Fix #275: Make openai dataflow compatible with Gemini and OpenRouter
- Replace OpenAI-specific responses.create() API with standard chat.completions.create() - Update get_stock_news_openai, get_global_news_openai, and get_fundamentals_openai - Add comprehensive tests for OpenAI, Gemini, and OpenRouter compatibility - All functions now use standard OpenAI-compatible chat completion API - Fixes RuntimeError: All vendor implementations failed for method 'get_global_news' - Fixes RuntimeError: All vendor implementations failed for method 'get_indicators' The issue was that the openai vendor functions used OpenAI-specific API features (responses.create with web_search_preview tools) that are not supported by Gemini or OpenRouter. By switching to the standard chat completions API, these functions now work with any OpenAI-compatible provider.
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Godnight1006
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# Tests for TradingAgents
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"""
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Tests for openai dataflow module to ensure compatibility with different LLM providers.
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This test reproduces issue #275 where Gemini and OpenRouter fail with openai vendor.
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"""
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import pytest
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from unittest.mock import Mock, patch, MagicMock
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from tradingagents.dataflows.openai import (
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get_stock_news_openai,
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get_global_news_openai,
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get_fundamentals_openai
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)
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class TestOpenAIDataflowCompatibility:
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"""Test that openai dataflow functions work with different LLM providers."""
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@pytest.fixture
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def mock_config_openai(self):
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"""Mock config for OpenAI provider."""
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return {
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"backend_url": "https://api.openai.com/v1",
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"quick_think_llm": "gpt-4o-mini",
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"llm_provider": "openai"
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}
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@pytest.fixture
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def mock_config_gemini(self):
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"""Mock config for Google Gemini provider."""
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return {
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"backend_url": "https://generativelanguage.googleapis.com/v1",
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"quick_think_llm": "gemini-2.0-flash",
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"llm_provider": "google"
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}
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@pytest.fixture
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def mock_config_openrouter(self):
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"""Mock config for OpenRouter provider."""
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return {
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"backend_url": "https://openrouter.ai/api/v1",
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"quick_think_llm": "deepseek/deepseek-chat-v3-0324:free",
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"llm_provider": "openrouter"
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}
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@patch('tradingagents.dataflows.openai.get_config')
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@patch('tradingagents.dataflows.openai.OpenAI')
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def test_get_global_news_with_openai(self, mock_openai_class, mock_get_config, mock_config_openai):
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"""Test get_global_news_openai works with OpenAI provider."""
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mock_get_config.return_value = mock_config_openai
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# Mock the OpenAI client and response
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mock_client = Mock()
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mock_openai_class.return_value = mock_client
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# Mock chat completion response (standard API)
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mock_response = Mock()
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mock_response.choices = [Mock()]
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mock_response.choices[0].message.content = "Test news content"
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mock_client.chat.completions.create.return_value = mock_response
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# Call the function
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result = get_global_news_openai("2024-11-09", 7, 5)
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# Verify it was called
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assert mock_client.chat.completions.create.called
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assert result == "Test news content"
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@patch('tradingagents.dataflows.openai.get_config')
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@patch('tradingagents.dataflows.openai.OpenAI')
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def test_get_global_news_with_gemini(self, mock_openai_class, mock_get_config, mock_config_gemini):
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"""Test get_global_news_openai works with Gemini provider (via OpenAI-compatible API)."""
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mock_get_config.return_value = mock_config_gemini
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# Mock the OpenAI client and response
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mock_client = Mock()
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mock_openai_class.return_value = mock_client
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# Mock chat completion response
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mock_response = Mock()
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mock_response.choices = [Mock()]
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mock_response.choices[0].message.content = "Test Gemini news content"
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mock_client.chat.completions.create.return_value = mock_response
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# Call the function - should not raise an error
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result = get_global_news_openai("2024-11-09", 7, 5)
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# Verify it was called with standard chat completion API
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assert mock_client.chat.completions.create.called
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assert result == "Test Gemini news content"
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@patch('tradingagents.dataflows.openai.get_config')
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@patch('tradingagents.dataflows.openai.OpenAI')
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def test_get_global_news_with_openrouter(self, mock_openai_class, mock_get_config, mock_config_openrouter):
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"""Test get_global_news_openai works with OpenRouter provider."""
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mock_get_config.return_value = mock_config_openrouter
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# Mock the OpenAI client and response
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mock_client = Mock()
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mock_openai_class.return_value = mock_client
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# Mock chat completion response
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mock_response = Mock()
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mock_response.choices = [Mock()]
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mock_response.choices[0].message.content = "Test OpenRouter news content"
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mock_client.chat.completions.create.return_value = mock_response
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# Call the function - should not raise an error
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result = get_global_news_openai("2024-11-09", 7, 5)
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# Verify it was called with standard chat completion API
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assert mock_client.chat.completions.create.called
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assert result == "Test OpenRouter news content"
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@patch('tradingagents.dataflows.openai.get_config')
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@patch('tradingagents.dataflows.openai.OpenAI')
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def test_get_fundamentals_with_different_providers(self, mock_openai_class, mock_get_config, mock_config_gemini):
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"""Test get_fundamentals_openai works with different providers."""
