KiriAky 107
d189ea9620
- 添加 Markdown 文件上传和解析接口 - 实现流式分析和大纲提取功能 - 支持多种分析类型：摘要、大纲、关键点等 - 新增 markdown_ai_service 服务类 - 扩展 LLMService 支持流式调用 - 更新前端 API 接口定义和实现
332 lines
10 KiB
Python
332 lines
10 KiB
Python
"""
|
||
AI 分析 API 接口
|
||
"""
|
||
from fastapi import APIRouter, UploadFile, File, HTTPException, Query, Body
|
||
from fastapi.responses import StreamingResponse
|
||
from typing import Optional
|
||
import logging
|
||
import tempfile
|
||
import os
|
||
|
||
from app.services.excel_ai_service import excel_ai_service
|
||
from app.services.markdown_ai_service import markdown_ai_service
|
||
|
||
logger = logging.getLogger(__name__)
|
||
|
||
router = APIRouter(prefix="/ai", tags=["AI 分析"])
|
||
|
||
|
||
@router.post("/analyze/excel")
|
||
async def analyze_excel(
|
||
file: UploadFile = File(...),
|
||
user_prompt: str = Query("", description="用户自定义提示词"),
|
||
analysis_type: str = Query("general", description="分析类型: general, summary, statistics, insights"),
|
||
parse_all_sheets: bool = Query(False, description="是否分析所有工作表")
|
||
):
|
||
"""
|
||
上传并使用 AI 分析 Excel 文件
|
||
|
||
Args:
|
||
file: 上传的 Excel 文件
|
||
user_prompt: 用户自定义提示词
|
||
analysis_type: 分析类型
|
||
parse_all_sheets: 是否分析所有工作表
|
||
|
||
Returns:
|
||
dict: 分析结果，包含 Excel 数据和 AI 分析结果
|
||
"""
|
||
# 检查文件类型
|
||
if not file.filename:
|
||
raise HTTPException(status_code=400, detail="文件名为空")
|
||
|
||
file_ext = file.filename.split('.')[-1].lower()
|
||
if file_ext not in ['xlsx', 'xls']:
|
||
raise HTTPException(
|
||
status_code=400,
|
||
detail=f"不支持的文件类型: {file_ext}，仅支持 .xlsx 和 .xls"
|
||
)
|
||
|
||
# 验证分析类型
|
||
supported_types = ['general', 'summary', 'statistics', 'insights']
|
||
if analysis_type not in supported_types:
|
||
raise HTTPException(
|
||
status_code=400,
|
||
detail=f"不支持的分析类型: {analysis_type}，支持的类型: {', '.join(supported_types)}"
|
||
)
|
||
|
||
try:
|
||
# 读取文件内容
|
||
content = await file.read()
|
||
|
||
logger.info(f"开始分析文件: {file.filename}, 分析类型: {analysis_type}")
|
||
|
||
# 调用 AI 分析服务
|
||
if parse_all_sheets:
|
||
result = await excel_ai_service.batch_analyze_sheets(
|
||
content,
|
||
file.filename,
|
||
user_prompt=user_prompt,
|
||
analysis_type=analysis_type
|
||
)
|
||
else:
|
||
# 解析选项
|
||
parse_options = {"header_row": 0}
|
||
|
||
result = await excel_ai_service.analyze_excel_file(
|
||
content,
|
||
file.filename,
|
||
user_prompt=user_prompt,
|
||
analysis_type=analysis_type,
|
||
parse_options=parse_options
|
||
)
|
||
|
||
logger.info(f"文件分析完成: {file.filename}, 成功: {result['success']}")
|
||
|
||
return result
|
||
|
||
except HTTPException:
|
||
raise
|
||
except Exception as e:
|
||
logger.error(f"AI 分析过程中出错: {str(e)}")
|
||
raise HTTPException(status_code=500, detail=f"分析失败: {str(e)}")
|
||
|
||
|
||
@router.get("/analysis/types")
|
||
async def get_analysis_types():
|
||
"""
|
||
获取支持的分析类型列表
|
||
|
||
Returns:
|
||
dict: 支持的分析类型（包含 Excel 和 Markdown）
|
||
"""
|
||
return {
|
||
"excel_types": excel_ai_service.get_supported_analysis_types(),
|
||
"markdown_types": markdown_ai_service.get_supported_analysis_types()
