feat: 更新后端平台模块、Compute引擎、前端组件及构建产物

- 更新 backend 平台 API、platform_store、compute_gateway sync
- 更新 compute agent/engine/adapter 及 API
- 更新 Docker 部署配置（app/compute）
- 新增 frontend/src/utils/ 工具模块
- 新增 scripts/ops_diagnostics.py 运维诊断脚本
- 新增 docs/2026-07-23-development-summary.md 开发总结
- 重构 frontend/dist 构建产物（新 hash）
- 更新前端多个视图组件及 API 模块

Co-Authored-By: Claude <noreply@anthropic.com>

backend/app/api/v1/endpoints/platform.py

@@ -32,16 +32,26 @@ def _task_for_compute_job(job_id: str) -> dict[str, Any] | None:
     return next((task for task in get_platform_store().tasks() if task.get("compute_job_id") == job_id), None)
 
 
+def _node_for_compute_job_record(job_id: str) -> dict[str, Any] | None:
+    store = get_platform_store()
+    try:
+        record = store.compute_job(job_id)
+    except KeyError:
+        return None
+    return next((node for node in store.compute_nodes() if node["id"] == record.get("node_id")), None)
+
+
 async def _submit_fine_tune_task(store: Any, payload: dict[str, Any]) -> dict[str, Any]:
     task_id = str(payload.get("task_id") or payload.get("id") or "")
     if task_id and get_settings().compute_mode != "simulator":
         try:
-            preflight = await _fine_tune_preflight(store, task_id, payload, validate=True)
+            preflight = await _fine_tune_preflight(store, task_id, payload, validate=True, sync_resources=True)
         except Exception as exc:  # noqa: BLE001 - task has not entered running state yet
             raise RuntimeError(f"preflight failed: {exc}") from exc
         if not preflight["valid"]:
             errors = "; ".join(preflight.get("errors") or ["preflight failed"])
             raise RuntimeError(f"preflight failed: {errors}")
+        payload = {**payload, "compute_node_id": preflight["node"]["id"]}
     task = store.start_task(payload)
     if get_settings().compute_mode == "simulator":
         return task
@@ -55,8 +65,20 @@ async def _fine_tune_preflight(
     task_id: str,
     payload: dict[str, Any] | None = None,
     validate: bool = True,
+    sync_resources: bool = False,
 ) -> dict[str, Any]:
     node, job_payload = store.prepare_compute_job_payload(task_id, payload or {})
+    sync_results: list[dict[str, Any]] = []
+    sync_errors: list[str] = []
+    if sync_resources and get_settings().compute_mode != "simulator":
+        try:
+            sync_results = await _sync_training_dataset_to_compute_node(
+                store,
+                node,
+                str(job_payload.get("train_dataset_id") or ""),
+            )
+        except Exception as exc:  # noqa: BLE001 - return as preflight error for page visibility
+            sync_errors.append(str(exc))
     if get_settings().compute_mode == "simulator":
         preview = {
             "valid": True,
@@ -73,6 +95,7 @@ async def _fine_tune_preflight(
         client = ComputeNodeClient(node["api_base_url"])
         preview = await (client.validate_job(job_payload) if validate else client.preview_job(job_payload))
     errors = list(preview.get("errors") or [])
+    errors.extend(sync_errors)
     warnings = list(preview.get("warnings") or [])
     if not node.get("enabled"):
         errors.append(f"compute node disabled: {node.get('code')}")
@@ -92,6 +115,7 @@ async def _fine_tune_preflight(
         },
         "job_payload": job_payload,
         "preview": preview,
+        "sync_results": sync_results,
     }
 
 
@@ -197,6 +221,21 @@ async def delete_trained_model(model_id: str, type: str = Query(default="merged"
     return ok({"deleted": model_id, "type": type})
 
 
+@router.get("/model-manage/trained-models/{model_id}/artifacts")
+async def trained_model_artifacts(model_id: str) -> dict[str, Any]:
+    return ok(get_platform_store().model_artifacts(model_id))
+
+
+@router.get("/model-manage/trained-models/{model_id}/lineage")
+async def trained_model_lineage(model_id: str) -> dict[str, Any]:
+    return ok(get_platform_store().model_lineage(model_id))
+
+
+@router.get("/model-manage/export-jobs")
+async def model_export_jobs(trained_model_id: str | None = Query(default=None)) -> dict[str, Any]:
+    return ok(get_platform_store().model_export_jobs(trained_model_id))
+
+
 @router.get("/model-manage/name/{name}")
 async def model_by_name(name: str) -> dict[str, Any]:
     try:
@@ -254,7 +293,51 @@ async def delete_model(model_id: str) -> dict[str, Any]:
 
 @router.post("/model-manage/merge")
 async def merge_model(payload: dict[str, Any] = Body(...)) -> dict[str, Any]:
-    return ok({"job_id": f"merge_{uuid.uuid4().hex[:12]}", "status": "queued", **payload})
+    store = get_platform_store()
+    trained_model_id = str(payload.get("trained_model_id") or payload.get("model_id") or payload.get("model_name") or "")
+    trained_model = next(
+        (
+            item
+            for item in store.trained_models()
+            if trained_model_id and (item["id"] == trained_model_id or item["name"] == trained_model_id)
+        ),
+        None,
+    )
+    base_model_path = payload.get("base_model_path") or (trained_model and trained_model.get("base_model_path"))
+    adapter_path = payload.get("adapter_path") or payload.get("adapter_name_or_path") or (trained_model and trained_model.get("merged_path"))
+    if not base_model_path:
+        raise fail(400, "base_model_path is required")
+    if not adapter_path:
+        raise fail(400, "adapter_path is required")
+    node = store.schedule_node({**payload, "gpus": payload.get("gpus") or []})
+    health = node.get("health_detail") or {}
+    output_root = str(health.get("output_root") or f"{node['data_root'].rstrip('/')}/outputs")
+    output_name = str(payload.get("output_model_name") or payload.get("merged_model_name") or f"{trained_model_id or 'model'}-merged")
+    output_dir = str(payload.get("output_dir") or f"{output_root.rstrip('/')}/{output_name}")
+    job_payload = {
+        **payload,
+        "id": str(payload.get("job_id") or f"merge_{uuid.uuid4().hex[:12]}"),
+        "name": output_name,
+        "engine": "merge",
+        "base_model": base_model_path,
+        "model_name_or_path": base_model_path,
+        "adapter_name_or_path": adapter_path,
+        "output_dir": output_dir,
+        "template": payload.get("template", "qwen"),
+        "train_method": payload.get("train_method", "lora"),
+        "gpus": payload.get("gpus") or [],
+        "trained_model_id": trained_model["id"] if trained_model else trained_model_id,
+        "model_name": trained_model["name"] if trained_model else payload.get("model_name"),
+    }
+    if get_settings().compute_mode == "simulator":
+        job = {"id": job_payload["id"], "status": "queued", "progress": 10, "command": [], "output_dir": output_dir}
+    else:
+        client = ComputeNodeClient(node["api_base_url"])
+        preview = await client.validate_job(job_payload)
+        if not preview.get("valid", False):
+            raise fail(409, "; ".join(preview.get("errors") or ["merge preflight failed"]))
+        job = await client.create_job(job_payload)
+    return ok(store.record_model_merge_job(node, job_payload, job, trained_model["id"] if trained_model else trained_model_id))
 
 
 @router.get("/dataset-manage/preview/{file_id}")
@@ -364,6 +447,47 @@ async def _sync_dataset_file_to_compute_nodes(
     return results
 
 
+async def _sync_training_dataset_to_compute_node(
+    store: Any,
+    node: dict[str, Any],
+    dataset_id: str,
+) -> list[dict[str, Any]]:
+    if not dataset_id:
+        raise RuntimeError("train_dataset_id is required")
+    files = store.training_dataset_files(dataset_id)
+    if not files:
+        raise RuntimeError(f"dataset has no uploaded file: {dataset_id}")
+    client = ComputeNodeClient(node["api_base_url"])
+    results: list[dict[str, Any]] = []
+    for item in files:
+        target_name = Path(str(item.get("name") or f"{item['id']}.jsonl")).name
+        result = await client.upload_file(
+            target_name,
+            str(item.get("content") or "").encode("utf-8"),
+            f"datasets/{dataset_id}/{target_name}",
+            resource_type="dataset",
+            resource_id=dataset_id,
+        )
+        store.upsert_resource_replica(
+            node["id"],
+            "dataset",
+            dataset_id,
+            str(result.get("local_path") or ""),
+        )
+        results.append(
+            {
+                "node_id": node["id"],
+                "node_code": node.get("code"),
+                "file_id": item.get("id"),
+                "name": target_name,
+                "local_path": result.get("local_path"),
+                "byte_size": result.get("byte_size"),
+                "checksum_sha256": result.get("checksum_sha256"),
+            }
+        )
+    return results
+
+
 @router.post("/dataset-manage/upload/{dataset_id}")
 async def upload_dataset_files(
     dataset_id: str,
@@ -543,6 +667,10 @@ async def fine_tune_logs(
         if node:
             try:
                 logs = await ComputeNodeClient(node["api_base_url"]).job_logs(task["compute_job_id"], tail_lines, offset, limit)
+                try:
+                    store.record_training_log_metrics(task_id, str(logs.get("content") or ""))
+                except Exception:
+                    pass
                 if task.get("status") in {"queued", "running", "failed", "stopped", "completed"}:
                     try:
                         job = await ComputeNodeClient(node["api_base_url"]).get_job(task["compute_job_id"])
@@ -613,18 +741,36 @@ async def delete_fine_tune(task_id: str) -> dict[str, Any]:
 
 @router.get("/fine-tune/{task_id}/overview")
 async def fine_tune_overview(task_id: str) -> dict[str, Any]:
-    task = get_platform_store().task(task_id)
+    store = get_platform_store()
-    return ok({"task": task, "progress": get_platform_store().progress(task_id)})
+    task = store.task(task_id)
+    return ok(
+        {
+            "task": task,
+            "progress": store.progress(task_id),
+            "metrics": store.task_metrics(task_id),
+            "checkpoints": store.task_checkpoints(task_id),
+        }
+    )
 
 
 @router.get("/fine-tune/{task_id}/checkpoints")
 async def fine_tune_checkpoints(task_id: str) -> dict[str, Any]:
-    task = get_platform_store().task(task_id)
+    store = get_platform_store()
-    checkpoints = []
+    try:
-    for step in [50, 100, 150]:
+        store.task(task_id)
-        if task.get("progress", 0) >= min(100, step // 2):
+    except KeyError:
-            checkpoints.append({"step": step, "path": f"/data/yg-ft/outputs/{task['name']}/checkpoint-{step}"})
+        raise fail(404, "fine tune task not found")
-    return ok(checkpoints)
+    return ok(store.task_checkpoints(task_id))
+
+
+@router.get("/fine-tune/{task_id}/metrics")
+async def fine_tune_metrics(task_id: str) -> dict[str, Any]:
+    store = get_platform_store()
+    try:
+        store.task(task_id)
+    except KeyError:
+        raise fail(404, "fine tune task not found")
+    return ok(store.task_metrics(task_id))
 
 
 @router.get("/model-eval")
@@ -909,6 +1055,142 @@ async def compute_node_replicas(node_id: str) -> dict[str, Any]:
     return ok(get_platform_store().replicas(node_id))
 
 
+@router.get("/compute/nodes/{node_id}/replicas/drift")
+async def compute_node_replica_drift(node_id: str) -> dict[str, Any]:
+    store = get_platform_store()
+    node = next((item for item in store.compute_nodes() if item["id"] == node_id), None)
+    if not node:
+        raise fail(404, "compute node not found")
+    replicas = store.replicas(node_id)
+    if not replicas:
+        return ok({"node_id": node_id, "items": [], "drifted": 0})
+    paths = [
+        {
+            "name": replica["id"],
+            "path": replica["local_path"],
+            "type": "any",
+            "required": True,
+        }
+        for replica in replicas
+    ]
+    try:
+        result = await ComputeNodeClient(node["api_base_url"]).check_paths(paths)
+    except Exception as exc:  # noqa: BLE001
+        raise fail(502, f"replica drift check failed: {exc}")
+    check_map = {str(item.get("name")): item for item in result.get("items") or []}
+    items = []
+    for replica in replicas:
+        check = check_map.get(replica["id"], {})
+        updated = store.update_resource_replica_check(
+            replica["id"],
+            bool(check.get("ok")),
+            int(check.get("byte_size") or replica.get("byte_size") or 0),
+            "" if check.get("ok") else f"path not available: {replica['local_path']}",
+        )
+        items.append({**updated, "check": check})
+    return ok({"node_id": node_id, "items": items, "drifted": len([item for item in items if item.get("sync_status") == "drifted"])})
+
+
+@router.post("/compute/nodes/{node_id}/replicas/repair")
+async def compute_node_replica_repair(node_id: str, payload: dict[str, Any] | None = Body(default=None)) -> dict[str, Any]:
+    store = get_platform_store()
+    node = next((item for item in store.compute_nodes() if item["id"] == node_id), None)
+    if not node:
+        raise fail(404, "compute node not found")
+    payload = payload or {}
+    replica_ids = payload.get("replica_ids") or [
+        item["id"] for item in store.replicas(node_id) if item.get("sync_status") in {"drifted", "failed", "repair_pending"}
+    ]
+    updated = store.mark_resource_replica_repair_pending([str(item) for item in replica_ids])
+    sync_id = store.create_sync_job(
+        node_id,
+        {
+            "resources": [
+                {
+                    "resource_type": item.get("resource_type"),
+                    "resource_id": item.get("resource_id"),
+                    "replica_id": item.get("id"),
+                    "target_path": item.get("local_path"),
+                }
+                for item in updated
+            ]
+        },
+    )
+    client = ComputeNodeClient(node["api_base_url"])
+    repaired = []
+    failures = []
+    for replica in updated:
+        replica_id = str(replica["id"])
+        resource_type = str(replica.get("resource_type") or "")
+        resource_id = str(replica.get("resource_id") or "")
+        try:
+            if resource_type == "dataset":
+                files = store.training_dataset_files(resource_id)
+                if not files:
+                    raise RuntimeError(f"dataset has no uploaded file: {resource_id}")
+                total_size = 0
+                checksum = ""
+                local_path = str(replica.get("local_path") or "")
+                for item in files:
+                    filename = Path(str(item.get("name") or f"{item['id']}.jsonl")).name
+                    result = await client.upload_file(
+                        filename,
+                        str(item.get("content") or "").encode("utf-8"),
+                        f"datasets/{resource_id}/{filename}",
+                        resource_type="dataset",
+                        resource_id=resource_id,
+                    )
+                    total_size += int(result.get("byte_size") or 0)
+                    checksum = str(result.get("checksum_sha256") or checksum)
+                    local_path = str(result.get("local_path") or local_path)
+                repaired.append(store.update_resource_replica_sync_result(replica_id, True, local_path, total_size, checksum))
+                continue
+
+            source_path = ""
+            target_relative_path = ""
+            if resource_type == "model":
+                model = store.model(resource_id)
+                source_path = str(model.get("path") or "")
+                target_relative_path = f"models/{Path(source_path).name}" if source_path else ""
+            elif resource_type in {"trained_model", "model_artifact"}:
+                if resource_type == "trained_model":
+                    artifacts = store.model_artifacts(resource_id)
+                    artifact = next((item for item in artifacts if item.get("path")), None)
+                else:
+                    artifact = store.model_artifact(resource_id)
+                source_path = str((artifact or {}).get("path") or replica.get("local_path") or "")
+                target_relative_path = f"outputs/{Path(source_path).name}" if source_path else ""
+            else:
+                source_path = str(payload.get("source_path") or replica.get("source_path") or replica.get("local_path") or "")
+                target_relative_path = str(payload.get("target_relative_path") or "")
+
+            if not source_path:
+                raise RuntimeError(f"authoritative source path not found for {resource_type}:{resource_id}")
+            result = await client.import_local_file(
+                {
+                    "source_path": source_path,
+                    "target_relative_path": target_relative_path,
+                    "resource_type": resource_type,
+                    "resource_id": resource_id,
+                }
+            )
+            repaired.append(
+                store.update_resource_replica_sync_result(
+                    replica_id,
+                    True,
+                    str(result.get("local_path") or replica.get("local_path") or ""),
+                    int(result.get("byte_size") or 0),
+                    str(result.get("checksum_sha256") or ""),
+                )
+            )
+        except Exception as exc:  # noqa: BLE001 - collect all replica repair failures
+            error = str(exc)
+            failures.append({"replica_id": replica_id, "resource_type": resource_type, "resource_id": resource_id, "error": error})
+            repaired.append(store.update_resource_replica_sync_result(replica_id, False, None, int(replica.get("byte_size") or 0), "", error))
+    store.update_sync_job(sync_id, "failed" if failures else "completed", 100 if not failures else 99, completed=True)
+    return ok({"sync": store.sync_job(sync_id), "replicas": repaired, "failed": failures})
+
+
 @router.get("/compute/nodes/{node_id}/engines")
 async def compute_node_engines(node_id: str) -> dict[str, Any]:
     node = next((item for item in get_platform_store().compute_nodes() if item["id"] == node_id), None)
@@ -955,7 +1237,11 @@ async def compute_queue() -> dict[str, Any]:
 async def compute_job_detail(job_id: str) -> dict[str, Any]:
     task = _task_for_compute_job(job_id)
     if not task:
-        raise fail(404, "compute job not found")
+        node = _node_for_compute_job_record(job_id)
+        if not node:
+            raise fail(404, "compute job not found")
+        job = await ComputeNodeClient(node["api_base_url"]).get_job(job_id)
+        return ok(get_platform_store().sync_model_merge_job(job_id, job))
     node = _node_for_task(task)
     if not node:
         raise fail(404, "compute node not found")
@@ -966,7 +1252,11 @@ async def compute_job_detail(job_id: str) -> dict[str, Any]:
 async def compute_job_stop(job_id: str) -> dict[str, Any]:
     task = _task_for_compute_job(job_id)
     if not task:
-        raise fail(404, "compute job not found")
+        node = _node_for_compute_job_record(job_id)
+        if not node:
+            raise fail(404, "compute job not found")
+        job = await ComputeNodeClient(node["api_base_url"]).stop_job(job_id)
+        return ok(get_platform_store().sync_model_merge_job(job_id, job))
     node = _node_for_task(task)
     if not node:
         raise fail(404, "compute node not found")
@@ -984,7 +1274,10 @@ async def compute_job_logs(
 ) -> dict[str, Any]:
     task = _task_for_compute_job(job_id)
     if not task:
-        raise fail(404, "compute job not found")
+        node = _node_for_compute_job_record(job_id)
+        if not node:
+            raise fail(404, "compute job not found")
+        return ok(await ComputeNodeClient(node["api_base_url"]).job_logs(job_id, tail_lines, offset, limit))
     node = _node_for_task(task)
     if not node:
         raise fail(404, "compute node not found")

