fix: resolve lint and type issues in Google Batch Mode implementation

- Fix unused imports in test_batch_mode.py
- Update Google GenAI API calls to use correct client.batches methods
- Add proper type annotations and error handling
- Ensure compatibility with google-generativeai SDK

Co-Authored-By: João <joao@crewai.com>

src/crewai/llm.py

@@ -26,13 +26,10 @@ from pydantic import BaseModel, Field
 
 try:
     import google.generativeai as genai
-    from google.generativeai.types import BatchCreateJobRequest, BatchJob
     GOOGLE_GENAI_AVAILABLE = True
 except ImportError:
     GOOGLE_GENAI_AVAILABLE = False
-    # Create dummy types for type annotations when genai is not available
-    BatchJob = object
-    BatchCreateJobRequest = object
+    genai = None  # type: ignore
 
 from crewai.utilities.events.llm_events import (
     LLMCallCompletedEvent,
@@ -380,8 +377,8 @@ class LLM(BaseLLM):
         self.batch_mode = batch_mode
         self.batch_size = batch_size or 10
         self.batch_timeout = batch_timeout
-        self._batch_requests = []
-        self._current_batch_job = None
+        self._batch_requests: List[Dict[str, Any]] = []
+        self._current_batch_job: Optional[str] = None
 
         litellm.drop_params = True
 
@@ -474,7 +471,7 @@ class LLM(BaseLLM):
         
         formatted_messages = self._format_messages_for_provider(messages)
         
-        request = {
+        request: Dict[str, Any] = {
             "contents": [],
             "generationConfig": {
                 "temperature": self.temperature,
@@ -505,32 +502,37 @@ class LLM(BaseLLM):
             raise ValueError("API key is required for batch mode")
         
         genai.configure(api_key=self.api_key)
+        client = genai.Client()
         
-        batch_request = BatchCreateJobRequest(
-            requests=requests,
-            display_name=f"crewai-batch-{int(time.time())}"
+        batch_job = client.batches.create(
+            model=f"models/{self.model.replace('gemini/', '')}",
+            src=requests,
+            config={
+                'display_name': f"crewai-batch-{int(time.time())}"
+            }
         )
         
-        batch_job = genai.create_batch_job(batch_request)
         return batch_job.name
 
-    def _poll_batch_job(self, job_name: str) -> BatchJob:
+    def _poll_batch_job(self, job_name: str) -> Any:
         """Poll batch job status until completion."""
         if not GOOGLE_GENAI_AVAILABLE:
             raise ImportError("google-generativeai is required for batch mode")
         
         genai.configure(api_key=self.api_key)
+        client = genai.Client()
         
         start_time = time.time()
-        while time.time() - start_time < self.batch_timeout:
-            batch_job = genai.get_batch_job(job_name)
+        timeout = self.batch_timeout or 300
+        while time.time() - start_time < timeout:
+            batch_job = client.batches.get(name=job_name)
             
-            if batch_job.state in ["JOB_STATE_SUCCEEDED", "JOB_STATE_FAILED", "JOB_STATE_CANCELLED"]:
+            if batch_job.state.name in ["JOB_STATE_SUCCEEDED", "JOB_STATE_FAILED", "JOB_STATE_CANCELLED"]:
                 return batch_job
             
             time.sleep(5)
         
-        raise TimeoutError(f"Batch job {job_name} did not complete within {self.batch_timeout} seconds")
+        raise TimeoutError(f"Batch job {job_name} did not complete within {timeout} seconds")
 
     def _retrieve_batch_results(self, job_name: str) -> List[str]:
         """Retrieve results from a completed batch job."""
@@ -538,22 +540,29 @@ class LLM(BaseLLM):
             raise ImportError("google-generativeai is required for batch mode")
         
         genai.configure(api_key=self.api_key)
+        client = genai.Client()
         
-        batch_job = genai.get_batch_job(job_name)
+        batch_job = client.batches.get(name=job_name)
         
-        if batch_job.state != "JOB_STATE_SUCCEEDED":
-            raise RuntimeError(f"Batch job failed with state: {batch_job.state}")
+        if batch_job.state.name != "JOB_STATE_SUCCEEDED":
+            raise RuntimeError(f"Batch job failed with state: {batch_job.state.name}")
         
         results = []
-        for response in genai.list_batch_job_responses(job_name):
-            if response.response and response.response.candidates:
-                content = response.response.candidates[0].content
-                if content and content.parts:
-                    results.append(content.parts[0].text)
+        if batch_job.dest and batch_job.dest.inlined_responses:
+            for inline_response in batch_job.dest.inlined_responses:
+                if inline_response.response and hasattr(inline_response.response, 'text'):
+                    results.append(inline_response.response.text)
+                elif inline_response.response and hasattr(inline_response.response, 'candidates'):
+                    if inline_response.response.candidates and inline_response.response.candidates[0].content:
+                        content = inline_response.response.candidates[0].content
+                        if content.parts:
+                            results.append(content.parts[0].text)
+                        else:
+                            results.append("")
+                    else:
+                        results.append("")
                 else:
                     results.append("")
-            else:
-                results.append("")
         
         return results
 

tests/test_batch_mode.py

@@ -1,7 +1,6 @@
 import pytest
-import time
-from unittest.mock import Mock, patch, MagicMock
-from crewai.llm import LLM, BatchJobStartedEvent, BatchJobCompletedEvent, BatchJobFailedEvent
+from unittest.mock import Mock, patch
+from crewai.llm import LLM
 
 
 class TestBatchMode: