[AUR-389] Add base interface and embedding model (#17)

This change provides the base interface of an embedding, and wrap the Langchain's OpenAI embedding. Usage as follow:

```python
from kotaemon.embeddings import AzureOpenAIEmbeddings

model = AzureOpenAIEmbeddings(
    model="text-embedding-ada-002",
    deployment="embedding-deployment",
    openai_api_base="https://test.openai.azure.com/",
    openai_api_key="some-key",
)
output = model("Hello world")
```

tests/test_embedding_models.py

@@ -0,0 +1,46 @@
+import json
+from pathlib import Path
+from unittest.mock import patch
+
+from kotaemon.embeddings.openai import AzureOpenAIEmbeddings
+
+with open(Path(__file__).parent / "resources" / "embedding_openai_batch.json") as f:
+    openai_embedding_batch = json.load(f)
+
+with open(Path(__file__).parent / "resources" / "embedding_openai.json") as f:
+    openai_embedding = json.load(f)
+
+
+@patch(
+    "openai.api_resources.embedding.Embedding.create",
+    side_effect=lambda *args, **kwargs: openai_embedding,
+)
+def test_azureopenai_embeddings_raw(openai_embedding_call):
+    model = AzureOpenAIEmbeddings(
+        model="text-embedding-ada-002",
+        deployment="embedding-deployment",
+        openai_api_base="https://test.openai.azure.com/",
+        openai_api_key="some-key",
+    )
+    output = model("Hello world")
+    assert isinstance(output, list)
+    assert isinstance(output[0], float)
+    openai_embedding_call.assert_called()
+
+
+@patch(
+    "openai.api_resources.embedding.Embedding.create",
+    side_effect=lambda *args, **kwargs: openai_embedding_batch,
+)
+def test_azureopenai_embeddings_batch_raw(openai_embedding_call):
+    model = AzureOpenAIEmbeddings(
+        model="text-embedding-ada-002",
+        deployment="embedding-deployment",
+        openai_api_base="https://test.openai.azure.com/",
+        openai_api_key="some-key",
+    )
+    output = model(["Hello world", "Goodbye world"])
+    assert isinstance(output, list)
+    assert isinstance(output[0], list)
+    assert isinstance(output[0][0], float)
+    openai_embedding_call.assert_called()