Gemini chat wrapper fix

src/agentscope/models/gemini_model.py

@@ -13,8 +13,12 @@
 
 try:
     import google.generativeai as genai
+
+    # This package will be installed when the google-generativeai is installed
+    import google.ai.generativelanguage as glm
 except ImportError:
     genai = None
+    glm = None
 
 
 class GeminiWrapperBase(ModelWrapperBase, ABC):
@@ -42,6 +46,13 @@ def __init__(
         """
         super().__init__(config_name=config_name)
 
+        # Test if the required package is installed
+        if genai is None:
+            raise ImportError(
+                "The google-generativeai package is not installed, "
+                "please install it first.",
+            )
+
         # Load the api_key from argument or environment variable
         api_key = api_key or os.environ.get("GOOGLE_API_KEY")
 
@@ -149,7 +160,50 @@ def __call__(
             **kwargs,
         )
 
-        # step3: record the api invocation if needed
+        # step3: Check for candidates and handle accordingly
+        if (
+            not response.candidates[0].content
+            or not response.candidates[0].content.parts
+            or not response.candidates[0].content.parts[0].text
+        ):
+            # If we cannot get the response text from the model
+            finish_reason = response.candidates[0].finish_reason
+            reasons = glm.Candidate.FinishReason
+
+            if finish_reason == reasons.STOP:
+                error_info = (
+                    "Natural stop point of the model or provided stop "
+                    "sequence."
+                )
+            elif finish_reason == reasons.MAX_TOKENS:
+                error_info = (
+                    "The maximum number of tokens as specified in the request "
+                    "was reached."
+                )
+            elif finish_reason == reasons.SAFETY:
+                error_info = (
+                    "The candidate content was flagged for safety reasons."
+                )
+            elif finish_reason == reasons.RECITATION:
+                error_info = (
+                    "The candidate content was flagged for recitation reasons."
+                )
+            elif finish_reason in [
+                reasons.FINISH_REASON_UNSPECIFIED,
+                reasons.OTHER,
+            ]:
+                error_info = "Unknown error."
+            else:
+                error_info = "No information provided from Gemini API."
+
+            raise ValueError(
+                "The Google Gemini API failed to generate text response with "
+                f"the following finish reason: {error_info}\n"
+                f"YOUR INPUT: {contents}\n"
+                f"RAW RESPONSE FROM GEMINI API: {response}\n",
+            )
+
+        # step4: record the api invocation if needed
         self._save_model_invocation(
             arguments={
                 "contents": contents,
@@ -160,9 +214,6 @@ def __call__(
         )
 
         # step5: update monitor accordingly
-        # TODO: Up to 2024/03/11, the response from Gemini doesn't contain
-        #  the detailed information about cost. Here we simply count
-        #  the tokens manually.
         token_prompt = self.model.count_tokens(contents).total_tokens
         token_response = self.model.count_tokens(response.text).total_tokens
         self.update_monitor(

tests/gemini_test.py

@@ -20,11 +20,31 @@ def flush() -> None:
     MonitorFactory.flush()
 
 
+class DummyPart:
+    """Dummy part for testing."""
+
+    text = "Hello! How can I help you?"
+
+
+class DummyContent:
+    """Dummy content for testing."""
+
+    parts = [DummyPart()]
+
+
+class DummyCandidate:
+    """Dummy candidate for testing."""
+
+    content = DummyContent()
+
+
 class DummyResponse:
     """Dummy response for testing."""
 
     text = "Hello! How can I help you?"
 
+    candidates = [DummyCandidate]
+
     def __str__(self) -> str:
         """Return string representation."""
         return str({"text": self.text})