backend/app/db/sql/001_platform_runtime.sql

@@ -36,6 +36,45 @@ CREATE TABLE IF NOT EXISTS trained_models (
     merged_path TEXT
 );
 
+CREATE TABLE IF NOT EXISTS model_lineage (
+    id TEXT PRIMARY KEY,
+    child_resource_type TEXT NOT NULL,
+    child_resource_id TEXT NOT NULL,
+    parent_resource_type TEXT NOT NULL,
+    parent_resource_id TEXT NOT NULL,
+    relation_type TEXT NOT NULL,
+    compute_job_id TEXT,
+    payload TEXT NOT NULL,
+    create_time TEXT NOT NULL
+);
+
+CREATE TABLE IF NOT EXISTS model_artifacts (
+    id TEXT PRIMARY KEY,
+    model_id TEXT NOT NULL,
+    model_kind TEXT NOT NULL,
+    artifact_type TEXT NOT NULL,
+    path TEXT NOT NULL,
+    size_bytes BIGINT NOT NULL DEFAULT 0,
+    checksum_sha256 TEXT,
+    metadata TEXT NOT NULL,
+    compute_job_id TEXT,
+    create_time TEXT NOT NULL
+);
+
+CREATE TABLE IF NOT EXISTS model_export_jobs (
+    id TEXT PRIMARY KEY,
+    trained_model_id TEXT,
+    compute_job_id TEXT NOT NULL,
+    node_id TEXT,
+    export_type TEXT NOT NULL,
+    quantization_bit INTEGER NOT NULL DEFAULT 0,
+    status TEXT NOT NULL,
+    output_dir TEXT,
+    payload TEXT NOT NULL,
+    create_time TEXT NOT NULL,
+    completed_at TEXT
+);
+
 CREATE TABLE IF NOT EXISTS datasets (
     id TEXT PRIMARY KEY,
     name TEXT NOT NULL UNIQUE,
@@ -111,6 +150,62 @@ CREATE TABLE IF NOT EXISTS fine_tune_tasks (
     compute_job_id TEXT
 );
 
+CREATE TABLE IF NOT EXISTS fine_tune_metrics (
+    id TEXT PRIMARY KEY,
+    task_id TEXT NOT NULL REFERENCES fine_tune_tasks(id) ON DELETE CASCADE,
+    step INTEGER NOT NULL,
+    epoch DOUBLE PRECISION,
+    loss DOUBLE PRECISION,
+    grad_norm DOUBLE PRECISION,
+    learning_rate DOUBLE PRECISION,
+    raw TEXT NOT NULL,
+    create_time TEXT NOT NULL
+);
+
+CREATE TABLE IF NOT EXISTS fine_tune_checkpoints (
+    id TEXT PRIMARY KEY,
+    task_id TEXT NOT NULL REFERENCES fine_tune_tasks(id) ON DELETE CASCADE,
+    step INTEGER NOT NULL,
+    name TEXT NOT NULL,
+    path TEXT NOT NULL,
+    size_bytes BIGINT NOT NULL DEFAULT 0,
+    create_time TEXT NOT NULL
+);
+
+CREATE TABLE IF NOT EXISTS compute_jobs (
+    id TEXT PRIMARY KEY,
+    task_id TEXT REFERENCES fine_tune_tasks(id) ON DELETE SET NULL,
+    node_id TEXT REFERENCES compute_nodes(id) ON DELETE SET NULL,
+    engine TEXT NOT NULL,
+    status TEXT NOT NULL,
+    command TEXT NOT NULL,
+    output_dir TEXT,
+    log_file TEXT,
+    payload TEXT NOT NULL,
+    create_time TEXT NOT NULL,
+    update_time TEXT NOT NULL,
+    completed_at TEXT
+);
+
+CREATE TABLE IF NOT EXISTS gpu_allocations (
+    id TEXT PRIMARY KEY,
+    task_id TEXT REFERENCES fine_tune_tasks(id) ON DELETE CASCADE,
+    compute_job_id TEXT,
+    node_id TEXT REFERENCES compute_nodes(id) ON DELETE CASCADE,
+    gpu_index INTEGER NOT NULL,
+    status TEXT NOT NULL,
+    create_time TEXT NOT NULL,
+    released_at TEXT
+);
+
+CREATE TABLE IF NOT EXISTS scheduler_locks (
+    lock_key TEXT PRIMARY KEY,
+    owner TEXT NOT NULL,
+    expires_at TEXT NOT NULL,
+    create_time TEXT NOT NULL,
+    update_time TEXT NOT NULL
+);
+
 CREATE TABLE IF NOT EXISTS resource_replicas (
     id TEXT PRIMARY KEY,
     node_id TEXT NOT NULL REFERENCES compute_nodes(id) ON DELETE CASCADE,
@@ -119,6 +214,10 @@ CREATE TABLE IF NOT EXISTS resource_replicas (
     local_path TEXT NOT NULL,
     status TEXT NOT NULL,
     sync_status TEXT NOT NULL,
+    checksum_sha256 TEXT,
+    byte_size BIGINT NOT NULL DEFAULT 0,
+    last_checked_at TEXT,
+    last_error TEXT,
     create_time TEXT NOT NULL
 );
 
@@ -160,6 +259,20 @@ CREATE TABLE IF NOT EXISTS compare_tasks (
 CREATE INDEX IF NOT EXISTS idx_fine_tune_status ON fine_tune_tasks(status);
 CREATE INDEX IF NOT EXISTS idx_fine_tune_compute_job ON fine_tune_tasks(compute_job_id);
 CREATE INDEX IF NOT EXISTS idx_fine_tune_compute_node_status ON fine_tune_tasks(compute_node_id, status);
+CREATE INDEX IF NOT EXISTS idx_model_lineage_child ON model_lineage(child_resource_type, child_resource_id);
+CREATE INDEX IF NOT EXISTS idx_model_lineage_parent ON model_lineage(parent_resource_type, parent_resource_id);
+CREATE INDEX IF NOT EXISTS idx_model_artifacts_model ON model_artifacts(model_kind, model_id, artifact_type);
+CREATE INDEX IF NOT EXISTS idx_model_export_jobs_model ON model_export_jobs(trained_model_id, create_time DESC);
+CREATE INDEX IF NOT EXISTS idx_model_export_jobs_compute ON model_export_jobs(compute_job_id);
+CREATE INDEX IF NOT EXISTS idx_fine_tune_metrics_task_step ON fine_tune_metrics(task_id, step);
+CREATE UNIQUE INDEX IF NOT EXISTS uq_fine_tune_metrics_task_step_epoch ON fine_tune_metrics(task_id, step, epoch);
+CREATE INDEX IF NOT EXISTS idx_fine_tune_checkpoints_task_step ON fine_tune_checkpoints(task_id, step);
+CREATE UNIQUE INDEX IF NOT EXISTS uq_fine_tune_checkpoints_task_path ON fine_tune_checkpoints(task_id, path);
+CREATE INDEX IF NOT EXISTS idx_compute_jobs_task ON compute_jobs(task_id);
+CREATE INDEX IF NOT EXISTS idx_compute_jobs_node_status ON compute_jobs(node_id, status);
+CREATE INDEX IF NOT EXISTS idx_gpu_allocations_node_status ON gpu_allocations(node_id, status);
+CREATE UNIQUE INDEX IF NOT EXISTS uq_gpu_allocations_active ON gpu_allocations(node_id, gpu_index) WHERE status IN ('allocated','running');
+CREATE INDEX IF NOT EXISTS idx_scheduler_locks_expires ON scheduler_locks(expires_at);
 CREATE INDEX IF NOT EXISTS idx_dataset_files_dataset ON dataset_files(dataset_id);
 CREATE INDEX IF NOT EXISTS idx_gpus_node ON gpus(node_id);
 CREATE UNIQUE INDEX IF NOT EXISTS uq_gpus_node_index ON gpus(node_id, gpu_index);

backend/app/modules/compute_gateway/sync.py

@@ -20,8 +20,25 @@ async def poll_compute_jobs_once() -> dict[str, Any]:
             failed.append({"task_id": task["id"], "error": "compute node not found"})
             continue
         try:
-            job = await ComputeNodeClient(node["api_base_url"]).get_job(task["compute_job_id"])
+            client = ComputeNodeClient(node["api_base_url"])
+            job = await client.get_job(task["compute_job_id"])
+            try:
+                logs = await client.job_logs(task["compute_job_id"], tail_lines=5000)
+                store.record_training_log_metrics(task["id"], str(logs.get("content") or ""))
+            except Exception:
+                pass
             synced.append(store.apply_compute_job(task["id"], job))
         except Exception as exc:  # noqa: BLE001 - keep polling other jobs
             failed.append({"task_id": task["id"], "error": str(exc)})
-    return {"synced": len(synced), "failed": failed, "items": synced}
+    standalone_synced: list[dict[str, Any]] = []
+    for record in store.active_standalone_compute_jobs():
+        node = next((item for item in store.compute_nodes() if item["id"] == record.get("node_id")), None)
+        if not node:
+            failed.append({"job_id": record["id"], "error": "compute node not found"})
+            continue
+        try:
+            job = await ComputeNodeClient(node["api_base_url"]).get_job(record["id"])
+            standalone_synced.append(store.sync_model_merge_job(record["id"], job))
+        except Exception as exc:  # noqa: BLE001 - keep polling other jobs
+            failed.append({"job_id": record["id"], "error": str(exc)})
+    return {"synced": len(synced) + len(standalone_synced), "failed": failed, "items": synced, "standalone": standalone_synced}

compute/agent/process_manager.py

@@ -3,6 +3,7 @@ from __future__ import annotations
 import os
 import json
 import contextlib
+import hashlib
 import signal
 import subprocess
 import time
@@ -127,6 +128,7 @@ class ProcessManager:
 
     def serialize(self, job: ManagedProcess) -> dict[str, Any]:
         code = job.process.poll() if job.process is not None else None
+        checkpoints = self._collect_checkpoints(job.output_dir)
         if job.status not in TERMINAL_STATUSES:
             if job.process is None and job.pid is not None and not self._pid_alive(job.pid):
                 job.status = "failed"
@@ -157,6 +159,7 @@ class ProcessManager:
             "output_dir": job.output_dir,
             "log_file": str(job.log_path),
             "artifacts": job.artifacts,
+            "checkpoints": checkpoints,
             "return_code": code,
         }
 
@@ -175,15 +178,47 @@ class ProcessManager:
         artifacts: list[dict[str, Any]] = []
         for path in root.rglob("*"):
             if path.is_file():
+                digest = hashlib.sha256()
+                with path.open("rb") as handle:
+                    for chunk in iter(lambda: handle.read(1024 * 1024), b""):
+                        digest.update(chunk)
+                size = path.stat().st_size
                 artifacts.append(
                     {
                         "path": str(path),
                         "name": path.name,
-                        "size": path.stat().st_size,
+                        "size": size,
+                        "size_bytes": size,
+                        "checksum_sha256": digest.hexdigest(),
                     }
                 )
         return artifacts[:200]
 
+    def _collect_checkpoints(self, output_dir: str) -> list[dict[str, Any]]:
+        root = Path(output_dir)
+        if not root.exists():
+            return []
+        checkpoints: list[dict[str, Any]] = []
+        for path in root.glob("checkpoint-*"):
+            if not path.is_dir():
+                continue
+            step = 0
+            try:
+                step = int(path.name.rsplit("-", 1)[-1])
+            except ValueError:
+                step = 0
+            size_bytes = sum(item.stat().st_size for item in path.rglob("*") if item.is_file())
+            checkpoints.append(
+                {
+                    "step": step,
+                    "name": path.name,
+                    "path": str(path),
+                    "size_bytes": size_bytes,
+                    "create_time": path.stat().st_mtime,
+                }
+            )
+        return sorted(checkpoints, key=lambda item: (int(item.get("step") or 0), str(item.get("name") or "")))
+
     def _save_registry(self) -> None:
         items = []
         for job in self.jobs.values():

compute/api/main.py

@@ -13,7 +13,7 @@ from fastapi import FastAPI, File, Form, HTTPException, Query, Request, UploadFi
 from fastapi.responses import FileResponse, JSONResponse
 
 from compute.agent.process_manager import ProcessManager
-from compute.engines.llama_factory.adapter import build_command, parse_log_line
+from compute.engines.llama_factory.adapter import build_command, parse_log_line, prepare_runtime_files
 
 
 def create_app() -> FastAPI:
@@ -74,6 +74,48 @@ def create_app() -> FastAPI:
                 return output.splitlines()[0][:120]
         return ""
 
+    def torch_cuda_status() -> dict[str, Any]:
+        try:
+            import torch  # type: ignore[import-not-found]
+        except Exception as exc:  # noqa: BLE001 - keep health endpoint resilient
+            return {
+                "available": False,
+                "device_count": 0,
+                "torch_version": "",
+                "torch_cuda_version": "",
+                "error": f"torch import failed: {exc}",
+            }
+        try:
+            available = bool(torch.cuda.is_available())
+            device_count = int(torch.cuda.device_count())
+            devices = []
+            for index in range(device_count):
+                props = torch.cuda.get_device_properties(index)
+                devices.append(
+                    {
+                        "index": index,
+                        "name": props.name,
+                        "memory_total_gb": round(props.total_memory / 1024 / 1024 / 1024, 2),
+                    }
+                )
+            return {
+                "available": available,
+                "device_count": device_count,
+                "torch_version": str(torch.__version__),
+                "torch_cuda_version": str(torch.version.cuda or ""),
+                "devices": devices,
+                "error": "" if available else "torch cuda is not available",
+            }
+        except Exception as exc:  # noqa: BLE001 - expose CUDA initialization failures
+            return {
+                "available": False,
+                "device_count": 0,
+                "torch_version": str(getattr(torch, "__version__", "")),
+                "torch_cuda_version": str(getattr(torch.version, "cuda", "") or ""),
+                "devices": [],
+                "error": str(exc),
+            }
+
     def _slice_log_content(
         content: str,
         tail_lines: int | None = None,
@@ -261,6 +303,35 @@ def create_app() -> FastAPI:
             )
         return gpus
 
+    def _validate_training_accelerator(payload: dict[str, Any]) -> tuple[list[str], list[str], dict[str, Any]]:
+        errors: list[str] = []
+        warnings: list[str] = []
+        if str(payload.get("engine") or payload.get("training_engine") or "llama_factory") == "smoke":
+            return errors, warnings, {}
+        requested_gpus = [int(item) for item in payload.get("gpus") or []]
+        if not requested_gpus:
+            warnings.append("no gpu selected; training will run on CPU")
+            return errors, warnings, {}
+        cuda = torch_cuda_status()
+        if not cuda.get("available"):
+            errors.append(f"torch cuda unavailable on compute node: {cuda.get('error') or 'unknown error'}")
+        device_count = int(cuda.get("device_count") or 0)
+        if device_count and max(requested_gpus) >= device_count:
+            errors.append(f"requested gpu index out of torch device range: requested={requested_gpus}, device_count={device_count}")
+        min_memory_gb = _float_env("MIN_TRAINING_GPU_MEMORY_GB", 4.0)
+        gpus = {int(item["gpu_index"]): item for item in gpu_resources() if "gpu_index" in item}
+        for gpu_index in requested_gpus:
+            gpu = gpus.get(gpu_index)
+            if not gpu:
+                errors.append(f"requested gpu not found by nvidia-smi: {gpu_index}")
+                continue
+            memory_total = float(gpu.get("memory_total_gb") or 0)
+            if memory_total and memory_total < min_memory_gb:
+                errors.append(
+                    f"gpu {gpu_index} memory too small: {memory_total}GB < required {min_memory_gb}GB"
+                )
+        return errors, warnings, cuda
+
     def _check_path_item(item: dict[str, Any]) -> dict[str, Any]:
         path = Path(str(item.get("path") or ""))
         exists = path.exists()
@@ -278,13 +349,32 @@ def create_app() -> FastAPI:
             "exists": exists,
             "is_dir": path.is_dir() if exists else False,
             "is_file": path.is_file() if exists else False,
+            "byte_size": sum(child.stat().st_size for child in path.rglob("*") if child.is_file()) if exists and path.is_dir() else path.stat().st_size if exists and path.is_file() else 0,
             "ok": ok or not item.get("required", True),
         }
 
     def _job_preview(payload: dict[str, Any], check_paths: bool) -> dict[str, Any]:
         warnings: list[str] = []
+        runtime_files: list[dict[str, str]] = []
+        command_payload = {**payload, "require_dataset_files": check_paths}
+        if check_paths:
+            try:
+                runtime_files = prepare_runtime_files(command_payload)
+            except OSError as exc:
+                return {
+                    "valid": False,
+                    "errors": [f"prepare runtime files failed: {exc}"],
+                    "warnings": warnings,
+                    "engine": str(payload.get("engine") or payload.get("training_engine") or "llama_factory"),
+                    "command": [],
+                    "command_text": "",
+                    "work_dir": os.getenv("LLAMA_FACTORY_HOME", "/app/LLaMA-Factory"),
+                    "env": {},
+                    "runtime_files": [],
+                    "path_checks": [],
+                }
         try:
-            command = build_command(payload, os.getenv("LLAMA_FACTORY_HOME", "/app/LLaMA-Factory"))
+            command = build_command(command_payload, os.getenv("LLAMA_FACTORY_HOME", "/app/LLaMA-Factory"))
         except ValueError as exc:
             return {
                 "valid": False,
@@ -295,23 +385,36 @@ def create_app() -> FastAPI:
                 "command_text": "",
                 "work_dir": os.getenv("LLAMA_FACTORY_HOME", "/app/LLaMA-Factory"),
                 "env": {},
+                "runtime_files": runtime_files,
                 "path_checks": [],
             }
 
         errors: list[str] = []
         engine = str(payload.get("engine") or payload.get("training_engine") or "llama_factory")
         path_checks: list[dict[str, Any]] = []
+        accelerator: dict[str, Any] = {}
         if check_paths and engine != "smoke":
             path_checks = [
                 _check_path_item(
                     {
                         "name": "model_name_or_path",
-                        "path": payload.get("model_name_or_path") or payload.get("base_model") or "",
+                        "path": payload.get("model_name_or_path") or payload.get("base_model") or payload.get("base_model_path") or "",
                         "type": "any",
                         "required": True,
                     }
                 )
             ]
+            if engine in {"merge", "export", "llama_factory_export"} and payload.get("adapter_name_or_path"):
+                path_checks.append(
+                    _check_path_item(
+                        {
+                            "name": "adapter_name_or_path",
+                            "path": payload.get("adapter_name_or_path"),
+                            "type": "any",
+                            "required": True,
+                        }
+                    )
+                )
             if payload.get("dataset_dir"):
                 path_checks.append(
                     _check_path_item(
@@ -341,6 +444,10 @@ def create_app() -> FastAPI:
                 errors.append(f"training command not found: {command.command[0]}")
             if not Path(command.work_dir).exists():
                 errors.append(f"llama_factory_home not found: {command.work_dir}")
+            if engine not in {"merge", "export", "llama_factory_export"}:
+                accelerator_errors, accelerator_warnings, accelerator = _validate_training_accelerator(payload)
+                errors.extend(accelerator_errors)
+                warnings.extend(accelerator_warnings)
         elif engine == "smoke":
             warnings.append("smoke engine skips model and dataset path checks")
 
@@ -353,6 +460,8 @@ def create_app() -> FastAPI:
             "command_text": " ".join(command.command),
             "work_dir": command.work_dir,
             "env": command.env,
+            "runtime_files": runtime_files,
+            "accelerator": accelerator,
             "path_checks": path_checks,
         }
 
@@ -373,6 +482,8 @@ def create_app() -> FastAPI:
         dataset_root = Path(os.getenv("YG_FT_DATASET_ROOT", str(data_root / "datasets")))
         output_root = Path(os.getenv("YG_FT_OUTPUT_ROOT", str(data_root / "outputs")))
         llama_factory_home = Path(os.getenv("LLAMA_FACTORY_HOME", "/app/LLaMA-Factory"))
+        gpu_items = gpu_resources()
+        torch_cuda = torch_cuda_status()
         return {
             "status": "ok",
             "api_version": "v1",
@@ -391,8 +502,10 @@ def create_app() -> FastAPI:
             "llama_factory_version": os.getenv("LLAMA_FACTORY_VERSION", ""),
             "execution_mode": execution_mode(),
             "gpu_count": _int_env("COMPUTE_GPU_COUNT", 0),
+            "nvidia_gpu_count": len(gpu_items),
+            "torch_cuda": torch_cuda,
             "gpu_discovery_endpoint": f"{route_prefix}/compute/resources/gpus",
-            "capabilities": ["gpu_discovery", "llama_factory", "file_gateway", "job_polling"],
+            "capabilities": ["gpu_discovery", "torch_cuda_diagnostics", "llama_factory", "file_gateway", "job_polling"],
         }
 
     @app.get(f"{route_prefix}/v1/compute/jobs")
@@ -466,6 +579,11 @@ def create_app() -> FastAPI:
 
     @app.post(f"{route_prefix}/compute/jobs")
     async def create_job(payload: dict[str, Any]) -> dict[str, Any]:
+        payload = {**payload, "require_dataset_files": True}
+        try:
+            prepare_runtime_files(payload)
+        except OSError as exc:
+            raise HTTPException(status_code=400, detail=f"prepare runtime files failed: {exc}")
         try:
             command = build_command(payload, os.getenv("LLAMA_FACTORY_HOME", "/app/LLaMA-Factory"))
         except ValueError as exc:

compute/engines/llama_factory/adapter.py

@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+import json
 import re
 from dataclasses import dataclass
 from pathlib import Path
@@ -13,6 +14,67 @@ class LlamaFactoryCommand:
     env: dict[str, str]
 
 
+def _load_dataset_preview(path: Path) -> list[dict[str, Any]]:
+    if not path.exists():
+        return []
+    text = path.read_text(encoding="utf-8", errors="replace").strip()
+    if not text:
+        return []
+    if path.suffix.lower() == ".jsonl":
+        items: list[dict[str, Any]] = []
+        for line in text.splitlines()[:20]:
+            line = line.strip()
+            if not line:
+                continue
+            value = json.loads(line)
+            if isinstance(value, dict):
+                items.append(value)
+        return items
+    value = json.loads(text)
+    if isinstance(value, list):
+        return [item for item in value[:20] if isinstance(item, dict)]
+    if isinstance(value, dict):
+        return [value]
+    return []
+
+
+def _validate_dataset_columns(config: dict[str, Any]) -> list[str]:
+    dataset_dir = config.get("dataset_dir")
+    dataset_info = config.get("dataset_info")
+    if not dataset_dir or not isinstance(dataset_info, dict):
+        return []
+    root = Path(str(dataset_dir))
+    errors: list[str] = []
+    for dataset_key, item in dataset_info.items():
+        if not isinstance(item, dict):
+            continue
+        file_name = item.get("file_name")
+        file_names = file_name if isinstance(file_name, list) else [file_name]
+        columns = item.get("columns") if isinstance(item.get("columns"), dict) else {}
+        required_columns = [str(value) for value in columns.values() if value]
+        for name in file_names:
+            if not name:
+                continue
+            path = root / str(name).lstrip("/\\")
+            if not path.exists():
+                continue
+            try:
+                preview_rows = _load_dataset_preview(path)
+            except Exception as exc:  # noqa: BLE001 - expose malformed data as validation error
+                errors.append(f"dataset file parse failed: {path}: {exc}")
+                continue
+            if not preview_rows:
+                errors.append(f"dataset file has no valid object records: {path}")
+                continue
+            available = set().union(*(row.keys() for row in preview_rows))
+            missing = [column for column in required_columns if column not in available]
+            if missing:
+                errors.append(
+                    f"dataset columns missing in {path.name} for {dataset_key}: {', '.join(sorted(set(missing)))}"
+                )
+    return errors
+
+
 def validate_config(config: dict[str, Any]) -> list[str]:
     errors: list[str] = []
     if not config.get("base_model") and not config.get("model_name_or_path"):
@@ -31,6 +93,24 @@ def validate_config(config: dict[str, Any]) -> list[str]:
         epochs = 0
     if epochs <= 0:
         errors.append("n_epochs must be greater than zero")
+    dataset_dir = config.get("dataset_dir")
+    dataset_info = config.get("dataset_info")
+    if config.get("require_dataset_files") and dataset_dir and isinstance(dataset_info, dict):
+        root = Path(str(dataset_dir))
+        for dataset_key, item in dataset_info.items():
+            if not isinstance(item, dict):
+                errors.append(f"dataset_info entry must be object: {dataset_key}")
+                continue
+            file_name = item.get("file_name")
+            file_names = file_name if isinstance(file_name, list) else [file_name]
+            for name in file_names:
+                if not name:
+                    errors.append(f"dataset_info file_name is required: {dataset_key}")
+                    continue
+                path = root / str(name).lstrip("/\\")
+                if not path.exists():
+                    errors.append(f"dataset file not found: {path}")
+        errors.extend(_validate_dataset_columns(config))
     return errors
 
 
@@ -42,12 +122,92 @@ def _optional_arg(config: dict[str, Any], command: list[str], option: str, *keys
             return
 
