759 lines
25 KiB
Python
759 lines
25 KiB
Python
# novel_generator.py
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# -*- coding: utf-8 -*-
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import os
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import logging
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import re
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import time
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import traceback
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from typing import List, Optional, Tuple
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from langchain_chroma import Chroma
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from chromadb.config import Settings
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from langchain.docstore.document import Document
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# nltk、sentence_transformers 及文本处理相关
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import nltk
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from sentence_transformers import SentenceTransformer
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from sklearn.metrics.pairwise import cosine_similarity
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# 工具函数
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from utils import (
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read_file, append_text_to_file, clear_file_content,
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save_string_to_txt
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)
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# prompt模板
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from prompt_definitions import (
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core_seed_prompt,
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character_dynamics_prompt,
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world_building_prompt,
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plot_architecture_prompt,
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chapter_blueprint_prompt,
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summary_prompt,
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update_character_state_prompt,
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chapter_draft_prompt,
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summarize_recent_chapters_prompt
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)
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# 章节目录解析
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from chapter_directory_parser import get_chapter_info_from_blueprint
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from llm_adapters import create_llm_adapter
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from embedding_adapters import create_embedding_adapter
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logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s")
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# ============ 工具函数 ============
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def remove_think_tags(text: str) -> str:
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"""移除 <think>...</think> 包裹的内容"""
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return re.sub(r'<think>.*?</think>', '', text, flags=re.DOTALL)
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def debug_log(prompt: str, response_content: str):
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logging.info(
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f"\n[######################################### Prompt #########################################]\n{prompt}\n"
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)
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logging.info(
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f"\n[######################################### Response #########################################]\n{response_content}\n"
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)
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def invoke_with_cleaning(llm_adapter, prompt: str) -> str:
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"""通用封装：调用 LLM，并移除 <think>...</think> 文本，记录日志后返回"""
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response = llm_adapter.invoke(prompt)
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if not response:
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logging.warning("No response from model.")
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return ""
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cleaned_text = remove_think_tags(response)
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debug_log(prompt, cleaned_text)
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return cleaned_text.strip()
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# ============ 获取 vectorstore 路径 ============
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def get_vectorstore_dir(filepath: str) -> str:
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return os.path.join(filepath, "vectorstore")
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# ============ 清空向量库 ============
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def clear_vector_store(filepath: str) -> bool:
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import shutil
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store_dir = get_vectorstore_dir(filepath)
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if not os.path.exists(store_dir):
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logging.info("No vector store found to clear.")
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return False
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try:
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shutil.rmtree(store_dir)
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logging.info(f"Vector store directory '{store_dir}' removed.")
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return True
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except Exception as e:
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logging.error(f"无法删除向量库文件夹，请关闭程序后手动删除 {store_dir}。\n {str(e)}")
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traceback.print_exc()
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return False
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# ============ 根据 embedding 接口创建/加载 Chroma ============
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def init_vector_store(
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embedding_adapter,
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texts: List[str],
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filepath: str
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) -> Chroma:
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"""
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在 filepath 下创建/加载一个 Chroma 向量库并插入 texts。
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这里 embedding_adapter 是一个实现了 embed_documents(texts) 的对象
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"""
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store_dir = get_vectorstore_dir(filepath)
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os.makedirs(store_dir, exist_ok=True)
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# 将文本封装为 Document
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documents = [Document(page_content=str(t)) for t in texts]
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# 因为我们是自定义的 embeddings，对接Chroma时需包装一个“langchain兼容对象”
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# 这里示例：写一个包装函数
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from langchain.embeddings.base import Embeddings as LCEmbeddings
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class LCEmbeddingWrapper(LCEmbeddings):
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def embed_documents(self, doc_texts: List[str]) -> List[List[float]]:
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return embedding_adapter.embed_documents(doc_texts)
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def embed_query(self, query_text: str) -> List[float]:
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return embedding_adapter.embed_query(query_text)
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chroma_embedding = LCEmbeddingWrapper()
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vectorstore = Chroma.from_documents(
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documents,
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embedding=chroma_embedding,
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persist_directory=store_dir,
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client_settings=Settings(anonymized_telemetry=False),
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collection_name="novel_collection"
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)
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return vectorstore
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def load_vector_store(
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embedding_adapter,
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filepath: str
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) -> Optional[Chroma]:
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"""
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读取已存在的 Chroma 向量库。若不存在则返回 None。
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"""
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store_dir = get_vectorstore_dir(filepath)
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if not os.path.exists(store_dir):
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logging.info("Vector store not found. Will return None.")
