feat: async clustering job with progress UI
wuqinchuan
zanllp
parent
e522d8e878
commit
2edf9e52d7
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@ -25,6 +25,10 @@ _TOPIC_CLUSTER_JOBS_MAX = 16
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_EMBEDDING_MAX_TOKENS_SOFT = 7800
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# Some providers enforce a max token budget per *request* (sum of all inputs), not only per input.
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# Keep a conservative per-request budget to avoid "context length exceeded" across batched inputs.
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_EMBEDDING_REQUEST_MAX_TOKENS_SOFT = 7600
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_EMBEDDING_DEBUG = os.getenv("IIB_EMBEDDING_DEBUG", "0").strip().lower() in ["1", "true", "yes", "on"]
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def _estimate_tokens_soft(s: str) -> int:
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@ -75,6 +79,35 @@ def _truncate_for_embedding_tokens(s: str, max_tokens: int = _EMBEDDING_MAX_TOKE
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return ("".join(out)).strip()
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def _batched_by_token_budget(
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items: List[Dict],
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*,
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max_items: int,
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max_tokens_sum: int,
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) -> List[List[Dict]]:
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"""
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Split a list of items (each contains 'text') into batches constrained by:
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- max_items
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- sum(estimated_tokens(text)) <= max_tokens_sum
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"""
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batches: List[List[Dict]] = []
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cur: List[Dict] = []
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cur_tokens = 0
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for it in items:
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txt = str(it.get("text") or "")
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t = _estimate_tokens_soft(txt)
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# ensure progress even if one item is huge (should already be truncated per-input)
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if cur and (len(cur) >= max_items or (cur_tokens + t) > max_tokens_sum):
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batches.append(cur)
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cur = []
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cur_tokens = 0
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cur.append(it)
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cur_tokens += t
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if cur:
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batches.append(cur)
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return batches
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def _job_now() -> float:
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return time.time()
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@ -314,7 +347,10 @@ def _call_embeddings_sync(
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except requests.RequestException as e:
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raise HTTPException(status_code=502, detail=f"Embedding API request failed: {e}")
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if resp.status_code != 200:
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raise HTTPException(status_code=resp.status_code, detail=resp.text)
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# Do not leak upstream 401 to frontend (treat as bad request/config to avoid confusing auth state).
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status = 400 if resp.status_code == 401 else resp.status_code
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body = (resp.text or "")[:600]
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raise HTTPException(status_code=status, detail=body)
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data = resp.json()
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items = data.get("data") or []
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items.sort(key=lambda x: x.get("index", 0))
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@ -430,7 +466,8 @@ def _call_chat_title_sync(
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if resp.status_code != 200:
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# keep response body for debugging (truncated)
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body = (resp.text or "")[:600]
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raise HTTPException(status_code=resp.status_code, detail=body)
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status = 400 if resp.status_code == 401 else resp.status_code
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raise HTTPException(status_code=status, detail=body)
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try:
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data = resp.json()
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except Exception as e:
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@ -535,6 +572,97 @@ def mount_topic_cluster_routes(
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Mount embedding + topic clustering endpoints (MVP: manual, iib_output only).
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"""
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async def _run_cluster_job(job_id: str, req) -> None:
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try:
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_job_upsert(
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job_id,
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{
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"status": "running",
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"stage": "init",
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"created_at": _job_now(),
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"req": req.model_dump() if hasattr(req, "model_dump") else req.dict(),
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},
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)
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folders = _extract_and_validate_folders(req)
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_job_upsert(job_id, {"folders": folders, "stage": "embedding"})
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# Aggregate per-folder embedding progress into totals
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per_folder: Dict[str, Dict] = {}
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def _embed_cb(p: Dict) -> None:
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if not isinstance(p, dict):
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return
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if p.get("stage") != "embedding":
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return
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f = str(p.get("folder") or "")
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if f:
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per_folder[f] = dict(p)
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scanned = sum(int(x.get("scanned") or 0) for x in per_folder.values())
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to_embed = sum(int(x.get("to_embed") or 0) for x in per_folder.values())
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embedded_done = sum(int(x.get("embedded_done") or 0) for x in per_folder.values())
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updated = sum(int(x.get("updated") or 0) for x in per_folder.values())