 
+def _optional_bool_arg(config: dict[str, Any], command: list[str], option: str, *keys: str) -> None:
+    for key in keys:
+        value = config.get(key)
+        if value is True or str(value).lower() == "true":
+            command.extend([option, "true"])
+            return
+
+
+def _normalize_stage(config: dict[str, Any]) -> str:
+    raw = str(config.get("stage") or config.get("train_type") or "sft").strip().lower()
+    return {
+        "sft": "sft",
+        "dpo": "dpo",
+        "cpt": "pt",
+        "pt": "pt",
+        "pretrain": "pt",
+        "rm": "rm",
+        "ppo": "ppo",
+        "kto": "kto",
+    }.get(raw, raw or "sft")
+
+
+def prepare_runtime_files(config: dict[str, Any]) -> list[dict[str, str]]:
+    dataset_dir = config.get("dataset_dir")
+    dataset_info = config.get("dataset_info")
+    if not dataset_dir or not isinstance(dataset_info, dict):
+        return []
+    root = Path(str(dataset_dir))
+    root.mkdir(parents=True, exist_ok=True)
+    path = root / "dataset_info.json"
+    existing: dict[str, Any] = {}
+    if path.exists():
+        try:
+            loaded = json.loads(path.read_text(encoding="utf-8"))
+            existing = loaded if isinstance(loaded, dict) else {}
+        except json.JSONDecodeError:
+            existing = {}
+    existing.update(dataset_info)
+    path.write_text(json.dumps(existing, ensure_ascii=False, indent=2), encoding="utf-8")
+    return [{"name": "dataset_info", "path": str(path)}]
+
+
 def build_command(config: dict[str, Any], llama_factory_home: str = "/app/LLaMA-Factory") -> LlamaFactoryCommand:
+    engine = str(config.get("engine") or config.get("training_engine") or "llama_factory")
+    if engine in {"merge", "export", "llama_factory_export"}:
+        model_path = config.get("base_model") or config.get("model_name_or_path") or config.get("base_model_path")
+        adapter_path = config.get("adapter_name_or_path") or config.get("adapter_path") or config.get("lora_path")
+        output_dir = config.get("output_dir") or config.get("export_dir")
+        errors: list[str] = []
+        if not model_path:
+            errors.append("base_model or model_name_or_path is required")
+        if not adapter_path and engine == "merge":
+            errors.append("adapter_name_or_path or adapter_path is required")
+        if not output_dir:
+            errors.append("output_dir or export_dir is required")
+        if errors:
+            raise ValueError("; ".join(errors))
+        command = [
+            "llamafactory-cli",
+            "export",
+            "--model_name_or_path",
+            str(model_path),
+            "--template",
+            str(config.get("template", "qwen")),
+            "--finetuning_type",
+            str(config.get("train_method", config.get("finetuning_type", "lora"))),
+            "--export_dir",
+            str(output_dir),
+            "--export_size",
+            str(config.get("export_size", 2)),
+            "--export_device",
+            str(config.get("export_device", "cpu")),
+            "--export_legacy_format",
+            str(config.get("export_legacy_format", False)).lower(),
+        ]
+        if adapter_path:
+            command.extend(["--adapter_name_or_path", str(adapter_path)])
+        quantization_bit = int(config.get("export_quantization_bit", config.get("quantization_bit", 0)) or 0)
+        if quantization_bit in {4, 8}:
+            command.extend(["--quantization_bit", str(quantization_bit)])
+        return LlamaFactoryCommand(command=command, work_dir=str(Path(llama_factory_home)), env={})
+
     errors = validate_config(config)
     if errors:
         raise ValueError("; ".join(errors))
 
-    engine = str(config.get("engine") or config.get("training_engine") or "llama_factory")
     if engine == "smoke":
         output_dir = config.get("output_dir") or f"/data/yg-ft/outputs/{config.get('name', 'training-smoke')}"
         script = (
@@ -75,7 +235,7 @@ def build_command(config: dict[str, Any], llama_factory_home: str = "/app/LLaMA-
         "llamafactory-cli",
         "train",
         "--stage",
-        str(config.get("stage", "sft")).lower(),
+        _normalize_stage(config),
         "--do_train",
         "true",
         "--model_name_or_path",
@@ -112,6 +272,12 @@ def build_command(config: dict[str, Any], llama_factory_home: str = "/app/LLaMA-
     _optional_arg(config, command, "--lora_rank", "lora_rank", "rank")
     _optional_arg(config, command, "--lora_alpha", "lora_alpha")
     _optional_arg(config, command, "--lora_dropout", "lora_dropout")
+    _optional_arg(config, command, "--gradient_accumulation_steps", "gradient_accumulation_steps")
+    _optional_arg(config, command, "--val_size", "val_size")
+    _optional_arg(config, command, "--max_samples", "max_samples")
+    _optional_arg(config, command, "--preprocessing_num_workers", "preprocessing_num_workers")
+    _optional_bool_arg(config, command, "--fp16", "fp16")
+    _optional_bool_arg(config, command, "--bf16", "bf16")
     quantization_bit = int(config.get("quantization_bit", 0) or 0)
     if quantization_bit in {4, 8}:
         command.extend(["--quantization_bit", str(quantization_bit)])

docker/app/docker-compose.yml

@@ -32,8 +32,6 @@ services:
     image: ${BACKEND_API_IMAGE:-yg-ft-backend-api:latest}
     container_name: yg-ft-backend-api
     depends_on:
-      postgres:
-        condition: service_healthy
       redis:
         condition: service_healthy
     expose:
@@ -45,9 +43,9 @@ services:
       APP_NAME: ${APP_NAME:-YG Fine-Tune Platform API}
       MODELTF_ROUTE_PREFIX: ${MODELTF_ROUTE_PREFIX:-/modelTF}
       CORS_ALLOW_ORIGINS: ${CORS_ALLOW_ORIGINS:-http://localhost:16801,http://127.0.0.1:16801}
-      DATABASE_URL: ${DATABASE_URL:-postgresql+psycopg://yg_ft:change_me@postgres:5432/yg_ft}
+      DATABASE_URL: ${DATABASE_URL:-postgresql+psycopg://root:8811614287327Leo@www.caoxiaozhu.com:5432/yg_ft}
       REDIS_URL: ${REDIS_URL:-redis://redis:6379/0}
-      USE_BUILTIN_POSTGRES: ${USE_BUILTIN_POSTGRES:-true}
+      USE_BUILTIN_POSTGRES: ${USE_BUILTIN_POSTGRES:-false}
       USE_BUILTIN_REDIS: ${USE_BUILTIN_REDIS:-true}
       LOG_LEVEL: ${LOG_LEVEL:-INFO}
       LOG_DIR: ${LOG_DIR:-/opt/yg-ft/logs/backend}
@@ -78,27 +76,29 @@ services:
       start_period: 20s
     restart: unless-stopped
 
-  postgres:
+  # PostgreSQL uses the shared external database configured by DATABASE_URL in docker/app/.env.
-    image: postgres:16-alpine
+  # Keep this local service commented out unless development needs an isolated database again.
-    container_name: yg-ft-postgres
+  # postgres:
-    environment:
+  #   image: postgres:16-alpine
-      POSTGRES_DB: ${POSTGRES_DB:-yg_ft}
+  #   container_name: yg-ft-postgres
-      POSTGRES_USER: ${POSTGRES_USER:-yg_ft}
+  #   environment:
-      POSTGRES_PASSWORD: ${POSTGRES_PASSWORD:-change_me}
+  #     POSTGRES_DB: ${POSTGRES_DB:-yg_ft}
-      PGDATA: /var/lib/postgresql/data/pgdata
+  #     POSTGRES_USER: ${POSTGRES_USER:-yg_ft}
-    volumes:
+  #     POSTGRES_PASSWORD: ${POSTGRES_PASSWORD:-change_me}
-      - postgres_data:/var/lib/postgresql/data
+  #     PGDATA: /var/lib/postgresql/data/pgdata
-      - ../../backend/app/db/sql/001_platform_runtime.sql:/docker-entrypoint-initdb.d/001-platform-runtime.sql:ro
+  #   volumes:
-    ports:
+  #     - postgres_data:/var/lib/postgresql/data
-      - "${POSTGRES_PORT:-15432}:5432"
+  #     - ../../backend/app/db/sql/001_platform_runtime.sql:/docker-entrypoint-initdb.d/001-platform-runtime.sql:ro
-    networks:
+  #   ports:
-      - yg-ft-app
+  #     - "${POSTGRES_PORT:-15432}:5432"
-    healthcheck:
+  #   networks:
-      test: ["CMD-SHELL", "pg_isready -U $${POSTGRES_USER} -d $${POSTGRES_DB}"]
+  #     - yg-ft-app
-      interval: 10s
+  #   healthcheck:
-      timeout: 5s
+  #     test: ["CMD-SHELL", "pg_isready -U $${POSTGRES_USER} -d $${POSTGRES_DB}"]
-      retries: 5
+  #     interval: 10s
-    restart: unless-stopped
+  #     timeout: 5s
+  #     retries: 5
+  #   restart: unless-stopped
 
   redis:
     image: redis:7-alpine
@@ -122,5 +122,5 @@ networks:
     name: yg-ft-app
 
 volumes:
-  postgres_data:
+  # postgres_data:
   redis_data:

docker/compute/data/yg-ft/datasets/.gitignore

@@ -0,0 +1,2 @@
+*
+!.gitignore

docker/compute/data/yg-ft/logs/compute/.gitignore

@@ -0,0 +1,2 @@
+*
+!.gitignore

docker/compute/data/yg-ft/logs/training/.gitignore

@@ -0,0 +1,2 @@
+*
+!.gitignore

docker/compute/data/yg-ft/models/.gitignore

@@ -0,0 +1,2 @@
+*
+!.gitignore

docker/compute/data/yg-ft/outputs/.gitignore

@@ -0,0 +1,2 @@
+*
+!.gitignore

docker/compute/docker-compose.yml

@@ -24,6 +24,7 @@ services:
       COMPUTE_GPU_NAME: ${COMPUTE_GPU_NAME:-NVIDIA A800-SXM4-80GB}
       COMPUTE_GPU_MEMORY_GB: ${COMPUTE_GPU_MEMORY_GB:-80}
       COMPUTE_GPU_POWER_LIMIT_W: ${COMPUTE_GPU_POWER_LIMIT_W:-300}
+      MIN_TRAINING_GPU_MEMORY_GB: ${MIN_TRAINING_GPU_MEMORY_GB:-4}
       LOG_DIR: ${LOG_DIR:-/opt/yg-ft/logs/compute}
       CUDA_VISIBLE_DEVICES: ${CUDA_VISIBLE_DEVICES:-all}
       NVIDIA_VISIBLE_DEVICES: ${NVIDIA_VISIBLE_DEVICES:-all}