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return None
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# 同样要包装embedding_adapter
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from langchain.embeddings.base import Embeddings as LCEmbeddings
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class LCEmbeddingWrapper(LCEmbeddings):
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def embed_documents(self, doc_texts: List[str]) -> List[List[float]]:
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return embedding_adapter.embed_documents(doc_texts)
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def embed_query(self, query_text: str) -> List[float]:
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return embedding_adapter.embed_query(query_text)
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chroma_embedding = LCEmbeddingWrapper()
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return Chroma(
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persist_directory=store_dir,
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embedding_function=chroma_embedding,
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client_settings=Settings(anonymized_telemetry=False),
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collection_name="novel_collection"
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)
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# ============ 文本分段工具 ============
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def split_by_length(text: str, max_length: int = 500) -> List[str]:
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segments = []
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start_idx = 0
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while start_idx < len(text):
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end_idx = min(start_idx + max_length, len(text))
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segment = text[start_idx:end_idx]
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segments.append(segment.strip())
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start_idx = end_idx
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return segments
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def split_text_for_vectorstore(chapter_text: str,
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max_length: int = 500,
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similarity_threshold: float = 0.7) -> List[str]:
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"""
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对新的章节文本进行分段后，再用于存入向量库。
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先句子切分 -> 语义相似度合并 -> 再按 max_length 切分。
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"""
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if not chapter_text.strip():
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return []
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nltk.download('punkt', quiet=True)
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sentences = nltk.sent_tokenize(chapter_text)
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if not sentences:
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return []
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model = SentenceTransformer('paraphrase-MiniLM-L6-v2')
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embeddings = model.encode(sentences)
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merged_paragraphs = []
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current_sentences = [sentences[0]]
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current_embedding = embeddings[0]
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for i in range(1, len(sentences)):
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sim = cosine_similarity([current_embedding], [embeddings[i]])[0][0]
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if sim >= similarity_threshold:
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current_sentences.append(sentences[i])
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current_embedding = (current_embedding + embeddings[i]) / 2.0
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else:
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merged_paragraphs.append(" ".join(current_sentences))
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current_sentences = [sentences[i]]
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current_embedding = embeddings[i]
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if current_sentences:
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merged_paragraphs.append(" ".join(current_sentences))
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final_segments = []
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for para in merged_paragraphs:
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if len(para) > max_length:
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sub_segments = split_by_length(para, max_length=max_length)
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final_segments.extend(sub_segments)
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else:
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final_segments.append(para)
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return final_segments
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# ============ 更新向量库 ============
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def update_vector_store(
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embedding_adapter,
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new_chapter: str,
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filepath: str
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):
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"""
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将最新章节文本插入到向量库中。若库不存在则初始化。
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"""
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splitted_texts = split_text_for_vectorstore(new_chapter)
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if not splitted_texts:
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logging.warning("No valid text to insert into vector store. Skipping.")
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return
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store = load_vector_store(embedding_adapter, filepath)
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if not store:
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logging.info("Vector store does not exist. Initializing a new one for new chapter...")
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init_vector_store(embedding_adapter, splitted_texts, filepath)
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return
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docs = [Document(page_content=str(t)) for t in splitted_texts]
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store.add_documents(docs)
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logging.info("Vector store updated with the new chapter splitted segments.")
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# ============ 向量检索上下文 ============
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def get_relevant_context_from_vector_store(
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embedding_adapter,
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query: str,
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filepath: str,
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k: int = 2
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) -> str:
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"""
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从向量库中检索与 query 最相关的 k 条文本，拼接后返回。
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"""
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store = load_vector_store(embedding_adapter, filepath)
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if not store:
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logging.info("No vector store found. Returning empty context.")
