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1607 1608 1609 1610 1611 1612 1613 1614 1615 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625 | import hashlib
import json
import logging
import os
import queue as _queue
import re
import subprocess
import tempfile
import threading
import time
import warnings
import shutil
import atexit
import gradio as gr
from dotenv import load_dotenv
from openai import OpenAI
# ---------- 基础环境配置 ----------
_script_dir = os.path.dirname(os.path.abspath(__file__))
_temp_dir = os.path.join(_script_dir, ".gradio_temp")
os.makedirs(_temp_dir, exist_ok=True)
os.environ["GRADIO_TEMP_DIR"] = _temp_dir
tempfile.gettempdir = lambda: _temp_dir
load_dotenv()
warnings.filterwarnings("ignore")
logging.getLogger("httpx").setLevel(logging.WARNING)
from VideoAgent import QueryParam, VideoRAG
from VideoAgent.prompt import PROMPTS
from VideoAgent.query import _result_query_stream
from VideoAgent._utils import clean_output
# ---------- 并行处理补丁：解决 Spliting Video / Saving Video Segments 串行慢、CPU 利用率低的问题 ----------
from concurrent.futures import ProcessPoolExecutor, as_completed
import VideoAgent.vidrag_pipeline as _pipeline_mod
import VideoAgent._videoutil.split as _split_mod
_VIDEO_WORKERS = int(os.getenv("VIDEO_SPLIT_WORKERS", os.cpu_count() or 4))
def _extract_audio_seg(args):
"""Worker: 提取单个视频片段的音频（进程池调用）"""
video_path, start, end, output_path = args
from moviepy.video.io.VideoFileClip import VideoFileClip
try:
with VideoFileClip(video_path) as video:
subvideo = video.subclip(start, end)
subaudio = subvideo.audio
subaudio.write_audiofile(
output_path, codec='pcm_s16le', fps=16000,
nbytes=2, verbose=False, logger=None,
)
return True
except Exception:
return False
def _save_video_seg(args):
"""Worker: 提取并编码单个视频片段（进程池调用）"""
video_path, start, end, output_path = args
from moviepy.video.io.VideoFileClip import VideoFileClip
with VideoFileClip(video_path) as video:
subvideo = video.subclip(start, end)
subvideo.write_videofile(
output_path, codec='libx264',
ffmpeg_params=['-threads', '0'],
verbose=False, logger=None,
)
return True
def _parallel_split_video(
video_path, working_dir, segment_length,
num_frames_per_segment, audio_output_format='mp3',
):
"""split_video 的并行版本：先算元数据，再用进程池并行提取音频"""
import shutil as _shutil
import numpy as _np
from tqdm import tqdm as _tqdm
from moviepy.video.io.VideoFileClip import VideoFileClip as _VideoFileClip
unique_timestamp = str(int(time.time() * 1000))
video_name = os.path.basename(video_path).split('.')[0]
cache_dir = os.path.join(working_dir, '_cache', video_name)
if os.path.exists(cache_dir):
_shutil.rmtree(cache_dir)
os.makedirs(cache_dir, exist_ok=False)
segment_index2name, segment_times_info = {}, {}
with _VideoFileClip(video_path) as video:
total_length = int(video.duration)
start_times = list(range(0, total_length, segment_length))
if len(start_times) > 1 and (total_length - start_times[-1]) < 5:
start_times = start_times[:-1]
for idx, start in enumerate(start_times):
end = (min(start + segment_length, total_length)
if start != start_times[-1] else total_length)
frame_times = _np.linspace(0, end - start, num_frames_per_segment, endpoint=False)
frame_times += start
sid = str(idx)
segment_index2name[sid] = f"{unique_timestamp}-{idx}-{start}-{end}"
segment_times_info[sid] = {"frame_times": frame_times, "timestamp": (start, end)}
# 并行提取音频
tasks = []
for idx in segment_index2name:
s, e = segment_times_info[idx]["timestamp"]
out = os.path.join(cache_dir, f'{segment_index2name[idx]}.{audio_output_format}')
tasks.append((video_path, s, e, out))
nw = min(_VIDEO_WORKERS, len(tasks)) if tasks else 1
with ProcessPoolExecutor(max_workers=nw) as ex:
futs = {ex.submit(_extract_audio_seg, t): t for t in tasks}
for f in _tqdm(as_completed(futs), total=len(futs), desc=f"Spliting Video {video_name}"):
try:
f.result()
except Exception:
pass
return segment_index2name, segment_times_info
def _parallel_saving_video_segments(
video_name, video_path, working_dir, segment_index2name,
segment_times_info, error_queue, video_output_format='mp4',
):
"""saving_video_segments 的并行版本：进程池并行编码每个片段"""
from tqdm import tqdm as _tqdm2
try:
cache_dir = os.path.join(working_dir, '_cache', video_name)
tasks = []
for idx in segment_index2name:
s, e = segment_times_info[idx]["timestamp"]
out = os.path.join(cache_dir, f'{segment_index2name[idx]}.{video_output_format}')
tasks.append((video_path, s, e, out))
nw = min(_VIDEO_WORKERS, len(tasks)) if tasks else 1
with ProcessPoolExecutor(max_workers=nw) as ex:
futs = {ex.submit(_save_video_seg, t): t for t in tasks}
for f in _tqdm2(as_completed(futs), total=len(futs), desc=f"Saving Video Segments {video_name}"):
try:
f.result()
except Exception as e:
raise RuntimeError(f"Error in saving_video_segments: {str(e)}")
except Exception as e:
error_queue.put(f"Error in saving_video_segments:\n {str(e)}")
raise RuntimeError
def _parallel_speech_to_text(video_name, working_dir, segment_index2name, audio_output_format):
"""speech_to_text 的并行版本：线程池并行调用 ASR"""
from concurrent.futures import ThreadPoolExecutor, as_completed as _ac
from tqdm import tqdm as _tqdm3
cache_dir = os.path.join(working_dir, '_cache', video_name)
transcripts = {}
def _transcribe_one(idx_name):
idx, seg_name = idx_name
audio_file = os.path.join(cache_dir, f"{seg_name}.{audio_output_format}")
if not os.path.exists(audio_file):
return idx, ""
from VideoAgent._videoutil.asr import OnnxASRClient
result = OnnxASRClient.transcribe(audio_file)
if isinstance(result, tuple):
