fix: GenAI client(evals) - Fix TypeError in _build_generate_content_config

vertexai/_genai/_evals_common.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 #
 """Common utilities for evals."""
+
 import asyncio
 import base64
 import collections
@@ -24,8 +25,8 @@
 import os
 import threading
 import time
-import uuid
 from typing import Any, Callable, Literal, Optional, Union
+import uuid
 
 from google.api_core import exceptions as api_exceptions
 import vertexai
@@ -41,7 +42,6 @@
 from . import _evals_metric_loaders
 from . import _evals_utils
 from . import _gcs_utils
-
 from . import evals
 from . import types
 
@@ -90,7 +90,8 @@ def _get_api_client_with_location(
         return api_client
 
     logger.info(
-        "Model endpoint location set to %s, overriding client location %s for this API call.",
+        "Model endpoint location set to %s, overriding client location %s for"
+        " this API call.",
         location,
         api_client.location,
     )
@@ -208,7 +209,7 @@ def _generate_content_with_retry(
 
 
 def _build_generate_content_config(
-    request_dict: dict[str, Any],
+    request_dict: Union[dict[str, Any], str],
     global_config: Optional[genai_types.GenerateContentConfig] = None,
 ) -> genai_types.GenerateContentConfig:
     """Builds a GenerateContentConfig from the request dictionary or provided config."""
@@ -220,6 +221,9 @@ def _build_generate_content_config(
     else:
         merged_config_dict = {}
 
+    if not isinstance(request_dict, dict):
+        return genai_types.GenerateContentConfig(**merged_config_dict)
+
     for key in [
         "system_instruction",
         "tools",
@@ -264,7 +268,11 @@ def _execute_inference_concurrently(
     agent_engine: Optional[Union[str, types.AgentEngine]] = None,
     agent: Optional[LlmAgent] = None,
 ) -> list[
-    Union[genai_types.GenerateContentResponse, dict[str, Any], list[dict[str, Any]]]
+    Union[
+        genai_types.GenerateContentResponse,
+        dict[str, Any],
+        list[dict[str, Any]],
+    ]
 ]:
     """Internal helper to run inference with concurrency."""
     logger.info(
@@ -384,7 +392,11 @@ def _run_gemini_inference(
     prompt_dataset: pd.DataFrame,
     config: Optional[genai_types.GenerateContentConfig] = None,
 ) -> list[
-    Union[genai_types.GenerateContentResponse, dict[str, Any], list[dict[str, Any]]]
+    Union[
+        genai_types.GenerateContentResponse,
+        dict[str, Any],
+        list[dict[str, Any]],
+    ]
 ]:
     """Internal helper to run inference using Gemini model with concurrency."""
     return _execute_inference_concurrently(
@@ -410,7 +422,9 @@ def _run_custom_inference(
     )
 
 
-def _convert_prompt_row_to_litellm_messages(row: pd.Series) -> list[dict[str, Any]]:
+def _convert_prompt_row_to_litellm_messages(
+    row: pd.Series,
+) -> list[dict[str, Any]]:
     """Converts a DataFrame row into LiteLLM's messages format by detecting the input schema."""
     messages: list[dict[str, Any]] = []
     row_dict = row.to_dict()
@@ -442,10 +456,10 @@ def _convert_prompt_row_to_litellm_messages(row: pd.Series) -> list[dict[str, An
         return [{"role": USER_AUTHOR, "content": row_dict["prompt"]}]
 
     raise ValueError(
-        "Could not determine prompt/messages format from input row. "
-        "Expected OpenAI request body with a 'messages' key, or a 'request' key"
-        " with OpenAI request body, or Gemini request body with a 'contents'"
-        f" key, or a 'prompt' key with a raw string. Found keys: {list(row_dict.keys())}"
+        "Could not determine prompt/messages format from input row. Expected"
+        " OpenAI request body with a 'messages' key, or a 'request' key with"
+        " OpenAI request body, or Gemini request body with a 'contents' key, or"
+        f" a 'prompt' key with a raw string. Found keys: {list(row_dict.keys())}"
     )
 
