330 lines
12 KiB
Python
330 lines
12 KiB
Python
# -*- coding: utf-8 -*-
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"""
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向量存储 - 使用 Ollama 生成向量，FAISS 进行索引和检索
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"""
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import os
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import json
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import numpy as np
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import requests
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from config import INDEX_DIR, OLLAMA_URL, OLLAMA_EMBED_MODEL, TOP_K
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class VectorStore:
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def __init__(self):
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self.index = None
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self.documents = [] # 存储文档内容和元数据
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self.dimension = 768 # nomic-embed-text 的向量维度
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self.index_file = os.path.join(INDEX_DIR, "faiss.index")
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self.docs_file = os.path.join(INDEX_DIR, "documents.json")
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self.faiss = None
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def _load_faiss(self):
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"""懒加载 FAISS"""
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if self.faiss is None:
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import faiss
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self.faiss = faiss
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def _embed_with_ollama(self, text, retry_count=3):
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"""使用 Ollama 生成向量，带重试机制"""
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import time
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import urllib.request
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import urllib.error
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url = f"{OLLAMA_URL}/api/embeddings"
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# 确保文本不为空且是字符串
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if not text or not isinstance(text, str):
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text = "empty"
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# 清理文本中的特殊字符
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text = text.replace('\x00', '') # 移除 null 字符
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# 截断过长的文本（nomic-embed-text 上下文限制约 2048 tokens）
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# 中文约 1.5 字符/token，保守设置为 1000 字符
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max_length = 1000
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if len(text) > max_length:
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text = text[:max_length]
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payload = {
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"model": OLLAMA_EMBED_MODEL,
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"prompt": text
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}
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last_error = None
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for attempt in range(retry_count):
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try:
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# 使用 urllib 代替 requests，避免潜在的编码问题
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data = json.dumps(payload, ensure_ascii=False).encode('utf-8')
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req = urllib.request.Request(
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url,
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data=data,
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headers={'Content-Type': 'application/json; charset=utf-8'},
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method='POST'
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)
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with urllib.request.urlopen(req, timeout=120) as response:
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result = json.loads(response.read().decode('utf-8'))
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return result.get("embedding", [])
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except urllib.error.HTTPError as e:
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last_error = e
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error_body = e.read().decode('utf-8') if e.fp else 'N/A'
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print(f"Ollama HTTP 错误 (尝试 {attempt+1}/{retry_count}): {e.code} {e.reason}")
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print(f"响应内容: {error_body[:500]}")
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print(f"请求文本长度: {len(text)}")
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if attempt < retry_count - 1:
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wait_time = (attempt + 1) * 2
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print(f"等待 {wait_time} 秒后重试...")
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time.sleep(wait_time)
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except Exception as e:
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last_error = e
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print(f"Ollama 嵌入失败 (尝试 {attempt+1}/{retry_count}): {e}")
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if attempt < retry_count - 1:
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wait_time = (attempt + 1) * 2
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print(f"等待 {wait_time} 秒后重试...")
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time.sleep(wait_time)
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raise last_error
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def _embed_batch(self, texts):
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"""批量生成向量"""
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import time
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embeddings = []
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for i, text in enumerate(texts):
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# 打印文本信息用于调试
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print(f" 生成向量 {i+1}/{len(texts)}...")
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print(f" 文本长度: {len(text)}, 前50字符: {repr(text[:50])}")
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embedding = self._embed_with_ollama(text)
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embeddings.append(embedding)
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# 添加小延迟避免请求过快
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if i < len(texts) - 1:
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time.sleep(1.0)
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return embeddings
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for i, text in enumerate(texts):
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print(f" 生成向量 {i+1}/{len(texts)}...")
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embedding = self._embed_with_ollama(text)
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embeddings.append(embedding)
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# 添加小延迟避免请求过快
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if i < len(texts) - 1:
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time.sleep(0.5)
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return embeddings
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def _init_index(self):
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"""初始化 FAISS 索引"""
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self._load_faiss()
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if self.index is None:
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self.index = self.faiss.IndexFlatIP(self.dimension)
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def load_index(self):
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"""从磁盘加载索引"""
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self._load_faiss()
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if os.path.exists(self.index_file) and os.path.exists(self.docs_file):
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try:
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print("正在加载已有索引...")
