Dev

AGENTS.md

@@ -0,0 +1,52 @@
+# AGENTS.md
+
+## Purpose
+
+NovAI is intended to evolve toward an agentic novel-writing tool, closer to the interaction model of Claude Code / Vibe Coding tools than to a traditional chat app.
+
+The core loop should be:
+
+1. The user expresses intent in natural language.
+2. The AI maintains task context.
+3. The AI uses tools to read and write project files.
+4. Story artifacts are saved into the local project filesystem.
+5. The conversation acts as the collaboration interface, not the primary storage for story content.
+
+## Product Direction
+
+When making implementation decisions, prefer this framing:
+
+- The chat UI is an agent control surface.
+- The AI should operate on files, not mainly emit long final text into the chat stream.
+- Chapters, prompts, and elements belong in files.
+- Conversation history is for collaboration, clarification, planning, and action summaries.
+- Generated story content should be previewed in file/content panels and written back to the project.
+
+This means NovAI should gradually move away from a simple "single prompt -> single response" flow and toward a tool-using agent workflow for story creation and revision.
+
+## Reference Repository
+
+For implementation reference and comparative study, keep this external repository available next to the NovAI repo:
+
+- `/Users/honlnk/project/claude-code-sound`
+
+This repository is intentionally cloned outside the NovAI git repository so that:
+
+- it does not affect NovAI git status,
+- it is not accidentally committed,
+- it can still be read and compared during development.
+
+When useful, study that repository for patterns such as:
+
+- agent loop design,
+- conversation state management,
+- tool invocation structure,
+- streaming interaction flow,
+- file-oriented execution behavior.
+
+## Working Rule
+
+When documentation and code appear to conflict, prefer the clarified product intent above:
+
+- NovAI is not just a workspace with a chat box.
+- NovAI should become a conversation-driven AI agent for writing stories through tools and files.

README.md

@@ -24,24 +24,29 @@ NovAI 的思路是换一条路：
 
 ## 当前状态
 
-项目目前处于早期开发阶段，正在验证 MVP 的最小可用创作闭环。
+项目目前处于 MVP 早期实现阶段，正在优先验证 AI 最小可用创作闭环。
 
 当前仓库已经完成的内容主要包括：
 
 - Vue 3 + TypeScript + Vite 前端工程初始化
-- 首页、工作区、设置页的基础路由和页面骨架
 - 本地小说项目的创建与打开流程
+- 不合法项目目录的检测与修复流程
 - 标准项目目录初始化
-- 文件树扫描与基础文件预览链路
+- `novel.config.json` 读写
+- LLM / Embedding 配置测试连接
+- LLM 流式生成链路
+- `prompts/system.md` 读取与保存
+- 章节文件写入 `chapters/`
+- 测试页中的项目文档分组与原文预览
 - 项目规划、需求说明、UI 设计、技术架构等文档整理
 
 尚未完整落地的核心能力包括：
 
-- LLM / Embedding 真实接入
-- 章节流式生成
 - 要素抽取
 - Embedding 向量化与 RAG 检索
-- AI 精筛选与更完整的创作工作流
+- 近期章节上下文拼装
+- Rerank 精排与更完整的创作工作流
+- 正式工作台 UI
 
 ## MVP 目标
 
@@ -91,15 +96,27 @@ NovAI 采用“一个文件夹就是一个小说项目”的思路。当前默
 - Vite
 - Pinia
 - Vue Router
-- Sass
-- Tailwind CSS v4
+- File System Access API
 
 规划中的核心能力还包括：
 
-- File System Access API
 - Orama
 - isomorphic-git
 
+## 当前实现方式
+
+为了优先验证 AI 主链路，当前版本暂不继续推进正式工作台界面，而是采用一个极简测试页 `/test` 作为开发入口。
+
+当前测试页已经可以完成：
+
+- 创建 / 打开 / 修复小说项目
+- 编辑并保存项目配置
+- 测试 LLM / Embedding 连通性
+- 发起流式生成
+- 保存 SYSTEM Prompt
+- 保存生成章节
+- 浏览项目中的 Markdown / JSON / 文本文档原文
+
 ## 本地开发
 
 ### 环境要求

src/app/router.ts

@@ -1,5 +1,6 @@
 import { createRouter, createWebHistory } from 'vue-router'
 
+import SessionTestView from '../views/SessionTestView.vue'
 import TestLabView from '../views/TestLabView.vue'
 
 export const router = createRouter({
@@ -14,5 +15,10 @@ export const router = createRouter({
       name: 'test',
       component: TestLabView,
     },
+    {
+      path: '/session-test',
+      name: 'session-test',
+      component: SessionTestView,
+    },
   ],
 })