text = result[0] if result else ""
elif hasattr(result, 'text'):
text = result.text
else:
text = str(result)
return idx, text
items = list(segment_index2name.items())
nw = min(_VIDEO_WORKERS, len(items)) if items else 1
with ThreadPoolExecutor(max_workers=nw) as ex:
futs = {ex.submit(_transcribe_one, item): item for item in items}
for f in _tqdm3(_ac(futs), total=len(futs), desc=f"Speech Recognition {video_name}"):
idx, text = f.result()
transcripts[idx] = text
return transcripts
# 用并行版本替换 pipeline 模块中的串行实现
_pipeline_mod.split_video = _parallel_split_video
_pipeline_mod.saving_video_segments = _parallel_saving_video_segments
_pipeline_mod.speech_to_text = _parallel_speech_to_text
_split_mod.split_video = _parallel_split_video
_split_mod.saving_video_segments = _parallel_saving_video_segments
# 同样替换 asr 模块中的引用
import VideoAgent._videoutil.asr as _asr_mod
_asr_mod.speech_to_text = _parallel_speech_to_text
# ---------- 样式表 (CSS) ----------
custom_css = """
@import url('https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700&display=swap');
:root {
--radius-sm: 6px;
--radius-md: 8px;
--radius-lg: 10px;
--radius-xl: 12px;
--color-bg: #f1f5f9;
--color-surface: #ffffff;
--color-border: #e2e8f0;
--color-border-light: #f1f5f9;
--color-primary: #6366f1;
--color-primary-hover: #4f46e5;
--color-text: #1e293b;
--color-text-muted: #64748b;
--shadow-sm: 0 1px 2px rgba(0,0,0,0.04);
--shadow-md: 0 1px 3px rgba(0,0,0,0.06), 0 1px 2px rgba(0,0,0,0.04);
--shadow-lg: 0 4px 12px rgba(0,0,0,0.06), 0 2px 4px rgba(0,0,0,0.04);
}
.gradio-container {
background: var(--color-bg) !important;
font-family: 'Inter', -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif !important;
color: var(--color-text) !important;
max-width: 100% !important;
}
/* ---- 标题 ---- */
.app-title {
text-align: center;
padding: 6px 0 2px 0;
}
.app-title h1 {
margin: 0;
font-size: 18px;
font-weight: 700;
background: linear-gradient(135deg, #4338ca 0%, #6366f1 50%, #8b5cf6 100%);
-webkit-background-clip: text;
-webkit-text-fill-color: transparent;
background-clip: text;
letter-spacing: -0.02em;
}
/* ---- Tabs 导航栏 ---- */
.tabs {
gap: 0 !important;
}
.tabs > .tab-nav {
gap: 4px !important;
padding: 0 8px !important;
}
.tabs > .tab-nav > button {
font-size: 13px !important;
font-weight: 500 !important;
padding: 9px 22px !important;
border-radius: var(--radius-lg) var(--radius-lg) 0 0 !important;
transition: all 0.2s ease !important;
color: var(--color-text-muted) !important;
background: transparent !important;
border: none !important;
}
.tabs > .tab-nav > button.selected {
color: var(--color-primary) !important;
background: var(--color-surface) !important;
box-shadow: 0 -1px 3px rgba(0,0,0,0.04) !important;
}
/* ---- 卡片 ---- */
.card-style {
border-radius: var(--radius-lg) !important;
border: 1px solid var(--color-border) !important;
padding: 16px !important;
background: var(--color-surface) !important;
box-shadow: var(--shadow-sm) !important;
transition: box-shadow 0.2s ease !important;
margin-bottom: 10px !important;
}
.card-style:hover {
box-shadow: var(--shadow-lg) !important;
}
/* ---- 分区标题 ---- */
.section-label {
font-weight: 600;
font-size: 12px;
color: var(--color-primary);
margin-bottom: 10px;
display: flex;
align-items: center;
gap: 8px;
text-transform: uppercase;
letter-spacing: 0.06em;
opacity: 0.85;
}
.section-label::before {
content: '';
display: inline-block;
width: 3px;
height: 14px;
background: linear-gradient(180deg, #6366f1, #8b5cf6);
border-radius: var(--radius-sm);
}
/* ---- 按钮 ---- */
.gradio-container .gr-button-primary {
background: linear-gradient(135deg, #4f46e5 0%, #6366f1 100%) !important;
border: none !important;
font-weight: 500 !important;
font-size: 13px !important;
border-radius: var(--radius-md) !important;
padding: 6px 18px !important;
transition: all 0.2s ease !important;
box-shadow: 0 1px 3px rgba(79,70,229,0.25) !important;
}
.gradio-container .gr-button-primary:hover {
transform: translateY(-1px);
box-shadow: 0 4px 14px rgba(79,70,229,0.35) !important;
filter: brightness(1.06);
}
.gradio-container .gr-button-secondary {
border: 1px solid var(--color-border) !important;
background: var(--color-surface) !important;
color: #475569 !important;
font-weight: 500 !important;
font-size: 13px !important;
border-radius: var(--radius-md) !important;
padding: 6px 18px !important;
transition: all 0.15s ease !important;
}
.gradio-container .gr-button-secondary:hover {
background: #f8fafc !important;
border-color: #94a3b8 !important;
color: #334155 !important;
}
/* ---- 输入框全局 ---- */
.gradio-container input,
.gradio-container textarea {
border-radius: var(--radius-md) !important;
border: 1px solid var(--color-border) !important;
font-size: 13px !important;
transition: border-color 0.15s ease, box-shadow 0.15s ease !important;
background: var(--color-surface) !important;
}
.gradio-container input:focus,
.gradio-container textarea:focus {
outline: none !important;
border-color: var(--color-primary) !important;
box-shadow: 0 0 0 3px rgba(99,102,241,0.1) !important;
}
/* ---- 搜索面板 ---- */
.search-toolbar {
padding: 14px 16px !important;
margin-bottom: 8px;
}
.search-query textarea {
font-size: 14px !important;
line-height: 1.6 !important;
min-height: 68px !important;
border: 1px solid var(--color-border) !important;
background: #fafbff !important;
border-radius: var(--radius-lg) !important;
padding: 12px 14px !important;
}
.search-query textarea:focus {
background: var(--color-surface) !important;
