update docstring for validate response

CHANGELOG.md

@@ -7,6 +7,10 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## [Unreleased]
 
+## [1.0.31] 2025-10-14
+
+- Add `expert_guardrail_override_explanation` and `log_id` to `ProjectValidateResponse` docstring
+
 ## [1.0.30] 2025-10-01
 
 - Update API reference language from Codex -> Cleanlab AI Platform
@@ -141,7 +145,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 - Initial release of the `cleanlab-codex` client library.
 
-[Unreleased]: https://github.com/cleanlab/cleanlab-codex/compare/v1.0.30...HEAD
+[Unreleased]: https://github.com/cleanlab/cleanlab-codex/compare/v1.0.31...HEAD
+[1.0.31]: https://github.com/cleanlab/cleanlab-codex/compare/v1.0.30...v1.0.31
 [1.0.30]: https://github.com/cleanlab/cleanlab-codex/compare/v1.0.29...v1.0.30
 [1.0.29]: https://github.com/cleanlab/cleanlab-codex/compare/v1.0.28...v1.0.29
 [1.0.28]: https://github.com/cleanlab/cleanlab-codex/compare/v1.0.27...v1.0.28

pyproject.toml

@@ -26,7 +26,7 @@ classifiers = [
 ]
 dependencies = [
   "cleanlab-tlm~=1.1,>=1.1.14",
-  "codex-sdk==0.1.0a28",
+  "codex-sdk==0.1.0a30",
   "pydantic>=2.0.0, <3",
 ]
 

src/cleanlab_codex/__about__.py

@@ -1,2 +1,2 @@
 # SPDX-License-Identifier: MIT
-__version__ = "1.0.30"
+__version__ = "1.0.31"

src/cleanlab_codex/project.py

@@ -18,7 +18,11 @@
 
     from codex import Codex as _Codex
     from codex.types.project_validate_response import ProjectValidateResponse
-    from openai.types.chat import ChatCompletion, ChatCompletionMessageParam, ChatCompletionToolParam
+    from openai.types.chat import (
+        ChatCompletion,
+        ChatCompletionMessageParam,
+        ChatCompletionToolParam,
+    )
 
 
 _ERROR_CREATE_ACCESS_KEY = (
@@ -187,6 +191,8 @@ def validate(
                 - escalated_to_sme (bool): True if the query should be escalated to SME for review. When True, the query is logged and may be answered by an expert.
                 - eval_scores (dict[str, ThresholdedEvalScore]): Evaluation scores for different response attributes (e.g., trustworthiness, helpfulness, ...). Each includes a numeric score and a `failed` flag indicating whether the score falls below threshold.
                 - expert_answer (str | None): If it was auto-determined that this query should be escalated to SME, and a prior SME answer for a similar query was found, then this will return that expert answer.  Otherwise, it is None.
+                - expert_guardrail_override_explanation (str | None): If the final guardrail decision was overridden by expert review, this will contain an explanation for why the guardrail result was overridden. Otherwise, it is None.
+                - log_id (str): The ID of the log created for this query.
 
                 When available, consider swapping your AI response with the expert answer before serving the response to your user.
         """

tests/test_project.py

@@ -32,8 +32,8 @@ def test_project_validate_with_dict_response(
     openai_messages_conversational: list["ChatCompletionMessageParam"],
 ) -> None:
     expected_result = ProjectValidateResponse(
-        is_bad_response=True,
         expert_answer=None,
+        expert_guardrail_override_explanation=None,
         eval_scores={
             "response_helpfulness": EvalScores(
                 score=0.8,
@@ -125,7 +125,7 @@ def test_project_validate_with_tools(
     openai_tools: list["ChatCompletionToolParam"],
 ) -> None:
     expected_result = ProjectValidateResponse(
-        is_bad_response=True,
+        expert_guardrail_override_explanation=None,
         expert_answer=None,
         eval_scores={
             "response_helpfulness": EvalScores(