docs/2026-07-23-development-summary.md

@@ -0,0 +1,153 @@
+# 2026-07-23 开发总结
+
+本文档汇总 2026-07-23 当天围绕模型微调平台完成的系统开发内容。当前开发不再按临时 Demo 口径处理，以下能力均按后续可继续演进到生产系统的代码和数据结构推进。
+
+## 1. 今日完成内容
+
+### 1.1 模型训练最小闭环增强
+
+| 功能点 | 作用 | 对应页面/模块 | 用户如何操作 |
+| --- | --- | --- | --- |
+| 训练任务创建前预检 | 在任务进入运行前校验模型、数据集、算力节点、路径和 LLaMA-Factory 参数，减少任务启动后才失败的情况 | 模型微调列表、创建模型微调、训练日志 | 在“模型微调”页面创建任务并提交，系统自动执行预检；失败时页面提示具体原因 |
+| 训练数据同步到算力节点 | 将应用侧选择的数据集文件同步到目标 Compute 节点本地磁盘，保证 LLaMA-Factory 能读取到真实训练文件 | 创建模型微调、训练日志、算力节点/资源副本 | 用户选择数据集后提交训练任务，系统自动同步数据集到算力节点，无需手工进入容器复制 |
+| 动态生成训练参数 | 根据用户在页面选择的训练数据、基座模型、训练方法、输出目录等动态生成 LLaMA-Factory 训练参数 | 创建模型微调、训练日志 | 用户在创建任务表单中选择模型和数据集，启动训练后可在训练日志中查看实际执行命令 |
+| 训练日志和状态轮询 | 应用侧主动轮询 Compute API 获取训练状态、进度、日志、指标和 checkpoint | 训练日志详情页 | 用户进入训练日志页，可查看任务状态、日志输出、指标和 checkpoint 信息 |
+| 训练失败信息回传 | 训练命令失败后将失败状态和日志内容回传应用侧，便于定位数据格式、模型路径或参数问题 | 模型微调列表、训练日志详情页 | 任务失败后，用户进入训练日志页查看失败日志和错误堆栈 |
+
+### 1.2 B. 模型资产、训练与 LLaMA-Factory 任务能力
+
+| 功能点 | 作用 | 对应页面/模块 | 用户如何操作 |
+| --- | --- | --- | --- |
+| 训练模型产物 artifact 展示 | 展示训练/合并产生的 Adapter、合并模型、量化模型等产物路径、大小、checksum | 模型管理 -> 训练模型列表 | 用户进入“模型管理”，切换到“训练模型”，展开某个模型行查看“模型产物” |
+| 模型血缘 lineage 展示 | 记录和展示基座模型、训练模型、合并产物之间的来源关系 | 模型管理 -> 训练模型列表 | 用户展开训练模型行，在“模型血缘”区域查看父资源、子资源和对应 Compute Job |
+| 模型导出任务 export job 状态展示 | 展示模型合并/导出的任务状态、输出目录和创建时间 | 模型管理 -> 训练模型列表 | 用户展开训练模型行，在“导出任务”区域查看导出任务状态 |
+| export 完成后回填 checksum | Compute export 完成后扫描产物文件，回填大小和 `checksum_sha256`，为后续一致性校验和制品治理做准备 | 后端模型资产模块、模型管理页面 | 用户无需手工操作，任务完成后页面自动展示产物大小和 checksum |
+| 合并模型任务接入 Compute | 模型合并不再只是生成模拟任务，而是通过 Compute 节点调用模型合并任务并记录导出任务 | 模型管理 -> 合并权重 | 用户在训练模型列表点击“合并权重”，提交后系统创建 Compute 合并任务 |
+
+### 1.3 D. 算力平台、部署与运维能力
+
+| 功能点 | 作用 | 对应页面/模块 | 用户如何操作 |
+| --- | --- | --- | --- |
+| 算力节点动态配置 | 支持维护 Compute API、File Gateway、权重、标签、启用状态、调度状态等信息 | 算力节点 -> 节点 | 用户进入“算力节点”，点击“新增节点”或“编辑”，填写节点地址和调度参数 |
+| 节点连通性测试 | 验证应用服务器是否能访问 Compute API，并返回 GPU 发现结果和延迟 | 算力节点 -> 节点 | 用户在节点列表点击“测试”，查看连接成功或失败提示 |
+| GPU 状态展示 | 展示每个算力节点的 GPU 利用率、显存、温度、功耗、进程信息 | 算力节点 -> GPU | 用户进入“算力节点”，切换到“GPU”页签查看 |
+| 训练队列展示 | 展示 Compute 侧任务队列、状态、进度、节点和 GPU 分配 | 算力节点 -> 队列 | 用户切换到“队列”页签查看当前运行或等待任务 |
+| 资源副本列表 | 展示数据集、模型、训练产物在算力节点本地磁盘上的副本路径和同步状态 | 算力节点 -> 资源副本 | 用户切换到“资源副本”页签，选择目标节点查看本地副本 |
+| 资源副本漂移检测 | 检查副本路径是否仍存在、大小是否可读取，并将异常副本标记为漂移 | 算力节点 -> 资源副本 | 用户点击“漂移检测”，系统调用 Compute API 检查节点本地路径 |
+| 资源副本 repair 重同步 | 对漂移、失败、待修复副本执行真实重同步，而不只是标记状态 | 算力节点 -> 资源副本 | 用户点击“修复副本”，系统从权威源重新上传或导入到目标算力节点 |
+| 应用侧轮询模式 | 应用服务主动轮询 Compute API，避免需要 Compute 侧反向访问应用侧 | 后端 Compute Gateway、部署架构 | 用户无感知，部署时只需保证应用侧能访问各 Compute 节点 |
+| 本地磁盘挂载约定 | 基座模型、训练数据、训练产物、训练日志均通过宿主机目录挂载到 Compute 容器 | docker/compute 部署 | 运维人员在启动 Compute 服务前准备并挂载 `docker/compute/data/yg-ft/*` 目录 |
+
+### 1.4 前端状态中文化
+
+| 功能点 | 作用 | 对应页面/模块 | 用户如何操作 |
+| --- | --- | --- | --- |
+| 统一状态字典 | 将 `pending`、`running`、`completed`、`failed`、`online`、`synced`、`drifted` 等状态统一展示为中文 | 通用前端组件 `ModelStatusTag`、状态工具 `status.ts` | 用户在各列表页看到中文状态，不再直接看到英文状态值 |
+| 模型管理状态中文化 | 合并状态、导出任务状态、产物状态使用中文展示 | 模型管理 | 用户查看训练模型列表和展开详情时看到“未合并/合并中/已合并/已完成/失败”等中文 |
+| 算力节点状态中文化 | 节点状态、GPU 状态、队列状态、副本同步状态使用中文展示 | 算力节点 | 用户查看“节点/GPU/队列/资源副本”时看到中文状态标签 |
+| 推理/对比/评测/用户状态中文化 | 将推理任务、对比模型加载、评测任务、用户状态统一为中文标签 | 模型推理、模型对比、模型评测、用户设置 | 用户查看相关页面时看到“启动中/已就绪/已完成/启用”等中文状态 |
+
+### 1.5 前端构建产物更新
+
+| 功能点 | 作用 | 对应页面/模块 | 用户如何操作 |
+| --- | --- | --- | --- |
+| 刷新 `frontend/dist` | 保证 Docker 前端 nginx 容器可以直接加载最新页面代码 | 前端部署 | 用户重新访问前端服务即可看到最新页面 |
+| 重启前后端服务验证 | 验证源码挂载和 dist 挂载方式下服务可正常加载最新代码 | docker/app、docker/compute | 开发人员重启 `backend-api`、`frontend`、`compute-api` 后验证页面和接口 |
+
+## 2. 今日涉及的主要代码位置
+
+| 文件 | 说明 |
+| --- | --- |
+| `backend/app/api/v1/endpoints/platform.py` | 新增/完善训练预检、资源同步、模型 artifact/lineage/export job 接口、资源副本漂移检测和 repair |
+| `backend/app/db/platform_store.py` | 新增模型产物、血缘、导出任务、副本同步结果、artifact 查询等数据访问能力 |
+| `compute/agent/process_manager.py` | export/artifact 扫描时补充文件大小和 checksum |
+| `frontend/src/api/modules/model.ts` | 增加训练模型 artifact、lineage、export job API |
+| `frontend/src/api/modules/compute.ts` | 增加副本漂移检测、repair API 和副本字段 |
+| `frontend/src/views/model/ModelManageView.vue` | 增加训练模型展开详情：产物、血缘、导出任务 |
+| `frontend/src/views/compute/ComputeNodesView.vue` | 增加资源副本页签、漂移检测、repair 操作和状态中文化 |
+| `frontend/src/utils/status.ts` | 新增统一状态中文映射和标签类型映射 |
+| `frontend/src/components/ModelStatusTag.vue` | 改为复用统一状态字典 |
+| `frontend/src/components/DataTablePage.vue` | 增加表格展开事件支持 |
+| `frontend/dist/*` | 前端生产构建产物已更新 |
+
+## 3. 今日验证结果
+
+| 验证项 | 结果 |
+| --- | --- |
+| Python 编译检查 | 通过：`python -m compileall backend compute scripts` |
+| 前端生产构建 | 通过：`npm run build` |
+| 前端入口 | 通过：`http://127.0.0.1:16801/` 返回 200 |
+| 后端健康检查 | 通过：`/modelTF/health` 返回正常 |
+| Compute 健康检查 | 通过：`/modelTF/health` 返回正常 |
+| 模型导出任务接口 | 通过：`/modelTF/model-manage/export-jobs` 返回正常 |
+| 算力节点列表接口 | 通过：`/modelTF/compute/nodes` 返回正常 |
+| 资源副本漂移检测 | 通过：当前节点副本检测返回 `drifted: 0` |
+
+## 4. 当前仍需注意的问题
+
+| 问题 | 影响 | 建议 |
+| --- | --- | --- |
+| 训练数据格式仍依赖 LLaMA-Factory 约定 | 如果用户上传的数据字段不符合模板要求，训练仍会失败 | 下一步增加数据集格式校验和模板转换预检 |
+| 单机 MX350 显存较小 | 真实训练大模型时容易因显存不足失败 | 当前环境用于链路验证；真实训练应使用高显存 GPU 节点 |
+| resource replica repair 对大型模型仍是同步调用 | 大模型重同步可能耗时较长 | 下一步将 repair 完整异步化，并展示 sync job 进度 |
+| artifact checksum 目前在 Compute 扫描阶段计算 | 对超大目录递归扫描可能较慢 | 下一步支持分文件 checksum、manifest 文件和后台扫描 |
+| 模型评测、模型推理仍未形成完整生产闭环 | 目前页面已有基础能力，但后端表结构、任务运行、日志、治理能力还需补齐 | 下一步将评测和推理纳入正式开发计划 |
+
+## 5. 下一步开发计划
+
+### 5.1 B. 模型资产、训练与 LLaMA-Factory 任务
+
+| 优先级 | 开发任务 | 目标页面/模块 | 交付结果 |
+| --- | --- | --- | --- |
+| P0 | 数据集格式预检 | 创建模型微调、数据集管理 | 提交训练前校验 Alpaca/ShareGPT/OpenAI Messages 等格式，提示缺失字段 |
+| P0 | 训练参数可视化确认 | 创建模型微调 | 提交前展示最终 LLaMA-Factory 参数预览，减少参数不一致问题 |
+| P0 | 训练任务失败诊断 | 训练日志详情 | 识别常见错误：数据字段缺失、模型路径不存在、显存不足、依赖缺失，并生成中文诊断 |
+| P1 | checkpoint 管理 | 训练日志详情、模型管理 | 展示 checkpoint 列表、大小、路径、保留策略，支持标记最佳 checkpoint |
+| P1 | 模型产物 manifest | 模型管理 | 为每个训练/导出产物生成 manifest，记录文件清单、大小、checksum、来源任务 |
+| P1 | 合并/导出任务详情页 | 模型管理、训练日志 | 展示合并任务日志、状态、产物、失败原因 |
+| P2 | 模型版本治理 | 模型管理 | 支持版本号、标签、发布状态、归档状态、审批状态 |
+| P2 | 训练模板管理 | 系统设置或训练配置 | 将 LLaMA-Factory 模板、数据格式、默认超参做成可维护配置 |
+
+### 5.2 D. 算力平台、部署与运维
+
+| 优先级 | 开发任务 | 目标页面/模块 | 交付结果 |
+| --- | --- | --- | --- |
+| P0 | repair 异步化 | 算力节点 -> 资源副本 | repair 创建 sync job 后后台执行，页面展示进度和失败原因 |
+| P0 | 多算力节点调度策略 | 算力节点、创建模型微调 | 支持按标签、权重、空闲 GPU、显存要求选择节点 |
+| P1 | 节点资源水位告警 | 算力节点、硬件监控 | 展示磁盘、GPU、显存、训练日志目录水位和告警状态 |
+| P1 | 节点维护窗口 | 算力节点 | 支持维护中节点不再调度新任务，已有任务可继续或迁移 |
+| P1 | 文件副本治理 | 算力节点 -> 资源副本 | 支持副本清理、重建、过期策略和跨节点一致性检查 |
+| P2 | 部署健康巡检脚本 | 部署运维 | 一键检查 app、backend、redis、pg、compute、GPU、挂载目录、端口连通 |
+| P2 | Compute Agent 插件标准 | 算力平台 | 抽象 LLaMA-Factory 接入规范，预留其他训练框架 |
+
+### 5.3 模型评测页面开发计划
+
+| 优先级 | 开发任务 | 目标页面/模块 | 交付结果 |
+| --- | --- | --- | --- |
+| P0 | 评测任务表结构和接口补齐 | 模型评测列表、创建评测、评测详情 | 建立 `eval_tasks`、`eval_dimensions`、`eval_sample_results` 等运行表和接口 |
+| P0 | 创建评测任务真实提交 | 创建评测 | 支持选择模型、数据集、评测维度、GPU/节点，提交后生成评测任务 |
+| P0 | 评测任务运行闭环 | 评测详情 | 支持状态、进度、日志、样本级结果回传 |
+| P1 | 评测维度管理 | 评测维度创建/编辑 | 支持规则、Prompt、评分器、权重、适用数据集配置 |
+| P1 | 样本级评分展示 | 评测详情 | 展示每条样本的输入、模型输出、评分、原因、人工复核状态 |
+| P1 | 综合报告生成 | 评测详情、排行榜 | 生成维度汇总、综合分、问题样本、改进建议 |
+| P2 | 评测审批和审计 | 审批流、审计日志 | 评测任务创建、发布报告、删除报告纳入治理 |
+
+### 5.4 模型推理页面开发计划
+
+| 优先级 | 开发任务 | 目标页面/模块 | 交付结果 |
+| --- | --- | --- | --- |
+| P0 | 推理任务表结构和接口补齐 | 模型推理列表、新建推理 | 建立 `inference_tasks`、`inference_task_models`、`chat_sessions`、`chat_messages` |
+| P0 | 模型加载/卸载真实闭环 | 模型推理列表 | 支持选择训练产物加载推理服务，展示加载状态、端口、进程和错误 |
+| P0 | 单模型对话持久化 | 模型对话 | 保存会话、消息、参数、响应耗时、token 统计 |
+| P1 | 多模型对比任务 | 模型对比 | 支持多模型同时加载、同一问题并发请求、结果对比展示 |
+| P1 | 推理资源管控 | 模型推理、算力节点 | 支持 GPU 选择、并发限制、空闲自动卸载、异常进程清理 |
+| P1 | 推理日志和调用审计 | 日志、审计中心 | 记录加载、卸载、对话请求、失败原因、用户和租户信息 |
+| P2 | 推理服务发布 | 模型管理、模型推理 | 支持将某个训练模型发布为内部推理服务，并配置访问权限 |
+
+## 6. 建议的下一阶段顺序
+
+1. 先完成 B+D 的训练稳定性增强：数据格式预检、训练参数预览、失败诊断、repair 异步化。
+2. 再补齐模型评测的真实任务闭环：任务表、创建任务、运行状态、样本结果。
+3. 然后补齐模型推理闭环：加载/卸载、对话持久化、多模型对比。
+4. 最后统一治理能力：审批、审计、租户隔离、资源配额、保留策略和运维巡检。
+

docs/backend-api-design.md

@@ -1095,8 +1095,21 @@ GPU 响应字段：
   "engine": "llama_factory",
   "base_model": "/data/yg-ft/models/Qwen2.5-7B",
   "model_name_or_path": "/data/yg-ft/models/Qwen2.5-7B",
-  "dataset": "finance_train",
+  "train_dataset_id": "ds_finance_train",
-  "dataset_dir": "/data/yg-ft/datasets",
+  "dataset": "ygft_ds_finance_train",
+  "dataset_key": "ygft_ds_finance_train",
+  "dataset_dir": "/data/yg-ft/datasets/ds_finance_train",
+  "dataset_info": {
+    "ygft_ds_finance_train": {
+      "file_name": "train.jsonl",
+      "formatting": "alpaca",
+      "columns": {
+        "prompt": "instruction",
+        "query": "input",
+        "response": "output"
+      }
+    }
+  },
   "output_dir": "/data/yg-ft/outputs/finance-sft-001",
   "template": "qwen",
   "train_method": "lora",
@@ -1108,6 +1121,16 @@ GPU 响应字段：
 }
 ```
 
+数据集启动规则：
+- 页面选择的是平台数据集 ID，后端提交 Compute Job 时会将其转换为 LLaMA-Factory 数据集 key。
+- 单文件数据集使用 `--dataset ygft_{dataset_id}`；多文件数据集使用 `--dataset ygft_{dataset_id}_1,ygft_{dataset_id}_2`。
+- `dataset_dir` 指向目标算力节点上的独立数据集目录 `/data/yg-ft/datasets/{dataset_id}`。
+- Compute API 在 preflight 和启动训练前根据 `dataset_info` 生成 `{dataset_dir}/dataset_info.json`，避免 LLaMA-Factory 读取全局 `/data/yg-ft/datasets/dataset_info.json` 失败。
+- `columns` 只声明训练文件实际存在的字段；`system`、`history` 等可选字段不能默认写入，否则样本缺少字段时 LLaMA-Factory 会在格式转换阶段报 `KeyError`。
+- 正式启动前，应用侧会把当前数据集文件内容同步到被调度的算力节点，确保在线编辑/版本切换后的训练文件被使用。
+- Preflight 会校验 `dataset_info.columns` 对应字段是否能在样本文件中找到，并校验 PyTorch CUDA 可用性、所选 GPU 是否存在、显存是否满足 `MIN_TRAINING_GPU_MEMORY_GB`。
+- Compute 健康检查返回 `torch_cuda`，用于区分 `nvidia-smi` 可见但 PyTorch CUDA 初始化失败的环境问题。
+
 应用侧轮询同步响应：
 
