add embeddings

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

@@ -0,0 +1,54 @@
+
+---
+title: OCI (Oracle Cloud Infrastructure)
+weight: 2500
+aliases:
+  - /documentation/examples/oci-(oracle-cloud-infrastructure)-search/
+  - /documentation/tutorials/oci-(oracle-cloud-infrastructure)-search/
+  - /documentation/integrations/oci-(oracle-cloud-infrastructure)/ 
+---
+
+# Using OCI (Oracle Cloud Infrastructure) with Qdrant 
+
+OCI provides robust cloud-based embeddings for various media types. The Generative AI Embedding Models convert textual input - ranging from phrases and sentences to entire paragraphs - into a structured format known as embeddings. Each piece of text input is transformed into a numerical array consisting of 1024 distinct numbers.
+
+## Installation
+
+You can install the required package using the following pip command:
+
+```bash
+pip install oci
+```
+
+## Code Example
+
+Below is an example of how to obtain embeddings using OCI (Oracle Cloud Infrastructure)'s API and store them in a Qdrant collection:
+
+```python
+import qdrant_client
+from qdrant_client.models import Batch
+import oci
+
+# Initialize OCI client
+config = oci.config.from_file()
+ai_client = oci.ai_language.AIServiceLanguageClient(config)
+
+# Generate embeddings using OCI's AI service
+text = "OCI provides cloud-based AI services."
+response = ai_client.batch_detect_language_entities(text)
+embeddings = response.data[0].entities[0].embedding
+
+# Initialize Qdrant client
+qdrant_client = qdrant_client.QdrantClient(host="localhost", port=6333)
+
+# Upsert the embedding into Qdrant
+qdrant_client.upsert(
+    collection_name="CloudAI",
+    points=Batch(
+        ids=[1],
+        vectors=[embeddings],
+    )
+)
+
+```
+