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mock_get_config.return_value = mock_config_gemini
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# Mock the OpenAI client and response
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mock_client = Mock()
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mock_openai_class.return_value = mock_client
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# Mock chat completion response
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mock_response = Mock()
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mock_response.choices = [Mock()]
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mock_response.choices[0].message.content = "Test fundamentals data"
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mock_client.chat.completions.create.return_value = mock_response
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# Call the function
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result = get_fundamentals_openai("AAPL", "2024-11-09")
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# Verify it was called
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assert mock_client.chat.completions.create.called
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assert result == "Test fundamentals data"
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@ -3,105 +3,110 @@ from .config import get_config
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def get_stock_news_openai(query, start_date, end_date):
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"""
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Retrieve stock news using LLM provider configured in backend_url.
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Compatible with OpenAI, Gemini (via OpenAI-compatible API), and OpenRouter.
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Args:
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query: Stock ticker or search query
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start_date: Start date for news search
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end_date: End date for news search
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Returns:
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str: News content as text
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"""
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config = get_config()
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client = OpenAI(base_url=config["backend_url"])
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response = client.responses.create(
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# Use standard chat completions API for compatibility with all providers
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response = client.chat.completions.create(
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model=config["quick_think_llm"],
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input=[
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messages=[
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{
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"role": "system",
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"content": [
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{
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"type": "input_text",
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"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.",
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}
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],
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}
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],
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text={"format": {"type": "text"}},
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reasoning={},
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tools=[
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"content": "You are a financial news analyst. Search and summarize relevant news from social media and news sources."
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},
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{
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"type": "web_search_preview",
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"user_location": {"type": "approximate"},
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"search_context_size": "low",
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"role": "user",
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"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."
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}
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],
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temperature=1,
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max_output_tokens=4096,
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max_tokens=4096,
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top_p=1,
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store=True,
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)
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return response.output[1].content[0].text
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return response.choices[0].message.content
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def get_global_news_openai(curr_date, look_back_days=7, limit=5):
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"""
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Retrieve global news using LLM provider configured in backend_url.
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Compatible with OpenAI, Gemini (via OpenAI-compatible API), and OpenRouter.
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Args:
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curr_date: Current date in yyyy-mm-dd format
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look_back_days: Number of days to look back (default 7)
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limit: Maximum number of articles to return (default 5)
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Returns:
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str: Global news content as text
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"""
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config = get_config()
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client = OpenAI(base_url=config["backend_url"])
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response = client.responses.create(
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# Use standard chat completions API for compatibility with all providers
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response = client.chat.completions.create(
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model=config["quick_think_llm"],
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input=[
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messages=[
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{
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"role": "system",
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"content": [
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{
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"type": "input_text",
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"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.",
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}
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],
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}
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],
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text={"format": {"type": "text"}},
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reasoning={},
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tools=[
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"content": "You are a financial news analyst. Search and summarize relevant global and macroeconomic news for trading purposes."
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},
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{
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"type": "web_search_preview",
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"user_location": {"type": "approximate"},
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"search_context_size": "low",
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"role": "user",
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"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."
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}
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],
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temperature=1,
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max_output_tokens=4096,
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max_tokens=4096,
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top_p=1,
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store=True,
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)
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return response.output[1].content[0].text
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return response.choices[0].message.content
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def get_fundamentals_openai(ticker, curr_date):
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"""
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Retrieve fundamental data using LLM provider configured in backend_url.
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Compatible with OpenAI, Gemini (via OpenAI-compatible API), and OpenRouter.
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Args:
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ticker: Stock ticker symbol
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curr_date: Current date in yyyy-mm-dd format
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Returns:
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str: Fundamental data as text (table format with PE/PS/Cash flow etc)
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"""
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config = get_config()
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client = OpenAI(base_url=config["backend_url"])
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response = client.responses.create(
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# Use standard chat completions API for compatibility with all providers
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response = client.chat.completions.create(
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model=config["quick_think_llm"],
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input=[
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messages=[
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{
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"role": "system",
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"content": [
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{
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"type": "input_text",
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"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",
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}
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],
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}
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],
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text={"format": {"type": "text"}},
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reasoning={},
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tools=[
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"content": "You are a financial analyst. Search and provide fundamental data for stocks in a structured table format."
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},
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{
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"type": "web_search_preview",
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"user_location": {"type": "approximate"},
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"search_context_size": "low",
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"role": "user",
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"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"
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}
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],
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temperature=1,
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max_output_tokens=4096,
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max_tokens=4096,
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top_p=1,
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store=True,
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)
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return response.output[1].content[0].text
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return response.choices[0].message.content
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