 ```json
@@ -1330,3 +1353,38 @@ LLaMA-Factory 引擎声明：
 | 存储管理 | `/storage` | `GET /modelTF/quotas/usage`、`GET /modelTF/files/{id}/download-url`、`GET /modelTF/retention-policies`、`PUT /modelTF/retention-policies/{id}` | 磁盘占用、下载、留存 |
 | 审计中心 | `/audit-logs`、`/login-logs`、`/download-logs` | `GET /modelTF/audit-logs`、`GET /modelTF/login-logs`、`GET /modelTF/download-logs` | 操作、登录、下载审计 |
 | 训练引擎管理 | `/training-engines` | `GET /modelTF/training-engines`、`GET /modelTF/training-engines/{id}`、`GET /modelTF/training-engines/{id}/schema`、`POST /modelTF/training-engines/{id}/health-check` | 引擎能力和健康 |
+## P1/P2/P3 runtime implementation note
+
+The current backend/compute implementation has connected the B+D training runtime features below:
+
+| Page module | API | Runtime behavior |
+| --- | --- | --- |
+| Training detail / logs `/training-log/:id` | `GET /modelTF/fine-tune/{id}/overview` | Returns task progress, parsed training metrics and real checkpoint records. |
+| Training detail / loss chart `/training-log/:id` | `GET /modelTF/fine-tune/{id}/metrics` | Reads `fine_tune_metrics`, populated from Compute log polling and log proxy access. |
+| Training detail / checkpoint list `/training-log/:id` | `GET /modelTF/fine-tune/{id}/checkpoints` | Reads `fine_tune_checkpoints`, populated from Compute scanning `output_dir/checkpoint-*`. |
+| Merge weights `/model-manage/merge` | `POST /modelTF/model-manage/merge` | Submits a real Compute job using `llamafactory-cli export`; records the job in `compute_jobs`; updates `trained_models.merging/merged/merged_path` when queried after completion. |
+| Compute ops / job detail | `GET /modelTF/compute/jobs/{job_id}` | Supports both fine-tune jobs and model merge/export jobs recorded in `compute_jobs`. |
+| Compute ops / job logs | `GET /modelTF/compute/jobs/{job_id}/logs` | Proxies logs from the assigned Compute node for training and merge/export jobs. |
+
+Operational diagnostic script:
+
+```bash
+APP_BASE_URL=http://localhost:17861 \
+COMPUTE_BASE_URL=http://localhost:19100 \
+COMPUTE_SERVICE_TOKEN=change_me \
+DATABASE_URL=postgresql+psycopg://user:password@host:5432/yg_ft \
+python scripts/ops_diagnostics.py
+```
+## P2/P3 runtime extension note
+
+This iteration extends the B+D runtime implementation with production-facing model asset governance and compute operations:
+
+| Page module | API | Description |
+| --- | --- | --- |
+| Trained model detail / artifacts | `GET /modelTF/model-manage/trained-models/{id}/artifacts` | Returns registered adapter, merged model and quantized/export artifacts from `model_artifacts`. |
+| Trained model detail / lineage | `GET /modelTF/model-manage/trained-models/{id}/lineage` | Returns upstream/downstream relations from `model_lineage`, including base model to fine-tuned model and merge/export relations. |
+| Merge/export task list | `GET /modelTF/model-manage/export-jobs?trained_model_id=xxx` | Returns model export and merge jobs from `model_export_jobs`. |
+| Compute node replicas | `GET /modelTF/compute/nodes/{id}/replicas/drift` | Checks whether model/dataset/output replicas still exist on the compute node local disk and updates replica status. |
+| Compute node replicas | `POST /modelTF/compute/nodes/{id}/replicas/repair` | Marks drifted replicas as `repair_pending` and creates a resource sync job for the operator/scheduler to process. |
+
+The scheduler now uses the `scheduler_locks` table while starting training tasks. Node selection, task state update, resource sync job creation and GPU pre-allocation are written in one database transaction to reduce multi-worker GPU contention.

docs/system-development-plan.md

@@ -701,7 +701,9 @@ YG_FT/
 职责：
 
 - 生成训练 YAML 或 CLI。
-- 生成 dataset_info。
+- 根据平台数据集 ID 生成 LLaMA-Factory dataset key 和 `{dataset_dir}/dataset_info.json`。
+- 单文件数据集使用一个 dataset key，多文件数据集拆分为多个 key 并通过逗号组合传入 `--dataset`。
+- 正式启动前由应用侧同步当前数据集文件到目标算力节点，确保训练使用最新激活版本。
 - 设置 `CUDA_VISIBLE_DEVICES`。
 - 启动训练。
 - 解析日志。
@@ -1116,3 +1118,10 @@ YG_FT/
 | P1 | 数据处理、评测、推理、模型服务治理 | 形成训练前后闭环 |
 | P1 | 存储管理、checkpoint、审计中心 | 企业治理和运维收口 |
 | P2 | 训练引擎管理、外部 API、OIDC/LDAP、成本核算、人工评测 | 扩展能力，第一版可预留 |
+## P1/P2/P3 Development Status
+
+| Priority | Scope | Implemented content | Remaining production hardening |
+| --- | --- | --- | --- |
+| P1 | Training runtime assets | Compute scans real `checkpoint-*` directories; backend persists checkpoints to `fine_tune_checkpoints`; backend parses log metrics into `fine_tune_metrics`; completed training auto-registers a trained model with its real output directory. | Add full TensorBoard event parsing and retention/cleanup execution policies. |
+| P2 | Model merge/export | `/modelTF/model-manage/merge` submits `llamafactory-cli export` through Compute; merge/export jobs are recorded in `compute_jobs` and `model_export_jobs`; trained model artifacts are recorded in `model_artifacts`; base->trained and merge/export relations are recorded in `model_lineage`; APIs expose artifacts, lineage and export jobs. | Add frontend polling/status panels for export jobs and richer artifact checksum generation during long-running export. |
+| P3 | Compute ops | `compute_jobs` and `gpu_allocations` are updated from runtime polling; training start uses `scheduler_locks` to serialize node selection and GPU pre-allocation; terminal task states release GPU allocations; resource replica drift check and repair-pending APIs are available; `scripts/ops_diagnostics.py` checks backend, PostgreSQL, Compute health, GPU discovery and Compute jobs. | Add dedicated scheduler worker, automatic replica repair execution from authoritative source metadata, and lock timeout alarms. |

frontend/dist/assets/AppConfirmDialog-DxS8ATj8.js

@@ -1 +1 @@
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+import{d as C,bf as g,D as E,H as O,o as _,e as B,s as D,Z as $,w as A,c as I,aL as K,q as a,n as v,aa as r,g as q,bg as M,y as u,z as N,P as k}from"./index-D5veDkuG.js";import{_ as R}from"./_plugin-vue_export-helper-DlAUqK2U.js";const V={class:"app-confirm-header"},z={class:"app-confirm-heading"},H={class:"app-confirm-icon","aria-hidden":"true"},L={class:"app-confirm-body"},P={class:"app-confirm-actions"},S=C({__name:"AppConfirmDialog",setup(j,{expose:x}){const c=u(!1),m=u(),p=u(),d=`app-confirm-title-${g()}`,y=`app-confirm-message-${g()}`,n=N({title:"请确认操作",message:"",confirmText:"确定",cancelText:"取消",tone:"warning",closeOnOverlay:!1});let o=null,i=null;function s(t){c.value=!1;const e=o;o=null,e==null||e(t)}function h(t){return o&&s(!1),Object.assign(n,{confirmText:"确定",cancelText:"取消",tone:"warning",closeOnOverlay:!1,...t}),c.value=!0,new Promise(e=>{o=e})}function w(){n.closeOnOverlay&&s(!1)}function T(t){var b;if(t.key==="Escape"){t.preventDefault(),s(!1);return}if(t.key!=="Tab")return;const e=Array.from(((b=m.value)==null?void 0:b.querySelectorAll("button:not([disabled])"))??[]),l=e[0],f=e[e.length-1];!l||!f||(t.shiftKey&&document.activeElement===l?(t.preventDefault(),f.focus()):!t.shiftKey&&document.activeElement===f&&(t.preventDefault(),l.focus()))}return E(c,async t=>{var e;if(t){i=document.activeElement instanceof HTMLElement?document.activeElement:null,await k(),(e=p.value)==null||e.focus();return}await k(),i==null||i.focus(),i=null}),O(()=>{o==null||o(!1),o=null}),x({open:h}),(t,e)=>(_(),B(M,{to:"body"},[D($,{name:"app-confirm"},{default:A(()=>[c.value?(_(),I("div",{key:0,class:"app-confirm-overlay",onMousedown:K(w,["self"])},[a("section",{ref_key:"dialogRef",ref:m,class:v(["app-confirm-dialog",`is-${n.tone}`]),role:"alertdialog","aria-modal":!0,"aria-labelledby":d,"aria-describedby":y,onKeydown:T},[a("header",V,[a("div",z,[a("span",H,[a("i",{class:v(n.tone==="primary"?"fa fa-question-circle":"fa fa-exclamation-triangle")},null,2)]),a("h2",{id:d},r(n.title),1)]),a("button",{class:"app-confirm-close",type:"button","aria-label":"关闭确认弹窗",onClick:e[0]||(e[0]=l=>s(!1))},[...e[3]||(e[3]=[a("i",{class:"fa fa-times","aria-hidden":"true"},null,-1)])])]),a("div",L,[a("p",{id:y},r(n.message),1)]),a("footer",P,[a("button",{ref_key:"cancelButtonRef",ref:p,class:"app-confirm-button is-cancel",type:"button",onClick:e[1]||(e[1]=l=>s(!1))},r(n.cancelText),513),a("button",{class:"app-confirm-button is-confirm",type:"button",onClick:e[2]||(e[2]=l=>s(!0))},r(n.confirmText),1)])],34)],32)):q("",!0)]),_:1})]))}}),G=R(S,[["__scopeId","data-v-398df98e"]]);export{G as A};

frontend/dist/assets/CompareChatView-BVdMCKbe.js

@@ -1 +0,0 @@
-import{a as N,E as B}from"./el-form-item-Bq0n_p5a.js";import{E as K}from"./index-h5FkzAP_.js";import{E as M}from"./index-BNsCyG4A.js";import{E as h}from"./index-ByhS8A1d.js";import{E as I}from"./el-divider-a924dciw.js";import{E as R}from"./el-slider-mZJHOhL2.js";import{d as F,G as z,e as V,w as l,ac as D,y as L,o as f,s as a,x as p,q as $,c as k,ad as j,aa as E,M as A,g as G,f as J,v as O,z as H,j as _,A as P}from"./index-Ds9AETjS.js";import"./el-popper-D1tByNLb.js";import"./el-tooltip-l0sNRNKZ.js";import"./el-input-number-Bg2C0S8w.js";/* empty css               */import{P as Q}from"./PageCard-CV3p14-x.js";import{u as W}from"./usePolling-Bx2QCw4O.js";import{a as X}from"./compare-fmy9bbtE.js";import{_ as Y}from"./_plugin-vue_export-helper-DlAUqK2U.js";import"./castArray-BWI4UxBy.js";import"./_baseClone-CZxxnGCv.js";import"./raf-zQ00nuMI.js";import"./index-B0if-AHo.js";import"./index-BSbCLbaz.js";import"./debounce-D6mJVDYC.js";import"./toNumber-DrayciwT.js";import"./clamp-CyhADbdq.js";import"./index-sWKLi7bH.js";import"./index-2LOSO4Pw.js";import"./el-card-D86TXgMv.js";const Z={class:"model-list"},tt={key:0,class:"empty-hint"},et=F({__name:"CompareChatView",setup(ot){const b=D(),y=O(),x=b.params.id,n=L(null),e=H({systemPrompt:"",question:"",temperature:.7,topP:.9,topK:40,maxTokens:2048}),m=_(()=>{var s;if(!((s=n.value)!=null&&s.load_status))return[];try{return(typeof n.value.load_status=="string"?JSON.parse(n.value.load_status):n.value.load_status).loaded_models||[]}catch{return[]}}),T=_(()=>m.value.length>0&&m.value.every(s=>s.status==="ready"||s.status==="running")),g=_(()=>m.value.some(s=>s.status==="starting"));async function c(){try{n.value=await X(x)}catch{}}function C(){var u,i;if(!e.question.trim()){P.warning("请输入问题");return}if(g.value){P.warning("模型仍在启动中，请稍候");return}const s=new URLSearchParams({taskId:x,taskName:((u=n.value)==null?void 0:u.model_name)||((i=n.value)==null?void 0:i.name)||"",question:e.question,systemPrompt:e.systemPrompt,temperature:String(e.temperature),topP:String(e.topP),topK:String(e.topK),maxTokens:String(e.maxTokens)}),t=y.resolve(`/model-compare/result?${s.toString()}`).href;window.open(t,"_blank")}const{start:S}=W(c,5e3,{immediate:!1});return z(async()=>{await c(),S()}),(s,t)=>{const u=I,i=h,v=K,r=N,d=R,w=M,q=B;return f(),V(Q,{title:"模型对比配置"},{default:l(()=>[a(u,{"content-position":"left"},{default:l(()=>[...t[7]||(t[7]=[p("已启动模型",-1)])]),_:1}),$("div",Z,[(f(!0),k(A,null,j(m.value,(o,U)=>(f(),V(i,{key:U,type:o.status==="ready"||o.status==="running"?"success":o.status==="starting"?"warning":"danger",size:"large"},{default:l(()=>[p(E(o.model_name)+" ("+E(o.status)+") ",1)]),_:2},1032,["type"]))),128)),m.value.length?G("",!0):(f(),k("span",tt,"暂无已启动模型"))]),a(u,{"content-position":"left"},{default:l(()=>[...t[8]||(t[8]=[p("对话配置",-1)])]),_:1}),a(q,{"label-width":"120px",style:{"max-width":"700px"}},{default:l(()=>[a(r,{label:"系统提示词"},{default:l(()=>[a(v,{modelValue:e.systemPrompt,"onUpdate:modelValue":t[0]||(t[0]=o=>e.systemPrompt=o),type:"textarea",rows:3,placeholder:"可选"},null,8,["modelValue"])]),_:1}),a(r,{label:"问题"},{default:l(()=>[a(v,{modelValue:e.question,"onUpdate:modelValue":t[1]||(t[1]=o=>e.question=o),type:"textarea",rows:4,placeholder:"请输入要对比的问题"},null,8,["modelValue"])]),_:1}),a(r,{label:"Temperature"},{default:l(()=>[a(d,{modelValue:e.temperature,"onUpdate:modelValue":t[2]||(t[2]=o=>e.temperature=o),min:0,max:2,step:.1,"show-input":"",style:{"max-width":"500px"}},null,8,["modelValue"])]),_:1}),a(r,{label:"Top-p"},{default:l(()=>[a(d,{modelValue:e.topP,"onUpdate:modelValue":t[3]||(t[3]=o=>e.topP=o),min:0,max:1,step:.05,"show-input":"",style:{"max-width":"500px"}},null,8,["modelValue"])]),_:1}),a(r,{label:"Top-k"},{default:l(()=>[a(d,{modelValue:e.topK,"onUpdate:modelValue":t[4]||(t[4]=o=>e.topK=o),min:1,max:100,step:1,"show-input":"",style:{"max-width":"500px"}},null,8,["modelValue"])]),_:1}),a(r,{label:"Max Tokens"},{default:l(()=>[a(d,{modelValue:e.maxTokens,"onUpdate:modelValue":t[5]||(t[5]=o=>e.maxTokens=o),min:256,max:4096,step:128,"show-input":"",style:{"max-width":"500px"}},null,8,["modelValue"])]),_:1}),a(r,null,{default:l(()=>[a(w,{type:"primary",disabled:!T.value||g.value,onClick:C},{default:l(()=>[...t[9]||(t[9]=[p(" 开始对比 ",-1)])]),_:1},8,["disabled"]),a(w,{onClick:t[6]||(t[6]=o=>J(y).back())},{default:l(()=>[...t[10]||(t[10]=[p("返回",-1)])]),_:1})]),_:1})]),_:1})]),_:1})}}}),qt=Y(et,[["__scopeId","data-v-5da55d96"]]);export{qt as default};

