diff --git a/.gitignore b/.gitignore index dbbffd33ef8eb5bf97f64a3ad3badc1fea688d7e..7fd6d3ef2598ec7c6c84501b59f591c38acb3da5 100644 --- a/.gitignore +++ b/.gitignore @@ -16,11 +16,15 @@ wheels/ output/ data/ dashboard/dist/ -dashboard/node_modules/ docker/vendor/* !docker/vendor/.gitkeep .env +# Node.js and TypeScript generated files +node_modules/ +coverage/ +*.tsbuildinfo + # IDE and tools .claude/ .vscode/ diff --git a/api-tests/README.md b/api-tests/README.md new file mode 100644 index 0000000000000000000000000000000000000000..65c526e3f8b249f5932477d2d86f5e7a97c1fb16 --- /dev/null +++ b/api-tests/README.md @@ -0,0 +1,24 @@ +# TypeScript API 测试 + +测试直接调用已启动的 ikkyu HTTP 服务，不会 mock API。运行前需要服务已经完成本地模型预热，并准备一个 API Token。 + +```bash +cd api-tests +npm ci +IKKYU_API_TOKEN='' npm run test:basic +IKKYU_API_TOKEN='' npm run test:standard +``` + +`standard` 会同时运行 `basic`，并覆盖 PNG、PDF、Word、PowerPoint、Excel、Markdown 六种文件的 OCR 转换，以保证异常测试和文档处理测试建立在健康主链路之上。服务地址默认是 `http://127.0.0.1:8500`，可以通过 `IKKYU_API_BASE_URL` 修改；异步任务默认等待 120 秒，可以通过 `IKKYU_TASK_TIMEOUT_MS` 修改。 + +`external-full` 统一使用硅基流动服务，通过本地 Management API 临时写入 embedding、rerank、LLM 和 VL 配置。它会逐项测试模型连接，以 `t2q` 模式执行 basic 工作流，再完整执行 standard 的边界、异常、Chunk、空文件和六种文档类型用例。其中图片识别改用外部 VL，不使用本地 OCR；外部 t2q 入库最长等待 300 秒。测试结束后恢复原配置。Base URL 和模型标识均由测试固定，调用方只需提供 API Key： + +```bash +IKKYU_API_TOKEN='' \ +IKKYU_ADMIN_USERNAME='admin' \ +IKKYU_ADMIN_PASSWORD='' \ +IKKYU_SILICONFLOW_API_KEY='' \ +npm run test:external-full +``` + +固定 Base URL 为 `https://api.siliconflow.cn/v1`，模型为 `Qwen/Qwen3-Embedding-0.6B`、`Qwen/Qwen3-Reranker-0.6B`、`Qwen/Qwen3-8B` 和 `Qwen/Qwen3-VL-8B-Instruct`。Management API 使用登录后取得的 bearer token 鉴权。测试创建的领域和资源使用唯一名称，并在 suite 结束时清理。 diff --git a/api-tests/package-lock.json b/api-tests/package-lock.json new file mode 100644 index 0000000000000000000000000000000000000000..23f37fb2b50e797f7c1f2aaea527c3c8fe5e6a62 --- /dev/null +++ b/api-tests/package-lock.json @@ -0,0 +1,3583 @@ +{ + "name": "ikkyu-api-tests", + 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"scripts": { + "test": "vitest run", + "test:basic": "vitest run suites/basic.test.ts", + "test:standard": "vitest run suites/basic.test.ts suites/standard.test.ts", + "test:external-full": "vitest run suites/external-full.test.ts" + }, + "devDependencies": { + "@types/node": "^24.0.0", + "docx": "^9.5.1", + "exceljs": "^4.4.0", + "pdf-lib": "^1.17.1", + "pptxgenjs": "^4.0.1", + "sharp": "^0.34.3", + "typescript": "^5.8.3", + "vitest": "^3.2.4" + } +} diff --git a/api-tests/reports/standard-2026-07-17.md b/api-tests/reports/standard-2026-07-17.md new file mode 100644 index 0000000000000000000000000000000000000000..0c3b830a9a0779a93daca57f38e0490c323cfa8e --- /dev/null +++ b/api-tests/reports/standard-2026-07-17.md @@ -0,0 +1,65 @@ +# Standard API 测试报告 + +## 执行结论 + +- 结果：通过 +- 测试文件：3/3 通过 +- 测试用例：25/25 通过 +- Vitest 总耗时：3.87 秒 +- 执行时间：2026-07-17 20:33:23（Asia/Shanghai） +- 测试目标：Docker 容器暴露的 `http://127.0.0.1:8500` + +## 镜像与容器 + +- 镜像：`ikkyu-kb-arm64:latest` +- 镜像 ID：`sha256:fd7897d63350590da0fcfd698985461ab7f34132d98e69b3e691d051f6e648cf` +- 镜像大小：1,650,008,946 bytes +- 冷构建耗时：约 256 秒 +- 容器：`ikkyu-standard-test` +- 数据卷：`ikkyu-standard-test-data` +- 端口：`8500/tcp -> 8500` +- 模型预热：7.75 秒 +- 健康检查：`200 {"status":"healthy"}` +- 容器日志：未发现 ERROR、WARNING、failed 或 traceback + +## Suite 结果 + +| Suite | 通过 | 失败 | 耗时 | +| --- | ---: | ---: | ---: | +| basic：领域到资源再到查询 | 5 | 0 | 1.517 秒 | +| standard：边界与异常状态 | 14 | 0 | 0.229 秒 | +| standard：OCR 与文档类型覆盖 | 6 | 0 | 2.009 秒 | + +## Basic 用例 + +| 用例 | 结果 | 耗时 | +| --- | --- | ---: | +| 创建知识领域 | 通过 | 72 ms | +| 查询知识领域列表 | 通过 | 8 ms | +| 添加并等待资源入库 | 通过 | 533 ms | +| 查询资源列表 | 通过 | 4 ms | +| 查询知识并命中资源 | 通过 | 367 ms | + +## 边界与异常用例 + +以下 14 项全部通过：匿名访问、无效 Token、重复领域、非法领域名、分页上界、未知字段、错误 Data URL、不存在领域资源入库、空领域查询、`topK=0`、`topK=1000`、不存在任务、不存在资源删除、不存在领域删除。 + +## OCR 用例 + +| 文件类型 | 验证内容 | 结果 | 耗时 | +| --- | --- | --- | ---: | +| PNG 图片 | 独立图片 OCR 转 Markdown | 通过 | 361 ms | +| PDF | 扫描页图片 OCR | 通过 | 771 ms | +| Word (`.docx`) | 正文保留与内嵌图片 OCR | 通过 | 172 ms | +| PowerPoint (`.pptx`) | 文本框保留与内嵌图片 OCR | 通过 | 147 ms | +| Excel (`.xlsx`) | 单元格保留与工作表图片 OCR | 通过 | 74 ms | +| Markdown (`.md`) | 正文保留与 Data URI 图片 OCR | 通过 | 120 ms | + +## 清理状态 + +- 临时 API Token 已删除。 +- basic 和 standard 创建的测试领域及资源已删除。 +- 容器内 Markdown 测试目录无残留。 +- 管理员用户名和密码均已恢复为 `admin`，并验证 `admin/admin` 登录成功、测试账号登录失败。 +- 密码修改 API 要求至少 8 位，因此清理时使用容器内现有认证模块重置持久化密码，未修改业务源码。 +- 容器和独立测试 volume 保持运行，便于继续 API 验证。 diff --git a/api-tests/src/basicWorkflow.ts b/api-tests/src/basicWorkflow.ts new file mode 100644 index 0000000000000000000000000000000000000000..c98aa6bb971e0a76a971325be885e0a3118aa858 --- /dev/null +++ b/api-tests/src/basicWorkflow.ts @@ -0,0 +1,106 @@ +import { afterAll, beforeAll, describe, expect, test } from "vitest"; + +import { ApiClient, waitForTask } from "./client.js"; +import { TEST_CONFIG, uniqueName } from "./config.js"; + +interface WorkflowOptions { + suiteName: string; + enhancedMode: "none" | "t2q" | "t2qa"; + maxChunkSize?: number; + taskTimeoutMs?: number; + knowledge?: string; +} + +export function defineBasicWorkflow(options: WorkflowOptions): void { + describe.sequential(options.suiteName, () => { + const client = new ApiClient(); + const domainName = uniqueName("api-domain"); + const resourceName = "ikkyu-guide.txt"; + const knowledge = options.knowledge ?? "一休知识库的发布暗号是青竹七号。查询时应返回这段原始证据。"; + let domainCreated = false; + let resourceCreated = false; + + beforeAll(() => { + if (!TEST_CONFIG.apiToken) throw new Error("请先设置 IKKYU_API_TOKEN"); + }); + + afterAll(async () => { + if (resourceCreated) { + const deletion = await client.request<{ taskId: string }>("/api/resource/delete", { + domainName, + name: resourceName, + }); + if (deletion.status < 300) await waitForTask(client, deletion.body.taskId); + } + if (domainCreated) await client.request("/api/domain/delete", { name: domainName }); + }); + + // 验证可以创建隔离的知识领域，并正确保存不依赖外部模型的领域参数。 + test("创建知识领域", async () => { + const result = await client.ok<{ success: boolean; domain: { name: string; enhancedMode: string } }>( + "/api/domain/add", + { + name: domainName, + description: "TypeScript API 集成测试领域", + tags: "api-test,basic", + enhancedMode: options.enhancedMode, + maxChunkSize: options.maxChunkSize ?? 300, + vectorWeight: 0.8, + bm25Weight: 0.2, + }, + ); + domainCreated = true; + expect(result.success).toBe(true); + expect(result.domain).toMatchObject({ name: domainName, enhancedMode: options.enhancedMode }); + }); + + // 验证领域列表能按关键字找到刚创建的领域，覆盖领域读取链路。 + test("查询知识领域列表", async () => { + const result = await client.ok<{ domains: Array<{ name: string }>; total: number }>("/api/domain/", { + keyword: domainName, + limit: 10, + offset: 0, + }); + expect(result.total).toBe(1); + expect(result.domains.map((domain) => domain.name)).toContain(domainName); + }); + + // 验证 Data URL 文本资源可以异步完成转换、切块、向量化和索引入库。 + test("添加并等待资源入库", async () => { + const fileInBase64 = `data:text/plain;base64,${Buffer.from(knowledge, "utf8").toString("base64")}`; + const task = await client.ok<{ taskId: string }>("/api/resource/add", { + name: resourceName, + description: "用于验证完整查询链路", + tags: "api-test,guide", + creator: "vitest", + fileInBase64, + domainName, + }); + await waitForTask(client, task.taskId, options.taskTimeoutMs); + resourceCreated = true; + }, options.taskTimeoutMs); + + // 验证资源管理列表包含已完成入库的资源及非零切片数量。 + test("查询资源列表", async () => { + const result = await client.ok<{ resources: Array<{ name: string; chunkCount: number }>; total: number }>( + "/api/resource/", + { domainName, keyword: resourceName, limit: 10, offset: 0 }, + ); + expect(result.total).toBe(1); + expect(result.resources[0]).toMatchObject({ name: resourceName }); + expect(result.resources[0].chunkCount).toBeGreaterThan(0); + }); + + // 验证领域内混合检索可以召回刚入库资源，并保留可核验的原始文本证据。 + test("查询知识并命中资源", async () => { + const result = await client.ok<{ results: Array<{ resourceName: string; text: string }> }>("/api/query", { + domains: [domainName], + tags: "api-test", + text: "一休知识库的发布暗号是什么？", + topK: 5, + }); + expect(result.results.some((item) => item.resourceName === resourceName)).toBe(true); + expect(result.results.some((item) => item.text.includes("青竹七号"))).toBe(true); + }); + }); +} diff --git a/api-tests/src/client.ts b/api-tests/src/client.ts new file mode 100644 index 0000000000000000000000000000000000000000..e68be1081ce071b097a25587d75aecca63e6be63 --- /dev/null +++ b/api-tests/src/client.ts @@ -0,0 +1,69 @@ +import { expect } from "vitest"; + +import { TEST_CONFIG } from "./config.js"; + +export interface ApiResponse { + status: number; + body: T; + text: string; +} + +export class ApiClient { + public constructor( + private readonly token = TEST_CONFIG.apiToken, + private readonly baseUrl = TEST_CONFIG.baseUrl, + ) {} + + public async request(path: string, body?: unknown, method = "POST"): Promise> { + const headers: Record = {}; + if (this.token) headers.Authorization = `Bearer ${this.token}`; + if (body !== undefined) headers["Content-Type"] = "application/json"; + + const response = await fetch(`${this.baseUrl}${path}`, { + method, + headers, + body: body === undefined ? undefined : JSON.stringify(body), + }); + const text = await response.text(); + let parsed: unknown = text; + if (text) { + try { + parsed = JSON.parse(text); + } catch { + // Markdown 和文件下载接口返回的不是 JSON，保留原始文本供断言使用。 + } + } + return { status: response.status, body: parsed as T, text }; + } + + public async ok(path: string, body?: unknown, method = "POST"): Promise { + const response = await this.request(path, body, method); + expect(response.status, `${method} ${path}: ${response.text}`).toBeGreaterThanOrEqual(200); + expect(response.status, `${method} ${path}: ${response.text}`).toBeLessThan(300); + return response.body; + } +} + +export interface TaskInfo { + taskId: string; + status: string; + success: boolean | null; + errorMessage?: string | null; +} + +export async function waitForTask( + client: ApiClient, + taskId: string, + timeoutMs = TEST_CONFIG.taskTimeoutMs, +): Promise { + const deadline = Date.now() + timeoutMs; + while (Date.now() < deadline) { + const task = await client.ok("/api/task/", { taskId }); + if (task.success !== null) { + expect(task.success, task.errorMessage ?? `任务 ${taskId} 执行失败`).toBe(true); + return task; + } + await new Promise((resolve) => setTimeout(resolve, 500)); + } + throw new Error(`等待任务 ${taskId} 超时（${timeoutMs}ms）`); +} diff --git a/api-tests/src/config.ts b/api-tests/src/config.ts new file mode 100644 index 0000000000000000000000000000000000000000..aaf7f4b9e6f9bd656615fc2cde6563c628a8d573 --- /dev/null +++ b/api-tests/src/config.ts @@ -0,0 +1,11 @@ +export const TEST_CONFIG = { + baseUrl: (process.env.IKKYU_API_BASE_URL ?? "http://127.0.0.1:8500").replace(/\/$/, ""), + apiToken: process.env.IKKYU_API_TOKEN ?? "", + adminUsername: process.env.IKKYU_ADMIN_USERNAME ?? "admin", + adminPassword: process.env.IKKYU_ADMIN_PASSWORD ?? "admin", + taskTimeoutMs: Number(process.env.IKKYU_TASK_TIMEOUT_MS ?? 120_000), +}; + +export function uniqueName(prefix: string): string { + return `${prefix}-${Date.now()}-${process.pid}`; +} diff --git a/api-tests/src/documentFixtures.ts b/api-tests/src/documentFixtures.ts new file mode 100644 index 0000000000000000000000000000000000000000..c204fcbdcc2f6996124f4f6f93403afbcdda61d9 --- /dev/null +++ b/api-tests/src/documentFixtures.ts @@ -0,0 +1,71 @@ +import { Document, ImageRun, Packer, Paragraph, TextRun } from "docx"; +import ExcelJS from "exceljs"; +import { PDFDocument } from "pdf-lib"; +import pptxgen from "pptxgenjs"; +import sharp from "sharp"; + +export interface DocumentFixture { + name: string; + mimeType: string; + data: Buffer; + expectedText?: string; +} + +const OCR_TEXT = "OCR TEST 7319"; + +async function createOcrImage(): Promise { + return sharp({ + create: { width: 900, height: 240, channels: 3, background: "white" }, + }) + .composite([{ + input: Buffer.from(`${OCR_TEXT}`), + }]) + .png() + .toBuffer(); +} + +export async function createDocumentFixtures(): Promise { + const image = await createOcrImage(); + + const pdf = await PDFDocument.create(); + const pdfPage = pdf.addPage([900, 300]); + const pdfImage = await pdf.embedPng(image); + pdfPage.drawImage(pdfImage, { x: 0, y: 30, width: 900, height: 240 }); + + const wordText = "WORD BODY 7319"; + const word = new Document({ sections: [{ children: [ + new Paragraph({ children: [new TextRun(wordText)] }), + new Paragraph({ children: [new ImageRun({ data: image, transformation: { width: 600, height: 160 }, type: "png" })] }), + ] }] }); + + const pptText = "PPT BODY 7319"; + // pptxgenjs 4.0.1 的 ESM 运行时可构造，但其 NodeNext 导出类型被解析为模块命名空间。 + // @ts-expect-error 上游类型声明与 ESM exports 不一致。 + const presentation = new pptxgen(); + const slide = presentation.addSlide(); + slide.addText(pptText, { x: 0.5, y: 0.3, w: 4, h: 0.5 }); + slide.addImage({ data: `data:image/png;base64,${image.toString("base64")}`, x: 0.5, y: 1, w: 7.5, h: 2 }); + + const excelText = "EXCEL BODY 7319"; + const workbook = new ExcelJS.Workbook(); + const worksheet = workbook.addWorksheet("OCR"); + worksheet.getCell("A1").value = excelText; + const imageId = workbook.addImage({ buffer: image as unknown as ExcelJS.Buffer, extension: "png" }); + worksheet.addImage(imageId, { tl: { col: 0, row: 2 }, ext: { width: 600, height: 160 } }); + + const markdownText = "MARKDOWN BODY 7319"; + const markdown = Buffer.from(`# OCR fixture\n\n${markdownText}\n\n![OCR](data:image/png;base64,${image.toString("base64")})`, "utf8"); + + return [ + { name: "ocr-image.png", mimeType: "image/png", data: image }, + { name: "ocr-document.pdf", mimeType: "application/pdf", data: Buffer.from(await pdf.save()) }, + { name: "ocr-document.docx", mimeType: "application/vnd.openxmlformats-officedocument.wordprocessingml.document", data: await Packer.toBuffer(word), expectedText: wordText }, + { name: "ocr-document.pptx", mimeType: "application/vnd.openxmlformats-officedocument.presentationml.presentation", data: Buffer.from(await presentation.write({ outputType: "nodebuffer" }) as Buffer), expectedText: pptText }, + { name: "ocr-document.xlsx", mimeType: "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet", data: Buffer.from(await workbook.xlsx.writeBuffer()), expectedText: excelText }, + { name: "ocr-document.md", mimeType: "text/markdown", data: markdown, expectedText: markdownText }, + ]; +} + +export function toDataUri(fixture: DocumentFixture): string { + return `data:${fixture.mimeType};name=${fixture.name};base64,${fixture.data.toString("base64")}`; +} diff --git a/api-tests/src/standardSuites.ts b/api-tests/src/standardSuites.ts new file mode 100644 index 0000000000000000000000000000000000000000..f4dcdc743447fa16a9226fd09629708505292e75 --- /dev/null +++ b/api-tests/src/standardSuites.ts @@ -0,0 +1,294 @@ +import { afterAll, beforeAll, describe, expect, test } from "vitest"; + +import { ApiClient } from "./client.js"; +import { TEST_CONFIG, uniqueName } from "./config.js"; +import { createDocumentFixtures, DocumentFixture, toDataUri } from "./documentFixtures.js"; + +interface ConvertResult { + success: boolean; + markdown: string; + chunks: string[]; + logs: string; +} + +interface ChunkResult { + success: boolean; + chunks: string[]; + logs: string; +} + +interface StandardSuiteOptions { + suitePrefix: string; + imageProcessModel: "ocr" | "vl"; +} + +export function defineStandardSuites(options: StandardSuiteOptions): void { + describe.sequential(`${options.suitePrefix}：边界与异常状态`, () => { + const client = new ApiClient(); + const domainName = uniqueName("api-standard"); + + beforeAll(async () => { + if (!TEST_CONFIG.apiToken) throw new Error("请先设置 IKKYU_API_TOKEN"); + await client.ok("/api/domain/add", { name: domainName, enhancedMode: "none" }); + }); + + afterAll(async () => { + await client.request("/api/domain/delete", { name: domainName }); + }); + + // 验证受保护 API 在缺少 Bearer Token 时拒绝访问，避免鉴权配置意外失效。 + test("拒绝匿名访问受保护接口", async () => { + const response = await new ApiClient("").request("/api/domain/", {}); + expect(response.status).toBe(401); + }); + + // 验证错误 Bearer Token 与缺少 Token 一样返回未授权，而不是进入业务处理。 + test("拒绝无效 Bearer Token", async () => { + const response = await new ApiClient("invalid-api-token").request("/api/domain/", {}); + expect(response.status).toBe(401); + }); + + // 验证同名领域不能重复创建，确保领域唯一性约束通过 API 暴露为冲突错误。 + test("拒绝创建重复知识领域", async () => { + const response = await client.request("/api/domain/add", { name: domainName, enhancedMode: "none" }); + expect(response.status).toBe(409); + }); + + // 验证领域名禁止空白字符，覆盖名称格式校验的非法边界。 + test("拒绝包含空白的领域名", async () => { + const response = await client.request("/api/domain/add", { name: "invalid domain", enhancedMode: "none" }); + expect(response.status).toBe(400); + }); + + // 验证领域分页 limit 的上界由请求模型拦截，防止无界列表查询。 + test("拒绝超过上限的领域分页参数", async () => { + const response = await client.request("/api/domain/", { limit: 5001, offset: 0 }); + expect(response.status).toBe(422); + }); + + // 验证请求体出现未声明字段时返回校验错误，防止调用方拼错字段却静默成功。 + test("拒绝未知请求字段", async () => { + const response = await client.request("/api/domain/", { offset: 0, unexpected: true }); + expect(response.status).toBe(422); + }); + + // 验证资源 Data URL 必须包含 MIME、base64 标记和有效载荷。 + test("拒绝格式错误的资源 Data URL", async () => { + const response = await client.request("/api/resource/add", { + name: "invalid.txt", + creator: "vitest", + fileInBase64: "plain text", + domainName, + }); + expect(response.status).toBe(422); + }); + + // 验证资源入库接口拒绝零字节文件，避免创建无法检索的空资源和无效后台任务。 + test("拒绝零字节资源文件", async () => { + const response = await client.request("/api/resource/add", { + name: "empty.txt", + creator: "vitest", + fileInBase64: "data:text/plain;base64,", + domainName, + }); + expect(response.status).toBe(422); + }); + + // 验证向不存在领域添加资源会返回未找到，且不会产生后台脏任务。 + test("拒绝向不存在领域添加资源", async () => { + const data = `data:text/plain;base64,${Buffer.from("test").toString("base64")}`; + const response = await client.request("/api/resource/add", { + name: "missing.txt", + creator: "vitest", + fileInBase64: data, + domainName: `${domainName}-missing`, + }); + expect(response.status).toBe(404); + }); + + // 验证查询至少需要一个领域，覆盖空集合这一最小边界。 + test("拒绝空领域集合查询", async () => { + const response = await client.request("/api/query", { domains: [], text: "test", topK: 1 }); + expect(response.status).toBe(422); + }); + + // 验证 topK 不能为零，避免底层检索收到无意义的结果数量。 + test("拒绝 topK 等于零", async () => { + const response = await client.request("/api/query", { domains: [domainName], text: "test", topK: 0 }); + expect(response.status).toBe(422); + }); + + // 验证 topK 最大允许值 1000 可以正常进入业务查询，覆盖合法上边界。 + test("接受 topK 合法上边界", async () => { + const response = await client.request("/api/query", { domains: [domainName], text: "test", topK: 1000 }); + expect(response.status).toBe(200); + }); + + // 验证读取不存在任务时返回未找到，便于调用方区分任务失败和任务 ID 无效。 + test("查询不存在的异步任务", async () => { + const response = await client.request("/api/task/", { taskId: "missing-task-id" }); + expect(response.status).toBe(404); + }); + + // 验证删除不存在资源时立即返回未找到，而不是错误地报告已进入队列。 + test("删除不存在的资源", async () => { + const response = await client.request("/api/resource/delete", { domainName, name: "missing.txt" }); + expect(response.status).toBe(404); + }); + + // 验证删除不存在领域时返回未找到，并保持现有测试领域不受影响。 + test("删除不存在的知识领域", async () => { + const response = await client.request("/api/domain/delete", { name: `${domainName}-missing` }); + expect(response.status).toBe(404); + }); + }); + + describe.sequential(`${options.suitePrefix}：Chunk 边界与空文件`, () => { + const client = new ApiClient(""); + + async function chunk(content: string, maxChunkSize: number): Promise { + return client.ok("/api/util/chunk", { content, maxChunkSize }); + } + + function expectLosslessChunks(result: ChunkResult, content: string, maxChunkSize: number): void { + expect(result.success).toBe(true); + expect(result.chunks.join("")).toBe(content); + expect(result.chunks.every((item) => item.length <= maxChunkSize)).toBe(true); + } + + // 验证关闭切块时完整返回原文，不因标题、换行或标点产生隐式切片。 + test("maxChunkSize 为零时不切块", async () => { + const content = "# 标题\n\n第一段。\n\n第二段。"; + const result = await chunk(content, 0); + expect(result).toMatchObject({ success: true, chunks: [content] }); + }); + + // 验证空字符串执行切块时成功返回空数组，而不是产生一个无内容切片。 + test("空文本不产生 Chunk", async () => { + const result = await chunk("", 64); + expect(result).toMatchObject({ success: true, chunks: [] }); + }); + + // 验证纯空白文本不会产生 Chunk，避免进入后续扩增、向量化和索引管线。 + test("纯空白文本不产生 Chunk", async () => { + const content = " \n\t "; + const result = await chunk(content, 64); + expect(result).toMatchObject({ success: true, chunks: [] }); + }); + + // 验证有效正文两侧的空格、空行和制表符在切块过程中被完整保留。 + test("有效文本中的空白字符保持不变", async () => { + const content = " \n\t正文内容\n\n "; + const result = await chunk(content, 64); + expectLosslessChunks(result, content, 64); + }); + + // 验证文本长度恰好等于限制时，所有结果均不超过边界且原文可无损还原。 + test("文本长度等于 Chunk 上限", async () => { + const content = "边".repeat(64); + const result = await chunk(content, 64); + expectLosslessChunks(result, content, 64); + }); + + // 验证比限制多一个字符时会被安全拆分，并确保边界字符没有遗漏或重复。 + test("文本长度超过 Chunk 上限一个字符", async () => { + const content = "界".repeat(65); + const result = await chunk(content, 64); + expectLosslessChunks(result, content, 64); + expect(result.chunks.length).toBeGreaterThanOrEqual(2); + }); + + // 验证跨越两个完整边界的长文本至少产生三个合法切片，且顺序保持不变。 + test("文本跨越多个 Chunk 边界", async () => { + const content = "段".repeat(129); + const result = await chunk(content, 64); + expectLosslessChunks(result, content, 64); + expect(result.chunks.length).toBeGreaterThanOrEqual(3); + }); + + // 验证中英文、数字和标点混合内容按字符边界处理，不破坏 Unicode 文本。 + test("混合 Unicode 内容切块无损", async () => { + const content = "知识库API测试，Version 2。".repeat(12); + const result = await chunk(content, 48); + expectLosslessChunks(result, content, 48); + }); + + // 验证极小上限仍能终止处理，每个 Chunk 最多包含一个字符且全文无损。 + test("Chunk 上限为一", async () => { + const content = "边界测试"; + const result = await chunk(content, 1); + expectLosslessChunks(result, content, 1); + expect(result.chunks).toHaveLength(content.length); + }); + + // 验证缺少必填 content 字段时由 API 请求模型返回校验错误。 + test("拒绝缺少 content 的切块请求", async () => { + const response = await client.request("/api/util/chunk", { maxChunkSize: 64 }); + expect(response.status).toBe(422); + }); + + // 验证工具转换接口可以显式处理零字节文本文件，并返回可判定的空结果。 + test("转换零字节文本文件", async () => { + const result = await client.ok("/api/util/convert-file", { + data: "data:text/plain;base64,", + imageProcessModel: options.imageProcessModel, + maxChunkSize: 0, + }); + expect(result).toMatchObject({ success: true, markdown: "", chunks: [""] }); + }); + }); + + describe.sequential(`${options.suitePrefix}：图片识别与文档类型覆盖`, () => { + const client = new ApiClient(); + let fixtures: DocumentFixture[] = []; + + beforeAll(async () => { + fixtures = await createDocumentFixtures(); + }); + + async function convert(name: string): Promise { + const fixture = fixtures.find((item) => item.name === name); + if (!fixture) throw new Error(`找不到测试文件：${name}`); + const result = await client.ok("/api/util/convert-file", { + data: toDataUri(fixture), + imageProcessModel: options.imageProcessModel, + maxChunkSize: 0, + }); + expect(result.success).toBe(true); + expect(result.markdown.trim()).not.toBe(""); + if (fixture.expectedText) expect(result.markdown).toContain(fixture.expectedText); + expect(result.logs.toLowerCase()).toContain(options.imageProcessModel); + return result; + } + + // 验证独立 PNG 图片通过所选图片识别管线转换为非空 Markdown 文本。 + test("识别图片", async () => { + await convert("ocr-image.png"); + }); + + // 验证仅含扫描图片的 PDF 会逐页提取图片并识别，而不是返回空文档。 + test("识别 PDF 中的扫描图片", async () => { + await convert("ocr-document.pdf"); + }); + + // 验证 Word 正文与内嵌图片识别结果都会保留在转换后的 Markdown 中。 + test("识别 Word 内嵌图片", async () => { + await convert("ocr-document.docx"); + }); + + // 验证 PowerPoint 文本框与幻灯片内嵌图片可以共同进入转换结果。 + test("识别 PowerPoint 内嵌图片", async () => { + await convert("ocr-document.pptx"); + }); + + // 验证 Excel 单元格内容与工作表图片识别结果可以共同进入转换结果。 + test("识别 Excel 内嵌图片", async () => { + await convert("ocr-document.xlsx"); + }); + + // 验证 Markdown 中的 Data URI 图片会被识别文本替换，同时保留原始正文。 + test("识别 Markdown 内嵌图片", async () => { + await convert("ocr-document.md"); + }); + }); +} diff --git a/api-tests/suites/basic.test.ts b/api-tests/suites/basic.test.ts new file mode 100644 index 0000000000000000000000000000000000000000..60fc9816bd15f8bc5d375fa6776d4ee50ba3c2b2 --- /dev/null +++ b/api-tests/suites/basic.test.ts @@ -0,0 +1,3 @@ +import { defineBasicWorkflow } from "../src/basicWorkflow.js"; + +defineBasicWorkflow({ suiteName: "basic：领域到资源再到查询", enhancedMode: "none" }); diff --git a/api-tests/suites/external-full.test.ts b/api-tests/suites/external-full.test.ts new file mode 100644 index 0000000000000000000000000000000000000000..d2ab5f76080f980c376890b6d01f724c4c9bf07b --- /dev/null +++ b/api-tests/suites/external-full.test.ts @@ -0,0 +1,117 @@ +import { afterAll, beforeAll, describe, expect, test } from "vitest"; + +import { defineBasicWorkflow } from "../src/basicWorkflow.js"; +import { ApiClient } from "../src/client.js"; +import { TEST_CONFIG } from "../src/config.js"; +import { defineStandardSuites } from "../src/standardSuites.js"; + +const MODEL_KEYS = [ + "EMBEDDING_PROVIDER", "EMBEDDING_BASE_URL", "EMBEDDING_API_KEY", "EMBEDDING_MODEL", "EMBEDDING_DIMENSION", + "RERANK_PROVIDER", "RERANK_BASE_URL", "RERANK_API_KEY", "RERANK_MODEL", + "LLM_ENABLED", "LLM_BASE_URL", "LLM_API_KEY", "LLM_MODEL", + "VL_PROVIDER", "VL_BASE_URL", "VL_API_KEY", "VL_MODEL", +] as const; + +function requiredExternalValues(): Record { + const baseUrl = "https://api.siliconflow.cn/v1"; + const apiKey = process.env.IKKYU_SILICONFLOW_API_KEY ?? ""; + if (!apiKey) throw new Error("external-full suite 缺少 IKKYU_SILICONFLOW_API_KEY"); + + return { + EMBEDDING_PROVIDER: "openai", + EMBEDDING_BASE_URL: baseUrl, + EMBEDDING_API_KEY: apiKey, + EMBEDDING_MODEL: "Qwen/Qwen3-Embedding-0.6B", + EMBEDDING_DIMENSION: "512", + RERANK_PROVIDER: "openai", + RERANK_BASE_URL: baseUrl, + RERANK_API_KEY: apiKey, + RERANK_MODEL: "Qwen/Qwen3-Reranker-0.6B", + LLM_ENABLED: "1", + LLM_BASE_URL: baseUrl, + LLM_API_KEY: apiKey, + LLM_MODEL: "Qwen/Qwen3-8B", + VL_PROVIDER: "openai", + VL_BASE_URL: baseUrl, + VL_API_KEY: apiKey, + VL_MODEL: "Qwen/Qwen3-VL-8B-Instruct", + }; +} + +describe.sequential("external-full：全量外部模型集成测试", () => { + let managementClient: ApiClient; + let originalValues: Record = {}; + + beforeAll(async () => { + const anonymous = new ApiClient(""); + const login = await anonymous.ok<{ access_token: string }>("/management-api/auth/login", { + username: TEST_CONFIG.adminUsername, + password: TEST_CONFIG.adminPassword, + }); + managementClient = new ApiClient(login.access_token); + + const current = await managementClient.ok<{ variables: Array<{ key: string; value: string }> }>( + "/management-api/environment-config?reveal_secrets=true", undefined, "GET", + ); + originalValues = Object.fromEntries( + current.variables + .filter((item) => MODEL_KEYS.includes(item.key as typeof MODEL_KEYS[number])) + .map((item) => [item.key, item.value]), + ); + await managementClient.ok("/management-api/environment-config", { + values: requiredExternalValues(), + reveal_secrets: false, + }, "PATCH"); + }); + + afterAll(async () => { + if (managementClient && Object.keys(originalValues).length > 0) { + await managementClient.request("/management-api/environment-config", { values: originalValues }, "PATCH"); + } + }); + + // 验证 external-full 使用的外部 embedding 服务可连接，并返回配置的向量维度。 + test("验证外部 embedding 连接", async () => { + const result = await managementClient.ok<{ success: boolean; dimension: number }>( + "/management-api/model-connection-test", { service: "embedding", values: {} }, + ); + expect(result.success).toBe(true); + expect(result.dimension).toBe(512); + }); + + // 验证 external-full 使用的外部 rerank 服务可连接并返回排序结果。 + test("验证外部 rerank 连接", async () => { + const result = await managementClient.ok<{ success: boolean; result_count: number }>( + "/management-api/model-connection-test", { service: "rerank", values: {} }, + ); + expect(result.success).toBe(true); + expect(result.result_count).toBeGreaterThan(0); + }); + + // 验证 external-full 使用的外部 LLM 可完成真实 Completion 调用。 + test("验证外部 LLM 连接", async () => { + const result = await managementClient.ok<{ success: boolean; content: string }>( + "/management-api/model-connection-test", { service: "llm", values: {} }, + ); + expect(result.success).toBe(true); + expect(result.content.length).toBeGreaterThan(0); + }); + + // 验证 external-full 使用的外部 VL 可完成带图片的真实多模态调用。 + test("验证外部 VL 连接", async () => { + const result = await managementClient.ok<{ success: boolean; content: string }>( + "/management-api/model-connection-test", { service: "vl", values: {} }, + ); + expect(result.success).toBe(true); + expect(result.content.length).toBeGreaterThan(0); + }); + + defineBasicWorkflow({ + suiteName: "external-full：使用外部模型执行 basic 工作流", + enhancedMode: "t2q", + maxChunkSize: 0, + taskTimeoutMs: 300_000, + knowledge: "一休知识库的发布暗号是青竹七号。例行维护时间是每周三凌晨两点。服务负责人是竹林小组。", + }); + defineStandardSuites({ suitePrefix: "external-full", imageProcessModel: "vl" }); +}); diff --git a/api-tests/suites/standard.test.ts b/api-tests/suites/standard.test.ts new file mode 100644 index 0000000000000000000000000000000000000000..c9475eeb44351c11506c38659e5eed0545f22d23 --- /dev/null +++ b/api-tests/suites/standard.test.ts @@ -0,0 +1,3 @@ +import { defineStandardSuites } from "../src/standardSuites.js"; + +defineStandardSuites({ suitePrefix: "standard", imageProcessModel: "ocr" }); diff --git a/api-tests/tsconfig.json b/api-tests/tsconfig.json new file mode 100644 index 0000000000000000000000000000000000000000..265a064726dd429c05d333f7d6f9766ed6b57f1d --- /dev/null +++ b/api-tests/tsconfig.json @@ -0,0 +1,12 @@ +{ + "compilerOptions": { + "target": "ES2022", + "module": "NodeNext", + "moduleResolution": "NodeNext", + "esModuleInterop": true, + "strict": true, + "noEmit": true, + "types": ["node", "vitest/globals"] + }, + "include": ["**/*.ts"] +} diff --git a/api-tests/vitest.config.ts b/api-tests/vitest.config.ts new file mode 100644 index 0000000000000000000000000000000000000000..24c2d19fbc49efe7795402499eb5cbdf04da24a4 --- /dev/null +++ b/api-tests/vitest.config.ts @@ -0,0 +1,9 @@ +import { defineConfig } from "vitest/config"; + +export default defineConfig({ + test: { + testTimeout: 360_000, + hookTimeout: 360_000, + sequence: { concurrent: false }, + }, +}); diff --git a/app/config.py b/app/config.py index 52a127af501ebc6f41d549aee492132e4e6894c3..8aa44b5b64d1071936772a3e02403195372a300b 100644 --- a/app/config.py +++ b/app/config.py @@ -131,6 +131,7 @@ EMBEDDING_PROVIDER = os.getenv("EMBEDDING_PROVIDER", "builtin") EMBEDDING_BASE_URL = os.getenv("EMBEDDING_BASE_URL", "https://dashscope.aliyuncs.com/compatible-mode/v1") EMBEDDING_API_KEY = os.getenv("EMBEDDING_API_KEY", "") EMBEDDING_MODEL = os.getenv("EMBEDDING_MODEL", "") +EMBEDDING_DIMENSION = int(os.getenv("EMBEDDING_DIMENSION", "512")) EMBEDDING_API_TIMEOUT = int(os.getenv("EMBEDDING_API_TIMEOUT", "300")) # OCR 检测与识别模型路径（本地目录） @@ -164,7 +165,6 @@ REQUEST_SIZE_LIMIT_IN_MB = int(os.getenv("REQUEST_SIZE_LIMIT_IN_MB", "600")) IKKYU_ADMIN_USERNAME = os.getenv("IKKYU_ADMIN_USERNAME", "admin") IKKYU_ADMIN_PASSWORD = os.getenv("IKKYU_ADMIN_PASSWORD", "admin") IKKYU_MANAGEMENT_SESSION_TTL_SECONDS = int(os.getenv("IKKYU_MANAGEMENT_SESSION_TTL_SECONDS", "86400")) -IKKYU_MANAGEMENT_ALLOWED_HOSTS = os.getenv("IKKYU_MANAGEMENT_ALLOWED_HOSTS", "") # ==================== VL-Page 模式配置 ==================== # VL-Page 模式下 PDF 页面截图的目标宽度（像素） diff --git a/app/logic/chunk.py b/app/logic/chunk.py index fb4b8e28d2aacfb1a351f623dd7731c8cdf380ee..b1939678b437a9dd64b51a0dc4f1d87095c3ded5 100644 --- a/app/logic/chunk.py +++ b/app/logic/chunk.py @@ -336,7 +336,7 @@ def chunk_text_bert(content: str, max_size: int, logs: Optional[List[str]] = Non post_time = time.time() - post_start total_time = time.time() - start_time - result = [chunk.strip() for chunk in final_chunks if chunk.strip()] + result = [chunk for chunk in final_chunks if chunk.strip()] if logs is not None: min_size = max(1, math.ceil(max_size * MIN_CHUNK_SIZE_RATIO)) diff --git a/app/logic/embedding.py b/app/logic/embedding.py index a8fc08343e540ef834f9b2768a59fc25731ca7d8..afcf2d49c444dcd3eec818de6dd4cef97ca763a3 