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return ""
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docs = store.similarity_search(query, k=k)
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if not docs:
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logging.info(f"No relevant documents found for query '{query}'. Returning empty context.")
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return ""
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combined = "\n".join([d.page_content for d in docs])
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return combined
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# ============ 从目录中获取最近 n 章文本 ============
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def get_last_n_chapters_text(chapters_dir: str, current_chapter_num: int, n: int = 3) -> List[str]:
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texts = []
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start_chap = max(1, current_chapter_num - n)
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for c in range(start_chap, current_chapter_num):
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chap_file = os.path.join(chapters_dir, f"chapter_{c}.txt")
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if os.path.exists(chap_file):
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text = read_file(chap_file).strip()
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texts.append(text)
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else:
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texts.append("")
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return texts
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# ============ 提炼(短期摘要, 下一章关键字) ============
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def summarize_recent_chapters(
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interface_format: str,
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api_key: str,
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base_url: str,
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model_name: str,
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temperature: float,
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chapters_text_list: List[str]
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) -> Tuple[str, str]:
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"""
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生成 (short_summary, next_chapter_keywords)
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如果解析失败，则返回 (合并文本, "")
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"""
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combined_text = "\n".join(chapters_text_list).strip()
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if not combined_text:
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return ("", "")
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# 1) 构造 llm_adapter
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llm_adapter = create_llm_adapter(
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interface_format=interface_format,
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base_url=base_url,
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model_name=model_name,
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api_key=api_key,
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temperature=temperature
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)
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prompt = summarize_recent_chapters_prompt.format(combined_text=combined_text)
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response_text = invoke_with_cleaning(llm_adapter, prompt)
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short_summary = ""
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next_chapter_keywords = ""
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for line in response_text.splitlines():
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line = line.strip()
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if line.startswith("短期摘要:"):
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short_summary = line.replace("短期摘要:", "").strip()
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elif line.startswith("下一章关键字:"):
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next_chapter_keywords = line.replace("下一章关键字:", "").strip()
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if not short_summary and not next_chapter_keywords:
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short_summary = response_text
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return (short_summary, next_chapter_keywords)
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# ============ 1) 生成总体架构 ============
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def Novel_architecture_generate(
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api_key: str,
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base_url: str,
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llm_model: str,
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topic: str,
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genre: str,
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number_of_chapters: int,
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word_number: int,
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filepath: str,
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temperature: float = 0.7
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) -> None:
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"""
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依次调用:
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1. core_seed_prompt
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2. character_dynamics_prompt
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3. world_building_prompt
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4. plot_architecture_prompt
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最终输出 Novel_architecture.txt
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"""
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os.makedirs(filepath, exist_ok=True)
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# 通过工厂函数创建 LLM 适配器
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llm_adapter = create_llm_adapter(
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interface_format="openai", # 或根据你的实际：若你在UI中就是 "OpenAI" 就传递过来
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base_url=base_url,
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model_name=llm_model,
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api_key=api_key,
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temperature=temperature
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)
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# Step1: 核心种子
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prompt_core = core_seed_prompt.format(
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topic=topic,
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genre=genre,
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number_of_chapters=number_of_chapters,
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word_number=word_number
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)
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core_seed_result = invoke_with_cleaning(llm_adapter, prompt_core)
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# Step2: 角色动力学
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prompt_character = character_dynamics_prompt.format(core_seed=core_seed_result.strip())
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character_dynamics_result = invoke_with_cleaning(llm_adapter, prompt_character)
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# Step3: 世界观
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prompt_world = world_building_prompt.format(core_seed=core_seed_result.strip())
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world_building_result = invoke_with_cleaning(llm_adapter, prompt_world)
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# Step4: 三幕式情节
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prompt_plot = plot_architecture_prompt.format(
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core_seed=core_seed_result.strip(),
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character_dynamics=character_dynamics_result.strip(),
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world_building=world_building_result.strip()
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)
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plot_arch_result = invoke_with_cleaning(llm_adapter, prompt_plot)
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# 合并
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final_content = (
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"#=== 1) 核心种子 ===\n"
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f"{core_seed_result}\n\n"
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"#=== 2) 角色动力学 ===\n"
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f"{character_dynamics_result}\n\n"
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"#=== 3) 世界观 ===\n"
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f"{world_building_result}\n\n"
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"#=== 4) 三幕式情节架构 ===\n"
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f"{plot_arch_result}\n"
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)
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arch_file = os.path.join(filepath, "Novel_architecture.txt")
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clear_file_content(arch_file)
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save_string_to_txt(final_content, arch_file)
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logging.info("Novel_architecture.txt has been generated successfully.")
