MicroFish/.kiro/specs/i18n-oasis-profile-generator-prompts/tasks.md

Implementation Plan

• 1. Translate the system-prompt base string in _get_system_prompt

  • Replace the body of base_prompt (currently "你是社交媒体用户画像生成专家。生成详细、真实的人设用于舆论模拟,最大程度还原已有现实情况。必须返回有效的JSON格式，所有字符串值不能包含未转义的换行符。") with an English equivalent that preserves the same intent: define the LLM as an expert social-media-persona generator; require detailed, realistic personas grounded in supplied context; require valid JSON output; forbid unescaped newlines in string values
  • Preserve the trailing f"{base_prompt}\n\n{get_language_instruction()}" concatenation site exactly
  • Preserve the is_individual parameter (still accepted, still unused — no signature change)
  • Observable completion: _get_system_prompt(...) returns an English-only base prompt followed by the locale-appropriate get_language_instruction() postfix
  • Requirements: 1.1, 1.2, 1.3, 1.4

• 2. Translate the individual-persona user-message template in _build_individual_persona_prompt

  • Replace the introductory line ("为实体生成详细的社交媒体用户人设,...") with an English equivalent
  • Replace the field-label rows (实体名称, 实体类型, 实体摘要, 实体属性, 上下文信息) with English equivalents
  • Replace the 请生成JSON，包含以下字段: enumeration block with an English equivalent that preserves the eight required output keys verbatim by name (bio, persona, age, gender, mbti, country, profession, interested_topics)
  • Translate the per-field guidance: bio is a 200-character social-media bio; persona is a coherent ~2000-character text containing basic info, background, personality (with MBTI), social-media behavior, stance, distinctive traits, and event-specific memories; age must be an integer; gender must be the literal English token "male" or "female"; mbti is an MBTI four-letter code; country is a free-form country name; profession is a free-form occupation; interested_topics is a list of topics
  • Replace the trailing 重要: rules block with an English equivalent: all field values must be strings or numbers, no embedded newlines; persona must be a coherent single text block; gender must use English male/female; content must remain consistent with the entity information; age must be a valid integer
  • Preserve the call to get_language_instruction() interpolated into the rules block
  • Replace the attrs_str no-attributes placeholder "无" with "None" (or English equivalent) at line 677
  • Replace the context_str no-context placeholder "无额外上下文" with "No additional context" (or English equivalent) at line 678
  • Preserve every f-string interpolation by name and position: {entity_name}, {entity_type}, {entity_summary}, {attrs_str}, {context_str}, {get_language_instruction()}
  • Observable completion: _build_individual_persona_prompt(...) produces an English-only message body for any input combination, with zero CJK characters in any string literal it contributes; under the same inputs as before, all interpolated values still appear in the rendered output
  • Requirements: 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9

• 3. Translate the group-persona user-message template in _build_group_persona_prompt

  • Replace the introductory line ("为机构/群体实体生成详细的社交媒体账号设定,...") with an English equivalent
  • Replace the field-label rows (实体名称, 实体类型, 实体摘要, 实体属性, 上下文信息) with English equivalents (matching task 2)
  • Replace the 请生成JSON，包含以下字段: enumeration block with an English equivalent that preserves the eight required output keys verbatim by name (bio, persona, age, gender, mbti, country, profession, interested_topics)
  • Translate the per-field guidance: bio is a polished ~200-character official-account bio; persona is a coherent ~2000-character text covering institutional background, account positioning, voice, content patterns, official stance, distinctive traits, and event-specific memories; age must be the integer literal 30; gender must be the literal English token "other"; mbti describes account voice; country is a free-form country name; profession is the institution's role; interested_topics is a list of focus areas
  • Replace the trailing 重要: rules block with an English equivalent: all field values must be strings or numbers (no nulls); persona must be a coherent single text block (no embedded newlines); gender must use English "other"; age must be the integer 30; the institutional account's voice must match its identity
  • Preserve the call to get_language_instruction() interpolated into the rules block
  • Replace the attrs_str and context_str placeholders the same way as in task 2 (lines 726, 727)
  • Preserve every f-string interpolation by name and position
  • Observable completion: _build_group_persona_prompt(...) produces an English-only message body for any input combination, with zero CJK characters; under the same inputs as before, all interpolated values still appear in the rendered output
  • Requirements: 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9

• 4. Translate the section labels in _search_zep_for_entity and _build_entity_context

  • Replace the related-node prefix f"相关实体: {node.name}" with an English equivalent (e.g. f"Related entity: {node.name}") at line 384
  • Replace the facts block heading "事实信息:\n" with "Facts:\n" (or equivalent) at line 390
  • Replace the related-entities block heading "相关实体:\n" with "Related entities:\n" (or equivalent) at line 392
  • Replace the entity-attributes section heading "### 实体属性\n" with "### Entity attributes\n" (or equivalent) at line 422
  • Replace the inline edge-direction placeholder (相关实体) with (related entity) (or equivalent) at lines 438 and 440 (both outgoing and incoming branches)
  • Replace the related-facts/relationships section heading "### 相关事实和关系\n" with "### Related facts and relationships\n" (or equivalent) at line 443
  • Replace the related-entity-information section heading "### 关联实体信息\n" with "### Related entity information\n" (or equivalent) at line 463
  • Replace the Zep-retrieved facts section heading "### Zep检索到的事实信息\n" with "### Facts retrieved from the graph\n" (or equivalent) at line 472
  • Replace the Zep-retrieved related-nodes section heading "### Zep检索到的相关节点\n" with "### Related nodes retrieved from the graph\n" (or equivalent) at line 475
  • Preserve the structure (heading + bulleted body, joined by "\n".join(...))
  • Observable completion: the context string returned by _build_entity_context(...) contains zero CJK characters in section labels for any input
  • Requirements: 4.1, 4.2, 4.3, 4.4, 4.5, 4.6, 4.7, 4.8, 4.9, 4.10