 
@@ -466,7 +480,8 @@ def _run_litellm_inference(
 ) -> list[Optional[dict[str, Any]]]:
     """Runs inference using LiteLLM with concurrency."""
     logger.info(
-        "Generating responses for %d prompts using LiteLLM for third party model: %s",
+        "Generating responses for %d prompts using LiteLLM for third party"
+        " model: %s",
         len(prompt_dataset),
         model,
     )
@@ -776,8 +791,8 @@ def _execute_inference(
 
     Args:
         api_client: The API client.
-        src: The source of the dataset. Can be a string (path to a local file,
-          a GCS path, or a BigQuery table) or a Pandas DataFrame.
+        src: The source of the dataset. Can be a string (path to a local file, a
+          GCS path, or a BigQuery table) or a Pandas DataFrame.
         model: The model to use for inference. Can be a callable function or a
           string representing a model.
         agent_engine: The agent engine to use for inference. Can be a resource
@@ -847,8 +862,8 @@ def _execute_inference(
             raise TypeError(
                 f"Unsupported agent_engine type: {type(agent_engine)}. Expecting a"
                 " string (agent engine resource name in"
-                " 'projects/{project_id}/locations/{location_id}/reasoningEngines/{reasoning_engine_id}' format)"
-                " or a types.AgentEngine instance."
+                " 'projects/{project_id}/locations/{location_id}/reasoningEngines/{reasoning_engine_id}'"
+                " format) or a types.AgentEngine instance."
             )
         if (
             _evals_constant.INTERMEDIATE_EVENTS in prompt_dataset.columns
@@ -1193,7 +1208,8 @@ def _execute_evaluation(  # type: ignore[no-untyped-def]
             validated_agent_info = agent_info
         else:
             raise TypeError(
-                f"agent_info values must be of type types.evals.AgentInfo or dict, but got {type(agent_info)}'"
+                "agent_info values must be of type types.evals.AgentInfo or dict,"
+                f" but got {type(agent_info)}'"
             )
 
     processed_eval_dataset, num_response_candidates = _resolve_dataset_inputs(
@@ -1323,8 +1339,9 @@ def _run_agent_internal(
         processed_responses
     ) != len(processed_intermediate_events):
         raise RuntimeError(
-            "Critical prompt/response/intermediate_events count mismatch: %d prompts vs %d vs %d"
-            " responses. This indicates an issue in response collection."
+            "Critical prompt/response/intermediate_events count mismatch: %d"
+            " prompts vs %d vs %d responses. This indicates an issue in response"
+            " collection."
             % (
                 len(prompt_dataset),
                 len(processed_responses),
@@ -1354,7 +1371,11 @@ def _run_agent(
     agent: Optional[LlmAgent],
     prompt_dataset: pd.DataFrame,
 ) -> list[
-    Union[list[dict[str, Any]], dict[str, Any], genai_types.GenerateContentResponse]
+    Union[
+        list[dict[str, Any]],
+        dict[str, Any],
+        genai_types.GenerateContentResponse,
+    ]
 ]:
     """Internal helper to run inference using Gemini model with concurrency."""
     if agent_engine:
@@ -1645,12 +1666,15 @@ def _convert_request_to_dataset_row(
     return dict_row
 
 
-def _transform_dataframe(rows: list[dict[str, Any]]) -> list[types.EvaluationDataset]:
+def _transform_dataframe(
+    rows: list[dict[str, Any]],
+) -> list[types.EvaluationDataset]:
     """Transforms rows to a list of EvaluationDatasets.
 
     Args:
       rows: A list of rows, each row is a dictionary of candidate name to response
         text.
+
     Returns:
       A list of EvaluationDatasets, one for each candidate.
     """
@@ -1677,6 +1701,7 @@ def _get_eval_cases_eval_dfs_from_eval_items(
     Args:
       api_client: The API client.
       evaluation_set_name: The name of the evaluation set.
+
     Returns:
       A tuple of two lists:
         - eval_case_results: A list of EvalCaseResults, one for each evaluation
@@ -1748,6 +1773,7 @@ def _get_eval_result_from_eval_items(
     Args:
       results: The EvaluationRunResults object.
       eval_items: The list of EvaluationItems.
+
     Returns:
       An EvaluationResult object.
     """