feat: add avatar payments and finance management

digital-avatar-app/backend/requirements.txt

@@ -5,7 +5,6 @@ pydantic
 python-multipart
 httpx
 pypdf
-PyMuPDF>=1.24,<2
 python-docx
 openpyxl
 apscheduler>=3.10

digital-avatar-app/backend/services/knowledge_vectorizer.py

@@ -8,8 +8,7 @@ import threading
 from datetime import datetime, timezone
 
 from database import SessionLocal
-from models import Avatar, KnowledgeChunk, KnowledgeDoc
-from services.pdf_ocr_service import extract_scanned_pdf_text
+from models import KnowledgeChunk, KnowledgeDoc
 import embeddings
 
 logger = logging.getLogger(__name__)
@@ -77,23 +76,7 @@ class KnowledgeVectorizer:
 
             self._set_progress(db, doc, "extracting", 8)
             text = embeddings.extract_text(path, f".{doc.file_type}")
-            if doc.file_type == "pdf" and not text.strip():
-                avatar = db.get(Avatar, doc.avatar_id)
-                if not avatar:
-                    raise ValueError("文档所属分身不存在")
-
-                def ocr_progress(done: int, total: int):
-                    percent = 8 + int((done / max(1, total)) * 20)
-                    self._set_progress(db, doc, "ocr", min(percent, 28))
-
-                self._set_progress(db, doc, "ocr", 8)
-                text = extract_scanned_pdf_text(
-                    db,
-                    avatar,
-                    path,
-                    on_progress=ocr_progress,
-                )
-            self._set_progress(db, doc, "chunking", 29)
+            self._set_progress(db, doc, "chunking", 22)
             chunks = embeddings.chunk_text(text)
             if not chunks:
                 raise ValueError("文档没有可建立索引的文字内容")

digital-avatar-app/backend/services/pdf_ocr_service.py

@@ -1,130 +0,0 @@
-"""OCR fallback for image-only PDF knowledge documents."""
-
-import logging
-import os
-import time
-from typing import Callable
-
-from sqlalchemy.orm import Session
-
-from models import Avatar
-from services.chat_model_config import get_chat_model_config
-from services.token_billing import (
-    estimate_fallback_usage,
-    release_reservation,
-    reserve_avatar_tokens,
-    settle_reservation,
-)
-from services.vision_service import call_vision_model, prepare_image
-
-
-logger = logging.getLogger(__name__)
-
-PDF_OCR_PROMPT = (
-    "请逐字转录这一页扫描文档中的全部可见文字和表格，只输出转录内容，不要解释，不要使用 Markdown 代码块。"
-    "保留标题、段落、项目编号、数值和自然换行；看不清的内容写作[无法辨认]，不要猜测、纠错或补全。"
-)
-
-
-def _positive_int(name: str, default: int, minimum: int, maximum: int) -> int:
-    try:
-        value = int(os.getenv(name, str(default)))
-    except ValueError:
-        value = default
-    return max(minimum, min(maximum, value))
-
-
-def extract_scanned_pdf_text(
-    db: Session,
-    avatar: Avatar,
-    path: str,
-    *,
-    on_progress: Callable[[int, int], None] | None = None,
-) -> str:
-    """Render and OCR an image-only PDF while preserving page order."""
-    try:
-        import pymupdf
-    except ImportError as exc:
-        raise RuntimeError("扫描型 PDF 识别组件未安装") from exc
-
-    max_pages = _positive_int("KNOWLEDGE_PDF_OCR_MAX_PAGES", 80, 1, 300)
-    render_dpi = _positive_int("KNOWLEDGE_PDF_OCR_DPI", 144, 96, 200)
-    max_attempts = _positive_int("KNOWLEDGE_PDF_OCR_ATTEMPTS", 3, 1, 5)
-    model_config = get_chat_model_config()
-    model = model_config.ocr_model or model_config.vision_model
-    if not model_config.api_key or not model:
-        raise RuntimeError("扫描型 PDF 需要配置视觉 OCR 模型")
-
-    texts: list[str] = []
-    with pymupdf.open(path) as document:
-        total_pages = document.page_count
-        if total_pages <= 0:
-            raise ValueError("PDF 没有可识别页面")
-        if total_pages > max_pages:
-            raise ValueError(
-                f"扫描型 PDF 共 {total_pages} 页，超过单次 OCR 上限 {max_pages} 页，请拆分后上传"
-            )
-
-        scale = render_dpi / 72
-        for page_index in range(total_pages):
-            page = document.load_page(page_index)
-            pixmap = page.get_pixmap(
-                matrix=pymupdf.Matrix(scale, scale),
-                colorspace=pymupdf.csRGB,
-                alpha=False,
-            )
-            prepared = prepare_image(pixmap.tobytes("jpeg", jpg_quality=88))
-            estimate_messages = [{
-                "role": "user",
-                "content": f"[扫描 PDF 第 {page_index + 1}/{total_pages} 页]\n{PDF_OCR_PROMPT}",
-            }]
-            reservation = reserve_avatar_tokens(
-                db,
-                avatar,
-                "knowledge_pdf_ocr",
-                model,
-                estimate_messages,
-                model_config.vision_max_tokens,
-            )
-            try:
-                result = None
-                for attempt in range(1, max_attempts + 1):
-                    try:
-                        result = call_vision_model(
-                            prepared,
-                            model_config,
-                            model=model,
-                            prompt=PDF_OCR_PROMPT,
-                            json_output=False,
-                        )
-                        break
-                    except RuntimeError:
-                        if attempt == max_attempts:
-                            raise
-                        time.sleep(min(4, attempt))
-                content = str((result or {}).get("content") or "").strip()
-                if not content:
-                    raise RuntimeError("扫描型 PDF 页面识别结果为空")
-                settle_reservation(
-                    db,
-                    reservation,
-                    (result or {}).get("usage"),
-                    fallback_total=estimate_fallback_usage(estimate_messages, content),
-                )
-            except Exception as exc:
-                release_reservation(db, reservation, str(exc))
-                raise RuntimeError(
-                    f"扫描型 PDF 第 {page_index + 1}/{total_pages} 页识别失败：{exc}"
-                ) from exc
-
-            texts.append(f"[第 {page_index + 1} 页]\n{content}")
-            if on_progress:
-                on_progress(page_index + 1, total_pages)
-            logger.info(
-                "Scanned PDF OCR completed avatar=%s page=%s/%s",
-                avatar.id,
-                page_index + 1,
-                total_pages,
-            )
-
-    return "\n\n".join(texts).strip()

digital-avatar-app/backend/tests/test_knowledge_storage.py

@@ -238,53 +238,6 @@ def test_background_vectorizer_keeps_failure_reason_for_retry(
         db.close()
 
 
-def test_background_vectorizer_uses_ocr_for_image_only_pdf(
-    tmp_path: Path,
-    authorization_context,
-):
-    context = authorization_context
-    with (
-        patch("routers.knowledge.UPLOAD_DIR", str(tmp_path)),
-        patch("routers.knowledge.knowledge_vectorizer.enqueue"),
-    ):
-        response = client.post(
-            f"/api/avatar/{context['avatar'].id}/knowledge/docs",
-            headers=context["owner_headers"],
-            files={"file": ("scanned.pdf", b"image-only-pdf", "application/pdf")},
-        )
-
-    payload = response.json()["data"]
-    progress = []
-    with (
-        patch("services.knowledge_vectorizer.UPLOAD_DIR", str(tmp_path)),
-        patch("services.knowledge_vectorizer.embeddings.extract_text", return_value=""),
-        patch(
-            "services.knowledge_vectorizer.extract_scanned_pdf_text",
-            side_effect=lambda _db, _avatar, _path, on_progress: (
-                on_progress(1, 2), on_progress(2, 2), "扫描页文字"
-            )[-1],
-        ) as ocr,
-        patch("services.knowledge_vectorizer.embeddings.embed", return_value=[[1.0, 0.0]]),
-        patch.object(knowledge_vectorizer, "_set_progress", wraps=knowledge_vectorizer._set_progress) as set_progress,
-    ):
-        knowledge_vectorizer.vectorize_document(payload["id"])
-        progress = [(call.args[2], call.args[3]) for call in set_progress.call_args_list]
-
-    db = SessionLocal()
-    try:
-        stored = db.query(KnowledgeDoc).filter(KnowledgeDoc.id == payload["id"]).one()
-        assert stored.status == "ready"
-        assert stored.chunk_count == 1
-        assert ("ocr", 18) in progress
-        assert ("ocr", 28) in progress
-        ocr.assert_called_once()
-        db.query(KnowledgeChunk).filter(KnowledgeChunk.doc_id == stored.id).delete()
-        db.delete(stored)
-        db.commit()
-    finally:
-        db.close()
-
-
 def test_retry_queues_a_failed_document_again(
     tmp_path: Path,
     authorization_context,