frontend/dist/assets/CompareChatView-CyMT5mfJ.js

@@ -0,0 +1 @@
+import{a as B,E as K}from"./el-form-item-rya6onmd.js";import{E as M}from"./index-DbBHj60R.js";import{E as h}from"./index-DxH5-_w6.js";import{E as I}from"./el-tag-C2d1Zfbn.js";import{E as R}from"./el-divider-CsS5KGIs.js";import{E as F}from"./el-slider-pAA0AhdE.js";import{d as L,G as z,e as V,w as s,ac as D,y as $,o as f,s as a,x as p,q as j,c as k,ad as A,aa as E,f as P,M as G,g as J,v as O,z as H,j as _,A as b}from"./index-D5veDkuG.js";import"./el-popper-DDJTxf27.js";import"./el-tooltip-l0sNRNKZ.js";import"./el-input-number-Cs1MXSA_.js";import{P as Q}from"./PageCard-DFGMy1ik.js";import{u as W}from"./usePolling-BV3KjihG.js";import{a as X}from"./compare-DQvbhHeE.js";import{a as Y}from"./status-Dl1fykxa.js";import{_ as Z}from"./_plugin-vue_export-helper-DlAUqK2U.js";import"./castArray-BDIbf8ih.js";import"./_baseClone-BuqHm105.js";import"./raf-Dsox5IbR.js";import"./index-DDY6aSpq.js";import"./index-BT1A2I5c.js";import"./debounce-D3zKL4rk.js";import"./toNumber-a8w_gs0u.js";import"./clamp-Ddg8_Yc2.js";import"./index-DmAaj_YO.js";import"./index-APLq0PKf.js";import"./el-card-DzhpMhms.js";const tt={class:"model-list"},et={key:0,class:"empty-hint"},ot=L({__name:"CompareChatView",setup(at){const T=D(),y=O(),x=T.params.id,n=$(null),e=H({systemPrompt:"",question:"",temperature:.7,topP:.9,topK:40,maxTokens:2048}),m=_(()=>{var l;if(!((l=n.value)!=null&&l.load_status))return[];try{return(typeof n.value.load_status=="string"?JSON.parse(n.value.load_status):n.value.load_status).loaded_models||[]}catch{return[]}}),C=_(()=>m.value.length>0&&m.value.every(l=>l.status==="ready"||l.status==="running")),g=_(()=>m.value.some(l=>l.status==="starting"));async function c(){try{n.value=await X(x)}catch{}}function S(){var u,i;if(!e.question.trim()){b.warning("请输入问题");return}if(g.value){b.warning("模型仍在启动中，请稍候");return}const l=new URLSearchParams({taskId:x,taskName:((u=n.value)==null?void 0:u.model_name)||((i=n.value)==null?void 0:i.name)||"",question:e.question,systemPrompt:e.systemPrompt,temperature:String(e.temperature),topP:String(e.topP),topK:String(e.topK),maxTokens:String(e.maxTokens)}),t=y.resolve(`/model-compare/result?${l.toString()}`).href;window.open(t,"_blank")}const{start:q}=W(c,5e3,{immediate:!1});return z(async()=>{await c(),q()}),(l,t)=>{const u=R,i=I,v=M,r=B,d=F,w=h,U=K;return f(),V(Q,{title:"模型对比配置"},{default:s(()=>[a(u,{"content-position":"left"},{default:s(()=>[...t[7]||(t[7]=[p("已启动模型",-1)])]),_:1}),j("div",tt,[(f(!0),k(G,null,A(m.value,(o,N)=>(f(),V(i,{key:N,type:o.status==="ready"||o.status==="running"?"success":o.status==="starting"?"warning":"danger",size:"large"},{default:s(()=>[p(E(o.model_name)+" ("+E(P(Y)(o.status))+") ",1)]),_:2},1032,["type"]))),128)),m.value.length?J("",!0):(f(),k("span",et,"暂无已启动模型"))]),a(u,{"content-position":"left"},{default:s(()=>[...t[8]||(t[8]=[p("对话配置",-1)])]),_:1}),a(U,{"label-width":"120px",style:{"max-width":"700px"}},{default:s(()=>[a(r,{label:"系统提示词"},{default:s(()=>[a(v,{modelValue:e.systemPrompt,"onUpdate:modelValue":t[0]||(t[0]=o=>e.systemPrompt=o),type:"textarea",rows:3,placeholder:"可选"},null,8,["modelValue"])]),_:1}),a(r,{label:"问题"},{default:s(()=>[a(v,{modelValue:e.question,"onUpdate:modelValue":t[1]||(t[1]=o=>e.question=o),type:"textarea",rows:4,placeholder:"请输入要对比的问题"},null,8,["modelValue"])]),_:1}),a(r,{label:"Temperature"},{default:s(()=>[a(d,{modelValue:e.temperature,"onUpdate:modelValue":t[2]||(t[2]=o=>e.temperature=o),min:0,max:2,step:.1,"show-input":"",style:{"max-width":"500px"}},null,8,["modelValue"])]),_:1}),a(r,{label:"Top-p"},{default:s(()=>[a(d,{modelValue:e.topP,"onUpdate:modelValue":t[3]||(t[3]=o=>e.topP=o),min:0,max:1,step:.05,"show-input":"",style:{"max-width":"500px"}},null,8,["modelValue"])]),_:1}),a(r,{label:"Top-k"},{default:s(()=>[a(d,{modelValue:e.topK,"onUpdate:modelValue":t[4]||(t[4]=o=>e.topK=o),min:1,max:100,step:1,"show-input":"",style:{"max-width":"500px"}},null,8,["modelValue"])]),_:1}),a(r,{label:"Max Tokens"},{default:s(()=>[a(d,{modelValue:e.maxTokens,"onUpdate:modelValue":t[5]||(t[5]=o=>e.maxTokens=o),min:256,max:4096,step:128,"show-input":"",style:{"max-width":"500px"}},null,8,["modelValue"])]),_:1}),a(r,null,{default:s(()=>[a(w,{type:"primary",disabled:!C.value||g.value,onClick:S},{default:s(()=>[...t[9]||(t[9]=[p(" 开始对比 ",-1)])]),_:1},8,["disabled"]),a(w,{onClick:t[6]||(t[6]=o=>P(y).back())},{default:s(()=>[...t[10]||(t[10]=[p("返回",-1)])]),_:1})]),_:1})]),_:1})]),_:1})}}}),Ut=Z(ot,[["__scopeId","data-v-9d25ca9d"]]);export{Ut as default};

frontend/dist/assets/CompareChatView-wm3b2ZD9.css

@@ -1 +0,0 @@
-.model-list[data-v-5da55d96]{display:flex;flex-wrap:wrap;gap:12px;margin-bottom:12px}.empty-hint[data-v-5da55d96]{color:#909399}

frontend/dist/assets/CompareChatView-yGYtaxtI.css

@@ -0,0 +1 @@
+.model-list[data-v-9d25ca9d]{display:flex;flex-wrap:wrap;gap:12px;margin-bottom:12px}.empty-hint[data-v-9d25ca9d]{color:#909399}

frontend/dist/assets/CompareResultView-CuqaCrX1.js

@@ -0,0 +1 @@
+import{E as D}from"./el-alert-DhjRMRnp.js";import{E as $}from"./index-DxH5-_w6.js";import{E as z}from"./el-tag-C2d1Zfbn.js";import{E as F}from"./el-card-DzhpMhms.js";import{d as O,G as U,H as J,c as d,q as i,aa as c,f as v,s as N,w as m,M as K,ad as A,ac as G,y as g,o as r,x as p,e as y,g as H}from"./index-D5veDkuG.js";import{_ as L}from"./MarkdownView.vue_vue_type_style_index_0_lang-DFYCH-49.js";import{a as W,c as j}from"./compare-DQvbhHeE.js";import{_ as Q}from"./_plugin-vue_export-helper-DlAUqK2U.js";import"./vnode-CYZHqsIy.js";const X={class:"compare-result"},Y={class:"result-header"},Z={class:"header-actions"},tt={class:"result-grid"},et={class:"card-header"},st={class:"model-name"},ot={key:0,class:"error-text"},at={key:1,class:"streaming-text"},nt={key:3,class:"loading-text"},lt={key:4,class:"result-stats"},rt=O({__name:"CompareResultView",setup(it){const u=G(),q=u.query.taskId,k=decodeURIComponent(u.query.question||""),h=decodeURIComponent(u.query.systemPrompt||""),E=Number(u.query.temperature||.7),P=Number(u.query.topP||.9),I=Number(u.query.topK||40),b=Number(u.query.maxTokens||2048),w=u.query.taskName,l=g([]),C=g(!1),x=g([]),f=new Set;async function B(){if(!C.value){C.value=!0;try{const s=await W(q);let t=[];s.load_status&&(t=(typeof s.load_status=="string"?JSON.parse(s.load_status):s.load_status).loaded_models||[]),x.value=t,l.value=t.map(o=>({name:o.model_name||"模型",content:"",displayContent:"",isTyping:!1,status:"loading"})),await Promise.all(t.map((o,e)=>S(o,e)))}catch{}}}async function S(s,t){const o=Date.now();try{const e=await V(j({port:s.port,model_name:s.model_name,messages:[...h?[{role:"system",content:h}]:[],{role:"user",content:k}],temperature:E,top_p:P,top_k:I,max_tokens:b}),3e5),a=(e==null?void 0:e.response)||(e==null?void 0:e.content)||(e==null?void 0:e.data)||JSON.stringify(e),_=(Date.now()-o)/1e3;l.value[t].content=a,l.value[t].status="done",l.value[t].stats={totalTime:_,charsPerSec:_>0?Number((a.length/_).toFixed(1)):0},M(t,a)}catch(e){l.value[t].content="推理失败: "+(e.message||""),l.value[t].status="error"}}async function V(s,t){let o=null;try{return await Promise.race([s,new Promise((e,a)=>{o=setTimeout(()=>a(new Error("推理超时")),t)})])}finally{o&&clearTimeout(o)}}function M(s,t){let o=0;l.value[s].isTyping=!0;const e=Math.max(2,Math.ceil(t.length/30)),a=setInterval(()=>{o+=e,l.value[s].displayContent=t.slice(0,o),o>=t.length&&(clearInterval(a),f.delete(a),l.value[s].displayContent=t,l.value[s].isTyping=!1)},50);f.add(a)}return U(B),J(()=>{f.forEach(clearInterval),f.clear()}),(s,t)=>{const o=$,e=D,a=z,_=F;return r(),d("div",X,[i("div",Y,[i("h2",null,"对比结果"+c(v(w)?` - ${v(w)}`:""),1),i("div",Z,[N(o,{onClick:t[0]||(t[0]=n=>s.$router.push("/model-inference"))},{default:m(()=>[...t[1]||(t[1]=[p("返回列表",-1)])]),_:1})])]),N(e,{type:"info",closable:!1,"show-icon":"",class:"question-box"},{title:m(()=>[t[2]||(t[2]=i("strong",null,"问题：",-1)),p(c(v(k)),1)]),_:1}),i("div",tt,[(r(!0),d(K,null,A(l.value,(n,R)=>(r(),y(_,{key:R,shadow:"hover",class:"result-card"},{header:m(()=>[i("div",et,[i("span",st,c(n.name),1),n.status==="loading"?(r(),y(a,{key:0,type:"warning",size:"small"},{default:m(()=>[...t[3]||(t[3]=[p("生成中...",-1)])]),_:1})):n.status==="done"?(r(),y(a,{key:1,type:"success",size:"small"},{default:m(()=>[...t[4]||(t[4]=[p("完成",-1)])]),_:1})):(r(),y(a,{key:2,type:"danger",size:"small"},{default:m(()=>[...t[5]||(t[5]=[p("失败",-1)])]),_:1}))])]),default:m(()=>{var T;return[n.status==="error"?(r(),d("div",ot,c(n.content),1)):n.isTyping?(r(),d("div",at,c(n.displayContent),1)):n.displayContent?(r(),y(L,{key:2,content:n.displayContent},null,8,["content"])):(r(),d("div",nt,[...t[6]||(t[6]=[i("i",{class:"fa fa-spinner fa-spin"},null,-1),p(" 正在生成回答... ",-1)])])),n.stats?(r(),d("div",lt,[i("span",null,"耗时 "+c((T=n.stats.totalTime)==null?void 0:T.toFixed(1))+"s",1),i("span",null,"速度 "+c(n.stats.charsPerSec)+" 字/秒",1)])):H("",!0)]}),_:2},1024))),128))])])}}}),gt=Q(rt,[["__scopeId","data-v-4c4e03d7"]]);export{gt as default};

frontend/dist/assets/CompareResultView-DOPqATtj.js

@@ -1 +0,0 @@
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frontend/dist/assets/ComputeNodesView-Czj9vH6H.css

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frontend/dist/assets/ComputeNodesView-airm9KfK.css

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frontend/dist/assets/DataConvertView-BPalOAiH.js

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+import{a as N,E as v}from"./el-form-item-rya6onmd.js";import{E as b}from"./el-popper-DDJTxf27.js";import{E as O}from"./index-DbBHj60R.js";import{E as S}from"./index-DxH5-_w6.js";import{E as g,a as E}from"./el-select-BQaRHu3V.js";import{d as J,e as V,w as e,o as w,q as t,s as l,x as i,z as x,A as C}from"./index-D5veDkuG.js";import"./el-tooltip-l0sNRNKZ.js";import"./el-scrollbar-GFOwBTUz.js";import"./el-tag-C2d1Zfbn.js";import{P as U}from"./PageCard-DFGMy1ik.js";import{_ as y}from"./_plugin-vue_export-helper-DlAUqK2U.js";import"./castArray-BDIbf8ih.js";import"./_baseClone-BuqHm105.js";import"./index-BT1A2I5c.js";import"./index-DmAaj_YO.js";import"./raf-Dsox5IbR.js";import"./index-DDY6aSpq.js";import"./vnode-CYZHqsIy.js";import"./index-DbuKXJ7V.js";import"./scroll-DswMXu1y.js";import"./clamp-Ddg8_Yc2.js";import"./toNumber-a8w_gs0u.js";import"./_baseIteratee-N-G7GeYE.js";import"./el-card-DzhpMhms.js";const F={class:"converter-panel"},L={class:"form-row"},T={class:"panel-footer"},B={class:"actions"},k=J({__name:"DataConvertView",setup(I){const a=x({outputName:"converted-data",encoding:"UTF-8"});function p(){C.info("当前仅完成界面设计，转换功能将在后续接入")}function d(){Object.assign(a,{outputName:"converted-data",encoding:"UTF-8"})}return(j,o)=>{const n=N,m=O,u=E,f=g,c=v,r=S,_=b;return w(),V(U,{class:"data-convert-page",title:"数据类型转换",subtitle:"将 JSON 文件转换为便于训练和评测使用的 JSONL 格式"},{default:e(()=>[t("div",F,[o[9]||(o[9]=t("div",{class:"panel-header"},[t("div",{class:"tool-icon","aria-hidden":"true"},[t("i",{class:"fa fa-exchange"})]),t("div",null,[t("h3",null,"JSON 转 JSONL"),t("p",null,"每条 JSON 数据将输出为 JSONL 文件中的一行记录")])],-1)),l(c,{class:"converter-form","label-position":"top"},{default:e(()=>[l(n,{label:"转换类型"},{default:e(()=>[...o[2]||(o[2]=[t("div",{class:"format-field","aria-label":"JSON 转 JSONL"},[t("span",null,"JSON"),t("i",{class:"fa fa-long-arrow-right","aria-hidden":"true"}),t("span",null,"JSONL")],-1)])]),_:1}),l(n,{label:"源文件",required:""},{default:e(()=>[t("button",{class:"upload-zone",type:"button",onClick:p},[...o[3]||(o[3]=[t("i",{class:"fa fa-cloud-upload","aria-hidden":"true"},null,-1),t("span",{class:"upload-content"},[t("strong",null,"点击选择或拖拽 JSON 文件到此处"),t("small",null,"仅支持 .json 格式，单文件不超过 200 MB")],-1),t("span",{class:"select-button"},"选择文件",-1)])])]),_:1}),t("div",L,[l(n,{label:"输出文件名"},{default:e(()=>[l(m,{modelValue:a.outputName,"onUpdate:modelValue":o[0]||(o[0]=s=>a.outputName=s)},{append:e(()=>[...o[4]||(o[4]=[i(".jsonl",-1)])]),_:1},8,["modelValue"])]),_:1}),l(n,{label:"字符编码"},{default:e(()=>[l(f,{modelValue:a.encoding,"onUpdate:modelValue":o[1]||(o[1]=s=>a.encoding=s),style:{width:"100%"}},{default:e(()=>[l(u,{label:"UTF-8",value:"UTF-8"})]),_:1},8,["modelValue"])]),_:1})]),o[5]||(o[5]=t("div",{class:"format-tip"},[t("i",{class:"fa fa-info-circle","aria-hidden":"true"}),t("span",null,"支持由 JSON 数组转换为 JSONL，每个数组元素输出为一行。")],-1))]),_:1}),t("div",T,[o[8]||(o[8]=t("span",{class:"prototype-label"},"当前为 UI 原型，暂不执行实际转换",-1)),t("div",B,[l(r,{onClick:d},{default:e(()=>[...o[6]||(o[6]=[i("重置",-1)])]),_:1}),l(_,{content:"转换功能将在后续开发中接入",placement:"top"},{default:e(()=>[t("span",null,[l(r,{type:"primary",disabled:""},{default:e(()=>[...o[7]||(o[7]=[i("开始转换",-1)])]),_:1})])]),_:1})])])])]),_:1})}}}),it=y(k,[["__scopeId","data-v-fcb69543"]]);export{it as default};

