# Ask questions with specific context
+naval_bot.query("What unique capacity does Naval argue humans possess when it comes to understanding explanations or concepts?", where={'chapter': 'philosophy'})
+```
+
+## 🚀 How it works?
+
+Embedchain abstracts out the following steps from you to easily create LLM powered apps:
+
+1. Detect the data type and load data
+2. Create meaningful chunks
+3. Create embeddings for each chunk
+4. Store chunks in a vector database
+
+When a user asks a query, the following process happens to find the answer:
+
+1. Create an embedding for the query
+2. Find similar documents for the query from the vector database
+3. Pass the similar documents as context to LLM to get the final answer
+
+The process of loading the dataset and querying involves multiple steps, each with its own nuances:
+
+- How should I chunk the data? What is a meaningful chunk size?
+- How should I create embeddings for each chunk? Which embedding model should I use?
+- How should I store the chunks in a vector database? Which vector database should I use?
+- Should I store metadata along with the embeddings?
+- How should I find similar documents for a query? Which ranking model should I use?
+
+Embedchain takes care of all these nuances and provides a simple interface to create apps on any data.