100644 --- a/app/logic/embedding.py +++ b/app/logic/embedding.py @@ -130,9 +130,9 @@ def __prefixed_text(text: str, text_type: str) -> str: return f"{text_type}: {text}" -def __normalize_embedding(vector: list[float]) -> list[float]: - if len(vector) != ORIGINAL_DIMENSION: - raise ValueError(f"Embedding dimension must be {ORIGINAL_DIMENSION}, got {len(vector)}") +def __normalize_embedding(vector: list[float], expected_dimension: int = ORIGINAL_DIMENSION) -> list[float]: + if len(vector) != expected_dimension: + raise ValueError(f"Embedding dimension must be {expected_dimension}, got {len(vector)}") normalized = [float(value) for value in vector] if not all(math.isfinite(value) for value in normalized): @@ -199,10 +199,11 @@ def generate_openai_embedding( if not api_key or not model: raise ValueError("EMBEDDING_API_KEY and EMBEDDING_MODEL must be configured for external embedding") + dimension = int(app_config.EMBEDDING_DIMENSION) payload = { "model": model, "input": __prefixed_text(text, text_type), - "dimensions": ORIGINAL_DIMENSION, + "dimensions": dimension, } headers = { "Authorization": f"Bearer {api_key}", @@ -226,9 +227,15 @@ def generate_openai_embedding( if not isinstance(raw_embedding, list): raise RuntimeError("External embedding response did not include an embedding list") - embedding_vector = __normalize_embedding(raw_embedding) + embedding_vector = __normalize_embedding(raw_embedding, expected_dimension=dimension) duration = time.time() - start_time - return embedding_vector, ORIGINAL_DIMENSION, duration + return embedding_vector, dimension, duration + + +def configured_embedding_dimension() -> int: + if app_config.EMBEDDING_PROVIDER == "openai": + return int(app_config.EMBEDDING_DIMENSION) + return ORIGINAL_DIMENSION def generate_embedding( diff --git a/app/logic/environment_config.py b/app/logic/environment_config.py index 7ed896383773b09af43d4522702e7da51411d9e1..1d3f81b80f5956e95be2355a9160804f90cda586 100644 --- a/app/logic/environment_config.py +++ b/app/logic/environment_config.py @@ -39,6 +39,7 @@ _ENV_ITEMS = [ ("EMBEDDING_BASE_URL", "Embedding OpenAI 兼容地址", "Embedding", False, False), ("EMBEDDING_API_KEY", "Embedding API 密钥", "Embedding", True, False), ("EMBEDDING_MODEL", "Embedding OpenAI 兼容模型", "Embedding", False, False), + ("EMBEDDING_DIMENSION", "Embedding 输出维度", "Embedding", False, False), ("EMBEDDING_API_TIMEOUT", "Embedding 调用超时秒数", "Embedding", False, False), ("RERANK_MODEL_PATH", "Rerank 模型路径", "模型", False, True), ("RERANK_PROVIDER", "Rerank 提供方", "Rerank", False, False), @@ -71,6 +72,7 @@ _INT_KEYS = { "DOWNLOAD_TIMEOUT", "VL_API_TIMEOUT", "EMBEDDING_API_TIMEOUT", + "EMBEDDING_DIMENSION", "RERANK_API_TIMEOUT", "LLM_API_TIMEOUT", "LLM_MAX_CONCURRENCY", @@ -80,6 +82,13 @@ _INT_KEYS = { _FLOAT_KEYS = {"VL_PAGE_MIN_ZOOM", "VL_PAGE_MAX_ZOOM"} _PROVIDER_KEYS = {"EMBEDDING_PROVIDER", "RERANK_PROVIDER", "VL_PROVIDER"} _BOOLEAN_KEYS = {"LLM_ENABLED"} +_EMBEDDING_CONFIG_KEYS = { + "EMBEDDING_PROVIDER", + "EMBEDDING_BASE_URL", + "EMBEDDING_API_KEY", + "EMBEDDING_MODEL", + "EMBEDDING_DIMENSION", +} _ALLOWED_KEYS = {item[0] for item in _ENV_ITEMS} _RESTART_REQUIRED_KEYS = {item[0] for item in _ENV_ITEMS if item[4]} _PENDING_RESTART_VALUES: dict[str, str] = {} @@ -100,6 +109,8 @@ def update_environment_variables(values: dict[str, Any], reveal_secrets: bool = __validate_value(key, value) normalized_values[key] = value + __normalize_embedding_config(normalized_values) + __validate_embedding_config_mutation(normalized_values) __validate_vector_database_config(normalized_values) __write_environment_file(normalized_values) @@ -128,6 +139,8 @@ def __environment_info( if value is None and hasattr(app_config, key): value = str(getattr(app_config, key)) value = value or "" + if key == "EMBEDDING_DIMENSION" and __effective_value("EMBEDDING_PROVIDER", {}) != "openai": + value = "384" should_mask = is_secret or key.endswith(_SECRET_SUFFIXES) masked = should_mask and bool(value) and not reveal_secrets @@ -156,6 +169,8 @@ def __validate_value(key: str, value: str) -> None: raise BadRequestError(f"{key} must be an integer") from exc if key == "LLM_MAX_CONCURRENCY" and integer_value < 1: raise BadRequestError("LLM_MAX_CONCURRENCY must be greater than or equal to 1") + if key == "EMBEDDING_DIMENSION" and integer_value < 1: + raise BadRequestError("EMBEDDING_DIMENSION must be greater than or equal to 1") if key in _FLOAT_KEYS: try: float(value) @@ -175,6 +190,37 @@ def __validate_vector_database_config(values: dict[str, str]) -> None: raise BadRequestError("QDRANT_URL must be configured when VECTOR_DB_PROVIDER is qdrant") +def __normalize_embedding_config(values: dict[str, str]) -> None: + provider = __effective_value("EMBEDDING_PROVIDER", values) or "builtin" + if provider == "builtin": + if "EMBEDDING_DIMENSION" in values and values["EMBEDDING_DIMENSION"] != "384": + raise BadRequestError("Built-in embedding dimension is fixed at 384") + if "EMBEDDING_PROVIDER" in values: + values["EMBEDDING_DIMENSION"] = "384" + return + + current_provider = __effective_value("EMBEDDING_PROVIDER", {}) or "builtin" + if current_provider != "openai" and "EMBEDDING_DIMENSION" not in values: + values["EMBEDDING_DIMENSION"] = "512" + + +def __validate_embedding_config_mutation(values: dict[str, str]) -> None: + changed_keys = { + key + for key in _EMBEDDING_CONFIG_KEYS.intersection(values) + if values[key] != __effective_value(key, {}) + } + if not changed_keys: + return + + from app.logic.knowledge_base import get_knowledge_base_summary + + if get_knowledge_base_summary().resource_file_count > 0: + raise BadRequestError( + "Embedding configuration cannot be changed while resource files exist" + ) + + def __effective_value(key: str, values: dict[str, str]) -> str: if key in values: return values[key] diff --git a/app/logic/knowledge_base.py b/app/logic/knowledge_base.py index a608c6c2fefb88dd5133552b7c9379135e2ef84c..7d381971717b20505be3f02fcb8c127c473fb9c9 100644 --- a/app/logic/knowledge_base.py +++ b/app/logic/knowledge_base.py @@ -23,7 +23,7 @@ from typing import Any import app.config as app_config from app.config import IKKYU_DATA_DIR -from app.logic.embedding import ORIGINAL_DIMENSION, generate_embedding +from app.logic.embedding import ORIGINAL_DIMENSION, configured_embedding_dimension, generate_embedding from app.logic.errors import BadRequestError, LogicError, ProcessingError, TaskAbortedError from app.logic.models import ( KnowledgeAugmentationMode, @@ -928,7 +928,7 @@ def _ensure_default_resource_domain() -> None: create_knowledge_domain( name=_DEFAULT_RESOURCE_DOMAIN, description="Dashboard 使用的默认资源文件库", - dimension=ORIGINAL_DIMENSION, + dimension=configured_embedding_dimension(), metric="cosine", augmentation="none", vector_weight=1.0, diff --git a/app/logic/model_connection.py b/app/logic/model_connection.py index a74cc6d4253561765f79bb19ffb226b7fc3938fb..8df574a3ed6cca5661f69729c2507fa5234b43aa 100644 --- a/app/logic/model_connection.py +++ b/app/logic/model_connection.py @@ -15,7 +15,7 @@ from app.logic.rerank import rerank_openai_documents _TEST_IMAGE_DATA_URI = ( "data:image/png;base64," - "iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAQAAAC1HAwCAAAAC0lEQVR42mP8/x8AAwMCAO+/p9sAAAAASUVORK5CYII=" + "iVBORw0KGgoAAAANSUhEUgAAACAAAAAgCAIAAAD8GO2jAAAAN0lEQVR4nO3RwQ0AMAjDwJT9d05HMB9+vgGCZF7bXJrT9XhgwR8gEyETIRMhEyETIRMhEyEThXzH8QM9OMM6fAAAAABJRU5ErkJggg==" ) diff --git a/app/site/auth.py b/app/site/auth.py index 267d46a32c0c798c3577e7b26eea9c1f72498ab3..54cf873a67b205caf5939aa91cca633ce44d2f77 100644 --- a/app/site/auth.py +++ b/app/site/auth.py @@ -2,14 +2,10 @@ from __future__ import annotations -import os -from ipaddress import ip_address - from fastapi import Request from fastapi.responses import JSONResponse from starlette.middleware.base import BaseHTTPMiddleware -import app.config as app_config from app.logic.auth import validate_api_token, validate_management_session from app.logic.errors import LogicError @@ -23,9 +19,6 @@ class AuthenticationMiddleware(BaseHTTPMiddleware): return