• 5. Translate the fallback persona templates

  • Replace f"{entity_name}是一个{entity_type}。" with f"{entity_name} is a {entity_type}." (or equivalent) at line 547 (_generate_profile_with_llm, missing-persona branch)
  • Replace the same template at line 644 (_try_fix_json, regex-extraction branch)
  • Replace the same template at line 659 (_try_fix_json, catastrophic-failure branch)
  • Preserve the entity_summary or template priority order at every site
  • Observable completion: when the LLM fails JSON parse and the fallback template is invoked, the resulting persona value is English
  • Requirements: 5.1, 5.2, 5.3

• 6. Translate the console-output formatting in _print_generated_profile and the surrounding banners

  • Replace the section headings in _print_generated_profile: f"【简介】" → English equivalent (e.g. "[Bio]"), f"【详细人设】" → English equivalent (e.g. "[Persona]"), f"【基本属性】" → English equivalent (e.g. "[Basic attributes]")
  • Replace the row labels in _print_generated_profile: f"用户名:" → f"Username: {profile.user_name}", f"年龄: {profile.age} | 性别: {profile.gender} | MBTI: {profile.mbti}" → f"Age: {profile.age} | Gender: {profile.gender} | MBTI: {profile.mbti}", f"职业: {profile.profession} | 国家: {profile.country}" → f"Profession: {profile.profession} | Country: {profile.country}", f"兴趣话题: {topics_str}" → f"Interested topics: {topics_str}"
  • Replace the empty-topics sentinel '无' with 'None' (or equivalent) at line 1011
  • Replace the start-of-batch banner in generate_profiles_from_entities (currently f"开始生成Agent人设 - 共 {total} 个实体，并行数: {parallel_count}" at line 945) with an English equivalent (e.g. f"Generating agent profiles — {total} entities, parallel: {parallel_count}")
  • Replace the end-of-batch banner (currently f"人设生成完成！共生成 {len([p for p in profiles if p])} 个Agent" at line 1001) with an English equivalent (e.g. f"Profile generation complete — produced {len([p for p in profiles if p])} agents")
  • Preserve all f-string interpolations
  • Preserve the existing t('progress.profileGenerated', name=entity_name, type=entity_type) call (already locale-keyed)
  • Observable completion: the console output stream contains zero CJK characters in literals contributed by _print_generated_profile and the two batch banners (the entity name itself may still contain CJK because it is data, not a literal)
  • Requirements: 6.1, 6.2, 6.3, 6.4, 6.5, 6.6, 6.7

• 7. Confirm boundary commitments around the translation

  • Confirm logger.warning(...), logger.info(...), logger.error(...), logger.debug(...) calls and their t("log.profile_generator.*") keys in this file are unchanged
  • Confirm the module/class/method docstrings and inline comments are unchanged (including lines 65, 93, 641, 804–807, 816–819)
  • Confirm _normalize_gender mapping table (Chinese keys 男/女/机构/其他) is unchanged
  • Confirm the rule-based country: "中国" default at lines 807, 819 is unchanged
  • Confirm the ValueError("LLM_API_KEY 未配置") raise at line 194 is unchanged
  • Confirm public signatures (__init__, generate_profile_from_entity, generate_profiles_from_entities, set_graph_id, save_profiles, save_profiles_to_json) and private helper signatures are unchanged
  • Confirm the OasisAgentProfile dataclass schema is unchanged
  • Confirm the LLM call (response_format={"type": "json_object"}, temperature=0.7 - (attempt * 0.1), no max_tokens) is unchanged
  • Confirm backend/app/utils/locale.py, /locales/languages.json, /locales/en.json, /locales/zh.json are not modified
  • Confirm backend/pyproject.toml, backend/uv.lock, and any file outside backend/app/services/oasis_profile_generator.py are not modified
  • Observable completion: a git diff review against main shows changes only inside backend/app/services/oasis_profile_generator.py, only inside the seven owned regions
  • Requirements: 7.1, 7.4, 8.1, 8.2, 8.3, 8.4, 8.5, 8.6, 10.1, 10.2, 10.3, 10.4, 10.5, 10.6, 10.7

• 8. Verify CJK-free invariant in the seven owned regions

  • Run a one-shot script that imports OasisProfileGenerator, calls _build_individual_persona_prompt(...), _build_group_persona_prompt(...), _get_system_prompt(...), and _build_entity_context(...) with representative inputs that contain no CJK in the inputs themselves, and asserts the rendered output contains zero matches against the regex [一-鿿]
  • Manually inspect the seven owned regions in the patched file with a CJK regex (grep -nP '[\x{4e00}-\x{9fff}]') and confirm there are no remaining matches inside the owned regions
  • Observable completion: the inspection passes; if it fails, fix the offending region and re-run before completing this task
  • Requirements: 1.1, 2.8, 3.8, 4.10, 5.3, 6.6

• 9. Verify locale-driven output language under both en and zh

  • Set the thread-local locale to en via set_locale("en"), run OasisProfileGenerator().generate_profile_from_entity(...) against the configured LLM with a small representative entity, and confirm the returned bio and persona are in English
  • Set the thread-local locale to zh via set_locale("zh"), run the same round-trip, and confirm the returned bio and persona are in Chinese, equivalent in quality to the pre-change baseline
  • Observable completion: both runs succeed; the en run is CJK-free in bio and persona; the zh run continues to produce Chinese; results recorded in the PR description
  • Requirements: 7.2, 7.3