diff --git a/backend/api/chat_file.py b/backend/api/chat_file.py
index 21b23af..f901f7d 100644
--- a/backend/api/chat_file.py
+++ b/backend/api/chat_file.py
@@ -17,6 +17,7 @@ from core.database import get_db_pool
from services.chat_thread_file_service import ChatThreadFileService
from services.vector_service import get_vector_service
from services.oss_service import get_oss_service
+from services.kb_text_limits import decode_txt_char_count, validate_chat_file_text_length
from models.chat_thread_file import (
ChatThreadFileUploadResponse,
ChatThreadFileListResponse
@@ -438,6 +439,14 @@ async def upload_chat_file(
)
logger.info(f"✅ 文件大小验证通过: {file_size_mb:.2f}MB ({file_size} bytes)")
+
+ # .txt 文件上传时直接校验字符数，其他格式在后台处理时校验
+ if file_ext == ".txt":
+ try:
+ validate_chat_file_text_length(decode_txt_char_count(content))
+ except ValueError as e:
+ logger.warning(f"❌ 文本文件字数超限: {file.filename}, {e}")
+ raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail=str(e))
# 生成唯一文件名（使用时间戳）
timestamp = int(time.time() * 1000)
diff --git a/backend/api/kb_file_router.py b/backend/api/kb_file_router.py
index 614ede6..85315bf 100644
--- a/backend/api/kb_file_router.py
+++ b/backend/api/kb_file_router.py
@@ -23,6 +23,7 @@ from services.knowledge_base_service import KnowledgeBaseService
from services.knowledge_base_file_service import KnowledgeBaseFileService
from services.audit_service import AuditService
from services.vector_service import get_vector_service
+from services.kb_text_limits import decode_txt_char_count, validate_kb_text_length
from services.oss_service import get_oss_service
from utils.helpers import BaseResponse
from logger.logging import get_logger
@@ -356,6 +357,13 @@ async def upload_file(
raise BadRequestError(f"文件大小超过限制，当前: {file_size_mb:.2f}MB，最大允许: 15MB")
logger.info(f"✅ 文件大小验证通过: {file_size_mb:.2f}MB ({file_size} bytes)")
+
+ if file_ext == ".txt":
+ try:
+ validate_kb_text_length(decode_txt_char_count(content))
+ except ValueError as e:
+ logger.warning(f"❌ 文本文件字数超限: {file.filename}, {e}")
+ raise BadRequestError(str(e)) from e
# 生成唯一文件名
timestamp = int(time.time() * 1000)
diff --git a/backend/api/knowledge_graph_router.py b/backend/api/knowledge_graph_router.py
index b9f180b..724896e 100644
--- a/backend/api/knowledge_graph_router.py
+++ b/backend/api/knowledge_graph_router.py
@@ -22,6 +22,7 @@ from services import neo4j_service
from services.novel_kg_service import (
extract_and_import_knowledge_graph,
extract_knowledge_document_text,
+ validate_knowledge_graph_text_length,
)
from utils.helpers import BaseResponse
from logger.logging import get_logger
@@ -139,6 +140,7 @@ async def create_knowledge_graph(
try:
text = await extract_knowledge_document_text(file.filename, raw)
+ validate_knowledge_graph_text_length(text)
except ValueError as e:
raise HTTPException(status_code=400, detail=str(e)) from e
diff --git a/backend/services/kb_text_limits.py b/backend/services/kb_text_limits.py
new file mode 100644
index 0000000..0f932b2
--- /dev/null
+++ b/backend/services/kb_text_limits.py
@@ -0,0 +1,57 @@
+"""
+知识库与对话文件的正文长度限制。
+
+处理流程对比：
+ 知识图谱 每块 1 次串行 LLM 抽关系 → 最严格，8 万字
+ 知识库 批量 embedding + 单次摘要 → 较宽松，30 万字
+ 对话文件 批量 embedding + 单次摘要（临时会话上下文）→ 20 万字
+"""
+
+# ---------- 知识库 ----------
+# chunk_size=4096、overlap=200 → 有效步长约 3896 字/块
+# 30 万字 ≈ 77 块向量化，通常 1-2 分钟可完成
+MAX_KB_INPUT_CHARS = 300_000
+MAX_KB_VECTOR_CHUNKS = 80
+
+# ---------- 对话文件（聊天时上传） ----------
+# 对话文件是单次会话临时上下文，精度要求高于知识库；过长会稀释检索精度
+# 20 万字 ≈ 51 块，兼顾处理速度与上下文覆盖
+MAX_CHAT_FILE_INPUT_CHARS = 200_000
+MAX_CHAT_FILE_VECTOR_CHUNKS = 60