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# ============ 2) 生成章节蓝图 ============
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def Chapter_blueprint_generate(
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api_key: str,
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base_url: str,
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llm_model: str,
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filepath: str,
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temperature: float = 0.7
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) -> None:
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arch_file = os.path.join(filepath, "Novel_architecture.txt")
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if not os.path.exists(arch_file):
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logging.warning("Novel_architecture.txt not found. Please generate architecture first.")
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return
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architecture_text = read_file(arch_file).strip()
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if not architecture_text:
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logging.warning("Novel_architecture.txt is empty.")
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return
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match_chaps = re.search(r'约(\d+)章', architecture_text)
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if match_chaps:
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number_of_chapters = int(match_chaps.group(1))
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else:
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number_of_chapters = 10
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# 提取三幕式文本
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plot_arch_text = ""
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pat_plot = r'#=== 4\) 三幕式情节架构 ===\n([\s\S]+)$'
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m = re.search(pat_plot, architecture_text)
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if m:
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plot_arch_text = m.group(1).strip()
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llm_adapter = create_llm_adapter(
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interface_format="openai", # 或实际由UI传入
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base_url=base_url,
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model_name=llm_model,
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api_key=api_key,
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temperature=temperature
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)
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prompt = chapter_blueprint_prompt.format(
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plot_architecture=plot_arch_text,
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number_of_chapters=number_of_chapters
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)
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blueprint_text = invoke_with_cleaning(llm_adapter, prompt)
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if not blueprint_text.strip():
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logging.warning("Chapter blueprint generation result is empty.")
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return
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filename_dir = os.path.join(filepath, "Novel_directory.txt")
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clear_file_content(filename_dir)
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save_string_to_txt(blueprint_text, filename_dir)
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logging.info("Novel_directory.txt (chapter blueprint) has been generated successfully.")
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# ============ 3) 生成章节草稿 ============
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def generate_chapter_draft(
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api_key: str,
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base_url: str,
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model_name: str,
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filepath: str,
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novel_number: int,
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word_number: int,
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temperature: float,
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user_guidance: str,
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characters_involved: str,
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key_items: str,
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scene_location: str,
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time_constraint: str,
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embedding_api_key: str,
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embedding_url: str,
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embedding_interface_format: str,
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embedding_model_name: str,
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embedding_retrieval_k: int = 2
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) -> str:
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arch_file = os.path.join(filepath, "Novel_architecture.txt")
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novel_architecture_text = read_file(arch_file)
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directory_file = os.path.join(filepath, "Novel_directory.txt")
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blueprint_text = read_file(directory_file)
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global_summary_file = os.path.join(filepath, "global_summary.txt")
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global_summary_text = read_file(global_summary_file)
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character_state_file = os.path.join(filepath, "character_state.txt")
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character_state_text = read_file(character_state_file)
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# 解析本章信息
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chapter_info = get_chapter_info_from_blueprint(blueprint_text, novel_number)
|
|
chapter_title = chapter_info["chapter_title"]
|
|
chapter_role = chapter_info["chapter_role"]
|
|
chapter_purpose = chapter_info["chapter_purpose"]
|
|
suspense_level = chapter_info["suspense_level"]
|
|
foreshadowing = chapter_info["foreshadowing"]
|
|
plot_twist_level = chapter_info["plot_twist_level"]
|
|
chapter_summary = chapter_info["chapter_summary"]
|
|
|
|
chapters_dir = os.path.join(filepath, "chapters")
|
|
os.makedirs(chapters_dir, exist_ok=True)
|
|
|
|
# 获取最近3章 => (短期摘要, 下一章关键字)
|
|
recent_3_texts = get_last_n_chapters_text(chapters_dir, novel_number, n=3)
|
|
short_summary, next_chapter_keywords = summarize_recent_chapters(
|
|
interface_format="openai", # 或由UI传进
|
|
api_key=api_key,
|
|
base_url=base_url,
|
|
model_name=model_name,
|
|
temperature=temperature,
|
|
chapters_text_list=recent_3_texts
|
|
)
|
|
|
|
# 上一章片段(末尾1500字)
|
|
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
|
|
|
|
# 使用embedding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）"
|
|
|
|
# 组装 Prompt
|
|
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,
|
|
|
|
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,
|
|
previous_chapter_excerpt=previous_chapter_excerpt,
|
|
context_excerpt=relevant_context
|
|
)
|
|
|
|
# 调用 LLM 生成
|
|
llm_adapter = create_llm_adapter(
|
|
interface_format="openai", # 或由UI传进
|
|
base_url=base_url,
|
|
model_name=model_name,
|
|
api_key=api_key,
|
|
temperature=temperature
|
|
)
|
|
chapter_content = invoke_with_cleaning(llm_adapter, prompt_text)
|
|
if not chapter_content.strip():
|
|
logging.warning("Generated chapter draft is empty.")
