WIP: PAIN

tests/test_deepseek_provider.py

@@ -1,5 +1,7 @@
 from unittest.mock import patch, MagicMock
-import ai_client
+from src import ai_client
+import json
+import pytest
 
 def test_deepseek_model_selection() -> None:
  """
@@ -9,117 +11,104 @@ def test_deepseek_model_selection() -> None:
  assert ai_client._provider == "deepseek"
  assert ai_client._model == "deepseek-chat"
 
-def test_deepseek_completion_logic() -> None:
+@patch("requests.post")
+def test_deepseek_completion_logic(mock_post: MagicMock) -> None:
  """
     Verifies that ai_client.send() correctly calls the DeepSeek API and returns content.
     """
  ai_client.set_provider("deepseek", "deepseek-chat")
- with patch("requests.post") as mock_post:
+ with patch("src.ai_client._load_credentials", return_value={"deepseek": {"api_key": "test-key"}}):
   mock_response = MagicMock()
   mock_response.status_code = 200
   mock_response.json.return_value = {
-   "choices": [{
-     "message": {"role": "assistant", "content": "DeepSeek Response"},
-     "finish_reason": "stop"
-    }],
-   "usage": {"prompt_tokens": 10, "completion_tokens": 5}
+   "choices": [{"message": {"content": "Hello World"}, "finish_reason": "stop"}]
   }
   mock_post.return_value = mock_response
-  result = ai_client.send(md_content="Context", user_message="Hello", base_dir=".")
-  assert result == "DeepSeek Response"
+  
+  result = ai_client.send(md_content="Context", user_message="Hi", base_dir=".")
+  assert result == "Hello World"
   assert mock_post.called
 
-def test_deepseek_reasoning_logic() -> None:
+@patch("requests.post")
+def test_deepseek_reasoning_logic(mock_post: MagicMock) -> None:
  """
     Verifies that reasoning_content is captured and wrapped in <thinking> tags.
     """
  ai_client.set_provider("deepseek", "deepseek-reasoner")
- with patch("requests.post") as mock_post:
+ with patch("src.ai_client._load_credentials", return_value={"deepseek": {"api_key": "test-key"}}):
   mock_response = MagicMock()
   mock_response.status_code = 200
   mock_response.json.return_value = {
    "choices": [{
-     "message": {
-      "role": "assistant", 
-      "content": "Final Answer", 
-      "reasoning_content": "Chain of thought"
-     },
-     "finish_reason": "stop"
-    }],
-   "usage": {"prompt_tokens": 10, "completion_tokens": 20}
+    "message": {"content": "Final answer", "reasoning_content": "Chain of thought"},
+    "finish_reason": "stop"
+   }]
   }
   mock_post.return_value = mock_response
-  result = ai_client.send(md_content="Context", user_message="Reasoning test", base_dir=".")
+  
+  result = ai_client.send(md_content="Context", user_message="Hi", base_dir=".")
   assert "<thinking>\nChain of thought\n</thinking>" in result
-  assert "Final Answer" in result
+  assert "Final answer" in result
 
-def test_deepseek_tool_calling() -> None:
+@patch("requests.post")
+def test_deepseek_tool_calling(mock_post: MagicMock) -> None:
  """
     Verifies that DeepSeek provider correctly identifies and executes tool calls.
     """
  ai_client.set_provider("deepseek", "deepseek-chat")
- with patch("requests.post") as mock_post, \
- patch("mcp_client.dispatch") as mock_dispatch:
- # 1. Mock first response with a tool call
+ with patch("src.ai_client._load_credentials", return_value={"deepseek": {"api_key": "test-key"}}), \
+   patch("src.mcp_client.dispatch") as mock_dispatch:
+  
+  # Round 1: Model calls a tool
   mock_resp1 = MagicMock()
   mock_resp1.status_code = 200
   mock_resp1.json.return_value = {
    "choices": [{
-     "message": {
-      "role": "assistant", 
-      "content": "Let me read that file.",
-      "tool_calls": [{
-        "id": "call_123",
-        "type": "function",
-        "function": {
-         "name": "read_file",
-         "arguments": '{"path": "test.txt"}'
-        }
-       }]
-     },
-     "finish_reason": "tool_calls"
-    }],
-   "usage": {"prompt_tokens": 50, "completion_tokens": 10}
+    "message": {
+     "content": "I will read the file",
+     "tool_calls": [{
+      "id": "call_1",
+      "type": "function",
+      "function": {"name": "read_file", "arguments": '{"path": "test.txt"}'}
+     }]
+    },
+    "finish_reason": "tool_calls"
+   }]
   }
-  # 2. Mock second response (final answer)
+  
+  # Round 2: Model provides final answer
   mock_resp2 = MagicMock()
   mock_resp2.status_code = 200
   mock_resp2.json.return_value = {
-   "choices": [{
-     "message": {
-      "role": "assistant", 
-      "content": "File content is: Hello World"
-     },
-     "finish_reason": "stop"
-    }],
-   "usage": {"prompt_tokens": 100, "completion_tokens": 20}
+   "choices": [{"message": {"content": "File content is: Hello World"}, "finish_reason": "stop"}]
   }
+  
   mock_post.side_effect = [mock_resp1, mock_resp2]
   mock_dispatch.return_value = "Hello World"
+  
   result = ai_client.send(md_content="Context", user_message="Read test.txt", base_dir=".")
   assert "File content is: Hello World" in result
   assert mock_dispatch.called
-  assert mock_dispatch.call_args[0][0] == "read_file"
-  assert mock_dispatch.call_args[0][1] == {"path": "test.txt"}
+  mock_dispatch.assert_called_with("read_file", {"path": "test.txt"})
 
-def test_deepseek_streaming() -> None:
+@patch("requests.post")
+def test_deepseek_streaming(mock_post: MagicMock) -> None:
  """
     Verifies that DeepSeek provider correctly aggregates streaming chunks.
     """
  ai_client.set_provider("deepseek", "deepseek-chat")
- with patch("requests.post") as mock_post:
- # Mock a streaming response
+ with patch("src.ai_client._load_credentials", return_value={"deepseek": {"api_key": "test-key"}}):
   mock_response = MagicMock()
   mock_response.status_code = 200
-  # Simulate OpenAI-style server-sent events (SSE) for streaming
-  # Each line starts with 'data: ' and contains a JSON object
+  
+  # Mocking an iterable response for stream=True
   chunks = [
-   'data: {"choices": [{"delta": {"role": "assistant", "content": "Hello"}, "index": 0, "finish_reason": null}]}',
-   'data: {"choices": [{"delta": {"content": " World"}, "index": 0, "finish_reason": null}]}',
-   'data: {"choices": [{"delta": {}, "index": 0, "finish_reason": "stop"}]}',
-   'data: [DONE]'
+   'data: {"choices": [{"delta": {"content": "Hello "}}]}\n',
+   'data: {"choices": [{"delta": {"content": "World"}}]}\n',
+   'data: [DONE]\n'
   ]
   mock_response.iter_lines.return_value = [c.encode('utf-8') for c in chunks]
   mock_post.return_value = mock_response
+  
   result = ai_client.send(md_content="Context", user_message="Stream test", base_dir=".", stream=True)
   assert result == "Hello World"