When LLM_PROVIDER=gemini, LLMClient automatically uses the Google AI Studio
OpenAI-compatible endpoint instead of requiring manual base_url configuration.
An explicit base_url argument still takes precedence.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Translate all Chinese comments, docstrings, log messages, error messages,
and LLM prompt text to English across the entire backend codebase.
Locale translation files (locales/*.json) are unchanged.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Background threads (graph building, simulation prep, report generation,
profile generation) now inherit the requesting user's locale preference.
Previously these fell back to 'zh' because Flask request context was
unavailable in spawned threads.
Ensure poster_type stays PascalCase English and stance stays English enum
values regardless of language setting. Only natural language fields follow
the user's language preference.
The language instruction was causing LLM to change entity/relation naming
conventions. Now explicitly enforce PascalCase/UPPER_SNAKE_CASE for technical
identifiers while only applying language preference to description fields.
- Implemented `_get_report_id_for_simulation` to find the most recent report ID associated with a simulation ID by scanning the reports directory.
- Updated `get_simulation_history` to include the retrieved report ID in the response, enhancing the simulation data returned to the client.
- Updated simulation history retrieval to read project details directly from the Simulation file.
- Improved simulation configuration handling by reading simulation requirements from JSON.
- Added project file listing to the simulation history, displaying up to three associated files.
- Refined card layout in HistoryDatabase.vue to accommodate new file display features and improved responsiveness.
- Decreased the maximum tool calls per section from 8 to 5.
- Reduced the maximum iterations in the ReACT loop from 8 to 5, streamlining the report generation process.
- Reduced maximum tool calls per chat from 5 to 2 for improved efficiency.
- Simplified system prompt to focus on concise responses and report content.
- Implemented report content retrieval with length limitation to prevent context overflow.
- Adjusted tool call execution to limit to one call per iteration, enhancing clarity in responses.
- Updated user message prompts to encourage concise answers based on retrieved data.
- Increased the maximum tool calls per section from 4 to 8, enhancing the agent's capabilities.
- Raised the maximum reflection rounds from 2 to 3 to allow for deeper analysis.
- Adjusted the maximum tool calls per chat from 3 to 5 for improved interaction.
- Expanded the maximum agents for interviews from 5 to 20, facilitating more comprehensive data gathering.
- Increased the maximum iterations for ReACT loops from 5 to 8 and from 3 to 5 in different contexts, optimizing the report generation process.
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