diff --git a/README.md b/README.md
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--- a/README.md
+++ b/README.md
@@ -1,23 +1,73 @@
 # ✨PyPi Scout
 
-https://drive.google.com/file/d/1huR7-VD3AieBRCcQyRX9MWbPLMb_czjq/view?usp=sharing
+<video width="820">
+  <source src="./pypi-scout.mp4" type="video/mp4">
+</video>
 
-# setup
+![](./pypi-scout.mp4)
 
-```sh
-cp .env.template .env
-```
+PyPi Scout helps you find PyPi packages using natural language prompts powered by Large Language Models (LLMs).
 
-add API token
+The project works by collecting project summaries and descriptions for all packages on PyPI with more than 50 weekly downloads. These are then converted into vector representations using [Sentence Transformers](https://www.sbert.net/). When the user enters a query, it is converted into a vector representation, and the most similar package descriptions are fetched from the vector database. Additional weight is given to weekly downloads before presenting the results to the user in a dashboard.
 
-```
-docker build -t pypi-scout .
-```
+## Getting Started
 
-```
-docker run --rm \
-  --env-file .env \
-  -v $(pwd)/data:/code/data \
-  pypi-scout \
-  python /code/pypi_scout/scripts/setup.py
-```
+### Prerequisites
+
+1. **Create a `.env` File**
+
+   Copy the `.env.template` to create a new `.env` file:
+
+   ```sh
+   cp .env.template .env
+   ```
+
+2. **Set Up Pinecone**
+
+   Since PyPi Scout uses [Pinecone](https://www.pinecone.io/) as the vector database, register for a free account on their website. Obtain your API key from [here](https://docs.pinecone.io/guides/get-started/quickstart) and add it to your `.env` file.
+
+### Build and Setup
+
+1. **Build the Docker Image**
+
+   From the root of the project, build the Docker image:
+
+   ```sh
+   docker build -t pypi-scout .
+   ```
+
+2. **Run the Setup Script**
+
+   Execute the setup script to download and process the PyPI dataset, set up your Pinecone index, create vector embeddings, and upsert them to the Pinecone index:
+
+   ```sh
+   docker run --rm \
+     --env-file .env \
+     -v $(pwd)/data:/code/data \
+     pypi-scout \
+     python /code/pypi_scout/scripts/setup.py
+   ```
+
+   This script will:
+
+   - Download and process the PyPI dataset and store the results in the `data` directory
+   - Set up your Pinecone index
+   - Create vector embeddings for the PyPI dataset and upsert them to the Pinecone index
+
+3. **Run the Application**
+
+   Start the application using Docker Compose:
+
+   ```sh
+   docker-compose up
+   ```
+
+   After a short while, your application will be live at [http://localhost:3000](http://localhost:3000).
+
+## Data
+
+The dataset for this project is created using the [PyPI dataset on Google BigQuery](https://console.cloud.google.com/marketplace/product/gcp-public-data-pypi/pypi?project=regal-net-412415). The SQL query used can be found in [pypi_bigquery.sql](./pypi_bigquery.sql). The resulting dataset is available as a CSV file on [Google Drive](https://drive.google.com/file/d/1huR7-VD3AieBRCcQyRX9MWbPLMb_czjq/view?usp=sharing).
+
+---
+
+By following these instructions, you'll have PyPi Scout up and running, enabling you to find the best PyPi packages with ease using natural language queries. Enjoy exploring!
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