chore: import upstream snapshot with attribution

python/samples/concepts/embedding/text_embedding_generation.py

@@ -0,0 +1,61 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+
+from samples.concepts.setup.text_embedding_services import Services, get_text_embedding_service_and_request_settings
+
+"""
+This sample shows how to generating embeddings for text data. This sample uses the following component:
+- an text embedding generator: This component is responsible for generating embeddings for text data.
+"""
+
+# You can select from the following text embedding services:
+# - Services.OPENAI
+# - Services.AZURE_OPENAI
+# - Services.AZURE_AI_INFERENCE
+# - Services.BEDROCK
+# - Services.GOOGLE_AI
+# - Services.HUGGING_FACE
+# - Services.MISTRAL_AI
+# - Services.OLLAMA
+# - Services.VERTEX_AI
+# Please make sure you have configured your environment correctly for the selected text embedding service.
+text_embedding_service, request_settings = get_text_embedding_service_and_request_settings(Services.OPENAI)
+
+TEXTS = [
+    "A dog ran joyfully through the green field, chasing after butterflies in the warm afternoon sun.",
+    "A happy puppy sprinted across the grassy meadow, playfully pursuing insects under the bright sky.",
+]
+
+
+def cosine_similarity(a, b):
+    from scipy.spatial.distance import cosine
+
+    # Note that scipy.spatial.distance.cosine computes the cosine distance, which is 1 - cosine similarity.
+    # https://en.wikipedia.org/wiki/Cosine_similarity#Cosine_distance
+    return 1 - cosine(a, b)
+
+
+async def main() -> None:
+    # 1. Generate embeddings in batches.
+    embeddings = await text_embedding_service.generate_embeddings(TEXTS, request_settings)
+    print(embeddings)
+
+    # 2. Generate embeddings for a single text. Since the two texts are similar in meaning,
+    # the cosine similarity between the two embeddings should be high.
+    embedding_a = await text_embedding_service.generate_embeddings([TEXTS[0]], request_settings)
+    embedding_b = await text_embedding_service.generate_embeddings([TEXTS[1]], request_settings)
+    print(f"Similarity between the two texts: {cosine_similarity(embedding_a[0], embedding_b[0])}")
+
+    """
+    Sample output:
+    [[ 0.02221295 -0.00633203  0.00067574 ... -0.00513578 -0.0314321
+      -0.02128683]
+    [-0.00864875  0.02254905 -0.00182191 ...  0.01043635 -0.00777349
+      -0.02256389]]
+    Similarity between the two texts: 0.7263079790609065
+    """
+
+
+if __name__ == "__main__":
+    asyncio.run(main())