[docs]: Revamp embedchain docs (#799)

docs/get-started/faq.mdx

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+---
+title: ❓ FAQs
+description: 'Collections of all the frequently asked questions'
+---
+
+#### How to use GPT-4 as the LLM model?
+
+<CodeGroup>
+
+```python main.py
+import os
+from embedchain import App
+
+os.environ['OPENAI_API_KEY'] = 'xxx'
+
+# load llm configuration from gpt4.yaml file
+app = App.from_config(yaml_path="gpt4.yaml")
+```
+
+```yaml gpt4.yaml
+llm:
+  provider: openai
+  model: 'gpt-4'
+  config:
+    temperature: 0.5
+    max_tokens: 1000
+    top_p: 1
+    stream: false
+```
+
+</CodeGroup>
+
+#### I don't have OpenAI credits. How can I use some open source model?
+
+<CodeGroup>
+
+```python main.py
+import os
+from embedchain import App
+
+os.environ['OPENAI_API_KEY'] = 'xxx'
+
+# load llm configuration from opensource.yaml file
+app = App.from_config(yaml_path="opensource.yaml")
+```
+
+```yaml opensource.yaml
+llm:
+  provider: gpt4all
+  model: 'orca-mini-3b.ggmlv3.q4_0.bin'
+  config:
+    temperature: 0.5
+    max_tokens: 1000
+    top_p: 1
+    stream: false
+
+embedder:
+  provider: gpt4all
+  config:
+    model: 'all-MiniLM-L6-v2'
+```
+</CodeGroup>
+
+#### How to contact support?
+
+If docs aren't sufficient, please feel free to reach out to us using one of the following methods:
+
+<Snippet file="get-help.mdx" />

docs/get-started/introduction.mdx

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+---
+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.

docs/get-started/quickstart.mdx

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+---
+title: '🚀 Quickstart'
+description: '💡 Start building LLM powered apps under 30 seconds'
+---
+
+Install embedchain python package:
+
+```bash
+pip install embedchain
+```
+
+Creating an app involves 3 steps:
+
+<Steps>
+  <Step title="⚙️ Import app instance">
+```python
+from embedchain import App
+app = App()
+```
+  </Step>
+  <Step title="🗃️ Add data sources">
+```python
+# Embed online resources
+elon_bot.add("https://en.wikipedia.org/wiki/Elon_Musk")
+elon_bot.add("https://www.forbes.com/profile/elon-musk")
+```
+  </Step>
+  <Step title="💬 Query or chat on your data and get answers">
+```python
+elon_bot.query("What is the net worth of Elon Musk today?")
+# Answer: The net worth of Elon Musk today is $258.7 billion.
+```
+  </Step>
+</Steps>
+
+Putting it together, you can run your first app 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_bot = App()
+
+# Embed online resources
+elon_bot.add("https://en.wikipedia.org/wiki/Elon_Musk")
+elon_bot.add("https://www.forbes.com/profile/elon-musk")
+
+response = elon_bot.query("What is the net worth of Elon Musk today?")
+print(response)
+# Answer: The net worth of Elon Musk today is $258.7 billion.
+```