update readme

Author: bwzheng2010

doc/LLM_ChangeData_Example.md

@@ -23,7 +23,7 @@ To run this example, you need:
 2. A LLM model with a thinking capability.
 
 > [!TIP]
-> Our tests were conducted using the `Qwen/Qwen3-Omni-30B-A3B-Thinking` model, achieving over 99% recall and 100% accuracy.
+> Our tests were conducted using the [Qwen/Qwen3-Omni-30B-A3B-Thinking](https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Thinking) model, achieving over 99% recall and 100% accuracy.
 > 
 > For optimal results, it is recommended to use large language models with large parameters.
 

doc/LLM_ForecastData_Example.md

@@ -22,7 +22,7 @@ To run this example, you need:
 2. A LLM model with a thinking capability.
 
 > [!TIP]
-> Our tests were conducted using the `Qwen/Qwen3-Omni-30B-A3B-Thinking` model, achieving over 99% recall and 100% accuracy.
+> Our tests were conducted using the [Qwen/Qwen3-Omni-30B-A3B-Thinking](https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Thinking) model, achieving over 99% recall and 100% accuracy.
 > 
 > For optimal results, it is recommended to use large language models with large parameters.
 

test/test_ai_transcripts.py

@@ -9,7 +9,7 @@
 class TestAITranscripts(unittest.TestCase):
     @classmethod
     def setUpClass(cls):
-        cls.ticker = Ticker("JPM", http_proxy="http://127.0.0.1:33210", log_level=logging.DEBUG)
+        cls.ticker = Ticker("NFLX", http_proxy="http://127.0.0.1:33210", log_level=logging.DEBUG)
 
         key = Path(__file__).parent.joinpath("siliconflow_api.key").read_text(encoding="utf-8")
         cls.llm = OpenAI(
@@ -35,16 +35,16 @@ def test_analyze_financial_metrics_change_for_this_quarter_with_ai(self):
         transcripts = self.ticker.earning_call_transcripts()
         res = transcripts.analyze_financial_metrics_change_for_this_quarter_with_ai(
             2025,
-            3,
+            2,
             self.llm,
             OpenAIConfiguration(model='Qwen/Qwen3-Omni-30B-A3B-Thinking', temperature=0, top_p=0, tool_choice="auto"))
         print(res.to_string())
 
     def test_analyze_financial_metrics_forecast_for_future_with_ai(self):
         transcripts = self.ticker.earning_call_transcripts()
         res = transcripts.analyze_financial_metrics_forecast_for_future_with_ai(
-            2026,
-            1,
+            2025,
+            2,
             self.llm,
             OpenAIConfiguration(model='Qwen/Qwen3-Omni-30B-A3B-Thinking', temperature=0, top_p=0, tool_choice="auto"))
         print(res.to_string())