Keep the code coverage high (#80)

We still need to add unit tests for OpenAI, will add them in a separate
patch.

pyproject.toml

@@ -23,6 +23,7 @@ bandit = ">=1.7.10"
 build = ">=1.0.0"
 wheel = ">=0.40.0"
 litellm = ">=1.52.11"
+pytest-asyncio = "0.24.0"
 
 [build-system]
 requires = ["poetry-core"]

src/codegate/providers/litellmshim/generators.py

@@ -1,6 +1,8 @@
 import json
 from typing import Any, AsyncIterator
 
+from pydantic import BaseModel
+
 # Since different providers typically use one of these formats for streaming
 # responses, we have a single stream generator for each format that is then plugged
 # into the adapter.
@@ -10,7 +12,9 @@ async def sse_stream_generator(stream: AsyncIterator[Any]) -> AsyncIterator[str]
     """OpenAI-style SSE format"""
     try:
         async for chunk in stream:
-            if hasattr(chunk, "model_dump_json"):
+            if isinstance(chunk, BaseModel):
+                # alternatively we might want to just dump the whole object
+                # this might even allow us to tighten the typing of the stream
                 chunk = chunk.model_dump_json(exclude_none=True, exclude_unset=True)
             try:
                 yield f"data:{chunk}\n\n"

src/codegate/providers/litellmshim/litellmshim.py

@@ -14,8 +14,9 @@ class LiteLLmShim(BaseCompletionHandler):
     LiteLLM API.
     """
 
-    def __init__(self, adapter: BaseAdapter):
+    def __init__(self, adapter: BaseAdapter, completion_func=acompletion):
         self._adapter = adapter
+        self._completion_func = completion_func
 
     async def complete(self, data: Dict, api_key: str) -> AsyncIterator[Any]:
         """
@@ -28,7 +29,7 @@ async def complete(self, data: Dict, api_key: str) -> AsyncIterator[Any]:
         if completion_request is None:
             raise Exception("Couldn't translate the request")
 
-        response = await acompletion(**completion_request)
+        response = await self._completion_func(**completion_request)
 
         if isinstance(response, ModelResponse):
             return self._adapter.translate_completion_output_params(response)

tests/providers/anthropic/test_adapter.py

@@ -0,0 +1,154 @@
+from typing import AsyncIterator, Dict, List, Union
+
+import pytest
+from litellm import ModelResponse
+from litellm.adapters.anthropic_adapter import AnthropicStreamWrapper
+from litellm.types.llms.anthropic import (
+    ContentBlockDelta,
+    ContentBlockStart,
+    ContentTextBlockDelta,
+    MessageChunk,
+    MessageStartBlock,
+)
+from litellm.types.utils import Delta, StreamingChoices
+
+from codegate.providers.anthropic.adapter import AnthropicAdapter
+
+
+@pytest.fixture
+def adapter():
+    return AnthropicAdapter()
+
+def test_translate_completion_input_params(adapter):
+    # Test input data
+    completion_request = {
+        "model": "claude-3-haiku-20240307",
+        "max_tokens": 1024,
+        "stream": True,
+        "messages": [
+         {
+           "role": "user",
+           "system": "You are an expert code reviewer",
+           "content": [
+             {
+               "type": "text",
+               "text": "Review this code"
+             }
+           ]
+         }
+       ]
+    }
+    expected = {
+        'max_tokens': 1024,
+        'messages': [
+            {'content': [{'text': 'Review this code', 'type': 'text'}], 'role': 'user'}
+        ],
+        'model': 'claude-3-haiku-20240307',
+        'stream': True
+    }
+
+    # Get translation
+    result = adapter.translate_completion_input_params(completion_request)
+    assert result == expected
+
+@pytest.mark.asyncio
+async def test_translate_completion_output_params_streaming(adapter):
+    # Test stream data
+    async def mock_stream():
+        messages = [
+            ModelResponse(
+                id="test_id_1",
+                choices=[
+                    StreamingChoices(
+                        finish_reason=None,
+                        index=0,
+                        delta=Delta(content="Hello", role="assistant")),
+                ],
+                model="claude-3-haiku-20240307",
+            ),
+            ModelResponse(
+                id="test_id_2",
+                choices=[
+                    StreamingChoices(finish_reason=None,
+                                     index=0,
+                                     delta=Delta(content="world", role="assistant")),
+                ],
+                model="claude-3-haiku-20240307",
+            ),
+            ModelResponse(
+                id="test_id_2",
+                choices=[
+                    StreamingChoices(finish_reason=None,
+                                     index=0,
+                                     delta=Delta(content="!", role="assistant")),
+                ],
+                model="claude-3-haiku-20240307",
+            ),
+        ]
+        for msg in messages:
+            yield msg
+
+    expected: List[Union[MessageStartBlock,ContentBlockStart,ContentBlockDelta]] = [
+        MessageStartBlock(
+            type="message_start",
+            message=MessageChunk(
+                id="msg_1nZdL29xx5MUA1yADyHTEsnR8uuvGzszyY",
+                type="message",
+                role="assistant",
+                content=[],
+                # litellm makes up a message start block with hardcoded values
+                model="claude-3-5-sonnet-20240620",
+                stop_reason=None,
+                stop_sequence=None,
+                usage={"input_tokens": 25, "output_tokens": 1},
+            ),
+        ),
+        ContentBlockStart(
+            type="content_block_start",
+            index=0,
+            content_block={"type": "text", "text": ""},
+        ),
+        ContentBlockDelta(
+            type="content_block_delta",
+            index=0,
+            delta=ContentTextBlockDelta(type="text_delta", text="Hello"),
+        ),
+        ContentBlockDelta(
+            type="content_block_delta",
+            index=0,
+            delta=ContentTextBlockDelta(type="text_delta", text="world"),
+        ),
+        ContentBlockDelta(
+            type="content_block_delta",
+            index=0,
+            delta=ContentTextBlockDelta(type="text_delta", text="!"),
+        ),
+        # litellm doesn't seem to have a type for message stop
+        dict(type="message_stop"),
+    ]
+
+    stream = adapter.translate_completion_output_params_streaming(mock_stream())
+    assert isinstance(stream, AnthropicStreamWrapper)
+
+    # just so that we can zip over the expected chunks
+    stream_list = [chunk async for chunk in stream]
+    # Verify we got all chunks
+    assert len(stream_list) == 6
+
+    for chunk, expected_chunk in zip(stream_list, expected):
+        assert chunk == expected_chunk
+
+
+def test_stream_generator_initialization(adapter):
+    # Verify the default stream generator is set
+    from codegate.providers.litellmshim import anthropic_stream_generator
+    assert adapter.stream_generator == anthropic_stream_generator
+
+def test_custom_stream_generator():
+    # Test that we can inject a custom stream generator
+    async def custom_generator(stream: AsyncIterator[Dict]) -> AsyncIterator[str]:
+        async for chunk in stream:
+            yield "custom: " + str(chunk)
+
+    adapter = AnthropicAdapter(stream_generator=custom_generator)
+    assert adapter.stream_generator == custom_generator