border-color: var(--color-primary) !important;
box-shadow: 0 0 0 4px rgba(99,102,241,0.06) !important;
}
.search-actions {
margin-top: 10px;
justify-content: flex-end;
gap: 10px;
}
.search-actions .gr-button {
min-height: 44px !important;
font-size: 15px !important;
border-radius: var(--radius-lg) !important;
min-width: 130px;
}
.search-panel {
margin-top: 0;
gap: 10px !important;
}
/* ---- 控制台输出框 (暗色主题) ---- */
.console-font,
.console-font > div {
border-radius: var(--radius-lg) !important;
}
.console-font textarea {
border-radius: var(--radius-lg) !important;
overflow-y: auto !important;
}
.console-font textarea {
font-family: 'JetBrains Mono', 'Fira Code', ui-monospace, SFMono-Regular, Menlo, Monaco, monospace !important;
font-size: 12px !important;
line-height: 1.5 !important;
background: #0f172a !important;
color: #e2e8f0 !important;
border: 1px solid #1e293b !important;
padding: 14px !important;
}
.console-font textarea:focus {
border-color: #334155 !important;
box-shadow: 0 0 0 2px rgba(99,102,241,0.15) !important;
outline: none !important;
}
/* ---- 结果展示框 ---- */
.result-box {
min-height: 360px;
max-height: 360px;
overflow: auto;
}
.result-box textarea {
min-height: 360px !important;
max-height: 360px !important;
}
/* ---- 视频预览 ---- */
.video-box {
border-radius: var(--radius-lg) !important;
overflow: hidden !important;
border: 1px solid var(--color-border);
box-shadow: var(--shadow-sm);
background: #f8fafc !important;
}
.video-box video {
border-radius: var(--radius-lg) !important;
}
/* ---- 画廊 ---- */
.clip-gallery {
border: 1px solid var(--color-border);
border-radius: var(--radius-lg);
padding: 8px;
background: var(--color-surface);
box-shadow: var(--shadow-sm);
min-height: 360px;
}
.clip-gallery .grid-wrap {
gap: 8px !important;
}
.clip-gallery img,
.clip-gallery video {
border-radius: var(--radius-md) !important;
transition: transform 0.2s ease, box-shadow 0.2s ease !important;
}
.clip-gallery img:hover,
.clip-gallery video:hover {
transform: scale(1.03);
box-shadow: 0 4px 20px rgba(0,0,0,0.1) !important;
}
/* ---- 文件上传区域 ---- */
.gradio-container .file-preview {
border-radius: var(--radius-md) !important;
border: 2px dashed #cbd5e1 !important;
background: #f8fafc !important;
transition: all 0.2s ease !important;
padding: 8px !important;
}
.gradio-container .file-preview:hover {
border-color: var(--color-primary) !important;
background: #fafbff !important;
}
/* ---- 设置面板 ---- */
.settings-group {
margin-bottom: 20px;
}
.settings-section-title {
font-size: 13px !important;
font-weight: 600 !important;
color: #334155 !important;
margin-bottom: 12px !important;
padding-bottom: 8px !important;
border-bottom: 2px solid var(--color-border);
}
.config-card {
background: #f8fafc !important;
border-radius: var(--radius-md) !important;
padding: 14px !important;
border: 1px solid var(--color-border) !important;
margin-bottom: 12px;
}
.param-row {
display: flex !important;
gap: 14px !important;
margin-bottom: 12px !important;
}
.param-col {
flex: 1 !important;
display: flex !important;
flex-direction: column !important;
}
.param-label {
font-size: 12px !important;
font-weight: 500 !important;
color: #475569 !important;
margin-bottom: 4px !important;
}
.param-info {
font-size: 11px !important;
color: #94a3b8 !important;
margin-top: 2px !important;
}
.apply-btn-container {
text-align: center;
margin-top: 20px;
}
/* ---- Accordion ---- */
.gradio-accordion {
border-radius: var(--radius-md) !important;
border: 1px solid var(--color-border) !important;
margin-bottom: 6px !important;
overflow: hidden !important;
background: #ffffff !important;
}
.gradio-accordion:last-child {
margin-bottom: 0 !important;
}
.gradio-accordion .label-wrap {
padding: 9px 14px !important;
font-size: 13px !important;
font-weight: 500 !important;
color: #475569 !important;
background: transparent !important;
border: none !important;
border-radius: var(--radius-md) !important;
}
.gradio-accordion[open] > .label-wrap {
border-radius: var(--radius-md) var(--radius-md) 0 0 !important;
border-bottom: 1px solid var(--color-border) !important;
}
/* ---- Number Input ---- */
.gradio-container input[type="number"] {
font-size: 13px !important;
padding: 7px 10px !important;
border-radius: var(--radius-md) !important;
}
/* ---- 滚动条 ---- */
::-webkit-scrollbar { width: 5px; height: 5px; }
::-webkit-scrollbar-track { background: transparent; }
::-webkit-scrollbar-thumb { background: #cbd5e1; border-radius: 10px; }
::-webkit-scrollbar-thumb:hover { background: #94a3b8; }
/* ---- Footer ---- */
footer { display: none !important; }
/* ---- Tab 内容区 ---- */
.tabs > .tabitem {
padding-top: 8px !important;
}
/* ---- 表单行间距 ---- */
.form {
gap: 10px !important;
}
"""
# ---------- 全局状态控制 ----------
_videorag: VideoRAG | None = None
_rag_lock = threading.Lock()
# 添加清理缓存函数
def cleanup_temp_dir():
"""清理Gradio临时目录，但保留working_dir中的视频文件"""
try:
if os.path.exists(_temp_dir):
# 只删除非working_dir的临时文件
for item in os.listdir(_temp_dir):
item_path = os.path.join(_temp_dir, item)
# 跳过处理过的视频文件，只清理临时上传文件
if os.path.isfile(item_path):
os.remove(item_path)
elif os.path.isdir(item_path):
# 递归删除子目录
shutil.rmtree(item_path)
print(f"已清理Gradio临时目录: {_temp_dir}")
except Exception as e:
print(f"清理临时目录时出错: {e}")
# 注册退出时清理函数
atexit.register(cleanup_temp_dir)
_RAG_ENV_MAP = {
"video_segment_length": "VIDEORAG_VIDEO_SEGMENT_LENGTH",
"rough_num_frames_per_segment": "VIDEORAG_ROUGH_NUM_FRAMES_PER_SEGMENT",
"retrieval_topk_chunks": "VIDEORAG_RETRIEVAL_TOPK_CHUNKS",
