docs: updated readme

README.md

@@ -1,2 +1,81 @@
-# Qdrant-Spark Connector
+# Qdrant-Spark Connector 💥
 
+[Apache Spark](https://spark.apache.org/) is a distributed computing framework designed for big data processing and analytics. This connector enables [Qdrant](https://qdrant.tech/) to be a storage destination in Spark.
+
+## Installation 🚀
+
+### GitHub Releases 📦
+
+The packaged `jar` file releases can be found [here](https://github.com/qdrant/qdrant-spark/releases).
+
+### Building from source 🛠️
+
+To build the `jar` from source, you need [JDK@17](https://www.oracle.com/java/technologies/javase/jdk17-archive-downloads.html) and [Maven](https://maven.apache.org/) installed.
+Once the requirements have been satisfied, run the following command in the project root. 🛠️
+
+```bash
+mvn package -Passembly
+```
+This will build and store the fat JAR in the `target` directory by default.
+
+### Maven Central 📚
+
+The package will be available at the registry soon.
+
+## Usage 📝
+
+### Creating a Spark session (Single-node) with Qdrant support 🌟
+
+```python
+from pyspark.sql import SparkSession
+
+spark = (
+    SparkSession.builder.config(
+        "spark.jars",
+        "spark-1.0-SNAPSHOT-jar-with-dependencies.jar",  # specify the downloaded JAR file
+    )
+    .master("local[*]")
+    .appName("qdrant")
+    .getOrCreate()
+```
+
+### Loading data 📊
+
+To load data into Qdrant, a collection has to be created beforehand with the appropriate vector dimensions and configurations.
+
+```python
+   <pyspark.sql.DataFrame>
+    .write
+    .format("io.qdrant.spark.Qdrant")
+    .option("qdrant_url", <QDRANT_URL>)
+    .option("collection_name", <QDRANT_COLLECTION_NAME>)
+    .option("embedding_field", <EMBEDDING_FIELD_NAME>)
+    .option("schema", dataframe.schema.json())
+    .mode("append")
+    .save()
+```
+
+* By default, UUIDs are generated for each row. If you need to use custom IDs, you can do so by setting the `id_field` option.
+* An API key can be set using the `api_key` option to make authenticated requests.
+
+## Datatype support 📋
+
+Qdrant supports all the Spark data types, and the appropriate types are mapped based on the provided `schema`.
+
+## Options 🛠️
+
+| Option | Description     | Required      |
+| :-------- | :------- | :------------|
+| `qdrant_url` | `string` REST URL of the Qdrant instance | ✅ |
+| `collection_name` | `string` Name of the collection to write data into | ✅ |
+| `embedding_field` | `string` Name of the field holding the embeddings | ✅ |
+| `id_field` | `string` Name of the field holding the point IDs | ✅ |
+| `schema` | `string` JSON string of the dataframe schema | ✅ |
+| `mode` | `string` Write mode of the dataframe | ✅ |
+| `batch_size` | `int` Max size of the upload batch. Default: 100 | ❌ |
+| `retries` | `string` Number of upload retries. Default: 3 | ❌ |
+| `api_key` | `string` API key to be sent in the header. Default: null | ❌ |
+
+## LICENSE 📜
+
+Apache 2.0 © [2023](https://github.com/qdrant/qdrant-spark)