|
||
}
|
||
|
||
|
||
@router.post("/analyze/text")
|
||
async def analyze_text(
|
||
excel_data: dict = Body(..., description="Excel 解析后的数据"),
|
||
user_prompt: str = Body("", description="用户提示词"),
|
||
analysis_type: str = Body("general", description="分析类型")
|
||
):
|
||
"""
|
||
对已解析的 Excel 数据进行 AI 分析
|
||
|
||
Args:
|
||
excel_data: Excel 数据
|
||
user_prompt: 用户提示词
|
||
analysis_type: 分析类型
|
||
|
||
Returns:
|
||
dict: 分析结果
|
||
"""
|
||
try:
|
||
logger.info(f"开始文本分析, 分析类型: {analysis_type}")
|
||
|
||
# 调用 LLM 服务
|
||
from app.services.llm_service import llm_service
|
||
|
||
if user_prompt and user_prompt.strip():
|
||
result = await llm_service.analyze_with_template(
|
||
excel_data,
|
||
user_prompt
|
||
)
|
||
else:
|
||
result = await llm_service.analyze_excel_data(
|
||
excel_data,
|
||
user_prompt,
|
||
analysis_type
|
||
)
|
||
|
||
logger.info(f"文本分析完成, 成功: {result['success']}")
|
||
|
||
return result
|
||
|
||
except Exception as e:
|
||
logger.error(f"文本分析失败: {str(e)}")
|
||
raise HTTPException(status_code=500, detail=f"分析失败: {str(e)}")
|
||
|
||
|
||
@router.post("/analyze/md")
|
||
async def analyze_markdown(
|
||
file: UploadFile = File(...),
|
||
analysis_type: str = Query("summary", description="分析类型: summary, outline, key_points, questions, tags, qa, statistics, section"),
|
||
user_prompt: str = Query("", description="用户自定义提示词"),
|
||
section_number: Optional[str] = Query(None, description="指定章节编号，如 '一' 或 '（一）'")
|
||
):
|
||
"""
|
||
上传并使用 AI 分析 Markdown 文件
|
||
|
||
Args:
|
||
file: 上传的 Markdown 文件
|
||
analysis_type: 分析类型
|
||
user_prompt: 用户自定义提示词
|
||
section_number: 指定分析的章节编号
|
||
|
||
Returns:
|
||
dict: 分析结果
|
||
"""
|
||
# 检查文件类型
|
||
if not file.filename:
|
||
raise HTTPException(status_code=400, detail="文件名为空")
|
||
|
||
file_ext = file.filename.split('.')[-1].lower()
|
||
if file_ext not in ['md', 'markdown']:
|
||
raise HTTPException(
|
||
status_code=400,
|
||
detail=f"不支持的文件类型: {file_ext}，仅支持 .md 和 .markdown"
|
||
)
|
||
|
||
# 验证分析类型
|
||
supported_types = markdown_ai_service.get_supported_analysis_types()
|
||
if analysis_type not in supported_types:
|
||
raise HTTPException(
|
||
status_code=400,
|
||
detail=f"不支持的分析类型: {analysis_type}，支持的类型: {', '.join(supported_types)}"
|
||
)
|
||
|
||
try:
|
||
# 读取文件内容
|
||
content = await file.read()
|
||
|
||
# 保存到临时文件
|
||
with tempfile.NamedTemporaryFile(mode='wb', suffix='.md', delete=False) as tmp:
|
||
tmp.write(content)
|
||
tmp_path = tmp.name
|
||
|
||
try:
|
||
logger.info(f"开始分析 Markdown 文件: {file.filename}, 分析类型: {analysis_type}, 章节: {section_number}")
|
||
|
||
# 调用 AI 分析服务
|
||
result = await markdown_ai_service.analyze_markdown(
|
||
file_path=tmp_path,
|
||
analysis_type=analysis_type,
|
||
user_prompt=user_prompt,
|
||
section_number=section_number
|
||
)
|
||
|
||
logger.info(f"Markdown 分析完成: {file.filename}, 成功: {result['success']}")
|
||
|
||
if not result['success']:
|
||
raise HTTPException(status_code=500, detail=result.get('error', '分析失败'))
|
||
|
||
return result
|
||
|
||
finally:
|
||
# 清理临时文件
|
||
