Fix #275: Make openai dataflow compatible with Gemini and OpenRouter

tests/__init__.py

@@ -0,0 +1,2 @@
+# Tests for TradingAgents
+

tests/test_openai_dataflow.py

@@ -0,0 +1,135 @@
+"""
+Tests for openai dataflow module to ensure compatibility with different LLM providers.
+This test reproduces issue #275 where Gemini and OpenRouter fail with openai vendor.
+"""
+import pytest
+from unittest.mock import Mock, patch, MagicMock
+from tradingagents.dataflows.openai import (
+    get_stock_news_openai,
+    get_global_news_openai,
+    get_fundamentals_openai
+)
+
+
+class TestOpenAIDataflowCompatibility:
+    """Test that openai dataflow functions work with different LLM providers."""
+
+    @pytest.fixture
+    def mock_config_openai(self):
+        """Mock config for OpenAI provider."""
+        return {
+            "backend_url": "https://api.openai.com/v1",
+            "quick_think_llm": "gpt-4o-mini",
+            "llm_provider": "openai"
+        }
+
+    @pytest.fixture
+    def mock_config_gemini(self):
+        """Mock config for Google Gemini provider."""
+        return {
+            "backend_url": "https://generativelanguage.googleapis.com/v1",
+            "quick_think_llm": "gemini-2.0-flash",
+            "llm_provider": "google"
+        }
+
+    @pytest.fixture
+    def mock_config_openrouter(self):
+        """Mock config for OpenRouter provider."""
+        return {
+            "backend_url": "https://openrouter.ai/api/v1",
+            "quick_think_llm": "deepseek/deepseek-chat-v3-0324:free",
+            "llm_provider": "openrouter"
+        }
+
+    @patch('tradingagents.dataflows.openai.get_config')
+    @patch('tradingagents.dataflows.openai.OpenAI')
+    def test_get_global_news_with_openai(self, mock_openai_class, mock_get_config, mock_config_openai):
+        """Test get_global_news_openai works with OpenAI provider."""
+        mock_get_config.return_value = mock_config_openai
+
+        # Mock the OpenAI client and response
+        mock_client = Mock()
+        mock_openai_class.return_value = mock_client
+
+        # Mock chat completion response (standard API)
+        mock_response = Mock()
+        mock_response.choices = [Mock()]
+        mock_response.choices[0].message.content = "Test news content"
+        mock_client.chat.completions.create.return_value = mock_response
+
+        # Call the function
+        result = get_global_news_openai("2024-11-09", 7, 5)
+
+        # Verify it was called
+        assert mock_client.chat.completions.create.called
+        assert result == "Test news content"
+
+    @patch('tradingagents.dataflows.openai.get_config')
+    @patch('tradingagents.dataflows.openai.OpenAI')
+    def test_get_global_news_with_gemini(self, mock_openai_class, mock_get_config, mock_config_gemini):
+        """Test get_global_news_openai works with Gemini provider (via OpenAI-compatible API)."""
+        mock_get_config.return_value = mock_config_gemini
+
+        # Mock the OpenAI client and response
+        mock_client = Mock()
+        mock_openai_class.return_value = mock_client
+
+        # Mock chat completion response
+        mock_response = Mock()
+        mock_response.choices = [Mock()]
+        mock_response.choices[0].message.content = "Test Gemini news content"
+        mock_client.chat.completions.create.return_value = mock_response
+
+        # Call the function - should not raise an error
+        result = get_global_news_openai("2024-11-09", 7, 5)
+
+        # Verify it was called with standard chat completion API
+        assert mock_client.chat.completions.create.called
+        assert result == "Test Gemini news content"
+
+    @patch('tradingagents.dataflows.openai.get_config')
+    @patch('tradingagents.dataflows.openai.OpenAI')
+    def test_get_global_news_with_openrouter(self, mock_openai_class, mock_get_config, mock_config_openrouter):
+        """Test get_global_news_openai works with OpenRouter provider."""
+        mock_get_config.return_value = mock_config_openrouter
+
+        # Mock the OpenAI client and response
+        mock_client = Mock()
+        mock_openai_class.return_value = mock_client
+
+        # Mock chat completion response
+        mock_response = Mock()
+        mock_response.choices = [Mock()]
+        mock_response.choices[0].message.content = "Test OpenRouter news content"
+        mock_client.chat.completions.create.return_value = mock_response
+
+        # Call the function - should not raise an error
+        result = get_global_news_openai("2024-11-09", 7, 5)
+
+        # Verify it was called with standard chat completion API
+        assert mock_client.chat.completions.create.called
+        assert result == "Test OpenRouter news content"
+
+    @patch('tradingagents.dataflows.openai.get_config')
+    @patch('tradingagents.dataflows.openai.OpenAI')
+    def test_get_fundamentals_with_different_providers(self, mock_openai_class, mock_get_config, mock_config_gemini):
+        """Test get_fundamentals_openai works with different providers."""
+        mock_get_config.return_value = mock_config_gemini
+
+        # Mock the OpenAI client and response
+        mock_client = Mock()
+        mock_openai_class.return_value = mock_client
+
+        # Mock chat completion response
+        mock_response = Mock()
+        mock_response.choices = [Mock()]
+        mock_response.choices[0].message.content = "Test fundamentals data"
+        mock_client.chat.completions.create.return_value = mock_response
+
+        # Call the function
+        result = get_fundamentals_openai("AAPL", "2024-11-09")
+
+        # Verify it was called
+        assert mock_client.chat.completions.create.called
+        assert result == "Test fundamentals data"
+

tradingagents/dataflows/openai.py

@@ -3,105 +3,110 @@
 
 
 def get_stock_news_openai(query, start_date, end_date):
+    """
+    Retrieve stock news using LLM provider configured in backend_url.
+    Compatible with OpenAI, Gemini (via OpenAI-compatible API), and OpenRouter.
+
+    Args:
+        query: Stock ticker or search query
+        start_date: Start date for news search
+        end_date: End date for news search
+
+    Returns:
+        str: News content as text
+    """
     config = get_config()
     client = OpenAI(base_url=config["backend_url"])
 
