fix(bedrock): use OrderedDict for _fetch_locks to support move_to_end…

[ysano]

… method

Related Issues or Context

Fix 'dict' object has no attribute 'move_to_end' error when validating Bedrock inference profiles.

When adding a Bedrock model with a cross-region inference profile ID (e.g., jp.anthropic.claude-haiku-4-5-20251001-v1:0), the validation fails with:

Failed to validate inference profile: 'dict' object has no attribute 'move_to_end'

Root Cause: In models/bedrock/utils/inference_profile.py, _fetch_locks is declared as a regular dict (line 21), but move_to_end method is called on it (line 30). move_to_end is only available on OrderedDict.

Solution: Change _fetch_locks from dict to OrderedDict to enable LRU cache behavior as intended.

This PR contains Changes to Non-Plugin

• Documentation
• Other

This PR contains Changes to Non-LLM Models Plugin

• I have Run Comprehensive Tests Relevant to My Changes

This PR contains Changes to LLM Models Plugin

• My Changes Affect Message Flow Handling (System Messages and User→Assistant Turn-Taking)

• My Changes Affect Tool Interaction Flow (Multi-Round Usage and Output Handling, for both Agent App and Agent Node)

• My Changes Affect Multimodal Input Handling (Images, PDFs, Audio, Video, etc.)

• My Changes Affect Multimodal Output Generation (Images, Audio, Video, etc.)

• My Changes Affect Structured Output Format (JSON, XML, etc.)

• My Changes Affect Token Consumption Metrics

• My Changes Affect Other LLM Functionalities (Reasoning Process, Grounding, Prompt Caching, etc.)

• Other Changes (Add New Models, Fix Model Parameters etc.)

Version Control (Any Changes to the Plugin Will Require Bumping the Version)

• I have Bumped Up the Version in Manifest.yaml (Top-Level Version Field, Not in Meta Section)
  • 0.0.57 → 0.0.58

Dify Plugin SDK Version

• I have Ensured dify_plugin>=0.3.0,<0.6.0 is in requirements.txt (SDK docs)
  • Note: Current version is dify_plugin==0.7.0 (newer than template suggests)

Environment Verification (If Any Code Changes)

Local Deployment Environment

• Dify Version is: , I have Tested My Changes on Local Deployment Dify with a Clean Environment That Matches the Production Configuration.

SaaS Environment

• I have Tested My Changes on cloud.dify.ai with a Clean Environment That Matches the Production Configuration

[gemini-code-assist]

Summary of Changes

Hello @ysano, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request resolves a critical bug within the Bedrock plugin that prevented the successful validation of inference profiles, particularly for models used across different regions. The issue stemmed from an incorrect data structure being used for an internal locking mechanism, which lacked a method essential for its intended least-recently-used (LRU) caching logic. The change rectifies this by employing the correct ordered dictionary type, thereby restoring proper functionality and ensuring robust Bedrock model integration.

Highlights

• Bug Fix for Bedrock Inference Profile Validation: Resolved a TypeError that occurred when validating Bedrock inference profiles, specifically for cross-region model IDs, which previously failed with 'dict' object has no attribute 'move_to_end'.
• OrderedDict Implementation: Corrected the _fetch_locks variable in inference_profile.py from a standard dict to an OrderedDict to properly support the move_to_end method, which is crucial for the intended LRU cache behavior.
• Plugin Version Bump: Updated the Bedrock plugin version from 0.0.57 to 0.0.58 in manifest.yaml to reflect the bug fix and other changes.

🧠 New Feature in Public Preview: You can now enable Memory to help Gemini Code Assist learn from your team's feedback. This makes future code reviews more consistent and personalized to your project's style. Click here to enable Memory in your admin console.

Using Gemini Code Assist

The full guide for Gemini Code Assist can be found on our documentation page, here are some quick tips.

Invoking Gemini

You can request assistance from Gemini at any point by creating a comment using either /gemini <command> or @gemini-code-assist <command>. Below is a summary of the supported commands on the current page.

Feature	Command	Description
Code Review	/gemini review	Performs a code review for the current pull request in its current state.
Pull Request Summary	/gemini summary	Provides a summary of the current pull request in its current state.
Comment	@gemini-code-assist	Responds in comments when explicitly tagged, both in pull request comments and review comments.
Help	/gemini help	Displays a list of available commands.

Customization

To customize Gemini Code Assist for GitHub experience, repository maintainers can create a configuration file and/or provide a custom code review style guide (such as PEP-8 for Python) by creating and adding files to a .gemini/ folder in the base of the repository. Detailed instructions can be found here.

Limitations & Feedback

Gemini Code Assist may make mistakes. Please leave feedback on any instances where its feedback is incorrect or counter productive. You can react with 👍 and 👎 on @gemini-code-assist comments. If you're interested in giving your feedback about your experience with Gemini Code Assist for Github and other Google products, sign up here.

You can also get AI-powered code generation, chat, as well as code reviews directly in the IDE at no cost with the Gemini Code Assist IDE Extension.

Footnotes

1. Review the Privacy Notices, Generative AI Prohibited Use Policy, Terms of Service, and learn how to configure Gemini Code Assist in GitHub here. Gemini can make mistakes, so double check it and use code with caution. ↩

[gemini-code-assist]

Code Review

This pull request correctly addresses a AttributeError by changing the type of _fetch_locks from a standard dict to collections.OrderedDict. This change enables the use of the move_to_end method, which is crucial for the intended LRU cache behavior. The fix is accurate and the accompanying version bump in manifest.yaml is appropriate. I have one suggestion to further improve the type hinting for better code clarity.