if os.path.exists(tmp_path):
|
||
os.unlink(tmp_path)
|
||
|
||
except HTTPException:
|
||
raise
|
||
except Exception as e:
|
||
logger.error(f"Markdown AI 分析过程中出错: {str(e)}")
|
||
raise HTTPException(status_code=500, detail=f"分析失败: {str(e)}")
|
||
|
||
|
||
@router.post("/analyze/md/stream")
|
||
async def analyze_markdown_stream(
|
||
file: UploadFile = File(...),
|
||
analysis_type: str = Query("summary", description="分析类型"),
|
||
user_prompt: str = Query("", description="用户自定义提示词"),
|
||
section_number: Optional[str] = Query(None, description="指定章节编号")
|
||
):
|
||
"""
|
||
流式分析 Markdown 文件 (SSE)
|
||
|
||
Returns:
|
||
StreamingResponse: SSE 流式响应
|
||
"""
|
||
if not file.filename:
|
||
raise HTTPException(status_code=400, detail="文件名为空")
|
||
|
||
file_ext = file.filename.split('.')[-1].lower()
|
||
if file_ext not in ['md', 'markdown']:
|
||
raise HTTPException(
|
||
status_code=400,
|
||
detail=f"不支持的文件类型: {file_ext}，仅支持 .md 和 .markdown"
|
||
)
|
||
|
||
try:
|
||
content = await file.read()
|
||
|
||
with tempfile.NamedTemporaryFile(mode='wb', suffix='.md', delete=False) as tmp:
|
||
tmp.write(content)
|
||
tmp_path = tmp.name
|
||
|
||
try:
|
||
logger.info(f"开始流式分析 Markdown 文件: {file.filename}, 分析类型: {analysis_type}")
|
||
|
||
async def stream_generator():
|
||
async for chunk in markdown_ai_service.analyze_markdown_stream(
|
||
file_path=tmp_path,
|
||
analysis_type=analysis_type,
|
||
user_prompt=user_prompt,
|
||
section_number=section_number
|
||
):
|
||
yield chunk
|
||
|
||
return StreamingResponse(
|
||
stream_generator(),
|
||
media_type="text/event-stream",
|
||
headers={
|
||
"Cache-Control": "no-cache",
|
||
"Connection": "keep-alive",
|
||
"X-Accel-Buffering": "no"
|
||
}
|
||
)
|
||
|
||
finally:
|
||
if os.path.exists(tmp_path):
|
||
os.unlink(tmp_path)
|
||
|
||
except HTTPException:
|
||
raise
|
||
except Exception as e:
|
||
logger.error(f"Markdown AI 流式分析出错: {str(e)}")
|
||
raise HTTPException(status_code=500, detail=f"流式分析失败: {str(e)}")
|
||
|
||
|
||
@router.get("/analyze/md/outline")
|
||
async def get_markdown_outline(
|
||
file: UploadFile = File(...)
|
||
):
|
||
"""
|
||
获取 Markdown 文档的大纲结构（分章节信息）
|
||
|
||
Args:
|
||
file: 上传的 Markdown 文件
|
||
|
||
Returns:
|
||
dict: 文档大纲结构
|
||
"""
|
||
if not file.filename:
|
||
raise HTTPException(status_code=400, detail="文件名为空")
|
||
|
||
file_ext = file.filename.split('.')[-1].lower()
|
||
if file_ext not in ['md', 'markdown']:
|
||
raise HTTPException(
|
||
status_code=400,
|
||
detail=f"不支持的文件类型: {file_ext}，仅支持 .md 和 .markdown"
|
||
)
|
||
|
||
try:
|
||
content = await file.read()
|
||
|
||
with tempfile.NamedTemporaryFile(mode='wb', suffix='.md', delete=False) as tmp:
|
||
tmp.write(content)
|
||
tmp_path = tmp.name
|
||
|
||
try:
|
||
result = await markdown_ai_service.extract_outline(tmp_path)
|
||
return result
|
||
finally:
|
||
if os.path.exists(tmp_path):
|
||
os.unlink(tmp_path)
|
||
|
||
except Exception as e:
|
||
logger.error(f"获取 Markdown 大纲失败: {str(e)}")
|
||
raise HTTPException(status_code=500, detail=f"获取大纲失败: {str(e)}")
|