"query_better_than_threshold": "VIDEORAG_QUERY_BETTER_THAN_THRESHOLD",
"chunk_token_size": "VIDEORAG_CHUNK_TOKEN_SIZE",
"segment_retrieval_top_k": "VIDEORAG_SEGMENT_RETRIEVAL_TOP_K",
}
def _read_int_env(key: str, default: int) -> int:
try:
return int(os.getenv(key, str(default)).strip())
except Exception:
return default
def _read_float_env(key: str, default: float) -> float:
try:
return float(os.getenv(key, str(default)).strip())
except Exception:
return default
def _load_rag_runtime_settings() -> dict:
return {
"video_segment_length": _read_int_env(_RAG_ENV_MAP["video_segment_length"], 20),
"rough_num_frames_per_segment": _read_int_env(_RAG_ENV_MAP["rough_num_frames_per_segment"], 10),
"retrieval_topk_chunks": _read_int_env(_RAG_ENV_MAP["retrieval_topk_chunks"], 2),
"query_better_than_threshold": _read_float_env(_RAG_ENV_MAP["query_better_than_threshold"], 0.2),
"chunk_token_size": _read_int_env(_RAG_ENV_MAP["chunk_token_size"], 1000),
"segment_retrieval_top_k": _read_int_env(_RAG_ENV_MAP["segment_retrieval_top_k"], 3),
}
_rag_runtime_settings = _load_rag_runtime_settings()
def _get_rag(working_dir: str) -> VideoRAG:
global _videorag
with _rag_lock:
need_rebuild = _videorag is None or _videorag.working_dir != working_dir
if not need_rebuild and _videorag is not None:
for k, v in _rag_runtime_settings.items():
if getattr(_videorag, k, None) != v:
need_rebuild = True
break
if need_rebuild:
_videorag = VideoRAG(working_dir=working_dir, **_rag_runtime_settings)
return _videorag
def _read_indexed_videos(working_dir: str) -> list[str]:
kv_path = os.path.join(working_dir, "kv_store_video_path.json")
if not os.path.exists(kv_path):
return []
try:
with open(kv_path, "r", encoding="utf-8") as f:
data = json.load(f)
return list(data.keys())
except Exception:
return []
def _fmt_video_list(videos: list[str]) -> str:
if not videos:
return "📦 暂无已索引视频"
return "\n".join(f"• {v}" for v in sorted(videos))
def _get_path_from_file(file_obj):
if file_obj is None:
return None
if isinstance(file_obj, str):
return file_obj
if isinstance(file_obj, os.PathLike):
return os.fspath(file_obj)
if isinstance(file_obj, dict):
for k in ("path", "name"):
v = file_obj.get(k)
if isinstance(v, str) and v.strip():
return v
return None
for attr in ("path", "name"):
v = getattr(file_obj, attr, None)
if isinstance(v, str) and v.strip():
return v
return None
def _parse_clock_text(clock_text: str) -> float:
t = clock_text.strip()
parts = t.split(":")
if len(parts) == 2:
mm, ss = parts
return int(mm) * 60 + int(ss)
if len(parts) == 3:
hh, mm, ss = parts
return int(hh) * 3600 + int(mm) * 60 + int(ss)
raise ValueError(f"无效时间格式: {clock_text}")
_TIME_TOKEN_RE = r"[0-9]{1,2}:[0-9]{1,2}(?::[0-9]{1,2})?"
_REF_LINE_RE = re.compile(
rf"^\s*(?:[-*•]\s*)?(?:\[(?P<idx>\d+)\]\s*)?(?:\d+[.)、]\s*)?"
rf"(?:(?:\*\*)?(?:reference|参考)(?:\*\*)?\s*[:：]\s*)?"
rf"(?P<video>[^,，]+?)\s*[,，]\s*(?P<start>{_TIME_TOKEN_RE})\s*[,，]\s*(?P<end>{_TIME_TOKEN_RE})\s*$",
flags=re.IGNORECASE,
)
def _is_reference_header(line: str) -> bool:
s = line.strip()
s = re.sub(r"\*\*", "", s)
s = s.replace("：", ":")
return bool(
re.match(r"^#{1,6}\s*(reference|参考)\s*:?\s*$", s, flags=re.IGNORECASE)
or re.match(r"^(reference|参考)\s*:?\s*$", s, flags=re.IGNORECASE)
)
def _parse_reference_line(line: str):
normalized = line.strip().replace("，", ",")
if not normalized:
return None
# 兼容“说明文字 ... 参考：video, t1, t2”
tail = re.search(r"(?:reference|参考)\s*[:：]\s*(.+)$", normalized, flags=re.IGNORECASE)
if tail:
normalized = tail.group(1).strip()
idx_text = None
m_idx = re.match(r"^\s*\[(\d+)\]\s*(.*)$", normalized)
if m_idx:
idx_text = m_idx.group(1)
normalized = m_idx.group(2).strip()
normalized = re.sub(r"^\s*[-*•]\s*", "", normalized)
normalized = re.sub(r"^(?:\d+[.)、])\s*", "", normalized)
normalized = re.sub(r"^(?:\*\*)?(?:reference|参考)(?:\*\*)?\s*[:：]\s*", "", normalized, flags=re.IGNORECASE)
# 兼容: [sanguo, 0:0:20, 0:0:40] / - [sanguo, ...]
if normalized.startswith("[") and normalized.endswith("]"):
normalized = normalized[1:-1].strip()
m = _REF_LINE_RE.match(normalized)
if m:
idx_text = idx_text or m.group("idx")
video_name = m.group("video").strip().strip("`").strip('"').strip("'").strip("[]")
start_text = m.group("start")
end_text = m.group("end")
else:
# 兜底：按逗号切分，最后两段视为时间
parts = [p.strip() for p in normalized.split(",") if p.strip()]
if len(parts) < 3:
return None
video_name = ",".join(parts[:-2]).strip().strip("`").strip('"').strip("'").strip("[]")
start_text, end_text = parts[-2], parts[-1]
if not video_name:
return None
try:
start = _parse_clock_text(start_text)
end = _parse_clock_text(end_text)
except Exception:
return None
if end <= start:
end = start + 1
return {
"ref_id": int(idx_text) if idx_text else None,
"video_name": video_name,
"start": float(start),
"end": float(end),
"start_text": start_text,
"end_text": end_text,
}
def _dedup_and_fill_ref_id(items: list[dict]):
uniq = []
seen = set()
for it in items:
key = (it["video_name"].lower(), int(it["start"]), int(it["end"]))
if key in seen:
continue
seen.add(key)
uniq.append(it)
for i, it in enumerate(uniq, start=1):
if it["ref_id"] is None:
it["ref_id"] = i
return sorted(uniq, key=lambda x: x["ref_id"])
def _extract_reference_items(answer: str):
lines = answer.splitlines()
in_ref = False
from_ref_section = []
from_all_lines = []
for raw in lines:
line = raw.strip()
if _is_reference_header(line):
in_ref = True
continue
if in_ref and re.match(r"^#{1,6}\s+\S+", line) and not _is_reference_header(line):
