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| import pandas as pd | |
| from model_handler import ModelHandler | |
| from config import LING_MINI_2_0 | |
| from .agent_common_utils import format_df_to_string | |
| def fetch_prompt_suggestions_agent(editor_content: str, style: str, kb_df: pd.DataFrame, short_outline_df: pd.DataFrame, long_outline_df: pd.DataFrame): | |
| """ | |
| Agent for fetching short prompt suggestions using LING_MINI_2_0. | |
| """ | |
| print("\n[Agent][fetch_prompt_suggestions_agent] === 推理类型：续写提示推荐 ===") | |
| try: | |
| # 1. Format context | |
| style_context = f"### 整体章程\n{style}\n\n" | |
| kb_context = format_df_to_string(kb_df, "知识库") | |
| short_outline_context = format_df_to_string(short_outline_df, "当前章节大纲") | |
| # 2. Build System Prompt | |
| system_prompt = ( | |
| "你是一个辅助写作的创意助手。请根据提供的故事背景和知识库，结合“互动”、“冲突”、“发展”、“对话”等动作，生成3个简短的续写提示短语。\n" | |
| "要求：\n" | |
| "1. 短语简洁明了，例如：“和Alpha争吵”、“探索废弃的地铁站”、“回忆起旧照片的往事”。\n" | |
| "2. 尽量使用知识库中的专有名词。\n" | |
| "3. 请严格遵守以下格式：输出3个短语，用 `|` 分隔。不要包含其他内容。\n" | |
| "例如：和Alpha争吵|探索废弃的地铁站|回忆起旧照片的往事" | |
| ) | |
| # 3. Build User Prompt | |
| full_context = style_context + kb_context + short_outline_context | |
| user_prompt = ( | |
| f"### 背景设定\n{full_context}\n" | |
| f"### 当前已写内容 (末尾部分)\n{editor_content[-500:]}\n\n" # Only need a little context | |
| f"### 任务\n生成3个续写提示。" | |
| ) | |
| # 4. Call LLM | |
| model_handler = ModelHandler() | |
| response_generator = model_handler.generate_code( | |
| system_prompt=system_prompt, | |
| user_prompt=user_prompt, | |
| model_choice=LING_MINI_2_0 | |
| ) | |
| full_response = "".join(chunk for chunk in response_generator) | |
| print("【收到的建议】", full_response) | |
| suggestions = full_response.split("|") | |
| # Ensure 3 suggestions | |
| suggestions += ["继续推进剧情"] * (3 - len(suggestions)) | |
| return suggestions[0].strip(), suggestions[1].strip(), suggestions[2].strip() | |
| except Exception as e: | |
| print(f"[Agent] Error fetching prompt suggestions: {e}") | |
| return "生成失败", "生成失败", "生成失败" | |