-    response = client.responses.create(
+    # Use standard chat completions API for compatibility with all providers
+    response = client.chat.completions.create(
         model=config["quick_think_llm"],
-        input=[
+        messages=[
             {
                 "role": "system",
-                "content": [
-                    {
-                        "type": "input_text",
-                        "text": f"Can you search Social Media for {query} from {start_date} to {end_date}? Make sure you only get the data posted during that period.",
-                    }
-                ],
-            }
-        ],
-        text={"format": {"type": "text"}},
-        reasoning={},
-        tools=[
+                "content": "You are a financial news analyst. Search and summarize relevant news from social media and news sources."
+            },
             {
-                "type": "web_search_preview",
-                "user_location": {"type": "approximate"},
-                "search_context_size": "low",
+                "role": "user",
+                "content": f"Can you search Social Media for {query} from {start_date} to {end_date}? Make sure you only get the data posted during that period."
             }
         ],
         temperature=1,
-        max_output_tokens=4096,
+        max_tokens=4096,
         top_p=1,
-        store=True,
     )
 
-    return response.output[1].content[0].text
+    return response.choices[0].message.content
 
 
 def get_global_news_openai(curr_date, look_back_days=7, limit=5):
+    """
+    Retrieve global news using LLM provider configured in backend_url.
+    Compatible with OpenAI, Gemini (via OpenAI-compatible API), and OpenRouter.
+
+    Args:
+        curr_date: Current date in yyyy-mm-dd format
+        look_back_days: Number of days to look back (default 7)
+        limit: Maximum number of articles to return (default 5)
+
+    Returns:
+        str: Global news content as text
+    """
     config = get_config()
     client = OpenAI(base_url=config["backend_url"])
 
-    response = client.responses.create(
+    # Use standard chat completions API for compatibility with all providers
+    response = client.chat.completions.create(
         model=config["quick_think_llm"],
-        input=[
+        messages=[
             {
                 "role": "system",
-                "content": [
-                    {
-                        "type": "input_text",
-                        "text": f"Can you search global or macroeconomics news from {look_back_days} days before {curr_date} to {curr_date} that would be informative for trading purposes? Make sure you only get the data posted during that period. Limit the results to {limit} articles.",
-                    }
-                ],
-            }
-        ],
-        text={"format": {"type": "text"}},
-        reasoning={},
-        tools=[
+                "content": "You are a financial news analyst. Search and summarize relevant global and macroeconomic news for trading purposes."
+            },
             {
-                "type": "web_search_preview",
-                "user_location": {"type": "approximate"},
-                "search_context_size": "low",
+                "role": "user",
+                "content": f"Can you search global or macroeconomics news from {look_back_days} days before {curr_date} to {curr_date} that would be informative for trading purposes? Make sure you only get the data posted during that period. Limit the results to {limit} articles."
             }
         ],
         temperature=1,
-        max_output_tokens=4096,
+        max_tokens=4096,
         top_p=1,
-        store=True,
     )
 
-    return response.output[1].content[0].text
+    return response.choices[0].message.content
 
 
 def get_fundamentals_openai(ticker, curr_date):
+    """
+    Retrieve fundamental data using LLM provider configured in backend_url.
+    Compatible with OpenAI, Gemini (via OpenAI-compatible API), and OpenRouter.
+
+    Args:
+        ticker: Stock ticker symbol
+        curr_date: Current date in yyyy-mm-dd format
+
+    Returns:
+        str: Fundamental data as text (table format with PE/PS/Cash flow etc)
+    """
     config = get_config()
     client = OpenAI(base_url=config["backend_url"])
 
-    response = client.responses.create(
+    # Use standard chat completions API for compatibility with all providers
+    response = client.chat.completions.create(
         model=config["quick_think_llm"],
-        input=[
+        messages=[
             {
                 "role": "system",
-                "content": [
-                    {
-                        "type": "input_text",
-                        "text": f"Can you search Fundamental for discussions on {ticker} during of the month before {curr_date} to the month of {curr_date}. Make sure you only get the data posted during that period. List as a table, with PE/PS/Cash flow/ etc",
-                    }
-                ],
-            }
-        ],
-        text={"format": {"type": "text"}},
-        reasoning={},
-        tools=[
+                "content": "You are a financial analyst. Search and provide fundamental data for stocks in a structured table format."
+            },
             {
-                "type": "web_search_preview",
-                "user_location": {"type": "approximate"},
-                "search_context_size": "low",
+                "role": "user",
+                "content": f"Can you search Fundamental for discussions on {ticker} during of the month before {curr_date} to the month of {curr_date}. Make sure you only get the data posted during that period. List as a table, with PE/PS/Cash flow/ etc"
             }
         ],
         temperature=1,
-        max_output_tokens=4096,
+        max_tokens=4096,
         top_p=1,
-        store=True,
     )
 
-    return response.output[1].content[0].text
+    return response.choices[0].message.content