|
|
|
|
# 写入 chapter_X.txt
|
|
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)
|
|
|
|
logging.info(f"[Draft] Chapter {novel_number} generated as a draft.")
|
|
return chapter_content
|
|
|
|
# ============ 4) 定稿章节 ============
|
|
|
|
def finalize_chapter(
|
|
novel_number: int,
|
|
word_number: int,
|
|
api_key: str,
|
|
base_url: str,
|
|
model_name: str,
|
|
temperature: float,
|
|
filepath: str,
|
|
embedding_api_key: str,
|
|
embedding_url: str,
|
|
embedding_interface_format: str,
|
|
embedding_model_name: str
|
|
):
|
|
chapters_dir = os.path.join(filepath, "chapters")
|
|
chapter_file = os.path.join(chapters_dir, f"chapter_{novel_number}.txt")
|
|
chapter_text = read_file(chapter_file).strip()
|
|
if not chapter_text:
|
|
logging.warning(f"Chapter {novel_number} is empty, cannot finalize.")
|
|
return
|
|
|
|
# 如果篇幅过短，可以扩写
|
|
if len(chapter_text) < 0.6 * word_number:
|
|
chapter_text = enrich_chapter_text(chapter_text, word_number, api_key, base_url, model_name, temperature)
|
|
clear_file_content(chapter_file)
|
|
save_string_to_txt(chapter_text, chapter_file)
|
|
|
|
# 读取全局摘要、角色状态
|
|
global_summary_file = os.path.join(filepath, "global_summary.txt")
|
|
old_global_summary = read_file(global_summary_file)
|
|
character_state_file = os.path.join(filepath, "character_state.txt")
|
|
old_character_state = read_file(character_state_file)
|
|
|
|
# 调用 LLM 更新全局摘要
|
|
llm_adapter = create_llm_adapter(
|
|
interface_format="openai",
|
|
base_url=base_url,
|
|
model_name=model_name,
|
|
api_key=api_key,
|
|
temperature=temperature
|
|
)
|
|
prompt_summary = summary_prompt.format(
|
|
chapter_text=chapter_text,
|
|
global_summary=old_global_summary
|
|
)
|
|
new_global_summary = invoke_with_cleaning(llm_adapter, prompt_summary)
|
|
if not new_global_summary.strip():
|
|
new_global_summary = old_global_summary
|
|
|
|
# 更新角色状态
|
|
prompt_char_state = update_character_state_prompt.format(
|
|
chapter_text=chapter_text,
|
|
old_state=old_character_state
|
|
)
|
|
new_char_state = invoke_with_cleaning(llm_adapter, prompt_char_state)
|
|
if not new_char_state.strip():
|
|
new_char_state = old_character_state
|
|
|
|
# 写回
|
|
clear_file_content(global_summary_file)
|
|
save_string_to_txt(new_global_summary, global_summary_file)
|
|
|
|
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,
|
|
embedding_url,
|
|
embedding_model_name
|
|
)
|
|
update_vector_store(embedding_adapter, chapter_text, filepath)
|
|
|
|
logging.info(f"Chapter {novel_number} has been finalized.")
