More precise testing OpenAILike (run-llama#9026)

Co-authored-by: Logan <[email protected]>

CHANGELOG.md

@@ -4,6 +4,7 @@
 
 ### New Features
 
+- More precise testing of `OpenAILike` (#9026)
 - Added callback manager to each retriever (#8871)
 
 ### Bug Fixes / Nits

tests/llms/test_openai_like.py

@@ -2,29 +2,34 @@
 from unittest.mock import MagicMock, patch
 
 from llama_index.llms import OpenAILike
-from llama_index.llms.base import ChatMessage
+from llama_index.llms.base import ChatMessage, MessageRole
+from llama_index.llms.openai import Tokenizer
 from openai.types import Completion, CompletionChoice
 from openai.types.chat.chat_completion import ChatCompletion, Choice
 from openai.types.chat.chat_completion_message import ChatCompletionMessage
 
 
-class MockTokenizer:
-    def encode(self, text: str) -> List[str]:
-        return text.split(" ")
+class StubTokenizer(Tokenizer):
+    def encode(self, text: str) -> List[int]:
+        return [sum(ord(letter) for letter in word) for word in text.split(" ")]
+
+
+STUB_MODEL_NAME = "models/stub.gguf"
+STUB_API_KEY = "stub_key"
 
 
 def test_interfaces() -> None:
-    llm = OpenAILike(model="placeholder")
+    llm = OpenAILike(model=STUB_MODEL_NAME, api_key=STUB_API_KEY)
     assert llm.class_name() == type(llm).__name__
-    assert llm.model == "placeholder"
+    assert llm.model == STUB_MODEL_NAME
 
 
 def mock_chat_completion(text: str) -> ChatCompletion:
     return ChatCompletion(
         id="chatcmpl-abc123",
         object="chat.completion",
         created=1677858242,
-        model="gpt-3.5-turbo-0301",
+        model=STUB_MODEL_NAME,
         usage={"prompt_tokens": 13, "completion_tokens": 7, "total_tokens": 20},
         choices=[
             Choice(
@@ -38,10 +43,10 @@ def mock_chat_completion(text: str) -> ChatCompletion:
 
 def mock_completion(text: str) -> Completion:
     return Completion(
-        id="chatcmpl-abc123",
+        id="cmpl-abc123",
         object="text_completion",
         created=1677858242,
-        model="gpt-3.5-turbo-0301",
+        model=STUB_MODEL_NAME,
         usage={"prompt_tokens": 13, "completion_tokens": 7, "total_tokens": 20},
         choices=[
             CompletionChoice(
@@ -61,14 +66,21 @@ def test_completion(MockSyncOpenAI: MagicMock) -> None:
     mock_instance.completions.create.return_value = mock_completion(text)
 
     llm = OpenAILike(
-        model="placeholder",
+        model=STUB_MODEL_NAME,
         is_chat_model=False,
         context_window=1024,
-        tokenizer=MockTokenizer(),
+        tokenizer=StubTokenizer(),
     )
 
     response = llm.complete("A long time ago in a galaxy far, far away")
     assert response.text == text
+    mock_instance.completions.create.assert_called_once_with(
+        prompt="A long time ago in a galaxy far, far away",
+        stream=False,
+        model=STUB_MODEL_NAME,
+        temperature=0.1,
+        max_tokens=1014,
+    )
 
 
 @patch("llama_index.llms.openai.SyncOpenAI")
@@ -79,22 +91,32 @@ def test_chat(MockSyncOpenAI: MagicMock) -> None:
     mock_instance.chat.completions.create.return_value = mock_chat_completion(content)
 
     llm = OpenAILike(
-        model="models/placeholder", is_chat_model=True, tokenizer=MockTokenizer()
+        model=STUB_MODEL_NAME, is_chat_model=True, tokenizer=StubTokenizer()
     )
 
-    response = llm.chat([ChatMessage(role="user", content="test message")])
+    response = llm.chat([ChatMessage(role=MessageRole.USER, content="test message")])
     assert response.message.content == content
+    mock_instance.chat.completions.create.assert_called_once_with(
+        messages=[{"role": MessageRole.USER, "content": "test message"}],
+        stream=False,
+        model=STUB_MODEL_NAME,
+        temperature=0.1,
+    )
 
 
 def test_serialization() -> None:
     llm = OpenAILike(
-        model="placeholder",
+        model=STUB_MODEL_NAME,
         is_chat_model=True,
-        context_window=42,
-        tokenizer=MockTokenizer(),
+        max_tokens=42,
+        context_window=43,
+        tokenizer=StubTokenizer(),
     )
 
     serialized = llm.to_dict()
-
+    # Check OpenAI base class specifics
+    assert "api_key" not in serialized
+    assert serialized["max_tokens"] == 42
+    # Check OpenAILike subclass specifics
+    assert serialized["context_window"] == 43
     assert serialized["is_chat_model"]
-    assert serialized["context_window"] == 42