digital-avatar-app/backend/tests/test_pdf_ocr_service.py

@@ -1,104 +0,0 @@
-from types import SimpleNamespace
-from unittest.mock import MagicMock, patch
-
-import pytest
-
-from services.pdf_ocr_service import extract_scanned_pdf_text
-
-
-class FakePixmap:
-    def tobytes(self, *_args, **_kwargs):
-        return b"jpeg-page"
-
-
-class FakePage:
-    def get_pixmap(self, **_kwargs):
-        return FakePixmap()
-
-
-class FakeDocument:
-    page_count = 2
-
-    def __enter__(self):
-        return self
-
-    def __exit__(self, *_args):
-        return None
-
-    def load_page(self, _index):
-        return FakePage()
-
-
-def test_scanned_pdf_ocr_preserves_page_order_and_reports_progress(monkeypatch):
-    fake_pymupdf = SimpleNamespace(
-        open=lambda _path: FakeDocument(),
-        Matrix=lambda x, y: (x, y),
-        csRGB="rgb",
-    )
-    monkeypatch.setitem(__import__("sys").modules, "pymupdf", fake_pymupdf)
-    progress = []
-    reservation = SimpleNamespace()
-    config = SimpleNamespace(
-        api_key="configured",
-        ocr_model="qwen-vl-ocr",
-        vision_model="vision",
-        vision_max_tokens=2048,
-    )
-
-    with (
-        patch("services.pdf_ocr_service.get_chat_model_config", return_value=config),
-        patch("services.pdf_ocr_service.prepare_image", return_value=SimpleNamespace()),
-        patch(
-            "services.pdf_ocr_service.call_vision_model",
-            side_effect=[
-                {"content": "第一页文字", "usage": {"total_tokens": 10}},
-                {"content": "第二页文字", "usage": {"total_tokens": 12}},
-            ],
-        ),
-        patch("services.pdf_ocr_service.reserve_avatar_tokens", return_value=reservation) as reserve,
-        patch("services.pdf_ocr_service.settle_reservation") as settle,
-    ):
-        text = extract_scanned_pdf_text(
-            MagicMock(),
-            SimpleNamespace(id="avatar-1"),
-            "/tmp/scanned.pdf",
-            on_progress=lambda done, total: progress.append((done, total)),
-        )
-
-    assert text == "[第 1 页]\n第一页文字\n\n[第 2 页]\n第二页文字"
-    assert progress == [(1, 2), (2, 2)]
-    assert reserve.call_count == 2
-    assert settle.call_count == 2
-
-
-def test_scanned_pdf_ocr_releases_tokens_after_retries_fail(monkeypatch):
-    fake_document = FakeDocument()
-    fake_document.page_count = 1
-    fake_pymupdf = SimpleNamespace(
-        open=lambda _path: fake_document,
-        Matrix=lambda x, y: (x, y),
-        csRGB="rgb",
-    )
-    monkeypatch.setitem(__import__("sys").modules, "pymupdf", fake_pymupdf)
-    monkeypatch.setenv("KNOWLEDGE_PDF_OCR_ATTEMPTS", "2")
-    reservation = SimpleNamespace()
-    config = SimpleNamespace(
-        api_key="configured",
-        ocr_model="qwen-vl-ocr",
-        vision_model="vision",
-        vision_max_tokens=2048,
-    )
-
-    with (
-        patch("services.pdf_ocr_service.get_chat_model_config", return_value=config),
-        patch("services.pdf_ocr_service.prepare_image", return_value=SimpleNamespace()),
-        patch("services.pdf_ocr_service.call_vision_model", side_effect=RuntimeError("timeout")) as call,
-        patch("services.pdf_ocr_service.reserve_avatar_tokens", return_value=reservation),
-        patch("services.pdf_ocr_service.release_reservation") as release,
-        patch("services.pdf_ocr_service.time.sleep"),
-    ):
-        with pytest.raises(RuntimeError, match="第 1/1 页识别失败"):
-            extract_scanned_pdf_text(MagicMock(), SimpleNamespace(id="avatar-1"), "/tmp/scanned.pdf")
-
-    assert call.call_count == 2
-    release.assert_called_once()

