add embeddings

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

@@ -0,0 +1,51 @@
+
+---
+title: MixedBread
+weight: 2200
+aliases:
+  - /documentation/examples/mixedbread-search/
+  - /documentation/tutorials/mixedbread-search/
+  - /documentation/integrations/mixedbread/ 
+---
+
+# Using MixedBread with Qdrant 
+
+MixedBread is a unique provider offering embeddings across multiple domains. Their models are versatile for various search tasks when integrated with Qdrant. MixedBread is creating state-of-the-art models and tools that make search smarter, faster, and more relevant. Whether you're building a next-gen search engine or RAG (Retrieval Augmented Generation) systems, or whether you're enhancing your existing search solution, they've got the ingredients to make it happen.
+
+## Installation
+
+You can install the required package using the following pip command:
+
+```bash
+pip install mixedbread
+```
+
+## Integration Example
+
+Below is an example of how to obtain embeddings using MixedBread's API and store them in a Qdrant collection:
+
+```python
+import qdrant_client
+from qdrant_client.models import Batch
+from mixedbread import MixedBreadModel
+
+# Initialize MixedBread model
+model = MixedBreadModel("mixedbread-variant")
+
+# Generate embeddings
+text = "MixedBread provides versatile embeddings for various domains."
+embeddings = model.embed(text)
+
+# Initialize Qdrant client
+qdrant_client = qdrant_client.QdrantClient(host="localhost", port=6333)
+
+# Upsert the embedding into Qdrant
+qdrant_client.upsert(
+    collection_name="VersatileEmbeddings",
+    points=Batch(
+        ids=[1],
+        vectors=[embeddings],
+    )
+)
+
+```