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skipped = sum(int(x.get("skipped") or 0) for x in per_folder.values())
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_job_upsert(
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job_id,
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{
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"stage": "embedding",
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"progress": {
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"scanned": scanned,
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"to_embed": to_embed,
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"embedded_done": embedded_done,
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"updated": updated,
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"skipped": skipped,
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"folder": f,
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"batch_n": int((p or {}).get("batch_n") or 0),
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},
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},
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)
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# Ensure embeddings exist (incremental per folder)
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model = req.model or embedding_model
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batch_size = max(1, min(int(req.batch_size or 64), 256))
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max_chars = max(256, min(int(req.max_chars or 4000), 8000))
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force = bool(req.force_embed)
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for f in folders:
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await _build_embeddings_one_folder(
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folder=f,
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model=model,
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force=force,
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batch_size=batch_size,
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max_chars=max_chars,
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progress_cb=_embed_cb,
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)
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# Clustering + titling progress
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def _cluster_cb(p: Dict) -> None:
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if not isinstance(p, dict):
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return
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st = str(p.get("stage") or "")
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if st:
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patch = {"stage": st, "status": "running"}
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# keep small progress fields only
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prog = {}
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for k in [
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"items_total",
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"items_done",
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"clusters_total",
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"clusters_done",
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"folder",
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]:
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if k in p:
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prog[k] = p.get(k)
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if prog:
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patch["progress"] = {**(_job_get(job_id) or {}).get("progress", {}), **prog}
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_job_upsert(job_id, patch)
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res = await _cluster_after_embeddings(req, folders, progress_cb=_cluster_cb)
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_job_upsert(job_id, {"status": "done", "stage": "done", "result": res})
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except HTTPException as e:
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_job_upsert(job_id, {"status": "error", "stage": "error", "error": str(e.detail)})
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except Exception as e:
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_job_upsert(job_id, {"status": "error", "stage": "error", "error": f"{type(e).__name__}: {e}"})
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async def _build_embeddings_one_folder(
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*,
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folder: str,
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@ -633,9 +761,19 @@ def mount_topic_cluster_routes(
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updated = 0
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embedded_done = 0
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for i in range(0, len(to_embed), batch_size):
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batch = to_embed[i : i + batch_size]
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batches = _batched_by_token_budget(
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to_embed,
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max_items=batch_size,
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max_tokens_sum=_EMBEDDING_REQUEST_MAX_TOKENS_SOFT,
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)
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for bi, batch in enumerate(batches):
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inputs = [x["text"] for x in batch]
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if _EMBEDDING_DEBUG:
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token_sum = sum(_estimate_tokens_soft(s) for s in inputs)
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token_max = max((_estimate_tokens_soft(s) for s in inputs), default=0)
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print(
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f"[iib][embed] folder={folder} batch={bi+1}/{len(batches)} n={len(inputs)} token_sum~={token_sum} token_max~={token_max}"
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)
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vectors = await _call_embeddings(
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inputs=inputs,
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model=model,
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@ -666,6 +804,7 @@ def mount_topic_cluster_routes(
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"embedded_done": embedded_done,
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"updated": updated,
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"skipped": skipped,
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"batch_n": len(inputs),
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}
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)
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# yield between batches
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@ -722,6 +861,30 @@ def mount_topic_cluster_routes(
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# Output language for titles/keywords (from frontend globalStore.lang)
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lang: Optional[str] = None
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@app.post(
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f"{db_api_base}/cluster_iib_output_job_start",
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dependencies=[Depends(verify_secret), Depends(write_permission_required)],
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)
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async def cluster_iib_output_job_start(req: ClusterIibOutputReq):
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"""
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Start a background job for embedding + clustering + LLM titling.
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Returns job_id immediately; frontend should poll job_status to show progress.