digital-avatar-app/src/router/index.ts

@@ -1,7 +1,6 @@
 import { createRouter, createWebHashHistory } from 'vue-router'
 import type { RouteRecordRaw } from 'vue-router'
 import { getAuthToken } from '@/api'
-import { isInUniWebView } from '@/utils/uniapp-bridge'
 
 const routes: RouteRecordRaw[] = [
   {
@@ -56,7 +55,7 @@ const routes: RouteRecordRaw[] = [
     path: '/token/charge',
     name: 'TokenCharge',
     component: () => import('@/views/TokenCharge.vue'),
-    meta: { title: '积分充值', requiresAuth: true, requiresUniWebView: true }
+    meta: { title: '积分充值', requiresAuth: true }
   },
   {
     path: '/avatar/card',
@@ -134,10 +133,6 @@ const router = createRouter({
 
 router.beforeEach((to, from, next) => {
   document.title = to.meta.title as string || '会会数字分身'
-  if (to.meta.requiresUniWebView && !isInUniWebView()) {
-    next({ path: '/avatar/manage' })
-    return
-  }
   const hasLocalSession = Boolean(localStorage.getItem('hh_app_token'))
   const hasInjectedSession = Boolean(getAuthToken())
   if (to.meta.requiresAuth && !hasLocalSession && !hasInjectedSession) {

digital-avatar-app/src/views/AvatarManage.vue

@@ -20,7 +20,7 @@
       </div>
     </section>
 
-    <!-- 积分余额条：仅在 uni-app 原生壳内开放充值购买。 -->
+    <!-- 积分余额条：暂时隐藏，保留完整实现便于后续恢复。 -->
     <section v-if="SHOW_POINTS_BALANCE_CARD" class="token-section">
       <div class="token-card">
         <div class="token-info">
@@ -28,7 +28,7 @@
           <span class="token-amount">{{ tokenBalance.toLocaleString() }}</span>
           <span class="token-used">累计使用 {{ tokenConsumed.toLocaleString() }}</span>
         </div>
-        <button class="recharge-btn" @click="goToRecharge">充值购买</button>
+        <button class="recharge-btn" @click="goToRecharge">充值</button>
       </div>
     </section>
 
@@ -89,15 +89,14 @@ import { useAvatarStore } from '@/store/avatar'
 import { useUserStore } from '@/store/user'
 import { createAvatarShareLink } from '@/api'
 import { isHuihuiEmbeddedMode } from '@/utils/embed-mode'
-import { isInUniWebView } from '@/utils/uniapp-bridge'
 
 const router = useRouter()
 const avatarStore = useAvatarStore()
 const userStore = useUserStore()
 const isEmbedded = isHuihuiEmbeddedMode()
 
-// 充值购买只在 uni-app 原生壳内提供，避免普通 H5 进入支付链路。
-const SHOW_POINTS_BALANCE_CARD = isInUniWebView()
+// 临时产品开关：余额卡片代码保留，后续改为 true 即可恢复展示。
+const SHOW_POINTS_BALANCE_CARD = false
 
 // 当前登录会会用户的资料（头像 / 昵称）
 const me = computed(() => userStore.user)

digital-avatar-app/src/views/KnowledgeManage.vue

@@ -147,7 +147,7 @@ const documentState = (doc: any) => {
   if (['uploaded', 'parsing'].includes(String(doc.status || '').toLowerCase())) {
     const stage = String(doc.indexStage || 'queued').toLowerCase()
     const labels: Record<string, string> = {
-      queued: '等待处理', extracting: '解析文档', ocr: '扫描件识别', chunking: '切分文本', embedding: '向量化中'
+      queued: '等待处理', extracting: '解析文档', chunking: '切分文本', embedding: '向量化中'
     }
     const progress = Math.max(0, Math.min(99, Number(doc.indexProgress || 0)))
     return { tone: 'pending', label: labels[stage] || '处理中', detail: `${labels[stage] || '正在建立知识索引'} ${progress}%`, progress }