fix: Update doc heading name (#730)

docs/get-start/introduction.mdx

@@ -0,0 +1,60 @@
+---
+title: 📚 Introduction
+description: '📝 Embedchain is a framework to easily create LLM powered bots over any dataset.'
+---
+
+## 🤔 What is Embedchain?
+
+Embedchain abstracts the entire process of loading a dataset, chunking it, creating embeddings, and storing it in a vector database.
+
+You can add a single or multiple datasets 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_chat_bot = App()
+# Embed Online Resources
+naval_chat_bot.add("https://www.youtube.com/watch?v=3qHkcs3kG44")
+naval_chat_bot.add("https://navalmanack.s3.amazonaws.com/Eric-Jorgenson_The-Almanack-of-Naval-Ravikant_Final.pdf")
+naval_chat_bot.add("https://nav.al/feedback")
+naval_chat_bot.add("https://nav.al/agi")
+naval_chat_bot.add("The Meanings of Life", 'text', metadata={'chapter': 'philosphy'})
+
+# Embed Local Resources
+naval_chat_bot.add(("Who is Naval Ravikant?", "Naval Ravikant is an Indian-American entrepreneur and investor."))
+
+naval_chat_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.
+
+# with where context filter
+naval_chat_bot.query("What unique capacity does Naval argue humans possess when it comes to understanding explanations or concepts?", where={'chapter': 'philosophy'})
+```
+
+## 🚀 How it works?
+
+Creating a chat bot over any dataset involves the following steps:
+
+1. Detect the data type and load the data
+2. Create meaningful chunks
+3. Create embeddings for each chunk
+4. Store the 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 bots over any dataset.
+
+In the first release, we make it easier for anyone to get a chatbot over any dataset up and running in less than a minute. Just create an app instance, add the datasets using the `.add` method, and use the `.query` method to get the relevant answers.

docs/get-start/quickstart.mdx

@@ -0,0 +1,35 @@
+---
+title: '🚀 Quickstart'
+description: '💡 Start building LLM powered bots under 30 seconds'
+---
+
+Install embedchain python package:
+
+```bash
+pip install --upgrade embedchain
+```
+
+Creating a chatbot involves 3 steps:
+
+- ⚙️ Import the App instance
+- 🗃️ Add Dataset
+- 💬 Query or Chat on the dataset and get answers (Interface Types)
+
+Run your first bot in python using the following code. Make sure to set the `OPENAI_API_KEY` 🔑 environment variable in the code.
+
+```python
+import os
+
+from embedchain import App
+
+os.environ["OPENAI_API_KEY"] = "xxx"
+elon_musk_bot = App()
+
+# Embed Online Resources
+elon_musk_bot.add("https://en.wikipedia.org/wiki/Elon_Musk")
+elon_musk_bot.add("https://www.forbes.com/profile/elon-musk")
+
+response = elon_musk_bot.query("How many companies does Elon Musk run and name those?")
+print(response)
+# Answer: 'Elon Musk currently runs several companies. As of my knowledge, he is the CEO and lead designer of SpaceX, the CEO and product architect of Tesla, Inc., the CEO and founder of Neuralink, and the CEO and founder of The Boring Company. However, please note that this information may change over time, so it's always good to verify the latest updates.'
+```