in_ref = False
parsed = _parse_reference_line(line)
if not parsed:
continue
from_all_lines.append(parsed)
if in_ref:
from_ref_section.append(parsed)
# 优先使用“Reference/参考”章节中的条目；没有再全局兜底
if from_ref_section:
return _dedup_and_fill_ref_id(from_ref_section)
return _dedup_and_fill_ref_id(from_all_lines)
def _resolve_video_path(rag: VideoRAG, referenced_name: str):
def _normalize_video_name(name: str) -> str:
n = (name or "").strip().strip("`").strip('"').strip("'").strip("[]")
n = n.replace("：", ":")
# 兼容 "video_name: sanguo" / "video: xxx" / "视频名: xxx"
n = re.sub(r"^(video_name|video|视频名|文件名|name)\s*:\s*", "", n, flags=re.IGNORECASE).strip()
n = re.sub(r"^\s*[-*•]\s*", "", n)
return os.path.splitext(n)[0]
path_map = rag.video_path_db._data
normalized = _normalize_video_name(referenced_name)
if normalized in path_map:
return normalized, path_map[normalized]
raw = (referenced_name or "").strip()
raw_wo_ext = _normalize_video_name(raw)
for k, v in path_map.items():
if k == raw_wo_ext:
return k, v
if k.lower() == raw.lower() or k.lower() == raw_wo_ext.lower():
return k, v
# 兜底：包含关系匹配，避免“video_name: xxx”这类前缀导致 miss
if raw_wo_ext.lower() in k.lower() or k.lower() in raw_wo_ext.lower():
return k, v
return None, None
def _export_clip(video_path: str, start: float, end: float, working_dir: str, cache_key: str):
clip_dir = os.path.join(working_dir, "_webui_query_clips")
os.makedirs(clip_dir, exist_ok=True)
video_stem = os.path.splitext(os.path.basename(video_path))[0]
clip_name = f"{video_stem}_{int(start)}_{int(end)}_{cache_key[:8]}.mp4"
clip_path = os.path.join(clip_dir, clip_name)
if os.path.exists(clip_path):
return clip_path
# 使用与 split.py 相同的 ffmpeg 选择逻辑
try:
import imageio_ffmpeg
_ffmpeg = imageio_ffmpeg.get_ffmpeg_exe()
except ImportError:
_ffmpeg = os.getenv("FFMPEG_BIN", "ffmpeg")
cmd = [
_ffmpeg,
"-y",
"-ss",
f"{start:.3f}",
"-to",
f"{end:.3f}",
"-i",
video_path,
"-c:v",
"libx264",
"-c:a",
"aac",
"-movflags",
"+faststart",
"-loglevel",
"error",
clip_path,
]
proc = subprocess.run(cmd, capture_output=True, text=True)
if proc.returncode != 0:
raise RuntimeError(proc.stderr.strip() or "ffmpeg 裁剪失败")
return clip_path
def _upsert_env_file(env_path: str, updates: dict[str, str]):
key_pattern = re.compile(r"^\s*([A-Za-z_][A-Za-z0-9_]*)\s*=")
lines = []
if os.path.exists(env_path):
with open(env_path, "r", encoding="utf-8") as f:
lines = f.readlines()
found = set()
new_lines = []
for line in lines:
m = key_pattern.match(line)
if not m:
new_lines.append(line)
continue
k = m.group(1)
if k in updates:
safe_v = str(updates[k]).replace('"', '\\"')
new_lines.append(f'{k} = "{safe_v}"\n')
found.add(k)
else:
new_lines.append(line)
for k, v in updates.items():
if k in found:
continue
safe_v = str(v).replace('"', '\\"')
new_lines.append(f'{k} = "{safe_v}"\n')
with open(env_path, "w", encoding="utf-8") as f:
f.writelines(new_lines)
def apply_system_settings(
llm_base_url: str,
llm_api_key: str,
llm_model_name: str,
vlm_base_url: str,
vlm_api_key: str,
vlm_model_name: str,
embedding_base_url: str,
embedding_api_key: str,
embedding_model_name: str,
video_segment_length: float,
rough_num_frames_per_segment: float,
retrieval_topk_chunks: float,
query_better_than_threshold: float,
chunk_token_size: float,
segment_retrieval_top_k: float, # 新增参数
):
global _rag_runtime_settings, _videorag
llm_base_url = (llm_base_url or "").strip()
llm_api_key = (llm_api_key or "").strip()
llm_model_name = (llm_model_name or "").strip()
vlm_base_url = (vlm_base_url or "").strip()
vlm_api_key = (vlm_api_key or "").strip()
vlm_model_name = (vlm_model_name or "").strip()
embedding_base_url = (embedding_base_url or "").strip()
embedding_api_key = (embedding_api_key or "").strip()
embedding_model_name = (embedding_model_name or "").strip()
required = {
"LLM_API_BASE_URL": llm_base_url,
"LLM_API_KEY": llm_api_key,
"LLM_MODEL_NAME": llm_model_name,
"VLM_API_BASE_URL": vlm_base_url,
"VLM_API_KEY": vlm_api_key,
"VLM_MODEL_NAME": vlm_model_name,
"EMBEDDING_API_BASE_URL": embedding_base_url,
"EMBEDDING_API_KEY": embedding_api_key,
"EMBEDDING_MODEL_NAME": embedding_model_name,
}
empties = [k for k, v in required.items() if not v]
if empties:
return "❌ 以下配置不能为空：\n- " + "\n- ".join(empties)
try:
video_segment_length = int(video_segment_length)
rough_num_frames_per_segment = int(rough_num_frames_per_segment)
retrieval_topk_chunks = int(retrieval_topk_chunks)
chunk_token_size = int(chunk_token_size)
query_better_than_threshold = float(query_better_than_threshold)
segment_retrieval_top_k = int(segment_retrieval_top_k) # 新增转换
except Exception as e:
return f"❌ 参数类型错误，请检查数值配置：{e}"
if video_segment_length <= 0 or rough_num_frames_per_segment <= 0:
return "❌ video_segment_length 和 rough_num_frames_per_segment 必须 > 0"
if retrieval_topk_chunks <= 0:
return "❌ retrieval_topk_chunks 必须 > 0"
if chunk_token_size <= 0:
return "❌ chunk_token_size 必须 > 0"
if segment_retrieval_top_k <= 0: # 新增验证
return "❌ segment_retrieval_top_k 必须 > 0"
if not (0 <= query_better_than_threshold <= 1):
return "❌ query_better_than_threshold 建议设置在 [0, 1] 区间"
updates = {
**required,
_RAG_ENV_MAP["video_segment_length"]: str(video_segment_length),
_RAG_ENV_MAP["rough_num_frames_per_segment"]: str(rough_num_frames_per_segment),
_RAG_ENV_MAP["retrieval_topk_chunks"]: str(retrieval_topk_chunks),
_RAG_ENV_MAP["query_better_than_threshold"]: str(query_better_than_threshold),
_RAG_ENV_MAP["chunk_token_size"]: str(chunk_token_size),
_RAG_ENV_MAP["segment_retrieval_top_k"]: str(segment_retrieval_top_k), # 新增