error_response if _is_management_path(path): - local_response = _validate_local_management_request(request) - if local_response is not None: - return local_response if not _is_public_management_path(path, request): auth_response = _validate_management_session_request(request) if auth_response is not None: @@ -56,12 +49,6 @@ def _validate_management_session_request(request: Request) -> JSONResponse | Non return None -def _validate_local_management_request(request: Request) -> JSONResponse | None: - if _is_local_request(request): - return None - return JSONResponse(status_code=403, content={"detail": "Management API is only available locally"}) - - def _bearer_token(request: Request) -> str | None: authorization = request.headers.get("authorization", "") scheme, _, token = authorization.partition(" ") @@ -102,43 +89,6 @@ def _is_public_management_path(path: str, request: Request) -> bool: ) -def _is_local_request(request: Request) -> bool: - client_host = request.client.host if request.client else "" - if _is_allowed_local_host(client_host): - return True - - host_header = request.headers.get("host", "") - host = _host_without_port(host_header) - return _is_allowed_local_host(host) - - -def _is_allowed_local_host(host: str) -> bool: - value = (host or "").strip().lower() - if value in {"testclient", "localhost"}: - return True - if value in _configured_management_hosts(): - return True - try: - return ip_address(value).is_loopback - except ValueError: - return False - - -def _configured_management_hosts() -> set[str]: - raw = os.getenv("IKKYU_MANAGEMENT_ALLOWED_HOSTS", app_config.IKKYU_MANAGEMENT_ALLOWED_HOSTS) - return {item.strip().lower() for item in raw.split(",") if item.strip()} - - -def _host_without_port(value: str) -> str: - host = value.strip() - if host.startswith("["): - end = host.find("]") - return host[1:end] if end >= 0 else host.strip("[]") - if host.count(":") == 1: - return host.split(":", 1)[0] - return host - - def _unauthorized(detail: str) -> JSONResponse: return JSONResponse( status_code=401, diff --git a/app/site/routes.py b/app/site/routes.py index d7ee29c08c84fb12e5ec242f523a946be01683f1..ca2154b0169941f474bfd9761d80329887733461 100644 --- a/app/site/routes.py +++ b/app/site/routes.py @@ -17,7 +17,7 @@ from app.logic.auth import ( login_admin, validate_resource_download_ticket, ) -from app.logic.embedding import ORIGINAL_DIMENSION, generate_embedding +from app.logic.embedding import configured_embedding_dimension, generate_embedding from app.logic.environment_config import list_environment_variables, update_environment_variables from app.logic.errors import LogicError from app.logic.file_resource import ( @@ -770,7 +770,7 @@ async def api_domain_add(request: KnowledgeDomainCreateRequest): domain = create_knowledge_domain( name=request.name, description=request.description, - dimension=ORIGINAL_DIMENSION, + dimension=configured_embedding_dimension(), metric="cosine", augmentation=request.enhanced_mode, max_chunk_size=request.max_chunk_size, diff --git a/doc/dev.md b/doc/dev.md index 09427b2d335627a820127d5f88cdd5ca3e131476..7cac7e43f238fda4091bdb3ec8c0dccf0da04c77 100644 --- a/doc/dev.md +++ b/doc/dev.md @@ -41,6 +41,12 @@ uv run python scripts/download_model.py ### 编译镜像 +Linux（宿主机只需安装 Docker Engine，产物固定为 `linux/amd64`）： + +```bash +./scripts/compile-linux.sh +``` + Windows： ```bash @@ -56,6 +62,7 @@ Mac： 强制重新下载模型并编译： ```bash +./scripts/compile-linux.sh -f scripts\compile-windows.bat -f ./scripts/compile-mac.sh -f ``` @@ -150,6 +157,7 @@ ikkyu/ │ └── vendor/ # AI 模型目录（离线运行必需） ├── scripts/ │ ├── compile-mac.sh # Mac 编译脚本 +│ ├── compile-linux.sh # Linux Docker-only 一键编译脚本 │ ├── compile-windows.bat # Windows 编译脚本 │ ├── download_model.py # 模型下载脚本 │ ├── run-mac.sh # Mac 本地启动脚本（不依赖 Docker） diff --git a/doc/install.md b/doc/install.md index c38d08d2e8b9fe1fdefb65f40b519e6b89081f52..032cc92a1c18ceac75fd18c2cc857bf020ecec8e 100644 --- a/doc/install.md +++ b/doc/install.md @@ -2,6 +2,26 @@ 本文档介绍部署方式、启动检查、模型预热和环境变量配置。 +## 在 Linux 服务器上一键编译 + +服务器已安装并启动 Docker Engine 后，在项目根目录执行： + +```bash +./scripts/compile-linux.sh +``` + +脚本不要求宿主机安装 Python、uv、Node.js 或 npm。模型下载、Dashboard 构建和镜像构建均固定使用 `linux/amd64` 平台，最终生成经过架构校验的 `ikkyu-kb-x64:latest` 镜像。首次执行需要访问 Docker Hub、Hugging Face（或配置的镜像源）以及 Debian 软件源。 + +自定义镜像名称、标签或模型下载参数： + +```bash +IMAGE_NAME=registry.example.com/team/ikkyu-kb IMAGE_TAG=1.0.0 ./scripts/compile-linux.sh +HF_ENDPOINT=https://hf-mirror.com HF_DOWNLOAD_MAX_WORKERS=1 ./scripts/compile-linux.sh +./scripts/compile-linux.sh --force +``` + +脚本会验证 Docker daemon 是否可用，并复用已经下载的模型、npm 缓存和未变化的镜像层。`--force` 会删除已有模型后重新下载。 + ## 使用预编译镜像部署 ### 拉取镜像 diff --git a/docker/assets/env.example b/docker/assets/env.example index 6b0a5b7ef9474d8c818df9470a271e32b32cfd6c..7a040c2b106accf22929e8c8e63e690a6d3e6e20 100644 --- a/docker/assets/env.example +++ b/docker/assets/env.example @@ -19,6 +19,9 @@ LLM_API_KEY= LLM_MODEL= LLM_MAX_CONCURRENCY=10 +# 外部 Embedding 输出维度，默认 512；内置模型固定为 384 +EMBEDDING_DIMENSION=512 + # 超时配置（秒）- 支持大文档处理 # DOWNLOAD_TIMEOUT=1200 # VL_API_TIMEOUT=300 diff --git a/docker/assets/runtime.env b/docker/assets/runtime.env index 174e914f4349e405d333e74f5201749b88629127..c909e433bfeeea80b2c1e9a9dacaccb4caaad949 100644 --- a/docker/assets/runtime.env +++ b/docker/assets/runtime.env @@ -26,7 +26,6 @@ DOWNLOAD_TIMEOUT=1200 IKKYU_ADMIN_USERNAME=admin IKKYU_ADMIN_PASSWORD=admin IKKYU_MANAGEMENT_SESSION_TTL_SECONDS=86400 -IKKYU_MANAGEMENT_ALLOWED_HOSTS= VL_PROVIDER=builtin VL_BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1 @@ -41,6 +40,7 @@ EMBEDDING_PROVIDER=builtin EMBEDDING_BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1 EMBEDDING_API_KEY= EMBEDDING_MODEL= +EMBEDDING_DIMENSION=512 EMBEDDING_API_TIMEOUT=300 RERANK_PROVIDER=builtin diff --git a/scripts/compile-linux.sh b/scripts/compile-linux.sh new file mode 100755 index 0000000000000000000000000000000000000000..9e114d056d963c96f48a785b2408c632c0dfd933 --- /dev/null +++ b/scripts/compile-linux.sh @@ -0,0 +1,163 @@ +#!/usr/bin/env bash +set -euo pipefail + +ROOT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" +cd "$ROOT_DIR" + +IMAGE_NAME="${IMAGE_NAME:-ikkyu-kb-x64}" +IMAGE_TAG="${IMAGE_TAG:-latest}" +IMAGE="${IMAGE_NAME}:${IMAGE_TAG}" +TARGET_PLATFORM="linux/amd64" +PYTHON_IMAGE="${PYTHON_IMAGE:-python:3.12-slim}" +NODE_IMAGE="${NODE_IMAGE:-node:22-bookworm-slim}" +HF_DOWNLOAD_MAX_WORKERS="${HF_DOWNLOAD_MAX_WORKERS:-2}" +FORCE_DOWNLOAD=0 + +usage() { + cat <<'EOF' +用法: ./scripts/compile-linux.sh [-f|--force] + +仅依赖 Linux、bash 和 Docker Engine，依次下载模型、构建 Dashboard 和 x64 Docker 镜像。 + +选项: + -f, --force 删除已有模型并重新下载 + -h, --help 显示帮助 + +可选环境变量: + IMAGE_NAME 镜像仓库名（默认: ikkyu-kb-x64） + IMAGE_TAG 镜像标签（默认: latest） + HF_DOWNLOAD_MAX_WORKERS 模型下载并发数（默认: 2） + HF_ENDPOINT Hugging Face 镜像地址 + HF_TOKEN Hugging Face 访问令牌 + HTTP_PROXY/HTTPS_PROXY 下载及构建代理 + PYTHON_IMAGE/NODE_IMAGE 临时构建容器镜像 +EOF +} + +while [[ $# -gt 0 ]]; do + case "$1" in + -f|--force) + FORCE_DOWNLOAD=1 + ;; + -h|--help) + usage + exit 0 + ;; + *) + echo "未知参数: $1" >&2 + usage >&2 + exit 2 + ;; + esac + shift +done + +if ! command -v docker >/dev/null 2>&1; then + echo "错误: 未找到 docker 命令，请先安装 Docker Engine。" >&2 + exit 1 +fi +if ! docker info >/dev/null 2>&1; then + echo "错误: 无法连接 Docker daemon，请确认服务已启动且当前用户有访问权限。" >&2 + exit 1 +fi +if [[ ! "$HF_DOWNLOAD_MAX_WORKERS" =~ ^[1-9][0-9]*$ ]]; then + echo "错误: HF_DOWNLOAD_MAX_WORKERS 必须是大于 0 的整数。" >&2 + exit 2 +fi + +mkdir -p docker/vendor dashboard/node_modules .cache/npm + +USER_ID="$(id -u)" +GROUP_ID="$(id -g)" +WORKSPACE_MOUNT="${ROOT_DIR}:/workspace" +COMMON_RUN_ARGS=( + --rm + --platform "$TARGET_PLATFORM" + --user "${USER_ID}:${GROUP_ID}" + --volume "$WORKSPACE_MOUNT" + --workdir /workspace +) +FORWARDED_ENV_VARS=( + HF_ENDPOINT HF_TOKEN HTTP_PROXY HTTPS_PROXY NO_PROXY + http_proxy https_proxy no_proxy +) +for variable_name in "${FORWARDED_ENV_VARS[@]}"; do + if [[ -n "${!variable_name:-}" ]]; then + COMMON_RUN_ARGS+=(--env "$variable_name") + fi +done + +python_in_container() { + docker run "${COMMON_RUN_ARGS[@]}" \ + --env HOME=/tmp \ + "$PYTHON_IMAGE" python "$@" +} + +image_label() { + docker image inspect "$1" --format "{{ index .Config.Labels \"$2\" }}" 2>/dev/null || true +} + +image_architecture() { + docker image inspect "$1" --format '{{.Architecture}}' 2>/dev/null || true +} + +echo "Step 1/4: 下载并校验模型..." +DOWNLOAD_ARGS=(--max-workers "$HF_DOWNLOAD_MAX_WORKERS") +if [[ "$FORCE_DOWNLOAD" == "1" ]]; then + DOWNLOAD_ARGS+=(--force) +fi +docker run "${COMMON_RUN_ARGS[@]}" \ + --env HOME=/tmp \ + "$PYTHON_IMAGE" \ + sh -c 'python -m pip install --quiet --disable-pip-version-check --target /tmp/python-deps "huggingface-hub[hf-xet]==1.23.0" && PYTHONPATH=/tmp/python-deps exec python scripts/download_model.py "$@"' \ + sh "${DOWNLOAD_ARGS[@]}" + +echo +echo "Step 2/4: 构建 Dashboard..." +docker run "${COMMON_RUN_ARGS[@]}" \ + --env HOME=/tmp \ + --env NPM_CONFIG_CACHE=/workspace/.cache/npm \ + --workdir /workspace/dashboard \ + "$NODE_IMAGE" \ + sh -c 'npm ci && npm run build' + +echo +echo "Step 3/4: 构建模型层镜像..." +MODEL_FINGERPRINT="$(python_in_container -c 'from scripts.build_cache import _model_fingerprint; print(_model_fingerprint())')" +MODEL_IMAGE="${IMAGE_NAME}-models:${MODEL_FINGERPRINT:0:16}" +if [[ "$(image_label "$MODEL_IMAGE" ikkyu.models.fingerprint)" == "$MODEL_FINGERPRINT" ]] \ + && [[ "$(image_architecture "$MODEL_IMAGE")" == "amd64" ]]; then + echo "模型层未变化，复用镜像: $MODEL_IMAGE" +else + docker build \ + --platform "$TARGET_PLATFORM" \ + --build-arg "MODEL_FINGERPRINT=$MODEL_FINGERPRINT" \ + -f docker/Dockerfile.models \ + -t "$MODEL_IMAGE" \ + . +fi + +echo +echo "Step 4/4: 构建应用镜像..." +BUILD_FINGERPRINT="$(python_in_container -c 'from scripts.build_cache import _docker_build_fingerprint, _model_fingerprint; print(_docker_build_fingerprint(_model_fingerprint()))')" +if [[ "$(image_label "$IMAGE" ikkyu.build.fingerprint)" == "$BUILD_FINGERPRINT" ]] \ + && [[ "$(image_architecture "$IMAGE")" == "amd64" ]]; then + echo "应用与模型均未变化，复用镜像: $IMAGE" +else + docker build \ + --platform "$TARGET_PLATFORM" \ + --build-arg "BUILD_FINGERPRINT=$BUILD_FINGERPRINT" \ + --build-arg "MODELS_IMAGE=$MODEL_IMAGE" \ + -f docker/Dockerfile \ + -t "$IMAGE" \ + . +fi + +echo +IMAGE_ARCHITECTURE="$(docker image inspect "$IMAGE" --format '{{.Architecture}}')" +if [[ "$IMAGE_ARCHITECTURE" != "amd64" ]]; then + echo "错误: 镜像架构应为 amd64，实际为 $IMAGE_ARCHITECTURE。" >&2 + exit 1 +fi +echo "x64 镜像构建完成: $IMAGE" +docker image inspect "$IMAGE" --format '镜像 ID: {{.Id}} 大小: {{.Size}} bytes' diff --git a/test/auth_test.py b/test/auth_test.py index 9564f856de1135b0f40f13f4db726fdf3b359f46..b0285ae451fe4b27988abe12f04db7aa19e57ad5 100644 --- a/test/auth_test.py +++ b/test/auth_test.py @@ -7,6 +7,9 @@ import unittest from pathlib import Path from unittest.mock import patch +from fastapi import FastAPI +from fastapi.testclient import TestClient + from app.logic.auth import ( create_resource_download_ticket, delete_api_token, @@ -15,11 +18,46 @@ from app.logic.auth import ( validate_resource_download_ticket, ) from app.logic.errors import LogicError +from app.site.auth import AuthenticationMiddleware _BUILTIN_API_TOKEN = "sk-1234567890qwertyuiop" +class AuthenticationMiddlewareTest(unittest.TestCase): + def test_remote_requests_use_token_authentication_without_a_host_restriction(self) -> None: + app = FastAPI() + app.add_middleware(AuthenticationMiddleware) + app.add_api_route("/management-api/auth/login", lambda: {"access_token": "session-token"}, methods=["POST"]) + app.add_api_route("/management-api/version", lambda: {"version": "test"}) + app.add_api_route("/api/domain", lambda: {"name": "test"}) + app.add_api_route("/api/util/chunk", lambda: {"chunks": []}, methods=["POST"]) + client = TestClient(app, client=("203.0.113.10", 50000)) + + self.assertEqual(client.post("/management-api/auth/login").status_code, 200) + self.assertEqual(client.get("/management-api/version").status_code, 401) + self.assertEqual(client.get("/api/domain").status_code, 401) + self.assertEqual(client.post("/api/util/chunk").status_code, 200) + + with ( + patch("app.site.auth.validate_management_session") as validate_management_session, + patch("app.site.auth.validate_api_token") as validate_api_token, + ): + management_response = client.get( + "/management-api/version", + headers={"Authorization": "Bearer session-token"}, + ) + api_response = client.get( + "/api/domain", + headers={"Authorization": "Bearer api-token"}, + ) + + self.assertEqual(management_response.status_code, 200) + self.assertEqual(api_response.status_code, 200) + validate_management_session.assert_called_once_with("session-token") + validate_api_token.assert_called_once_with("api-token") + + class BuiltinApiTokenTest(unittest.TestCase): def test_builtin_api_token_is_listed_and_accepted_without_persisted_auth_state(self) -> None: with tempfile.TemporaryDirectory() as temp_data_dir: diff --git a/test/chunk_test.py b/test/chunk_test.py index 128e75f4352fd78491e3e4ca0f607c0c2138e724..55145f40801eff92ab2987130efe486ffb774877 100644 --- a/test/chunk_test.py +++ b/test/chunk_test.py @@ -9,7 +9,8 @@ from unittest.mock import patch ROOT = Path(__file__).resolve().parents[1] sys.path.insert(0, str(ROOT)) -from app.logic import chunk +from app.logic import chunk, knowledge_base +from app.logic.errors import BadRequestError class ChunkTest(unittest.TestCase): @@ -83,6 +84,35 @@ class ChunkTest(unittest.TestCase): self.assertEqual(processed_windows[1][-100:], processed_windows[2][:100]) self.assertEqual("".join(chunks), content) + def test_preserves_whitespace_around_non_empty_chunk_content(self) -> None: + content = " \n\t正文内容\n\n " + + with ( + patch.object(chunk, "__initialize_model"), + patch.object( + chunk, + "__chunk_by_bert", + return_value=([content], [0]), + ), + ): + chunks = chunk.chunk_text_bert(content, max_size=100) + + self.assertEqual(chunks, [content]) + + def test_discards_content_that_contains_only_whitespace(self) -> None: + self.assertEqual(chunk.chunk_text_bert(" \n\t ", max_size=100), []) + + def test_resource_pipeline_filters_whitespace_only_chunks_before_indexing(self) -> None: + chunks = knowledge_base._normalized_document_chunks( + {"chunks": [" \n", "\t", " 正文 ", "\n\n"]} + ) + + self.assertEqual(chunks, [" 正文 "]) + + def test_resource_pipeline_rejects_document_with_only_whitespace_chunks(self) -> None: + with self.assertRaisesRegex(BadRequestError, "did not produce any chunks"): + knowledge_base._normalized_document_chunks({"chunks": [" \n", "\t"]}) + if __name__ == "__main__": unittest.main() diff --git a/test/environment_config_embedding_test.py b/test/environment_config_embedding_test.py new file mode 100644 index 0000000000000000000000000000000000000000..34b88ad119309a67a46f7c8c2d3c353f73652d09 --- /dev/null +++ b/test/environment_config_embedding_test.py @@ -0,0 +1,109 @@ +from __future__ import annotations + +import os +import sys +import tempfile +import unittest +from pathlib import Path +from types import SimpleNamespace +from unittest.mock import patch + + +ROOT = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(ROOT)) + +import app.config as app_config +from app.logic import environment_config +from app.logic.errors import BadRequestError + + +class EnvironmentConfigEmbeddingTest(unittest.TestCase): + def setUp(self) -> None: + self._original_environment = { + key: os.environ.get(key) + for key in ( + "EMBEDDING_PROVIDER", + "EMBEDDING_BASE_URL", + "EMBEDDING_API_KEY", + "EMBEDDING_MODEL", + "EMBEDDING_DIMENSION", + ) + } + self._original_config = { + key: getattr(app_config, key) + for key in self._original_environment + } + os.environ["EMBEDDING_PROVIDER"] = "builtin" + os.environ["EMBEDDING_DIMENSION"] = "512" + app_config.EMBEDDING_PROVIDER = "builtin" + app_config.EMBEDDING_DIMENSION = 512 + environment_config._PENDING_RESTART_VALUES.clear() + + def tearDown(self) -> None: + for key, value in self._original_environment.items(): + if value is None: + os.environ.pop(key, None) + else: + os.environ[key] = value + for key, value in self._original_config.items(): + setattr(app_config, key, value) + + def