Edge: Docs about on-device embeddings

qdrant-landing/content/documentation/edge/edge-fastembed-embeddings.md

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+---
+title: "On-Device Embeddings" 
+weight: 15
+---
+
+# On-Device Embeddings with Qdrant Edge and FastEmbed
+
+To generate embeddings for use with Qdrant Edge directly on a device, you can use the [FastEmbed](/documentation/fastembed/) library. FastEmbed provides multimodal models that run efficiently on edge devices to generate vector embeddings from text and images.
+
+# Provision the Device
+
+Assuming the devices on which you will run Qdrant Edge have intermittent or no internet connectivity, you need to provision them with the necessary dependencies and model files ahead of time. First, install FastEmbed and the Qdrant Edge Python bindings:
+
+```python
+pip install fastembed qdrant-edge-py
+```
+
+Next, download the embedding models and save them locally on the device. Instantiate instances of `ImageEmbedding` and `TextEmbedding`, setting the `cache_dir` parameter to a local directory:
+
+```python
+from fastembed import ImageEmbedding, TextEmbedding
+
+TEXT_MODEL_NAME='Qdrant/clip-ViT-B-32-text'
+VISION_MODEL_NAME='Qdrant/clip-ViT-B-32-vision'
+MODELS_DIR="./qdrant-edge-directory/models"
+
+ImageEmbedding(
+    model_name=VISION_MODEL_NAME, 
+    cache_dir=MODELS_DIR
+)
+
+TextEmbedding(
+    model_name=TEXT_MODEL_NAME, 
+    cache_dir=MODELS_DIR
+)
+```
+
+The models will be downloaded and cached in the specified `MODELS_DIR` directory, from where you can use them to generate embeddings.
+
+# Generate Image Embeddings
+
+First, initialize an Edge Shard as described in the [Qdrant Edge Quickstart Guide](/documentation/edge/edge-quickstart/).
+
+<details>
+<summary>Details</summary>
+
+```python
+from pathlib import Path
+from qdrant_edge import ( 
+    Distance, 
+    EdgeConfig,
+    EdgeShard, 
+    VectorDataConfig, 
+)
+
+SHARD_DIRECTORY = "./qdrant-edge-directory"
+VECTOR_DIMENSION = 512
+VECTOR_NAME="my-vector"
+
+Path(SHARD_DIRECTORY).mkdir(parents=True, exist_ok=True)
+config = EdgeConfig(
+    vector_data={
+        VECTOR_NAME: VectorDataConfig(
+            size=VECTOR_DIMENSION,
+            distance=Distance.Cosine,
+        )
+    }
+)
+
+edge_shard = EdgeShard(SHARD_DIRECTORY, config)
+```
+
+</details>
+
+Assuming you have an image file `temp.jpg`, you can generate an embedding for it using FastEmbed's `ImageEmbedding` class and then store it in the Edge Shard:
+
+```python
+from pathlib import Path
+from qdrant_edge import Point, UpdateOperation
+import uuid
+
+IMAGES_DIR = "images"
+
+model = ImageEmbedding(
+    model_name=VISION_MODEL_NAME, 
+    cache_dir=MODELS_DIR,
+    local_files_only=True
+)
+    
+embeddings = list(model.embed([Path(IMAGES_DIR) / "temp.jpg"]))[0]
+
+point = Point(
+    id=str(uuid.uuid4()), 
+    vector={VECTOR_NAME: embeddings.tolist()}
+)
+
+edge_shard.update(UpdateOperation.upsert_points([point]))
+```
+
+Note the use of `cache_dir=MODELS_DIR` and `local_files_only=True` to load the image embedding model from the local directory where it was previously downloaded.
+
+# Generate Text Embeddings
+
+At query time, you can generate text embeddings using FastEmbed's `TextEmbedding` class. For example, to query the Edge Shard:
+
+```python
+from qdrant_edge import Query, QueryRequest
+
+model = TextEmbedding(
+    model_name=TEXT_MODEL_NAME,
+    cache_dir=MODELS_DIR,
+    local_files_only=True
+)
+
+embeddings = list(model.embed(["<search terms>"]))[0]
+
+results = edge_shard.query(
+    QueryRequest(
+        query=Query.Nearest(embeddings.tolist(),using=VECTOR_NAME),
+        limit=10,
+        with_vector=False,
+        with_payload=True
+    )
+)
+```
+
+Again, using `cache_dir=MODELS_DIR` and `local_files_only=True` ensures the text embedding model is loaded from the local directory.