}
for k, v in updates.items():
os.environ[k] = v
env_path = os.path.join(_script_dir, ".env")
_upsert_env_file(env_path, updates)
applied_logs = []
try:
import VideoAgent.query as query_mod
if hasattr(query_mod, "qwen3_model"):
query_mod.qwen3_model.model_name = llm_model_name
query_mod.qwen3_model.base_url = llm_base_url
query_mod.qwen3_model.api_key = llm_api_key
query_mod.qwen3_model.client = OpenAI(base_url=llm_base_url, api_key=llm_api_key)
applied_logs.append("✅ LLM 客户端已热更新")
else:
applied_logs.append("⚠️ LLM 客户端未找到，需重启生效")
except Exception as e:
applied_logs.append(f"⚠️ LLM 热更新失败，需重启生效：{e}")
try:
import VideoAgent._videoutil.caption as caption_mod
if hasattr(caption_mod, "model"):
caption_mod.model.model_name = vlm_model_name
caption_mod.model.client = OpenAI(base_url=vlm_base_url, api_key=vlm_api_key)
applied_logs.append("✅ VLM 客户端已热更新")
else:
applied_logs.append("⚠️ VLM 客户端未找到，需重启生效")
except Exception as e:
applied_logs.append(f"⚠️ VLM 热更新失败，需重启生效：{e}")
try:
from VideoAgent._llm import Qwen3VLEmbedderC
import VideoAgent._videoutil.feature as feature_mod
feature_mod.model = Qwen3VLEmbedderC(
model_name=embedding_model_name,
base_url=embedding_base_url,
api_key=embedding_api_key,
)
applied_logs.append("✅ Embedding 客户端已热更新")
except Exception as e:
applied_logs.append(f"⚠️ Embedding 热更新失败，需重启生效：{e}")
_rag_runtime_settings = {
"video_segment_length": video_segment_length,
"rough_num_frames_per_segment": rough_num_frames_per_segment,
"retrieval_topk_chunks": retrieval_topk_chunks,
"query_better_than_threshold": query_better_than_threshold,
"chunk_token_size": chunk_token_size,
"segment_retrieval_top_k": segment_retrieval_top_k, # 新增
}
_videorag = None
applied_logs.append("✅ VideoAgent 参数已更新（下次索引/查询将按新参数实例化）")
return (
"🎉 系统设置已保存到 .env 并应用。\n"
+ "\n".join(applied_logs)
+ "\n\n若你替换了模型结构差异较大的服务，建议重启 webui 以确保完全生效。"
)
class _LogCapture(logging.Handler):
def __init__(self):
super().__init__()
self.q: _queue.Queue[str] = _queue.Queue()
def emit(self, record: logging.LogRecord):
self.q.put(self.format(record))
# ---------- 核心交互函数 ----------
def refresh_video_list(working_dir: str) -> str:
return _fmt_video_list(_read_indexed_videos(working_dir))
def index_videos(video_files, working_dir: str, progress=gr.Progress()):
if not working_dir.strip():
yield "❌ 错误：工作目录不能为空。", None
return
if not video_files:
yield "❌ 错误：未选择视频文件。", None
return
os.makedirs(working_dir, exist_ok=True)
# 创建processed目录
processed_dir = os.path.join(working_dir, "processed")
os.makedirs(processed_dir, exist_ok=True)
# 检查processed目录中已存在的视频文件
existing_videos = set()
for file in os.listdir(processed_dir):
if file.lower().endswith(('.mp4', '.avi', '.mov', '.mkv', '.wmv', '.flv', '.webm')):
video_name_without_ext = os.path.splitext(file)[0]
existing_videos.add(video_name_without_ext)
# 处理视频文件并获取处理后的路径
video_paths = []
original_to_processed_map = {} # 映射原始路径到处理后路径
skipped_videos = [] # 记录跳过的视频
for f in video_files:
original_path = _get_path_from_file(f)
if original_path and os.path.exists(original_path):
# 获取视频名称（不含扩展名）
original_video_name = os.path.basename(original_path)
video_name_without_ext = os.path.splitext(original_video_name)[0]
# 检查是否已存在同名视频
if video_name_without_ext in existing_videos:
skipped_videos.append(original_video_name)
continue
# 使用preprocess_video处理原始视频
from VideoAgent._videoutil.split import preprocess_video
video_output_path = preprocess_video(
original_path,
target_width=384, # 使用与VideoRAG类相同的默认值
target_height=384,
target_fps=5,
video_output_format="mp4"
)
# 将处理后的视频移动到working_dir/processed目录
video_name = os.path.basename(video_output_path)
target_path = os.path.join(processed_dir, video_name)
# 如果目标文件已存在（理论上不应该发生），则添加时间戳避免覆盖
if os.path.exists(target_path):
name, ext = os.path.splitext(video_name)
timestamp = int(time.time())
target_path = os.path.join(processed_dir, f"{name}_{timestamp}{ext}")
import shutil
shutil.move(video_output_path, target_path) # 使用move而不是copy，因为原文件不再需要
video_paths.append(target_path) # 现在使用处理后的路径调用insert_video
original_to_processed_map[original_path] = target_path # 保存映射关系
if skipped_videos:
log_lines = [f"⚠️ 以下视频已在processed目录中存在，跳过处理：{', '.join(skipped_videos)}"]
else:
log_lines = []
if not video_paths:
if skipped_videos:
yield f"⚠️ 所有视频均已在processed目录中存在，无需重复处理。\n{chr(10).join(log_lines)}", None
else:
yield "❌ 错误：上传文件不可用，请重新上传后重试。", None
return
cap = _LogCapture()
cap.setFormatter(logging.Formatter("%(asctime)s | %(message)s", "%H:%M:%S"))
root_logger = logging.getLogger()
root_logger.addHandler(cap)
events: _queue.Queue = _queue.Queue()
done_flag: dict[str, str | bool] = {}
total = len(video_paths)
def run():
try:
rag = _get_rag(working_dir)
for i, path in enumerate(video_paths, start=1):
events.put(("start", {"index": i, "path": path}))
rag.insert_video(video_path_list=[path])
# 获取video_name以便更新video_path_db
video_name = os.path.basename(path).split('.')[0]
# 使用always_get_an_event_loop获取事件循环并运行异步任务
from VideoAgent._utils import always_get_an_event_loop
loop = always_get_an_event_loop()
async def update_video_path():
await rag.video_path_db.upsert({video_name: path})
await rag.video_path_db.index_done_callback()
loop.run_until_complete(update_video_path())
events.put(("done", {"index": i, "path": path}))
done_flag["ok"] = True
except Exception as e:
done_flag["error"] = str(e)
worker = threading.Thread(target=run, daemon=True)
worker.start()
current_video = video_paths[0] if video_paths else None
log_lines.extend([f"🎬 准备索引 {total} 个视频..."])
