Test Edge code snippets (#2179)

* Add Edge and FastEmbed as Python dependencies

* Make Edge code snippets testable

qdrant-landing/content/documentation/headless/snippets/edge/fastembed/python.py

@@ -0,0 +1,87 @@
+# @block-start download-models
+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
+)
+# @block-end download-models
+
+# @block-start initialize-edge-shard
+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)
+# @block-end initialize-edge-shard
+
+# @block-start embed-and-store-image
+from pathlib import Path
+from qdrant_edge import Point, UpdateOperation
+import uuid
+
+IMAGES_DIR = "images"
+
+image_model = ImageEmbedding(
+    model_name=VISION_MODEL_NAME,
+    cache_dir=MODELS_DIR,
+    local_files_only=True
+)
+
+embeddings = list(image_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]))
+# @block-end embed-and-store-image
+
+# @block-start query-with-text-embedding
+from qdrant_edge import Query, QueryRequest
+
+text_model = TextEmbedding(
+    model_name=TEXT_MODEL_NAME,
+    cache_dir=MODELS_DIR,
+    local_files_only=True
+)
+
+embeddings = list(text_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
+    )
+)
+# @block-end query-with-text-embedding