diff --git a/novel_generator.py b/novel_generator.py index c02a2a2..6c4e47f 100644 --- a/novel_generator.py +++ b/novel_generator.py @@ -32,7 +32,8 @@ from prompt_definitions import ( chunked_chapter_blueprint_prompt, summary_prompt, update_character_state_prompt, - chapter_draft_prompt, + first_chapter_draft_prompt, + next_chapter_draft_prompt, summarize_recent_chapters_prompt ) @@ -415,11 +416,11 @@ def compute_chunk_size(number_of_chapters: int, max_tokens: int) -> int: 并确保 chunk_size 不会小于1或大于实际章节数。 """ tokens_per_chapter = 100.0 - ratio = max_tokens / tokens_per_chapter # 8192 / 100 = 81.92 + ratio = max_tokens / tokens_per_chapter # 例如：8192 / 100 = 81.92 # 先取到最接近的10倍 ratio_rounded_to_10 = int(ratio // 10) * 10 # => 80 # 再减10 - chunk_size = ratio_rounded_to_10 - 10 # => 70 + chunk_size = ratio_rounded_to_10 - 10 # => 70 if chunk_size < 1: chunk_size = 1 if chunk_size > number_of_chapters: @@ -527,7 +528,7 @@ def Chapter_blueprint_generate( logging.info("Novel_directory.txt (chapter blueprint) has been generated successfully (chunked).") -# ============ 3) 生成章节草稿 ============ +# ============ 3) 生成章节草稿（分「第一章」与「后续章节」） ============ def generate_chapter_draft( api_key: str, @@ -550,6 +551,11 @@ def generate_chapter_draft( interface_format: str = "openai", max_tokens: int = 2048 ) -> str: + """ + 根据 novel_number 判断是否为第一章。 + - 若是第一章，则使用 first_chapter_draft_prompt + - 否则使用 next_chapter_draft_prompt + """ arch_file = os.path.join(filepath, "Novel_architecture.txt") novel_architecture_text = read_file(arch_file) @@ -562,6 +568,7 @@ def generate_chapter_draft( character_state_file = os.path.join(filepath, "character_state.txt") character_state_text = read_file(character_state_file) + # 获取本章在目录中的信息 chapter_info = get_chapter_info_from_blueprint(blueprint_text, novel_number) chapter_title = chapter_info["chapter_title"] chapter_role = chapter_info["chapter_role"] @@ -571,68 +578,97 @@ def generate_chapter_draft( plot_twist_level = chapter_info["plot_twist_level"] chapter_summary = chapter_info["chapter_summary"] + # 准备章节目录文件夹 chapters_dir = os.path.join(filepath, "chapters") os.makedirs(chapters_dir, exist_ok=True) - recent_3_texts = get_last_n_chapters_text(chapters_dir, novel_number, n=3) - short_summary, next_chapter_keywords = summarize_recent_chapters( - interface_format=interface_format, - api_key=api_key, - base_url=base_url, - model_name=model_name, - temperature=temperature, - max_tokens=max_tokens, - chapters_text_list=recent_3_texts - ) + # 如果是第一章，不需要前情检索与前章结尾 + if novel_number == 1: + # 使用第一章提示词 + prompt_text = first_chapter_draft_prompt.format( + novel_number=novel_number, + chapter_title=chapter_title, + chapter_role=chapter_role, + chapter_purpose=chapter_purpose, + suspense_level=suspense_level, + foreshadowing=foreshadowing, + plot_twist_level=plot_twist_level, + chapter_summary=chapter_summary, - previous_chapter_excerpt = "" - for text_block in reversed(recent_3_texts): - if text_block.strip(): - if len(text_block) > 1500: - previous_chapter_excerpt = text_block[-1500:] - else: - previous_chapter_excerpt = text_block - break + characters_involved=characters_involved, + key_items=key_items, + scene_location=scene_location, + time_constraint=time_constraint, + user_guidance=user_guidance, - embedding_adapter = create_embedding_adapter( - embedding_interface_format, - embedding_api_key, - embedding_url, - embedding_model_name - ) - retrieval_query = short_summary + " " + next_chapter_keywords - relevant_context = get_relevant_context_from_vector_store( - embedding_adapter=embedding_adapter, - query=retrieval_query, - filepath=filepath, - k=embedding_retrieval_k - ) - if not relevant_context.strip(): - relevant_context = "（无检索到的上下文）" + novel_setting=novel_architecture_text + ) - prompt_text = chapter_draft_prompt.format( - novel_number=novel_number, - chapter_title=chapter_title, - chapter_role=chapter_role, - chapter_purpose=chapter_purpose, - suspense_level=suspense_level, - foreshadowing=foreshadowing, - plot_twist_level=plot_twist_level, - chapter_summary=chapter_summary, + else: + # 若不是第一章，则先获取最近几章文本，并做摘要与检索 + recent_3_texts = get_last_n_chapters_text(chapters_dir, novel_number, n=3) + short_summary, next_chapter_keywords = summarize_recent_chapters( + interface_format=interface_format, + api_key=api_key, + base_url=base_url, + model_name=model_name, + temperature=temperature, + max_tokens=max_tokens, + chapters_text_list=recent_3_texts + ) - characters_involved=characters_involved, - key_items=key_items, - scene_location=scene_location, - time_constraint=time_constraint, - user_guidance=user_guidance, + # 从最近章节中获取最后一段内容作为前章结尾 + previous_chapter_excerpt = "" + for text_block in reversed(recent_3_texts): + if text_block.strip(): + if len(text_block) > 1500: + previous_chapter_excerpt = text_block[-1500:] + else: + previous_chapter_excerpt = text_block + break - novel_setting=novel_architecture_text, - global_summary=global_summary_text, - character_state=character_state_text, - previous_chapter_excerpt=previous_chapter_excerpt, - context_excerpt=relevant_context - ) + # 从向量库检索上下文 + embedding_adapter = create_embedding_adapter( + embedding_interface_format, + embedding_api_key, + embedding_url, + embedding_model_name + ) + retrieval_query = short_summary + " " + next_chapter_keywords + relevant_context = get_relevant_context_from_vector_store( + embedding_adapter=embedding_adapter, + query=retrieval_query, + filepath=filepath, + k=embedding_retrieval_k + ) + if not relevant_context.strip(): + relevant_context = "（无检索到的上下文）" + # 使用后续章节提示词 + prompt_text = next_chapter_draft_prompt.format( + novel_number=novel_number, + chapter_title=chapter_title, + chapter_role=chapter_role, + chapter_purpose=chapter_purpose, + suspense_level=suspense_level, + foreshadowing=foreshadowing, + plot_twist_level=plot_twist_level, + chapter_summary=chapter_summary, + + characters_involved=characters_involved, + key_items=key_items, + scene_location=scene_location, + time_constraint=time_constraint, + user_guidance=user_guidance, + + novel_setting=novel_architecture_text, + global_summary=global_summary_text, + character_state=character_state_text, + context_excerpt=relevant_context, + previous_chapter_excerpt=previous_chapter_excerpt + ) + + # 调用LLM生成 llm_adapter = create_llm_adapter( interface_format=interface_format, base_url=base_url, @@ -645,6 +681,7 @@ def generate_chapter_draft( if not chapter_content.strip(): logging.warning("Generated chapter draft is empty.") + # 保存章节文本 chapter_file = os.path.join(chapters_dir, f"chapter_{novel_number}.txt") clear_file_content(chapter_file) save_string_to_txt(chapter_content, chapter_file) @@ -652,6 +689,7 @@ def generate_chapter_draft( logging.info(f"[Draft] Chapter {novel_number} generated as a draft.") return chapter_content + # ============ 4) 定稿章节 ============ def finalize_chapter( @@ -676,6 +714,7 @@ def finalize_chapter( logging.warning(f"Chapter {novel_number} is empty, cannot finalize.") return + # 如果内容过短，则尝试扩写 if len(chapter_text) < 0.7 * word_number: chapter_text = enrich_chapter_text(chapter_text, word_number, api_key, base_url, model_name, temperature, interface_format, max_tokens) clear_file_content(chapter_file) @@ -716,6 +755,7 @@ def finalize_chapter( clear_file_content(character_state_file) save_string_to_txt(new_char_state, character_state_file) + # 更新向量库 embedding_adapter = create_embedding_adapter( embedding_interface_format, embedding_api_key, diff --git a/prompt_definitions.py b/prompt_definitions.py index c7b5661..a6b2d2b 100644 --- a/prompt_definitions.py +++ b/prompt_definitions.py @@ -364,7 +364,7 @@ next_chapter_draft_prompt = """\ 前章结尾段： {previous_chapter_excerpt} -请从前章结尾处继续完成第 {novel_number} 章的正文，至少设计下方2个具有动态张力的场景： +请依据前章结尾片段，继续完成第 {novel_number} 章的正文，至少设计下方2个具有动态张力的场景： 1. 对话场景： - 潜台词冲突（表面谈论A，实际博弈B） - 权力关系变化（通过非对称对话长度体现）