* docs(index.md): update documentation to reflect changes in FastEmbe…

…d library

docs/index.md

@@ -1,17 +1,44 @@
-# Welcome to MkDocs
+# FastEmbed
 
-For full documentation visit [mkdocs.org](https://www.mkdocs.org).
+# 🪶 What is FastEmbed?
 
-## Commands
+FastEmbed is lightweight, fast, Python library built for retrieval and easy to use. 
 
-* `mkdocs new [dir-name]` - Create a new project.
-* `mkdocs serve` - Start the live-reloading docs server.
-* `mkdocs build` - Build the documentation site.
-* `mkdocs -h` - Print help message and exit.
 
-## Project layout
+## 🚀 Installation
 
-    mkdocs.yml    # The configuration file.
-    docs/
-        index.md  # The documentation homepage.
-        ...       # Other markdown pages, images and other files.
+To install the FastEmbed library, we recommend using Poetry, alternatively -- pip works: 
+
+```bash
+pip install fastembed
+```
+
+## 📖 Usage
+
+```python
+from fastembed.embedding import DefaultEmbedding
+
+documents: List[str] = [
+    "Hello, World!",
+    "This is an example document.",
+    "fastembed is supported by and maintained by Qdrant." * 128,
+]
+embedding_model = DeafultEmbedding() 
+embeddings: List[np.ndarray] = list(embedding_model.encode(documents))
+```
+
+## 🚒 Under the hood
+
+### Why fast?
+
+It's important we justify the "fast" in FastEmbed. FastEmbed is fast because:
+
+1. Quantized model weights
+2. ONNX Runtime which allows for inference on CPU, GPU, and other dedicated runtimes
+
+### Why light?
+1. No hidden dependencies on PyTorch or TensorFlow via Huggingface Transformers
+
+### Why accurate?
+1. Better than OpenAI Ada-002
+2. Top of the Embedding leaderboards e.g. [MTEB](https://huggingface.co/spaces/mteb/leaderboard)