test_external_embedding_defaults_to_dimension_512(self) -> None: + with self._configuration_context(resource_count=0): + variables = environment_config.update_environment_variables( + {"EMBEDDING_PROVIDER": "openai"} + ) + + values = {item.key: item.value for item in variables} + self.assertEqual(values["EMBEDDING_DIMENSION"], "512") + self.assertEqual(app_config.EMBEDDING_DIMENSION, 512) + + def test_builtin_embedding_forces_dimension_384(self) -> None: + with self._configuration_context(resource_count=0): + variables = environment_config.update_environment_variables( + {"EMBEDDING_PROVIDER": "builtin"} + ) + + values = {item.key: item.value for item in variables} + self.assertEqual(values["EMBEDDING_DIMENSION"], "384") + self.assertEqual(app_config.EMBEDDING_DIMENSION, 384) + + def test_rejects_embedding_changes_when_resource_exists(self) -> None: + with self._configuration_context(resource_count=1): + with self.assertRaisesRegex( + BadRequestError, + "cannot be changed while resource files exist", + ): + environment_config.update_environment_variables( + {"EMBEDDING_MODEL": "another-model"} + ) + + def _configuration_context(self, resource_count: int): + temp_dir = tempfile.TemporaryDirectory() + self.addCleanup(temp_dir.cleanup) + env_file = Path(temp_dir.name) / ".env" + return _EmbeddingConfigContext(env_file, resource_count) + + +class _EmbeddingConfigContext: + def __init__(self, env_file: Path, resource_count: int) -> None: + self._patches = ( + patch.object(app_config, "RUNTIME_ENV_FILE", env_file), + patch( + "app.logic.knowledge_base.get_knowledge_base_summary", + return_value=SimpleNamespace(resource_file_count=resource_count), + ), + ) + + def __enter__(self): + for current_patch in self._patches: + current_patch.start() + return self + + def __exit__(self, exc_type, exc_value, traceback): + for current_patch in reversed(self._patches): + current_patch.stop() + + +if __name__ == "__main__": + unittest.main() diff --git a/test/smoke_test.py b/test/smoke_test.py index b5476002b2d1b3d2462dde8aba10c70a4b1a29d0..eeda71b974e56e92ca3c4ca2a1fce1c88d0d8acf 100644 --- a/test/smoke_test.py +++ b/test/smoke_test.py @@ -593,6 +593,28 @@ def __test_api_smoke() -> None: from app.site.main import app client = TestClient(app) + remote_client = TestClient(app, client=("203.0.113.10", 50000)) + remote_missing_auth_response = remote_client.get("/management-api/version") + if remote_missing_auth_response.status_code != 401: + raise AssertionError("remote management endpoints should require management login") + remote_login_response = remote_client.post( + "/management-api/auth/login", + json={"username": "admin", "password": "admin"}, + ) + if remote_login_response.status_code != 200: + raise AssertionError( + "remote management login should be allowed: " + f"{remote_login_response.status_code} {remote_login_response.text}" + ) + remote_management_response = remote_client.get( + "/management-api/version", + headers={"Authorization": f"Bearer {remote_login_response.json()['access_token']}"}, + ) + if remote_management_response.status_code != 200: + raise AssertionError( + "remote management session should be accepted: " + f"{remote_management_response.status_code} {remote_management_response.text}" + ) missing_api_auth_response = client.post("/api/domain/", json={}) if missing_api_auth_response.status_code != 401: raise AssertionError("/api endpoints should require bearer authentication") @@ -759,6 +781,7 @@ def __test_api_smoke() -> None: raise AssertionError("/management-api/environment-config did not include IKKYU_ORIGINAL_ROOT") for key in ( "EMBEDDING_PROVIDER", + "EMBEDDING_DIMENSION", "RERANK_PROVIDER", "VL_PROVIDER", "LLM_ENABLED", @@ -791,9 +814,6 @@ def __test_api_smoke() -> None: ) if updated_provider["value"] != "qdrant" or not updated_provider["restart_required"]: raise AssertionError("environment config should expose the pending vector provider and restart requirement") - updated_environment_keys = {item["key"] for item in environment_update_response.json()["variables"]} - if "EMBEDDING_DIMENSION" in updated_environment_keys: - raise AssertionError("EMBEDDING_DIMENSION should not be configurable") runtime_env_file = Path(temp_data_dir) / ".env" if not runtime_env_file.exists(): raise AssertionError("environment config update should persist values to runtime .env") @@ -836,6 +856,7 @@ def __test_api_smoke() -> None: original_embedding_api_key = app_config.EMBEDDING_API_KEY original_embedding_model = app_config.EMBEDDING_MODEL original_embedding_base_url = app_config.EMBEDDING_BASE_URL + original_embedding_dimension = app_config.EMBEDDING_DIMENSION original_httpx_post = embedding_module.httpx.post captured_embedding_payload = {} @@ -844,7 +865,7 @@ def __test_api_smoke() -> None: return None def json(self): - return {"data": [{"embedding": [1.0] + [0.0] * 383}]} + return {"data": [{"embedding": [1.0] + [0.0] * 511}]} def fake_embedding_post(url, headers, json, timeout): captured_embedding_payload["url"] = url @@ -857,13 +878,14 @@ def __test_api_smoke() -> None: app_config.EMBEDDING_API_KEY = "smoke-key" app_config.EMBEDDING_MODEL = "smoke-embedding" app_config.EMBEDDING_BASE_URL = "https://embedding.example/v1/" + app_config.EMBEDDING_DIMENSION = 512 embedding_module.httpx.post = fake_embedding_post try: external_vector, external_dimension, _ = embedding_module.generate_embedding("alpha", text_type="query") - if external_dimension != 384 or len(external_vector) != 384: - raise AssertionError("external embedding should always return dimension 384") - if captured_embedding_payload["json"].get("dimensions") != 384: - raise AssertionError("external embedding request should force dimensions=384") + if external_dimension != 512 or len(external_vector) != 512: + raise AssertionError("external embedding should use the configured dimension") + if captured_embedding_payload["json"].get("dimensions") != 512: + raise AssertionError("external embedding request should use the configured dimension") if captured_embedding_payload["json"].get("input") != "query: alpha": raise AssertionError("external embedding request should preserve query prefix") if captured_embedding_payload["url"] != "https://embedding.example/v1/embeddings": @@ -873,6 +895,7 @@ def __test_api_smoke() -> None: app_config.EMBEDDING_API_KEY = original_embedding_api_key app_config.EMBEDDING_MODEL = original_embedding_model app_config.EMBEDDING_BASE_URL = original_embedding_base_url + app_config.EMBEDDING_DIMENSION = original_embedding_dimension embedding_module.httpx.post = original_httpx_post from app.logic import model_connection, rerank as rerank_module, llm as llm_module @@ -3263,6 +3286,29 @@ def __test_windows_batch_scripts() -> None: raise AssertionError("compile-windows.bat must use build_cache.py: " + ", ".join(missing)) +def __test_linux_compile_script() -> None: + compile_script = ROOT / "scripts" / "compile-linux.sh" + text = compile_script.read_text(encoding="utf-8") + required_fragments = [ + "docker info", + 'IMAGE_NAME="${IMAGE_NAME:-ikkyu-kb-x64}"', + 'IMAGE_TAG="${IMAGE_TAG:-latest}"', + 'TARGET_PLATFORM="linux/amd64"', + 'HF_DOWNLOAD_MAX_WORKERS="${HF_DOWNLOAD_MAX_WORKERS:-2}"', + "scripts/download_model.py", + "npm ci", + "npm run build", + "docker/Dockerfile.models", + "docker/Dockerfile", + "Architecture", + "amd64", + "--force", + ] + missing = [fragment for fragment in required_fragments if fragment not in text] + if missing: + raise AssertionError("compile-linux.sh missing required fragments: " + ", ".join(missing)) + + def __test_build_cache_config() -> None: helper = ROOT / "scripts" / "build_cache.py" mac_script = ROOT / "scripts" / "compile-mac.sh" @@ -3398,6 +3444,7 @@ def main() -> int: ("Rerank Transformers 5 compatibility", __test_rerank_transformers_5_compatibility), ("Dockerfile config", __test_dockerfile_config), ("Windows batch scripts", __test_windows_batch_scripts), + ("Linux Docker-only compile script", __test_linux_compile_script), ("build cache config", __test_build_cache_config), ("docker run docs", __test_docker_run_docs), ]