src/core/ai/rerank-client.ts

@@ -0,0 +1,189 @@
+import { createJsonHeaders, extractErrorMessage, normalizeBaseUrl, readJsonResponse, resolveApiUrl } from '../ai/shared'
+
+import type { ModelConnectionResult } from '../../types/ai'
+import type { RerankInput, RerankResult } from '../../types/rag'
+
+export type RerankConnectionInput = {
+  baseUrl: string
+  apiKey: string
+  model?: string
+}
+
+export async function testRerankConnection(
+  input: RerankConnectionInput,
+): Promise<ModelConnectionResult> {
+  const baseUrl = normalizeBaseUrl(input.baseUrl)
+
+  if (!baseUrl || !input.apiKey.trim()) {
+    return {
+      ok: false,
+      message: '请先填写 Rerank 的 API 地址和 API Key',
+    }
+  }
+
+  try {
+    const response = await fetch(`${baseUrl}/models`, {
+      method: 'GET',
+      headers: createJsonHeaders(input.apiKey, baseUrl),
+    })
+
+    if (!response.ok) {
+      const payload = await readJsonResponse(response)
+      return {
+        ok: false,
+        message: extractErrorMessage(payload, 'Rerank 测试连接失败'),
+      }
+    }
+
+    return {
+      ok: true,
+      message: 'Rerank 连接成功',
+    }
+  } catch (error) {
+    return {
+      ok: false,
+      message: error instanceof Error ? error.message : 'Rerank 测试连接失败',
+    }
+  }
+}
+
+export async function rerankCandidates(
+  input: RerankConnectionInput & RerankInput,
+): Promise<RerankResult> {
+  const baseUrl = normalizeBaseUrl(input.baseUrl)
+
+  if (!baseUrl || !input.apiKey.trim() || !input.model?.trim()) {
+    throw new Error('请先填写 Rerank 的 API 地址、API Key 和模型名称')
+  }
+
+  const request = buildRerankRequest(baseUrl, input)
+  const response = await fetch(request.url, {
+    method: 'POST',
+    headers: request.headers,
+    body: JSON.stringify(request.body),
+  })
+
+  if (!response.ok) {
+    const payload = await readJsonResponse(response)
+    throw new Error(extractErrorMessage(payload, 'Rerank 请求失败'))
+  }
+
+  const payload = await readJsonResponse(response)
+  return normalizeRerankResult(payload, input)
+}
+
+function normalizeRerankResult(payload: unknown, input: RerankInput): RerankResult {
+  const results = extractRerankResults(payload)
+
+  if (results) {
+    const items = results
+      .map((item) => {
+        if (
+          item &&
+          typeof item === 'object' &&
+          'index' in item &&
+          typeof item.index === 'number'
+        ) {
+          const candidate = input.candidates[item.index]
+
+          if (!candidate) {
+            return null
+          }
+
+          return {
+            id: candidate.id,
+            score:
+              'relevance_score' in item && typeof item.relevance_score === 'number'
+                ? item.relevance_score
+                : 0,
+          }
+        }
+
+        return null
+      })
+      .filter((item): item is NonNullable<typeof item> => item !== null)
+
+    return {
+      items,
+      model:
+        payload &&
+        typeof payload === 'object' &&
+        'model' in payload &&
+        typeof payload.model === 'string'
+          ? payload.model
+          : undefined,
+    }
+  }
+
+  return {
+    items: input.candidates.slice(0, input.topN).map((candidate, index) => ({
+      id: candidate.id,
+      score: input.candidates.length - index,
+    })),
+  }
+}
+
+function extractRerankResults(payload: unknown) {
+  if (
+    payload &&
+    typeof payload === 'object' &&
+    'results' in payload &&
+    Array.isArray(payload.results)
+  ) {
+    return payload.results
+  }
+
+  if (
+    payload &&
+    typeof payload === 'object' &&
+    'output' in payload &&
+    payload.output &&
+    typeof payload.output === 'object' &&
+    'results' in payload.output &&
+    Array.isArray(payload.output.results)
+  ) {
+    return payload.output.results
+  }
+
+  return null
+}
+
+function buildRerankRequest(baseUrl: string, input: RerankConnectionInput & RerankInput) {
+  const model = input.model?.trim() ?? ''
+
+  if (isDashScopeBaseUrl(baseUrl)) {
+    const dashScopeOrigin = new URL(baseUrl).origin
+    const dashScopePath = '/api/v1/services/rerank/text-rerank/text-rerank'
+
+    return {
+      url: resolveApiUrl(dashScopeOrigin, dashScopePath),
+      headers: createJsonHeaders(input.apiKey, dashScopeOrigin),
+      body: {
+        model,
+        input: {
+          query: input.query,
+          documents: input.candidates.map((candidate) => candidate.retrievalText),
+        },
+        parameters: {
+          top_n: input.topN,
+          return_documents: false,
+        },
+      },
+    }
+  }
+
+  return {
+    url: resolveApiUrl(baseUrl, '/rerank'),
+    headers: createJsonHeaders(input.apiKey, baseUrl),
+    body: {
+      model,
+      query: input.query,
+      top_n: input.topN,
+      documents: input.candidates.map((candidate) => candidate.retrievalText),
+    },
+  }
+}
+
+function isDashScopeBaseUrl(baseUrl: string) {
+  return /dashscope(-intl)?\.aliyuncs\.com/.test(baseUrl)
+}