tests/providers/litellmshim/test_generators.py

@@ -0,0 +1,80 @@
+from typing import AsyncIterator
+
+import pytest
+from litellm import ModelResponse
+
+from codegate.providers.litellmshim import (
+    anthropic_stream_generator,
+    sse_stream_generator,
+)
+
+
+@pytest.mark.asyncio
+async def test_sse_stream_generator():
+    # Mock stream data
+    mock_chunks = [
+        ModelResponse(id="1", choices=[{"text": "Hello"}]),
+        ModelResponse(id="2", choices=[{"text": "World"}])
+    ]
+
+    async def mock_stream():
+        for chunk in mock_chunks:
+            yield chunk
+
+    # Collect generated SSE messages
+    messages = []
+    async for message in sse_stream_generator(mock_stream()):
+        messages.append(message)
+
+    # Verify format and content
+    assert len(messages) == len(mock_chunks) + 1  # +1 for the [DONE] message
+    assert all(msg.startswith("data:") for msg in messages)
+    assert "Hello" in messages[0]
+    assert "World" in messages[1]
+    assert messages[-1] == "data: [DONE]\n\n"
+
+@pytest.mark.asyncio
+async def test_anthropic_stream_generator():
+    # Mock Anthropic-style chunks
+    mock_chunks = [
+        {"type": "message_start", "message": {"id": "1"}},
+        {"type": "content_block_start", "content_block": {"text": "Hello"}},
+        {"type": "content_block_stop", "content_block": {"text": "World"}}
+    ]
+
+    async def mock_stream():
+        for chunk in mock_chunks:
+            yield chunk
+
+    # Collect generated SSE messages
+    messages = []
+    async for message in anthropic_stream_generator(mock_stream()):
+        messages.append(message)
+
+    # Verify format and content
+    assert len(messages) == 3
+    for msg, chunk in zip(messages, mock_chunks):
+        assert msg.startswith(f"event: {chunk['type']}\ndata:")
+    assert "Hello" in messages[1]  # content_block_start message
+    assert "World" in messages[2]  # content_block_stop message
+
+@pytest.mark.asyncio
+async def test_generators_error_handling():
+    async def error_stream() -> AsyncIterator[str]:
+        raise Exception("Test error")
+        yield  # This will never be reached, but is needed for AsyncIterator typing
+
+    # Test SSE generator error handling
+    messages = []
+    async for message in sse_stream_generator(error_stream()):
+        messages.append(message)
+    assert len(messages) == 2
+    assert "Test error" in messages[0]
+    assert messages[1] == "data: [DONE]\n\n"
+
+    # Test Anthropic generator error handling
+    messages = []
+    async for message in anthropic_stream_generator(error_stream()):
+        messages.append(message)
+    assert len(messages) == 1
+    assert "Test error" in messages[0]