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"""
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job_id = uuid.uuid4().hex
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_job_upsert(job_id, {"status": "queued", "stage": "queued", "created_at": _job_now()})
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asyncio.create_task(_run_cluster_job(job_id, req))
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return {"job_id": job_id}
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@app.get(
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f"{db_api_base}/cluster_iib_output_job_status",
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dependencies=[Depends(verify_secret), Depends(write_permission_required)],
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)
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async def cluster_iib_output_job_status(job_id: str):
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j = _job_get(job_id)
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if not j:
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raise HTTPException(status_code=404, detail="job not found")
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return j
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def _extract_and_validate_folders(req: ClusterIibOutputReq) -> List[str]:
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folders: List[str] = []
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if req.folder_paths:
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@ -222,6 +222,45 @@ export const clusterIibOutput = async (req: ClusterIibOutputReq) => {
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return resp.data as ClusterIibOutputResp
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}
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// ===== Async clustering job (progress polling) =====
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export interface ClusterIibOutputJobStartResp {
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job_id: string
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}
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export interface ClusterIibOutputJobStatusResp {
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job_id: string
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status: 'queued' | 'running' | 'done' | 'error'
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stage?: string
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folders?: string[]
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progress?: {
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// embedding totals
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scanned?: number
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to_embed?: number
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embedded_done?: number
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updated?: number
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skipped?: number
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folder?: string
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// clustering
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items_total?: number
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items_done?: number
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// titling
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clusters_total?: number
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clusters_done?: number
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}
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error?: string
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result?: ClusterIibOutputResp
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}
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export const startClusterIibOutputJob = async (req: ClusterIibOutputReq) => {
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const resp = await axiosInst.value.post('/db/cluster_iib_output_job_start', req)
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return resp.data as ClusterIibOutputJobStartResp
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}
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export const getClusterIibOutputJobStatus = async (job_id: string) => {
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const resp = await axiosInst.value.get('/db/cluster_iib_output_job_status', { params: { job_id } })
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return resp.data as ClusterIibOutputJobStatusResp
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}
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// ===== Natural language prompt query (RAG-like retrieval) =====
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export interface PromptSearchReq {
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query: string
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@ -46,6 +46,17 @@ export const de: Partial<IIBI18nMap> = {
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topicSearchGuideStep3: 'Geben Sie einen Satz ein, um zu suchen; ähnliche Bilder werden abgerufen und die Ergebnisse geöffnet',
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topicSearchGuideEmptyReasonNoScope: 'Leer, weil: kein Bereich ausgewählt (standardmäßig deaktiviert). Klicken Sie auf „Bereich“, um Ordner zu wählen.',
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topicSearchGuideEmptyReasonNoTopics: 'Leer, weil: für diesen Bereich noch keine Themen erzeugt wurden (Aktualisieren oder Min. Cluster/Schwelle senken).',
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topicSearchJobFailed: 'Job fehlgeschlagen',
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topicSearchJobStage: 'Phase',
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topicSearchJobQueued: 'Job in Warteschlange…',
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topicSearchJobStageEmbedding: 'Vektorisierung…',
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topicSearchJobStageClustering: 'Clustering…',
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topicSearchJobStageTitling: 'Titel werden erzeugt…',
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topicSearchJobStageDone: 'Fertig',
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topicSearchJobStageError: 'Fehler',
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topicSearchJobEmbeddingDesc: '{0}/{1} vektorisiert (gescannt {2}); aktuell: {3}',
