update readme with latest changes

README.md

@@ -16,56 +16,76 @@ You can install the client in your PHP project using composer:
 composer require hkulekci/qdrant
 ```
 
-An example to create a collection :
+### Connecting to Qdrant 
 
 ```php
-use Qdrant\Endpoints\Collections;
-use Qdrant\Http\GuzzleClient;
-use Qdrant\Models\Request\CreateCollection;
-use Qdrant\Models\Request\VectorParams;
-
 include __DIR__ . "/../vendor/autoload.php";
 include_once 'config.php';
 
-$config = new \Qdrant\Config(QDRANT_HOST);
+use Qdrant\Config;
+use Qdrant\Http\Builder;
+
+$config = new Config(QDRANT_HOST);
 $config->setApiKey(QDRANT_API_KEY);
 
-$client = new Qdrant(new GuzzleClient($config));
+$transport = (new Builder())->build($config);
+$client = new Qdrant($transport);
+```
+
+### Creating a Collection
+
+```php
+use Qdrant\Endpoints\Collections;
+use Qdrant\Models\Request\CreateCollection;
+use Qdrant\Models\Request\VectorParams;
 
 $createCollection = new CreateCollection();
-$createCollection->addVector(new VectorParams(1024, VectorParams::DISTANCE_COSINE), 'image');
-$response = $client->collections('images')->create($createCollection);
+$createCollection->addVector(new VectorParams(1536, VectorParams::DISTANCE_COSINE), 'content');
+$response = $client->collections('contents')->create($createCollection);
 ```
 
-So now, we can insert a point : 
+### Inserting Points Into Collection
 
 ```php
 use Qdrant\Models\PointsStruct;
 use Qdrant\Models\PointStruct;
 use Qdrant\Models\VectorStruct;
 
+$openai = OpenAI::client(OPENAI_API_KEY);
+
+$query = 'sustainable agricultural startups';
+$response = $openai->embeddings()->create([
+    'model' => 'text-embedding-ada-002',
+    'input' => $query,
+]);
+$embedding = array_values($response->embeddings[0]->embedding);
+
 $points = new PointsStruct();
 $points->addPoint(
     new PointStruct(
         (int) $imageId,
-        new VectorStruct($data['embeddings'][0], 'image'),
+        new VectorStruct($embedding, 'content'),
         [
             'id' => 1,
             'meta' => 'Meta data'
         ]
     )
 );
-$client->collections('images')->points()->upsert($points);
+$client->collections('contents')->points()->upsert($points);
 ```
 
-While upsert data, if you want to wait for upsert to actually happen, you can use query paramaters:
+### Wait for Acknowledges
 
-```
-$client->collections('images')->points()->upsert($points, ['wait' => 'true']);
+While upsert data, if you want to wait for upsert to actually happen, you can use query parameters:
+
+```php
+$client->collections('contents')->points()->upsert($points, ['wait' => 'true']);
 ```
 
 You can check for more parameters : https://qdrant.github.io/qdrant/redoc/index.html#tag/points/operation/upsert_points
 
+### Search on Points
+
 Search with a filter :
 
 ```php
@@ -87,5 +107,32 @@ $searchRequest = (new SearchRequest(new VectorStruct($embedding, 'elev_pitch')))
     ])
     ->setWithPayload(true);
 
-$response = $client->collections('images')->points()->search($searchRequest);
+$response = $client->collections('contents')->points()->search($searchRequest);
 ```
+
+### Search on Points with OpenAI Embeddings
+
+```php
+$openai = OpenAI::client(OPENAI_API_KEY);
+
+$query = 'lorem ipsum dolor sit amed';
+$response = $openai->embeddings()->create([
+    'model' => 'text-embedding-ada-002',
+    'input' => $query,
+]);
+$embedding = array_values($response->embeddings[0]->embedding);
+
+$searchRequest = (new SearchRequest(new VectorStruct($embedding, 'content')))
+    ->setLimit(10)
+    ->setParams([
+        'hnsw_ef' => 128,
+        'exact' => false,
+    ])
+    ->setWithPayload(true);
+
+$response = $client->collections('contents')->points()->search($searchRequest);
+
+foreach ($response['result'] as $item) {
+    echo $item['score'] . ';' . $item['payload']['id'] . ';' . $item['payload']['meta_data'] . PHP_EOL;
+}
+```