docs: Added Bedrock embeddings examples (#518)

* docs: Added Bedrock embeddings example

* Update qdrant-landing/content/documentation/embeddings/bedrock.md

Co-authored-by: Mike Jang <michael@linuxexam.com>

* docs: Update bedrock.md

---------

Co-authored-by: Mike Jang <michael@linuxexam.com>

qdrant-landing/content/documentation/embeddings/bedrock.md

@@ -0,0 +1,114 @@
+---
+title: AWS Bedrock
+weight: 1000
+---
+
+# Bedrock Embeddings
+
+You can use [AWS Bedrock](https://aws.amazon.com/bedrock/) with Qdrant. AWS Bedrock supports multiple [embedding model providers](https://docs.aws.amazon.com/bedrock/latest/userguide/models-supported.html).
+
+You'll need the following information from your AWS account:
+
+- Region
+- Access key ID
+- Secret key
+
+To configure your credentials, review the following AWS article: [How do I create an AWS access key](https://repost.aws/knowledge-center/create-access-key)."
+
+With the following code sample, you can generate embeddings using the [Titan Embeddings G1 - Text model](https://docs.aws.amazon.com/bedrock/latest/userguide/titan-embedding-models.html) which produces sentence embeddings of size 1536.
+
+```python
+# Install the required dependencies
+# pip install boto3 qdrant_client
+
+import json
+import boto3
+
+from qdrant_client import QdrantClient, models
+
+session = boto3.Session()
+
+bedrock_client = session.client(
+    "bedrock-runtime",
+    region_name="<YOUR_AWS_REGION>",
+    aws_access_key_id="<YOUR_AWS_ACCESS_KEY_ID>",
+    aws_secret_access_key="<YOUR_AWS_SECRET_KEY>",
+)
+
+qdrant_client = QdrantClient(location="http://localhost:6333")
+
+qdrant_client.create_collection(
+    "{collection_name}",
+    vectors_config=models.VectorParams(size=1536, distance=models.Distance.COSINE),
+)
+
+body = json.dumps({"inputText": "Some text to generate embeddings for"})
+
+response = bedrock_client.invoke_model(
+    body=body,
+    modelId="amazon.titan-embed-text-v1",
+    accept="application/json",
+    contentType="application/json",
+)
+
+response_body = json.loads(response.get("body").read())
+
+qdrant_client.upsert(
+    "{collection_name}",
+    points=[models.PointStruct(id=1, vector=response_body["embedding"])],
+)
+```
+
+```javascript
+// Install the required dependencies
+// npm install @aws-sdk/client-bedrock-runtime @qdrant/js-client-rest
+
+import {
+    BedrockRuntimeClient,
+    InvokeModelCommand,
+} from "@aws-sdk/client-bedrock-runtime";
+import { QdrantClient } from '@qdrant/js-client-rest';
+
+const main = async () => {
+    const bedrockClient = new BedrockRuntimeClient({
+        region: "<YOUR_AWS_REGION>",
+        credentials: {
+            accessKeyId: "<YOUR_AWS_ACCESS_KEY_ID>",,
+            secretAccessKey: "<YOUR_AWS_SECRET_KEY>",
+        },
+    });
+
+    const qdrantClient = new QdrantClient({ url: 'http://localhost:6333' });
+
+    await qdrantClient.createCollection("{collection_name}", {
+        vectors: {
+            size: 1536,
+            distance: 'Cosine',
+        }
+    });
+
+    const response = await bedrockClient.send(
+        new InvokeModelCommand({
+            modelId: "amazon.titan-embed-text-v1",
+            body: JSON.stringify({
+                inputText: "Some text to generate embeddings for",
+            }),
+            contentType: "application/json",
+            accept: "application/json",
+        })
+    );
+
+    const body = new TextDecoder().decode(response.body);
+
+    await qdrantClient.upsert("{collection_name}", {
+        points: [
+            {
+                id: 1,
+                vector: JSON.parse(body).embedding,
+            },
+        ],
+    });
+}
+
+main();
+```