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topicSearchJobClusteringDesc: 'Clustering {0}/{1}',
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topicSearchJobTitlingDesc: 'Titel {0}/{1}',
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'auto.refreshed': 'Automatische Aktualisierung erfolgreich durchgeführt!',
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copied: 'In die Zwischenablage kopiert!',
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'index.expired': 'Index abgelaufen, automatische Aktualisierung wird durchgeführt',
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@ -46,6 +46,17 @@ export const en: IIBI18nMap = {
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topicSearchGuideStep3: 'Type a sentence to search; it will retrieve similar images and open the result page',
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topicSearchGuideEmptyReasonNoScope: 'Empty because: no scope selected (disabled by default). Click “Scope” to choose folders.',
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topicSearchGuideEmptyReasonNoTopics: 'Empty because: no topics generated yet for this scope (try Refresh or lower Min cluster/Threshold).',
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topicSearchJobFailed: 'Job failed',
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topicSearchJobStage: 'Stage',
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topicSearchJobQueued: 'Job queued…',
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topicSearchJobStageEmbedding: 'Embedding…',
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topicSearchJobStageClustering: 'Clustering…',
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topicSearchJobStageTitling: 'Generating titles…',
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topicSearchJobStageDone: 'Done',
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topicSearchJobStageError: 'Error',
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topicSearchJobEmbeddingDesc: 'Embedded {0}/{1} (scanned {2}); current: {3}',
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topicSearchJobClusteringDesc: 'Clustering {0}/{1}',
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topicSearchJobTitlingDesc: 'Titling {0}/{1}',
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success: 'Success',
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setCurrFrameAsVideoPoster: 'Set Current Frame as Video Cover',
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sync: 'Sync',
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@ -44,6 +44,17 @@ export const zhHans = {
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topicSearchGuideStep3: '输入一句话搜索，会召回相似图片并打开结果页',
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topicSearchGuideEmptyReasonNoScope: '当前为空：未选择范围（已默认关闭），请先点“范围”选择文件夹',
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topicSearchGuideEmptyReasonNoTopics: '当前为空：该范围内还未生成主题（可点刷新，或调低最小组/阈值）',
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topicSearchJobFailed: '任务失败',
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topicSearchJobStage: '阶段',
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topicSearchJobQueued: '已提交任务，准备开始…',
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topicSearchJobStageEmbedding: '向量化中（Embedding）',
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topicSearchJobStageClustering: '归类中（Clustering）',
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topicSearchJobStageTitling: '生成标题中（LLM）',
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topicSearchJobStageDone: '完成',
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topicSearchJobStageError: '失败',
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topicSearchJobEmbeddingDesc: '已向量化 {0}/{1}（扫描 {2}）；当前：{3}',
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topicSearchJobClusteringDesc: '正在归类 {0}/{1}',
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topicSearchJobTitlingDesc: '正在生成标题 {0}/{1}',
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success: '成功',
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setCurrFrameAsVideoPoster: '设置当前帧为视频封面',
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sync: '同步',
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@ -46,6 +46,17 @@ export const zhHant: Partial<IIBI18nMap> = {
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topicSearchGuideStep3: '輸入一句話搜尋，召回相似圖片並打開結果頁',
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topicSearchGuideEmptyReasonNoScope: '目前為空：尚未選擇範圍（預設關閉），請先點「範圍」選擇資料夾',
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topicSearchGuideEmptyReasonNoTopics: '目前為空：此範圍尚未生成主題（可點刷新，或調低最小組/閾值）',
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topicSearchJobFailed: '任務失敗',
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topicSearchJobStage: '階段',
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topicSearchJobQueued: '已提交任務，準備開始…',
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topicSearchJobStageEmbedding: '向量化中（Embedding）',
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topicSearchJobStageClustering: '歸類中（Clustering）',
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topicSearchJobStageTitling: '生成標題中（LLM）',
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topicSearchJobStageDone: '完成',
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topicSearchJobStageError: '失敗',
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topicSearchJobEmbeddingDesc: '已向量化 {0}/{1}（掃描 {2}）；目前：{3}',
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topicSearchJobClusteringDesc: '正在歸類 {0}/{1}',
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topicSearchJobTitlingDesc: '正在生成標題 {0}/{1}',
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clearCacheIfNotTakeEffect: '如果更改沒有生效，請嘗試清理頁面緩存',
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success: '成功',
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setCurrFrameAsVideoPoster: '設置當前幀為視頻封面',
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@ -1,9 +1,16 @@
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<script setup lang="ts">
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import { getGlobalSettingRaw, setAppFeSettingForce } from '@/api'
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import { clusterIibOutput, searchIibOutputByPrompt, type ClusterIibOutputResp, type PromptSearchResp } from '@/api/db'
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import {
