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/routers/chat.py

@@ -79,34 +79,6 @@ _WRITING_SYSTEM_PATTERNS = {
 _JAPANESE_KANA = re.compile(r"[\u3040-\u30ff]")
 _KOREAN_HANGUL = re.compile(r"[\uac00-\ud7af\u1100-\u11ff]")
 
-_LATIN_LANGUAGE_MARKERS = {
-    "English": re.compile(
-        r"\b(?:i|you|we|they|he|she|have|has|had|friend|who|what|where|when|why|how|"
-        r"symptoms?|disease|please|can|could|would|should|is|are|was|were|the|this|that)\b",
-        re.IGNORECASE,
-    ),
-    "French": re.compile(
-        r"\b(?:je|tu|vous|nous|ils|elle|une|des|avec|pour|pourquoi|comment|bonjour|est|sont)\b",
-        re.IGNORECASE,
-    ),
-    "Spanish": re.compile(
-        r"\b(?:yo|tu|usted|nosotros|ellos|ella|una|con|para|por que|como|hola|esta|son)\b",
-        re.IGNORECASE,
-    ),
-    "German": re.compile(
-        r"\b(?:ich|du|sie|wir|eine|mit|fur|warum|wie|hallo|ist|sind|haben)\b",
-        re.IGNORECASE,
-    ),
-    "Portuguese": re.compile(
-        r"\b(?:eu|voce|nos|eles|ela|uma|com|para|porque|como|ola|esta|sao|tenho)\b",
-        re.IGNORECASE,
-    ),
-    "Italian": re.compile(
-        r"\b(?:io|tu|voi|noi|loro|una|con|per|perche|come|ciao|sono|avere)\b",
-        re.IGNORECASE,
-    ),
-}
-
 
 class ChatMessage(BaseModel):
     model_config = ConfigDict(populate_by_name=True)
@@ -515,67 +487,15 @@ def _qa_requires_per_turn_rendering(
     return bool(history) or _qa_requires_language_adaptation(question, answer)
 
 
-def _latin_language_name(value: str) -> str:
-    scores = {
-        language: len(pattern.findall(value or ""))
-        for language, pattern in _LATIN_LANGUAGE_MARKERS.items()
-    }
-    language, score = max(scores.items(), key=lambda item: item[1])
-    return language if score else "the same natural language as the latest user message"
-
-
-def _turn_language_name(value: str) -> str:
-    writing_system = _dominant_writing_system(value)
-    return {
-        "han": "Chinese",
-        "japanese": "Japanese",
-        "korean": "Korean",
-        "cyrillic": "the same Cyrillic-script language as the latest user message",
-        "arabic": "the same Arabic-script language as the latest user message",
-        "hebrew": "Hebrew",
-        "devanagari": "the same Devanagari-script language as the latest user message",
-        "thai": "Thai",
-        "greek": "Greek",
-        "latin": _latin_language_name(value),
-    }.get(writing_system, "the same natural language as the latest user message")
-
-
-def _per_turn_language_instruction(question: str = "") -> str:
-    language = _turn_language_name(question)
+def _per_turn_language_instruction() -> str:
     return (
-        f"MANDATORY OUTPUT LANGUAGE FOR THIS TURN: {language}. "
-        "Write the entire answer only in that language. This instruction overrides the languages used by "
-        "conversation history, profile data, standard answers, retrieved documents, and custom prompts. "
-        "Translate grounded source material faithfully when necessary. Do not mention language detection, "
-        "translation, or this instruction."
+        "本轮语言覆盖指令：只根据紧随其后的最新用户消息判断本轮回答语言。"
+        "即使此前整段对话一直使用另一种语言，只要最新消息切换了语言，本轮就必须立即切换到相同语言；"
+        "不要沿用上一轮语言。若最新消息明确指定回答语言，以该指定为准；若混用多种语言，使用其中占主导的"
+        "自然语言。不要说明你检测、切换或翻译了语言。"
     )
 
 
-def _answer_requires_language_repair(question: str, answer: str) -> bool:
-    question_system = _dominant_writing_system(question)
-    answer_system = _dominant_writing_system(answer)
-    return (
-        question_system != "unknown"
-        and answer_system != "unknown"
-        and question_system != answer_system
-    )
-
-
-def _language_repair_messages(question: str, answer: str) -> list[dict]:
-    return [
-        {"role": "system", "content": _per_turn_language_instruction(question)},
-        {
-            "role": "system",
-            "content": (
-                "Rewrite the supplied draft in the mandatory output language. Preserve every grounded fact, "
-                "number, proper noun, uncertainty, and safety qualification. Add no new information and output "
-                "only the rewritten answer."
-            ),
-        },
-        {"role": "user", "content": answer.strip()},
-    ]
-
-
 def _canonicalize_question(value: str) -> str:
     value = _normalize_question(value)
     replacements = (
@@ -808,7 +728,7 @@ def _build_prompt(
     for item in history[-MAX_HISTORY_MESSAGES:]:
         messages.append({"role": item.role, "content": item.content} if hasattr(item, "role") else item)
     # Keep the language instruction adjacent to the current turn so long histories cannot override it.
-    messages.append({"role": "system", "content": _per_turn_language_instruction(question)})
+    messages.append({"role": "system", "content": _per_turn_language_instruction()})
     messages.append({"role": "user", "content": question.strip()})
     return messages
 
@@ -871,41 +791,6 @@ def _call_qwen(
     return {"answer": answer.strip(), "usage": data.get("usage") or {}}
 
