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Framework vector database module for LanceDB, pgvector, Qdrant, and Chroma.","maintainers":[{"name":"gudaoxuri","email":"i@sunisle.org"}],"readme":"# @h-ai/vecdb\n\n向量数据库模块，通过统一的 `vecdb` 对象访问 LanceDB、pgvector、Qdrant、Chroma。\n\n## 支持的向量数据库\n\n- LanceDB（嵌入式，本地文件存储，零配置）\n- pgvector（PostgreSQL + pgvector 扩展）\n- Qdrant（高性能向量搜索引擎）\n- Chroma（支持嵌入式自动拉起本地服务，或直连已有服务）\n\n## 快速开始\n\n```ts\nimport { HaiVecdbError, vecdb } from '@h-ai/vecdb'\n\n// 初始化（LanceDB）\nawait vecdb.init({ type: 'lancedb', path: './data/vecdb' })\n\n// 创建集合\nawait vecdb.collection.create('docs', { dimension: 1536 })\n\n// 插入向量\nawait vecdb.vector.insert('docs', [\n  { id: 'doc-1', vector: Array.from({ length: 1536 }).fill(0.1), content: '文档内容', metadata: { source: 'wiki' } },\n])\n\n// 搜索\nconst searchResult = await vecdb.vector.search('docs', Array.from({ length: 1536 }).fill(0.2), { topK: 5, minScore: 0.7 })\nif (searchResult.success) {\n  for (const item of searchResult.data) {\n    // item.id, item.score, item.content\n  }\n}\n\n// 关闭\nconst closeResult = await vecdb.close()\nif (!closeResult.success) {\n  throw new Error(closeResult.error.message)\n}\n```\n\n## 配置\n\n```ts\n// LanceDB（默认，嵌入式本地存储）\nawait vecdb.init({ type: 'lancedb', path: './data/vecdb' })\n\n// pgvector（连接字符串）\nawait vecdb.init({ type: 'pgvector', url: 'postgres://user:pass@localhost:5432/mydb' })\n\n// pgvector（分字段）\nawait vecdb.init({\n  type: 'pgvector',\n  host: 'localhost',\n  port: 5432,\n  database: 'mydb',\n  user: 'admin',\n  password: 'secret',\n  indexType: 'hnsw',\n  tablePrefix: 'vec_',\n})\n\n// Qdrant\nawait vecdb.init({ type: 'qdrant', url: 'http://localhost:6333', apiKey: 'optional-key' })\n\n// Chroma（嵌入式：自动拉起本地 `chroma run` 服务并持久化到 path）\nawait vecdb.init({ type: 'chroma', path: './data/chroma' })\n\n// Chroma（直连已有服务，不拉起进程）\nawait vecdb.init({ type: 'chroma', url: 'http://localhost:8000' })\n```\n\n> Chroma 在 Node 端只有 HTTP 客户端（无进程内嵌入式）。嵌入式模式下 `vecdb.init` 会\n> 通过 `serverCommand`（默认 `chroma`，来自 `chromadb` 包）拉起本地服务，`vecdb.close`\n> 时关闭进程。需安装可选依赖 `chromadb`；服务命令不可用时 `init` 返回 `CONNECTION_FAILED`。\n\n### 操作日志\n\n可在向量数据库配置中通过 `operationLog` 开启操作日志。日志在集合/向量操作进入真实 Provider 前输出，不在 `vecdb-main.ts` 中做统一包装。\n\n```yaml\n# config/_vecdb.yml\ntype: lancedb\npath: ./data/vecdb\noperationLog:\n  read: false # collection.exists/info/list, vector.search/count\n  write: false # collection.create/drop, vector.insert/upsert/delete\n  maxLength: 1000 # 参数序列化后的最大输出长度，超出会截断\n  level: debug # info | debug | trace，默认 debug\n```\n\n```ts\nawait vecdb.init({\n  type: 'lancedb',\n  path: './data/vecdb',\n  operationLog: {\n    read: true,\n    write: true,\n    maxLength: 500,\n    level: 'debug',\n  },\n})\n```\n\n> 注意：`vecdb.config` 返回的是**脱敏配置快照**；连接字符串中的用户名/密码、独立 `password` 字段和 `apiKey` 会被替换为 `[REDACTED]`。\n\n> 行为说明：空批量 `insert/upsert/delete` 会被视为 no-op；写入与搜索会校验向量维度，不匹配时返回 `HaiVecdbError.DIMENSION_MISMATCH`；缺少可选驱动时返回 `HaiVecdbError.DRIVER_NOT_FOUND`。\n\n## 错误处理\n\n所有操作返回 `HaiResult<T>`，通过 `result.success` 判断成功或失败。\n\n```ts\nconst result = await vecdb.collection.create('docs', { dimension: 1536 })\nif (!result.success) {\n  switch (result.error.code) {\n    case HaiVecdbError.NOT_INITIALIZED.code:\n      // 请先调用 vecdb.init()\n      break\n    case HaiVecdbError.COLLECTION_ALREADY_EXISTS.code:\n      // 集合已存在\n      break\n    case HaiVecdbError.CONNECTION_FAILED.code:\n      // 连接失败\n      break\n  }\n}\n```\n\n## 测试\n\n```bash\npnpm --filter @h-ai/vecdb test\n```\n\n> pgvector / Qdrant 测试需要 Docker。\n\n## License\n\nApache-2.0\n","readmeFilename":"README.md"}