+
+
+def decode_txt_char_count(raw: bytes) -> int:
+ """估算纯文本文件字符数（上传前快速校验）。"""
+ try:
+ text = raw.decode("utf-8")
+ except UnicodeDecodeError:
+ text = raw.decode("gb18030", errors="replace")
+ return len(text.strip())
+
+
+def validate_kb_text_length(char_count: int, *, chunk_count: int | None = None) -> None:
+ """校验知识库可处理的正文规模。"""
+ if char_count > MAX_KB_INPUT_CHARS:
+ raise ValueError(
+ f"提取的正文过长（约 {char_count:,} 字），知识库单文件上限为 {MAX_KB_INPUT_CHARS:,} 字。"
+ "请将文档拆分为多个文件分别上传。"
+ )
+ if chunk_count is not None and chunk_count > MAX_KB_VECTOR_CHUNKS:
+ raise ValueError(
+ f"文本分块过多（{chunk_count} 块，上限 {MAX_KB_VECTOR_CHUNKS} 块），"
+ f"请将正文控制在约 {MAX_KB_INPUT_CHARS:,} 字以内后重试。"
+ )
+
+
+def validate_chat_file_text_length(char_count: int, *, chunk_count: int | None = None) -> None:
+ """校验对话文件可处理的正文规模。"""
+ if char_count > MAX_CHAT_FILE_INPUT_CHARS:
+ raise ValueError(
+ f"提取的正文过长（约 {char_count:,} 字），对话文件上限为 {MAX_CHAT_FILE_INPUT_CHARS:,} 字。"
+ "请将文档拆分后上传，或使用知识库功能处理长文档。"
+ )
+ if chunk_count is not None and chunk_count > MAX_CHAT_FILE_VECTOR_CHUNKS:
+ raise ValueError(
+ f"文本分块过多（{chunk_count} 块，上限 {MAX_CHAT_FILE_VECTOR_CHUNKS} 块），"
+ f"请将正文控制在约 {MAX_CHAT_FILE_INPUT_CHARS:,} 字以内后重试。"
+ )
diff --git a/backend/services/novel_kg_service.py b/backend/services/novel_kg_service.py
index 7fd3743..1843c7d 100644
--- a/backend/services/novel_kg_service.py
+++ b/backend/services/novel_kg_service.py
@@ -23,7 +23,10 @@ from logger.logging import get_logger
logger = get_logger(__name__)
-MAX_INPUT_CHARS = 800_000
+# 知识图谱抽取上限：每块约 900 字、重叠 120，每块 1 次 DeepSeek 调用（串行）。
+# 8 万字 ≈ 100 次调用，后台约 5–10 分钟可完成；50 万字需 600+ 次，基本不可行。
+MAX_INPUT_CHARS = 80_000
+MAX_KG_EXTRACT_CHUNKS = 100
CHUNK_SIZE = 900
CHUNK_OVERLAP = 120
@@ -644,17 +647,32 @@ def merge_triplets(chunks: list[list[dict[str, Any]]]) -> list[dict[str, Any]]:
return merged
+def validate_knowledge_graph_text_length(text: str) -> None:
+ """上传/构建前校验正文长度，避免超长文本进入图谱抽取流水线。"""
+ char_count = len(text)
+ if char_count > MAX_INPUT_CHARS:
+ raise ValueError(
+ f"提取的正文过长（约 {char_count:,} 字），知识图谱构建上限为 {MAX_INPUT_CHARS:,} 字。"
+ "请拆分为多个文件分别上传，或使用知识库功能处理长文档问答。"
+ )
+
+
async def extract_and_import_knowledge_graph(text: str, graph_id: str) -> dict[str, Any]:
"""
对整篇文本分块调用 LLM，合并三元组后写入 Neo4j。
"""
- if len(text) > MAX_INPUT_CHARS:
- raise ValueError(f"文本过长，请控制在约 {MAX_INPUT_CHARS} 字以内")
+ validate_knowledge_graph_text_length(text)
chunks = split_novel_text(text)
if not chunks:
raise ValueError("文本为空")
+ if len(chunks) > MAX_KG_EXTRACT_CHUNKS:
+ raise ValueError(
+ f"文本分块过多（{len(chunks)} 块，上限 {MAX_KG_EXTRACT_CHUNKS} 块），"
+ f"请将正文控制在约 {MAX_INPUT_CHARS:,} 字以内后重试。"
+ )
+
logger.info("知识图谱：共 {} 个文本块", len(chunks))
batch_results: list[list[dict[str, Any]]] = []
for i, ch in enumerate(chunks):
diff --git a/backend/services/vector_service.py b/backend/services/vector_service.py
index 33f5760..e17486c 100644
--- a/backend/services/vector_service.py
+++ b/backend/services/vector_service.py
@@ -78,6 +78,7 @@ import bs4
from logger.logging import get_logger
from core.config import settings
+from services.kb_text_limits import validate_kb_text_length, validate_chat_file_text_length
logger = get_logger(__name__)
@@ -859,6 +860,14 @@ class VectorService:
error_msg = "未能从文件加载到任何内容"
logger.warning(error_msg)
return ProcessResult(success=False, chunks=[], chunk_count=0, error_message=error_msg)
+
+ total_chars = sum(len(d.page_content or "") for d in docs)
+ try:
+ validate_kb_text_length(total_chars)
+ except ValueError as e:
+ error_msg = str(e)
+ logger.warning(error_msg)