|
|
|
|
def enrich_chapter_text(
|
|
chapter_text: str,
|
|
word_number: int,
|
|
api_key: str,
|
|
base_url: str,
|
|
model_name: str,
|
|
temperature: float
|
|
) -> str:
|
|
llm_adapter = create_llm_adapter(
|
|
interface_format="openai",
|
|
base_url=base_url,
|
|
model_name=model_name,
|
|
api_key=api_key,
|
|
temperature=temperature
|
|
)
|
|
prompt = f"""以下章节文本较短，请在保持剧情连贯的前提下进行扩写，使其更充实，接近 {word_number} 字左右：
|
|
原内容：
|
|
{chapter_text}
|
|
"""
|
|
enriched_text = invoke_with_cleaning(llm_adapter, prompt)
|
|
return enriched_text if enriched_text else chapter_text
|
|
|
|
# ============ 导入知识文件到向量库 ============
|
|
|
|
def advanced_split_content(content: str,
|
|
similarity_threshold: float = 0.7,
|
|
max_length: int = 500) -> List[str]:
|
|
nltk.download('punkt', quiet=True)
|
|
sentences = nltk.sent_tokenize(content)
|
|
if not sentences:
|
|
return []
|
|
|
|
model = SentenceTransformer('paraphrase-MiniLM-L6-v2')
|
|
embeddings = model.encode(sentences)
|
|
|
|
merged_paragraphs = []
|
|
current_sentences = [sentences[0]]
|
|
current_embedding = embeddings[0]
|
|
|
|
for i in range(1, len(sentences)):
|
|
sim = cosine_similarity([current_embedding], [embeddings[i]])[0][0]
|
|
if sim >= similarity_threshold:
|
|
current_sentences.append(sentences[i])
|
|
current_embedding = (current_embedding + embeddings[i]) / 2.0
|
|
else:
|
|
merged_paragraphs.append(" ".join(current_sentences))
|
|
current_sentences = [sentences[i]]
|
|
current_embedding = embeddings[i]
|
|
|
|
if current_sentences:
|
|
merged_paragraphs.append(" ".join(current_sentences))
|
|
|
|
final_segments = []
|
|
for para in merged_paragraphs:
|
|
if len(para) > max_length:
|
|
sub_segments = split_by_length(para, max_length=max_length)
|
|
final_segments.extend(sub_segments)
|
|
else:
|
|
final_segments.append(para)
|
|
|
|
return final_segments
|
|
|
|
def import_knowledge_file(
|
|
embedding_api_key: str,
|
|
embedding_url: str,
|
|
embedding_interface_format: str,
|
|
embedding_model_name: str,
|
|
file_path: str,
|
|
filepath: str
|
|
):
|
|
logging.info(f"开始导入知识库文件: {file_path}, 接口格式: {embedding_interface_format}, 模型: {embedding_model_name}")
|
|
if not os.path.exists(file_path):
|
|
logging.warning(f"知识库文件不存在: {file_path}")
|
|
return
|
|
|
|
content = read_file(file_path)
|
|
if not content.strip():
|
|
logging.warning("知识库文件内容为空。")
|
|
return
|
|
|
|
paragraphs = advanced_split_content(content)
|
|
|
|
embedding_adapter = create_embedding_adapter(
|
|
interface_format=embedding_interface_format,
|
|
api_key=embedding_api_key,
|
|
base_url=embedding_url if embedding_url else "http://localhost:11434/api",
|
|
model_name=embedding_model_name
|
|
)
|
|
|
|
store = load_vector_store(embedding_adapter, filepath)
|
|
if not store:
|
|
logging.info("Vector store does not exist. Initializing a new one for knowledge import...")
|
|
init_vector_store(embedding_adapter, paragraphs, filepath)
|
|
else:
|
|
docs = [Document(page_content=str(p)) for p in paragraphs]
|
|
store.add_documents(docs)
|
|
logging.info("知识库文件已成功导入至向量库。")
|