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getClusterIibOutputJobStatus,
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searchIibOutputByPrompt,
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startClusterIibOutputJob,
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type ClusterIibOutputJobStatusResp,
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type ClusterIibOutputResp,
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type PromptSearchResp
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} from '@/api/db'
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import { t } from '@/i18n'
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import { useGlobalStore } from '@/store/useGlobalStore'
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import { computed, onMounted, ref, watch } from 'vue'
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import { computed, onBeforeUnmount, onMounted, ref, watch } from 'vue'
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import { uniqueId } from 'lodash-es'
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import { message } from 'ant-design-vue'
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@ -15,6 +22,56 @@ const threshold = ref(0.86)
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const minClusterSize = ref(2)
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const result = ref<ClusterIibOutputResp | null>(null)
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const job = ref<ClusterIibOutputJobStatusResp | null>(null)
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const jobId = ref<string>('')
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let _jobTimer: any = null
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const jobRunning = computed(() => {
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const st = job.value?.status
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return st === 'queued' || st === 'running'
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})
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const jobStageText = computed(() => {
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const st = String(job.value?.stage || '')
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if (!st || st === 'queued' || st === 'init') return t('topicSearchJobQueued')
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if (st === 'embedding') return t('topicSearchJobStageEmbedding')
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if (st === 'clustering') return t('topicSearchJobStageClustering')
|
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if (st === 'titling') return t('topicSearchJobStageTitling')
|
||||
if (st === 'done') return t('topicSearchJobStageDone')
|
||||
if (st === 'error') return t('topicSearchJobStageError')
|
||||
return `${t('topicSearchJobStage')}: ${st}`
|
||||
})
|
||||
const jobPercent = computed(() => {
|
||||
const p = job.value?.progress
|
||||
if (!p) return 0
|
||||
const total = Number(p.to_embed ?? 0)
|
||||
const done = Number(p.embedded_done ?? 0)
|
||||
if (total <= 0) return 0
|
||||
const v = Math.floor((done / total) * 100)
|
||||
return Math.max(0, Math.min(100, v))
|
||||
})
|
||||
const jobDesc = computed(() => {
|
||||
const p = job.value?.progress
|
||||
if (!p) return ''
|
||||
if (job.value?.stage === 'embedding') {
|
||||
const done = Number(p.embedded_done ?? 0)
|
||||
const total = Number(p.to_embed ?? 0)
|
||||
const scanned = Number(p.scanned ?? 0)
|
||||
const folder = String(p.folder ?? '')
|
||||
return t('topicSearchJobEmbeddingDesc', [done, total, scanned, folder])
|
||||
}
|
||||
if (job.value?.stage === 'clustering') {
|
||||
const done = Number(p.items_done ?? 0)
|
||||
const total = Number(p.items_total ?? 0)
|
||||
return t('topicSearchJobClusteringDesc', [done, total])
|
||||
}
|
||||
if (job.value?.stage === 'titling') {
|
||||
const done = Number(p.clusters_done ?? 0)
|
||||
const total = Number(p.clusters_total ?? 0)
|
||||
return t('topicSearchJobTitlingDesc', [done, total])
|
||||
}
|
||||
return ''
|
||||
})
|
||||
|
||||
const query = ref('')
|
||||
const qLoading = ref(false)
|
||||
const qResult = ref<PromptSearchResp | null>(null)
|
||||
|
|
@ -109,6 +166,29 @@ const scheduleSaveScope = () => {
|
|||
}, 500)
|
||||
}
|
||||
|
||||
const stopJobPoll = () => {
|
||||
if (_jobTimer) {
|
||||
clearInterval(_jobTimer)
|
||||
_jobTimer = null
|
||||
}
|
||||
}
|
||||
|
||||
const pollJob = async () => {
|
||||
const id = jobId.value
|
||||
if (!id) return
|
||||
const st = await getClusterIibOutputJobStatus(id)
|
||||
job.value = st
|
||||
if (st.status === 'done') {
|
||||
stopJobPoll()
|
||||
loading.value = false
|
||||
if (st.result) result.value = st.result
|
||||
} else if (st.status === 'error') {
|
||||
stopJobPoll()
|
||||
loading.value = false
|
||||
message.error(st.error || t('topicSearchJobFailed'))
|
||||
}
|
||||
}
|
||||
|
||||
const refresh = async () => {
|
||||
if (g.conf?.is_readonly) return
|
||||
if (!scopeCount.value) {
|
||||
|
|
@ -116,16 +196,26 @@ const refresh = async () => {
|
|||
scopeOpen.value = true
|
||||
return
|
||||
}
|
||||
stopJobPoll()
|
||||
loading.value = true
|
||||
job.value = null
|
||||
jobId.value = ''
|
||||
try {
|
||||
result.value = await clusterIibOutput({
|
||||
const started = await startClusterIibOutputJob({
|
||||
threshold: threshold.value,
|
||||
min_cluster_size: minClusterSize.value,
|
||||
lang: g.lang,
|
||||
folder_paths: scopeFolders.value
|
||||
})
|
||||
} finally {
|
||||
jobId.value = started.job_id
|
||||
// poll immediately + interval
|
||||
await pollJob()
|
||||
_jobTimer = setInterval(() => {
|
||||
void pollJob()
|
||||
}, 800)
|
||||
} catch (e) {
|
||||
loading.value = false
|
||||
throw e
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -193,6 +283,10 @@ onMounted(() => {
|
|||
})()
|
||||
})
|
||||
|
||||
onBeforeUnmount(() => {
|
||||
stopJobPoll()
|
||||
})
|
||||
|
||||
watch(
|
||||
() => scopeFolders.value,
|
||||
() => {
|
||||
|
|
@ -246,6 +340,11 @@ watch(
|
|||
show-icon
|
||||
/>
|
||||
|
||||
<div v-if="jobRunning" style="margin: 10px 0 0 0;">
|
||||
<a-alert type="info" show-icon :message="jobStageText" :description="jobDesc" />
|
||||
<a-progress :percent="jobPercent" size="small" style="margin-top: 8px;" />
|
||||
</div>
|
||||
|
||||
<a-spin :spinning="loading">
|
||||
<div v-if="qResult" style="margin-top: 10px;">
|
||||
<a-alert
|
||||
|
|
|
|||
Loading…
Reference in New Issue