+ return ProcessResult(success=False, chunks=[], chunk_count=0, error_message=error_msg)
# 2. 分割文本
all_splits = self.text_splitter.split_documents(docs)
@@ -869,6 +878,13 @@ class VectorService:
error_msg = "文档分割后没有内容，可能是空白文档"
logger.warning(error_msg)
return ProcessResult(success=False, chunks=[], chunk_count=0, error_message=error_msg)
+
+ try:
+ validate_kb_text_length(total_chars, chunk_count=len(all_splits))
+ except ValueError as e:
+ error_msg = str(e)
+ logger.warning(error_msg)
+ return ProcessResult(success=False, chunks=[], chunk_count=0, error_message=error_msg)
# 3. 向量化并存储
collection_name = f"kb_{knowledge_base_id}"
@@ -961,6 +977,14 @@ class VectorService:
error_msg = "未能从 URL 加载到任何内容"
logger.warning(error_msg)
return ProcessResult(success=False, chunks=[], chunk_count=0, error_message=error_msg)
+
+ total_chars = sum(len(d.page_content or "") for d in docs)
+ try:
+ validate_kb_text_length(total_chars)
+ except ValueError as e:
+ error_msg = str(e)
+ logger.warning(error_msg)
+ return ProcessResult(success=False, chunks=[], chunk_count=0, error_message=error_msg)
# 2. 分割文本
all_splits = self.text_splitter.split_documents(docs)
@@ -971,6 +995,13 @@ class VectorService:
error_msg = "网页分割后没有内容，可能是空白页面或无法提取文本"
logger.warning(error_msg)
return ProcessResult(success=False, chunks=[], chunk_count=0, error_message=error_msg)
+
+ try:
+ validate_kb_text_length(total_chars, chunk_count=len(all_splits))
+ except ValueError as e:
+ error_msg = str(e)
+ logger.warning(error_msg)
+ return ProcessResult(success=False, chunks=[], chunk_count=0, error_message=error_msg)
# 3. 向量化并存储
collection_name = f"kb_{knowledge_base_id}"
@@ -1081,6 +1112,14 @@ class VectorService:
error_msg = "未能加载到任何内容"
logger.warning(error_msg)
return ProcessResult(success=False, chunks=[], chunk_count=0, error_message=error_msg)
+
+ total_chars = sum(len(d.page_content or "") for d in docs)
+ try:
+ validate_chat_file_text_length(total_chars)
+ except ValueError as e:
+ error_msg = str(e)
+ logger.warning(error_msg)
+ return ProcessResult(success=False, chunks=[], chunk_count=0, error_message=error_msg)
# 分割文本
all_splits = self.text_splitter.split_documents(docs)
@@ -1091,6 +1130,13 @@ class VectorService:
error_msg = "文档分割后没有内容，可能是空白文档或无法提取文本"
logger.warning(error_msg)
return ProcessResult(success=False, chunks=[], chunk_count=0, error_message=error_msg)
+
+ try:
+ validate_chat_file_text_length(total_chars, chunk_count=len(all_splits))
+ except ValueError as e:
+ error_msg = str(e)
+ logger.warning(error_msg)
+ return ProcessResult(success=False, chunks=[], chunk_count=0, error_message=error_msg)
# 向量化并存储（使用 thread_id 作为集合名）
collection_name = f"thread_{thread_id}"
diff --git a/frontend/src/views/KnowledgeBase.vue b/frontend/src/views/KnowledgeBase.vue
index 1642c1e..3d996c4 100644
--- a/frontend/src/views/KnowledgeBase.vue
+++ b/frontend/src/views/KnowledgeBase.vue
@@ -216,7 +216,7 @@
ref="fileInput"
>
- 支持 PDF、DOCX、Excel（xlsx/xls）、CSV、TXT、图片（PNG、JPG、BMP）格式，文件将自动进行向量化处理
+ 支持 PDF、DOCX、Excel（xlsx/xls）、CSV、TXT、图片（PNG、JPG、BMP）格式，单文件不超过 15MB，提取正文不超过 30 万字（过长请拆分）
diff --git a/frontend/src/views/KnowledgeGraph.vue b/frontend/src/views/KnowledgeGraph.vue
index 2decb7c..8d18b81 100644
--- a/frontend/src/views/KnowledgeGraph.vue
+++ b/frontend/src/views/KnowledgeGraph.vue
@@ -219,8 +219,7 @@
class="form-control bg-dark text-white border-secondary"
@change="onFileSelect"
/>
- 支持 .txt、.pdf、.docx 及常见图片；扫描件将尝试 OCR + 通义视觉。单文件不超过约 15MB
-
+ 支持 .txt、.pdf、.docx 及常见图片；扫描件将尝试 OCR + 通义视觉。单文件不超过约 15MB，提取正文建议不超过 8 万字（过长请拆分或改用知识库）
{{ uploadError }}