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# FAISS 在 Windows 上不支持中文路径，使用临时文件
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import tempfile
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import shutil
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try:
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# 复制到临时文件再读取
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with tempfile.NamedTemporaryFile(delete=False, suffix='.index') as tmp:
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tmp_path = tmp.name
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shutil.copy2(self.index_file, tmp_path)
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self.index = self.faiss.read_index(tmp_path)
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os.unlink(tmp_path)
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except Exception as e:
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print(f"临时文件方式失败，尝试直接读取: {e}")
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self.index = self.faiss.read_index(self.index_file)
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with open(self.docs_file, 'r', encoding='utf-8') as f:
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self.documents = json.load(f)
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print(f"索引加载完成，共 {len(self.documents)} 个文档块")
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return True
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except Exception as e:
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print(f"加载索引失败: {e}")
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self._init_index()
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self.documents = []
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return False
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else:
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print("未找到已有索引，创建新索引")
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self._init_index()
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self.documents = []
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return False
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def save_index(self):
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"""保存索引到磁盘"""
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self._load_faiss()
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if self.index is not None:
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# 确保目录存在
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os.makedirs(os.path.dirname(self.index_file), exist_ok=True)
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# FAISS 在 Windows 上不支持中文路径，使用临时文件再移动
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import tempfile
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import shutil
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try:
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# 先写入临时文件
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with tempfile.NamedTemporaryFile(delete=False, suffix='.index') as tmp:
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tmp_path = tmp.name
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self.faiss.write_index(self.index, tmp_path)
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# 移动到目标位置
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shutil.move(tmp_path, self.index_file)
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except Exception as e:
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# 如果临时文件方式失败，尝试直接写入
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print(f"临时文件方式失败，尝试直接写入: {e}")
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self.faiss.write_index(self.index, self.index_file)
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with open(self.docs_file, 'w', encoding='utf-8') as f:
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json.dump(self.documents, f, ensure_ascii=False, indent=2)
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print(f"索引已保存，共 {len(self.documents)} 个文档块")
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def add_documents(self, chunks, metadata=None):
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"""添加文档块到索引"""
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if not chunks:
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return 0
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self._load_faiss()
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self._init_index()
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# 使用 Ollama 生成向量
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print(f"正在为 {len(chunks)} 个文本块生成向量...")
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embeddings = self._embed_batch(chunks)
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# 检查向量维度
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if embeddings and len(embeddings[0]) != self.dimension:
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self.dimension = len(embeddings[0])
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self.index = self.faiss.IndexFlatIP(self.dimension)
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print(f"更新向量维度为: {self.dimension}")
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# 归一化向量（用于余弦相似度）
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embeddings_np = np.array(embeddings).astype('float32')
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norms = np.linalg.norm(embeddings_np, axis=1, keepdims=True)
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embeddings_np = embeddings_np / (norms + 1e-10)
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# 添加到索引
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start_idx = len(self.documents)
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self.index.add(embeddings_np)
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# 保存文档内容和元数据
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for i, chunk in enumerate(chunks):
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doc = {
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'id': start_idx + i,
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'content': chunk,
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'metadata': metadata or {}
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}
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self.documents.append(doc)
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# 自动保存
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self.save_index()
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return len(chunks)
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def search(self, query, top_k=None):
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"""搜索相关文档"""
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if top_k is None:
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top_k = TOP_K
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if self.index is None or self.index.ntotal == 0:
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return []
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self._load_faiss()
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# 生成查询向量
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query_embedding = self._embed_with_ollama(query)
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query_np = np.array([query_embedding]).astype('float32')
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# 归一化
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norm = np.linalg.norm(query_np)
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query_np = query_np / (norm + 1e-10)
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# 搜索
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k = min(top_k, self.index.ntotal)
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scores, indices = self.index.search(query_np, k)
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# 构建结果
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results = []
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for i, idx in enumerate(indices[0]):
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if idx < len(self.documents) and idx >= 0:
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doc = self.documents[idx]
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metadata = doc.get('metadata', {})
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results.append({
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'content': doc['content'],
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'score': float(scores[0][i]),
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'filename': metadata.get('filename', '未知来源'),
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'similarity': float(scores[0][i]),
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'metadata': metadata
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})
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return results
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def delete_by_filename(self, filename):
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"""删除指定文件的所有文档块"""
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if not self.documents:
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return 0
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self._load_faiss()
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# 找出要保留的文档
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remaining_docs = []
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deleted_count = 0
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for doc in self.documents:
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if doc.get('metadata', {}).get('filename') != filename:
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remaining_docs.append(doc)
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else:
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deleted_count += 1
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if deleted_count > 0:
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# 重建索引
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self.documents = []
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self.index = self.faiss.IndexFlatIP(self.dimension)
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if remaining_docs:
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chunks = [doc['content'] for doc in remaining_docs]
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metadatas = [doc.get('metadata', {}) for doc in remaining_docs]
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embeddings = self._embed_batch(chunks)
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embeddings_np = np.array(embeddings).astype('float32')
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norms = np.linalg.norm(embeddings_np, axis=1, keepdims=True)
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embeddings_np = embeddings_np / (norms + 1e-10)
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self.index.add(embeddings_np)
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for i, (chunk, meta) in enumerate(zip(chunks, metadatas)):
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self.documents.append({
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'id': i,
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'content': chunk,
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'metadata': meta
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})
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self.save_index()
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return deleted_count
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def clear(self):
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"""清空所有索引"""
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self._load_faiss()
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self.index = self.faiss.IndexFlatIP(self.dimension)
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self.documents = []
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self.save_index()
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print("索引已清空")
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def get_stats(self):
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"""获取索引统计信息"""
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files = set()
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for doc in self.documents:
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filename = doc.get('metadata', {}).get('filename')
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if filename:
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files.add(filename)
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return {
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'total_chunks': len(self.documents),
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'total_files': len(files),
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'files': list(files)
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}
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# 全局实例
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vector_store = VectorStore()
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