last_log_snapshot = ""
last_video = None
while worker.is_alive() or not events.empty() or not cap.q.empty():
changed = False
while True:
try:
event_type, value = events.get_nowait()
if event_type == "start":
i = value["index"]
current_video = value["path"]
progress(float(i - 1) / total, desc=f"正在索引: {os.path.basename(current_video)}")
log_lines.append(f"🎞️ [{i}/{total}] 开始索引：{os.path.basename(current_video)}")
elif event_type == "done":
i = value["index"]
current_video = value["path"]
progress(float(i) / total, desc=f"已完成: {os.path.basename(current_video)}")
log_lines.append(f"✅ [{i}/{total}] 完成索引：{os.path.basename(current_video)}")
else:
log_lines.append(value)
changed = True
except _queue.Empty:
break
while True:
try:
log_line = cap.q.get_nowait()
log_lines.append(log_line)
changed = True
except _queue.Empty:
break
snapshot = "\n".join(log_lines[-120:])
if changed or snapshot != last_log_snapshot or current_video != last_video:
last_log_snapshot = snapshot
last_video = current_video
yield snapshot, current_video
else:
time.sleep(0.2)
root_logger.removeHandler(cap)
if skipped_videos:
final_log = f"{last_log_snapshot}\n🎉 完成索引。跳过的视频：{', '.join(skipped_videos)}"
else:
final_log = f"{last_log_snapshot}\n🎉 全部索引完成。"
if "error" in done_flag:
final_log = f"{last_log_snapshot}\n❌ 索引失败：{done_flag['error']}"
yield final_log, current_video
def query_videos(query_text: str, working_dir: str):
if not query_text.strip():
yield "❌ 请输入问题", []
return
# Immediate first yield so Gradio registers the generator as started
yield "⏳ 正在检索相关内容...", []
try:
rag = _get_rag(working_dir)
qparam = QueryParam()
qparam.naive_max_token_for_text_unit = int(
getattr(rag, "chunk_token_size", qparam.naive_max_token_for_text_unit)
)
# Phase 1: retrieve context (blocking, can take a while)
sys_prompt = rag._prepare_query_context(query=query_text, param=qparam)
if sys_prompt == PROMPTS["fail_response"]:
yield sys_prompt, []
return
# Phase 2: stream the LLM answer, filtering <think>...</think> blocks
accumulated = ""
try:
for chunk in _result_query_stream(query_text, sys_prompt):
accumulated += chunk
display = clean_output(accumulated)
if display:
yield display, []
except Exception as stream_err:
if accumulated:
yield f"{clean_output(accumulated)}\n\n❌ 流式输出中断: {stream_err}", []
else:
yield f"❌ 生成失败: {stream_err}", []
return
answer = clean_output(accumulated)
# --- Parse reference clips (after full answer is available) ---
refs = _extract_reference_items(answer)
if not refs:
yield (
f"{answer}\n\nℹ️ 未解析到可播放片段（请确保答案包含\"参考/Reference: 视频名, 开始时间, 结束时间\"格式）。",
[],
)
return
gallery_items = []
warnings = []
for ref in refs:
resolved_name, video_path = _resolve_video_path(rag, ref["video_name"])
if not video_path:
warnings.append(f"[{ref['ref_id']}] 未匹配到视频：{ref['video_name']}")
continue
cache_key = hashlib.md5(
f"{resolved_name}|{ref['start']}|{ref['end']}|{query_text}|{ref['ref_id']}".encode("utf-8")
).hexdigest()
try:
clip_path = _export_clip(
video_path=video_path,
start=ref["start"],
end=ref["end"],
working_dir=working_dir,
cache_key=cache_key,
)
except Exception as e:
warnings.append(f"[{ref['ref_id']}] 裁剪失败：{e}")
continue
label = f"[{ref['ref_id']}] {resolved_name} {ref['start_text']} - {ref['end_text']}"
gallery_items.append((clip_path, label))
if not gallery_items:
warn_text = ("\n".join(f"- {w}" for w in warnings)) if warnings else "- 未生成任何可播放片段"
yield (
f"{answer}\n\n⚠️ 参考片段解析完成，但无法生成可播放视频：\n{warn_text}",
[],
)
return
if warnings:
answer = answer + "\n\n⚠️ 部分片段处理失败：\n" + "\n".join(f"- {w}" for w in warnings[:5])
yield answer, gallery_items
except Exception as e:
yield f"❌ 查询异常: {e}", []
# ---------- UI 界面构建 ----------
DEFAULT_WORKING_DIR = os.path.join(_script_dir, "working_dir")
with gr.Blocks(
title="VideoRAG",
theme=gr.themes.Soft(
primary_hue="indigo",
secondary_hue="indigo",
neutral_hue="slate",
).set(
body_background_fill="#f1f5f9",
body_background_fill_dark="#0f172a",
block_background_fill="#ffffff",
block_border_color="#e2e8f0",
block_border_width="1px",
block_radius="12px",
button_primary_background_fill="linear-gradient(135deg, #4f46e5 0%, #6366f1 100%)",
button_primary_background_fill_hover="linear-gradient(135deg, #4338ca 0%, #4f46e5 100%)",
button_primary_border_color="transparent",
button_primary_text_color="#ffffff",
button_secondary_background_fill="#ffffff",
button_secondary_border_color="#cbd5e1",
button_secondary_text_color="#475569",
button_medium_radius="8px",
input_background_fill="#ffffff",
input_border_color="#e2e8f0",
input_border_color_focus="#818cf8",
input_radius="8px",
),
css=custom_css,
) as demo:
# gr.State 必须在 Blocks 上下文内创建，避免出现 KeyError: 0
working_dir_state = gr.State(DEFAULT_WORKING_DIR)
gr.HTML(
"""
<div class="app-title">
<h1>VideoAgent 控制台</h1>
</div>
"""
)
with gr.Tabs():
with gr.Tab("索引"):
with gr.Row(equal_height=True):
with gr.Column(scale=6):
with gr.Group(elem_classes="card-style"):
gr.HTML('<div class="section-label">上传视频</div>')
video_upload = gr.File(
file_count="multiple",
file_types=["video"],
type="filepath",
height=140,
)
with gr.Row():
index_btn = gr.Button("开始索引", variant="primary", size="sm")
refresh_btn = gr.Button("刷新", size="sm")
with gr.Group(elem_classes="card-style"):
gr.HTML('<div class="section-label">日志</div>')
index_log = gr.Textbox(
show_label=False,
interactive=False,
lines=16,
elem_classes="console-font",
placeholder="等待任务...",
)
with gr.Column(scale=4):
with gr.Group(elem_classes="card-style"):
gr.HTML('<div class="section-label">当前视频</div>')
index_video_preview = gr.Video(
show_label=False,
height=240,
interactive=False,
elem_classes="video-box",
)