 
-def _call_billed_qwen(
-    db: Session,
-    avatar: Avatar,
-    messages: list[dict],
-    temperature: float,
-    usage_source: str,
-    model_config: ChatModelConfig,
-) -> tuple[str, dict]:
-    reservation = reserve_avatar_tokens(
-        db,
-        avatar,
-        usage_source,
-        model_config.model,
-        messages,
-        model_config.max_tokens,
-    )
-    try:
-        model_result = _call_qwen(
-            messages=messages,
-            temperature=temperature,
-            model_config=model_config,
-        )
-        answer = model_result["answer"]
-        token_usage = settle_reservation(
-            db,
-            reservation,
-            model_result.get("usage"),
-            fallback_total=estimate_fallback_usage(messages, answer),
-        )
-        return answer, token_usage
-    except Exception as exc:
-        release_reservation(db, reservation, str(exc))
-        raise
-
-
 def _iter_qwen_stream(
     messages: list[dict], temperature: float, model_config: ChatModelConfig | None = None
 ):
@@ -1014,36 +899,32 @@ def _resolve_reply(
     token_usage = None
     if model_client is not None:
         answer = model_client(messages=messages, temperature=temperature)
-        if _answer_requires_language_repair(question, str(answer or "")):
-            answer = model_client(
-                messages=_language_repair_messages(question, str(answer)),
-                temperature=0.0,
-            )
     else:
         model_config = get_chat_model_config()
-        answer, token_usage = _call_billed_qwen(
+        reservation = reserve_avatar_tokens(
             db,
             avatar,
-            messages,
-            temperature,
             usage_source,
-            model_config,
+            model_config.model,
+            messages,
+            model_config.max_tokens,
         )
-        if _answer_requires_language_repair(question, answer):
-            logger.warning(
-                "chat response language mismatch avatar=%s source=%s expected=%s",
-                avatar.id,
-                usage_source,
-                _turn_language_name(question),
+        try:
+            model_result = _call_qwen(
+                messages=messages,
+                temperature=temperature,
+                model_config=model_config,
             )
-            answer, token_usage = _call_billed_qwen(
+            answer = model_result["answer"]
+            token_usage = settle_reservation(
                 db,
-                avatar,
-                _language_repair_messages(question, answer),
-                0.0,
-                f"{usage_source}_language_repair",
-                model_config,
+                reservation,
+                model_result.get("usage"),
+                fallback_total=estimate_fallback_usage(messages, answer),
             )
+        except Exception as exc:
+            release_reservation(db, reservation, str(exc))
+            raise
     answer = str(answer or "").strip()
     if image_contexts and _answer_denies_available_image(answer):
         logger.warning(

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_chat_orchestration.py

@@ -6,7 +6,6 @@ from fastapi import HTTPException
 
 from models import Avatar, User
 from routers.chat import (
-    _answer_requires_language_repair,
     _build_prompt,
     _iter_text_chunks,
     _match_standard_qa,
@@ -15,7 +14,6 @@ from routers.chat import (
     _qa_requires_per_turn_rendering,
     _require_owned_avatar,
     _resolve_reply,
-    _turn_language_name,
 )
 
 
@@ -109,8 +107,8 @@ class ChatOrchestrationTests(unittest.TestCase):
         messages = fake_model.call_args.kwargs["messages"]
         self.assertEqual(messages[-1], {"role": "user", "content": "Quelle est votre adresse ?"})
         self.assertEqual(messages[-2]["role"], "system")
-        self.assertIn("MANDATORY OUTPUT LANGUAGE", messages[-2]["content"])
-        self.assertIn("French", messages[-2]["content"])
+        self.assertIn("本轮语言覆盖指令", messages[-2]["content"])
+        self.assertIn("不要沿用上一轮语言", messages[-2]["content"])
 
     def test_latest_user_message_has_an_adjacent_language_override(self):
         history = [
@@ -121,37 +119,8 @@ class ChatOrchestrationTests(unittest.TestCase):
 
         self.assertEqual(messages[-1], {"role": "user", "content": "What can you help me with?"})
         self.assertEqual(messages[-2]["role"], "system")
-        self.assertIn("MANDATORY OUTPUT LANGUAGE", messages[-2]["content"])
-        self.assertIn("English", messages[-2]["content"])
-
-    def test_reported_alzheimer_question_is_explicitly_english(self):
-        question = "I have a friend who has symptoms of Alzheimer's disease"
-
-        self.assertEqual(_turn_language_name(question), "English")
-        messages = _build_prompt(self.avatar, [], question, [])
-        self.assertIn("MANDATORY OUTPUT LANGUAGE FOR THIS TURN: English", messages[-2]["content"])
-
-    def test_non_stream_reply_repairs_a_wrong_writing_system_before_sending(self):
-        question = "I have a friend who has symptoms of Alzheimer's disease"
-        fake_model = Mock(side_effect=["建议尽快就医评估。", "Please arrange a medical assessment soon."])
-
-        result = _resolve_reply(
-            None,
-            self.avatar,
-            question,
-            [],
-            qa_pairs=[],
-            search_fn=lambda *_args, **_kwargs: [],
-            model_client=fake_model,
-            usage_source="takeover",
-        )
-
-        self.assertEqual(result["answer"], "Please arrange a medical assessment soon.")
-        self.assertEqual(fake_model.call_count, 2)
-        repair_messages = fake_model.call_args.kwargs["messages"]
-        self.assertIn("English", repair_messages[0]["content"])
-        self.assertIn("建议尽快就医评估", repair_messages[-1]["content"])
-        self.assertTrue(_answer_requires_language_repair(question, "建议尽快就医评估。"))
+        self.assertIn("最新用户消息", messages[-2]["content"])
+        self.assertIn("立即切换到相同语言", messages[-2]["content"])
 
     def test_conversational_paraphrase_matches_standard_qa(self):
         for question in ("请问一下，你们公司在哪里呀？", "请问去你们那边怎么走"):

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/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 }