with gr.Group(elem_classes="card-style"):
gr.HTML('<div class="section-label">已索引</div>')
indexed_list = gr.Textbox(
show_label=False,
value=refresh_video_list(DEFAULT_WORKING_DIR),
lines=6,
max_lines=8,
interactive=False,
)
with gr.Tab("检索"):
with gr.Group(elem_classes=["card-style", "search-toolbar"]):
query_input = gr.Textbox(
placeholder="输入检索问题",
show_label=False,
lines=2,
elem_classes="search-query",
)
with gr.Row(equal_height=True, elem_classes="search-actions"):
query_btn = gr.Button("开始检索", variant="primary", size="lg")
clear_query = gr.Button("清空", variant="secondary", size="lg")
with gr.Row(equal_height=False, elem_classes="search-panel"):
with gr.Column(scale=6):
with gr.Group(elem_classes="card-style"):
gr.HTML('<div class="section-label">检索结果</div>')
response_box = gr.Textbox(
show_label=False,
value="等待检索...",
lines=20,
max_lines=30,
interactive=False,
elem_classes="console-font result-box",
)
with gr.Column(scale=6):
with gr.Group(elem_classes="card-style"):
gr.HTML('<div class="section-label">相关片段（点击播放）</div>')
query_clip_gallery = gr.Gallery(
show_label=False,
columns=2,
height=360,
interactive=False,
elem_classes="clip-gallery",
)
with gr.Tab("设置"):
with gr.Group(elem_classes="card-style"):
gr.HTML('<div class="section-label">模型 API 配置</div>')
with gr.Accordion("LLM 配置", open=False):
with gr.Row(equal_height=True):
llm_base_url_input = gr.Textbox(
label="Base URL",
value=os.getenv("LLM_API_BASE_URL", "http://localhost:8000/v1"),
placeholder="https://xxx/v1",
)
llm_api_key_input = gr.Textbox(
label="API Key",
type="password",
value=os.getenv("LLM_API_KEY", ""),
placeholder="API Key",
)
llm_model_name_input = gr.Textbox(
label="模型名称",
value=os.getenv("LLM_MODEL_NAME", ""),
placeholder="model-name",
)
with gr.Accordion("VLM 配置", open=False):
with gr.Row(equal_height=True):
vlm_base_url_input = gr.Textbox(
label="Base URL",
value=os.getenv("VLM_API_BASE_URL", "http://localhost:8012/v1"), # 修改为正确的端口
placeholder="https://xxx/v1",
)
vlm_api_key_input = gr.Textbox(
label="API Key",
type="password",
value=os.getenv("VLM_API_KEY", ""),
placeholder="API Key",
)
vlm_model_name_input = gr.Textbox(
label="模型名称",
value=os.getenv("VLM_MODEL_NAME", ""),
placeholder="model-name",
)
with gr.Accordion("Embedding 配置", open=False):
with gr.Row(equal_height=True):
embedding_base_url_input = gr.Textbox(
label="Base URL",
value=os.getenv("EMBEDDING_API_BASE_URL", "http://localhost:8010/v1"),
placeholder="https://xxx/v1",
)
embedding_api_key_input = gr.Textbox(
label="API Key",
type="password",
value=os.getenv("EMBEDDING_API_KEY", ""),
placeholder="API Key",
)
embedding_model_name_input = gr.Textbox(
label="模型名称",
value=os.getenv("EMBEDDING_MODEL_NAME", ""),
placeholder="model-name",
)
with gr.Group(elem_classes="card-style"):
gr.HTML('<div class="section-label">VideoAgent 参数配置</div>')
# 合并参数配置
with gr.Row(elem_classes="param-row"):
with gr.Column(elem_classes="param-col"):
video_segment_length_input = gr.Number(
label="视频分段长度 (秒)",
value=_rag_runtime_settings["video_segment_length"],
minimum=1,
info="每个视频片段的持续时间",
precision=0,
)
retrieval_topk_chunks_input = gr.Number(
label="文本段检索 Top-K",
value=_rag_runtime_settings["retrieval_topk_chunks"],
minimum=1,
info="检索相关片段的数量",
precision=0,
)
with gr.Column(elem_classes="param-col"):
rough_num_frames_per_segment_input = gr.Number(
label="每段采样帧数",
value=_rag_runtime_settings["rough_num_frames_per_segment"],
minimum=1,
info="每段视频采样的帧数",
precision=0,
)
segment_retrieval_top_k_input = gr.Number(
label="视频段检索 Top-K",
value=_rag_runtime_settings["segment_retrieval_top_k"],
minimum=1,
info="检索相关视频段的数量",
precision=0,
)
with gr.Row(elem_classes="param-row"):
with gr.Column(elem_classes="param-col"):
query_better_than_threshold_input = gr.Number(
label="查询阈值",
value=_rag_runtime_settings["query_better_than_threshold"],
minimum=0,
maximum=1,
info="查询匹配的最小阈值",
precision=3,
)
with gr.Column(elem_classes="param-col"):
chunk_token_size_input = gr.Number(
label="文本块最大Token数",
value=_rag_runtime_settings["chunk_token_size"],
minimum=1,
info="单个文本块的最大token数量",
precision=0,
)
apply_settings_btn = gr.Button("保存设置", variant="primary", size="lg", elem_classes="gr-button-primary apply-btn-container")
settings_status = gr.Textbox(
show_label=False,
lines=3,
max_lines=5,
interactive=False,
placeholder="等待保存...",
elem_classes="console-font"
)
# ---------- 事件流绑定 ----------
index_btn.click(
fn=index_videos,
inputs=[video_upload, working_dir_state],
outputs=[index_log, index_video_preview],
).then(
fn=refresh_video_list,
inputs=[working_dir_state],
outputs=[indexed_list],
)
refresh_btn.click(
fn=refresh_video_list,
inputs=[working_dir_state],
outputs=[indexed_list],
)
query_args = dict(
fn=query_videos,
inputs=[query_input, working_dir_state],
outputs=[response_box, query_clip_gallery],
stream_every=0.05,
)
query_btn.click(**query_args)
query_input.submit(**query_args)
clear_query.click(
lambda: ("", "等待检索...", []),
None,
[query_input, response_box, query_clip_gallery],
)
apply_settings_btn.click(
fn=apply_system_settings,
inputs=[
llm_base_url_input,
llm_api_key_input,
llm_model_name_input,
vlm_base_url_input,
vlm_api_key_input,
vlm_model_name_input,
embedding_base_url_input,
embedding_api_key_input,
embedding_model_name_input,
video_segment_length_input,
rough_num_frames_per_segment_input,
retrieval_topk_chunks_input,
query_better_than_threshold_input,
chunk_token_size_input,
segment_retrieval_top_k_input, # 新增输入参数
],
outputs=[settings_status],
)
if __name__ == "__main__":
demo.launch(server_name="0.0.0.0", server_port=7869, show_error=True)
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