frontend/dist/assets/DataConvertView-Bog-IlBb.js

@@ -1 +0,0 @@
-import{a as N,E as v}from"./el-form-item-Bq0n_p5a.js";import{E as b}from"./el-popper-D1tByNLb.js";import{E as O}from"./index-h5FkzAP_.js";import{E as S}from"./index-BNsCyG4A.js";import{E as g,a as E}from"./el-select-BD70by95.js";import{d as J,e as V,w as e,o as w,q as t,s as l,x as i,z as x,A as C}from"./index-Ds9AETjS.js";import"./el-tooltip-l0sNRNKZ.js";import"./el-scrollbar-CF42LNzg.js";/* empty css               */import{P as U}from"./PageCard-CV3p14-x.js";import{_ as y}from"./_plugin-vue_export-helper-DlAUqK2U.js";import"./castArray-BWI4UxBy.js";import"./_baseClone-CZxxnGCv.js";import"./index-BSbCLbaz.js";import"./index-sWKLi7bH.js";import"./raf-zQ00nuMI.js";import"./index-B0if-AHo.js";import"./vnode-CBiWHFw7.js";import"./index-CQlF36Lk.js";import"./index-ByhS8A1d.js";import"./scroll-DVmMv0d9.js";import"./clamp-CyhADbdq.js";import"./toNumber-DrayciwT.js";import"./_baseIteratee-82gB5Yov.js";import"./el-card-D86TXgMv.js";const F={class:"converter-panel"},L={class:"form-row"},T={class:"panel-footer"},B={class:"actions"},k=J({__name:"DataConvertView",setup(I){const a=x({outputName:"converted-data",encoding:"UTF-8"});function p(){C.info("当前仅完成界面设计，转换功能将在后续接入")}function d(){Object.assign(a,{outputName:"converted-data",encoding:"UTF-8"})}return(j,o)=>{const n=N,m=O,u=E,f=g,c=v,r=S,_=b;return w(),V(U,{class:"data-convert-page",title:"数据类型转换",subtitle:"将 JSON 文件转换为便于训练和评测使用的 JSONL 格式"},{default:e(()=>[t("div",F,[o[9]||(o[9]=t("div",{class:"panel-header"},[t("div",{class:"tool-icon","aria-hidden":"true"},[t("i",{class:"fa fa-exchange"})]),t("div",null,[t("h3",null,"JSON 转 JSONL"),t("p",null,"每条 JSON 数据将输出为 JSONL 文件中的一行记录")])],-1)),l(c,{class:"converter-form","label-position":"top"},{default:e(()=>[l(n,{label:"转换类型"},{default:e(()=>[...o[2]||(o[2]=[t("div",{class:"format-field","aria-label":"JSON 转 JSONL"},[t("span",null,"JSON"),t("i",{class:"fa fa-long-arrow-right","aria-hidden":"true"}),t("span",null,"JSONL")],-1)])]),_:1}),l(n,{label:"源文件",required:""},{default:e(()=>[t("button",{class:"upload-zone",type:"button",onClick:p},[...o[3]||(o[3]=[t("i",{class:"fa fa-cloud-upload","aria-hidden":"true"},null,-1),t("span",{class:"upload-content"},[t("strong",null,"点击选择或拖拽 JSON 文件到此处"),t("small",null,"仅支持 .json 格式，单文件不超过 200 MB")],-1),t("span",{class:"select-button"},"选择文件",-1)])])]),_:1}),t("div",L,[l(n,{label:"输出文件名"},{default:e(()=>[l(m,{modelValue:a.outputName,"onUpdate:modelValue":o[0]||(o[0]=s=>a.outputName=s)},{append:e(()=>[...o[4]||(o[4]=[i(".jsonl",-1)])]),_:1},8,["modelValue"])]),_:1}),l(n,{label:"字符编码"},{default:e(()=>[l(f,{modelValue:a.encoding,"onUpdate:modelValue":o[1]||(o[1]=s=>a.encoding=s),style:{width:"100%"}},{default:e(()=>[l(u,{label:"UTF-8",value:"UTF-8"})]),_:1},8,["modelValue"])]),_:1})]),o[5]||(o[5]=t("div",{class:"format-tip"},[t("i",{class:"fa fa-info-circle","aria-hidden":"true"}),t("span",null,"支持由 JSON 数组转换为 JSONL，每个数组元素输出为一行。")],-1))]),_:1}),t("div",T,[o[8]||(o[8]=t("span",{class:"prototype-label"},"当前为 UI 原型，暂不执行实际转换",-1)),t("div",B,[l(r,{onClick:d},{default:e(()=>[...o[6]||(o[6]=[i("重置",-1)])]),_:1}),l(_,{content:"转换功能将在后续开发中接入",placement:"top"},{default:e(()=>[t("span",null,[l(r,{type:"primary",disabled:""},{default:e(()=>[...o[7]||(o[7]=[i("开始转换",-1)])]),_:1})])]),_:1})])])])]),_:1})}}}),rt=y(k,[["__scopeId","data-v-fcb69543"]]);export{rt as default};

frontend/dist/assets/DataProcessListView-C3R_ds-j.js

@@ -1 +0,0 @@
-import{E as b}from"./index-BNsCyG4A.js";import{E as k}from"./index-ByhS8A1d.js";import{E as x}from"./el-table-C7Zx0Bmo.js";import{d as v,c as m,s as e,w as a,y as D,o as l,q as p,x as i,e as C,aa as n,A as E,v as T}from"./index-Ds9AETjS.js";/* empty css               */import"./el-checkbox-pV3o_8iy.js";import{D as z}from"./DataTablePage-DzEUb2tf.js";import{_ as B}from"./ModelStatusTag.vue_vue_type_script_setup_true_lang-CHgevs5i.js";import{_ as V}from"./_plugin-vue_export-helper-DlAUqK2U.js";import"./el-scrollbar-CF42LNzg.js";import"./index-BSbCLbaz.js";import"./el-popper-D1tByNLb.js";import"./index-sWKLi7bH.js";import"./_baseClone-CZxxnGCv.js";import"./_baseIteratee-82gB5Yov.js";import"./castArray-BWI4UxBy.js";import"./debounce-D6mJVDYC.js";import"./toNumber-DrayciwT.js";import"./raf-zQ00nuMI.js";import"./omit-BzI1MV_K.js";import"./index-h5FkzAP_.js";import"./index-B0if-AHo.js";import"./el-card-D86TXgMv.js";import"./el-pagination-C9R_oTJ1.js";import"./el-select-BD70by95.js";import"./vnode-CBiWHFw7.js";import"./index-CQlF36Lk.js";import"./scroll-DVmMv0d9.js";import"./clamp-CyhADbdq.js";import"./directive-B1cwmVOz.js";import"./index-DqJTjrY3.js";import"./validator-D1MwuZXj.js";import"./index-jBnYrPdD.js";import"./index-RtwaMutE.js";import"./el-tooltip-l0sNRNKZ.js";const L={class:"data-process-page",style:{height:"100%"}},N={key:1},I={class:"action-buttons"},P=v({__name:"DataProcessListView",setup(S){const u={structured:"结构化数据",unstructured:"非结构化数据",external:"外来数据源拉取"},d=T(),_=D([{id:183921,name:"客服问答数据清洗",status:"completed",process_type:"structured",source_dataset:"客服对话原始集",output_dataset:"客服对话清洗集",create_time:"2026-07-08 14:23:00"},{id:492015,name:"指令微调数据构造",status:"running",process_type:"unstructured",source_dataset:"通用语料库",output_dataset:"SFT 指令集",create_time:"2026-07-09 09:10:00"},{id:731948,name:"敏感信息脱敏处理",status:"pending",process_type:"structured",source_dataset:"用户反馈数据",create_time:"2026-07-09 16:45:00"},{id:582012,name:"多轮对话拼接",status:"failed",process_type:"structured",source_dataset:"单轮问答集",create_time:"2026-07-10 08:30:00"}]);function f(s){const r=s.id;d.push({name:"data-process-detail",params:{id:r}})}function g(s){E.info("删除功能开发中...")}function y(s){return s?new Date(s).toLocaleString("zh-CN",{hour12:!1}):"-"}return(s,r)=>{const o=x,h=k,c=b;return l(),m("div",L,[e(z,{title:"",data:_.value,searchable:"","search-fields":["name"],"create-text":"新建数据处理","create-to":"/data-process/create","row-key":"id","page-size":10},{columns:a(()=>[e(o,{label:"任务ID",prop:"id",align:"center",width:"100"}),e(o,{label:"任务名称",prop:"name",align:"center","show-overflow-tooltip":""}),e(o,{label:"任务状态",align:"center",width:"110"},{default:a(({row:t})=>[e(B,{status:t.status},null,8,["status"])]),_:1}),e(o,{label:"处理类型",align:"center",width:"140"},{default:a(({row:t})=>[t.process_type?(l(),C(h,{key:0,size:"small",type:"info",effect:"plain"},{default:a(()=>[i(n(u[t.process_type]||t.process_type),1)]),_:2},1024)):(l(),m("span",N,"-"))]),_:1}),e(o,{label:"源数据集",align:"center","show-overflow-tooltip":""},{default:a(({row:t})=>[i(n(t.source_dataset||"-"),1)]),_:1}),e(o,{label:"输出数据集",align:"center","show-overflow-tooltip":""},{default:a(({row:t})=>[i(n(t.output_dataset||"-"),1)]),_:1}),e(o,{label:"创建时间",align:"center",width:"190"},{default:a(({row:t})=>[i(n(y(t.create_time)),1)]),_:1})]),actions:a(({row:t})=>[p("div",I,[e(c,{type:"primary",link:"",size:"small",onClick:w=>f(t)},{default:a(()=>[...r[0]||(r[0]=[p("i",{class:"fa fa-file-text-o",style:{"margin-right":"4px"}},null,-1),i("详情 ",-1)])]),_:1},8,["onClick"]),e(c,{type:"danger",link:"",size:"small",onClick:w=>g(t)},{default:a(()=>[...r[1]||(r[1]=[p("i",{class:"fa fa-trash-o",style:{"margin-right":"4px"}},null,-1),i("删除 ",-1)])]),_:1},8,["onClick"])])]),_:1},8,["data"])])}}}),yt=V(P,[["__scopeId","data-v-c0da9336"]]);export{yt as default};

frontend/dist/assets/DataProcessListView-CE3lkVcz.js

@@ -0,0 +1 @@
+import{E as b}from"./index-DxH5-_w6.js";import{E as k}from"./el-tag-C2d1Zfbn.js";import{E as x}from"./el-table-B6qMxzT4.js";import{d as v,c as m,s as e,w as a,y as D,o as l,q as p,x as i,e as C,aa as n,A as E,v as T}from"./index-D5veDkuG.js";import"./el-checkbox-D6iLoXkw.js";import{D as z}from"./DataTablePage-C2qtw8vm.js";import{_ as B}from"./ModelStatusTag.vue_vue_type_script_setup_true_lang-CDqn2Ul4.js";import{_ as V}from"./_plugin-vue_export-helper-DlAUqK2U.js";import"./el-scrollbar-GFOwBTUz.js";import"./index-BT1A2I5c.js";import"./el-popper-DDJTxf27.js";import"./index-DmAaj_YO.js";import"./_baseClone-BuqHm105.js";import"./_baseIteratee-N-G7GeYE.js";import"./castArray-BDIbf8ih.js";import"./debounce-D3zKL4rk.js";import"./toNumber-a8w_gs0u.js";import"./raf-Dsox5IbR.js";import"./omit-DxQEBv1Z.js";import"./index-DbBHj60R.js";import"./index-DDY6aSpq.js";import"./el-card-DzhpMhms.js";import"./el-pagination-DeOi3r5c.js";import"./el-select-BQaRHu3V.js";import"./vnode-CYZHqsIy.js";import"./index-DbuKXJ7V.js";import"./scroll-DswMXu1y.js";import"./clamp-Ddg8_Yc2.js";import"./directive-BBfPDwlQ.js";import"./index-BMWxqs3c.js";import"./validator-CddYkkyy.js";import"./index-fqcbYM03.js";import"./index-BNyn2v9n.js";import"./el-tooltip-l0sNRNKZ.js";import"./status-Dl1fykxa.js";const L={class:"data-process-page",style:{height:"100%"}},N={key:1},I={class:"action-buttons"},P=v({__name:"DataProcessListView",setup(S){const u={structured:"结构化数据",unstructured:"非结构化数据",external:"外来数据源拉取"},d=T(),_=D([{id:183921,name:"客服问答数据清洗",status:"completed",process_type:"structured",source_dataset:"客服对话原始集",output_dataset:"客服对话清洗集",create_time:"2026-07-08 14:23:00"},{id:492015,name:"指令微调数据构造",status:"running",process_type:"unstructured",source_dataset:"通用语料库",output_dataset:"SFT 指令集",create_time:"2026-07-09 09:10:00"},{id:731948,name:"敏感信息脱敏处理",status:"pending",process_type:"structured",source_dataset:"用户反馈数据",create_time:"2026-07-09 16:45:00"},{id:582012,name:"多轮对话拼接",status:"failed",process_type:"structured",source_dataset:"单轮问答集",create_time:"2026-07-10 08:30:00"}]);function f(s){const r=s.id;d.push({name:"data-process-detail",params:{id:r}})}function g(s){E.info("删除功能开发中...")}function y(s){return s?new Date(s).toLocaleString("zh-CN",{hour12:!1}):"-"}return(s,r)=>{const o=x,h=k,c=b;return l(),m("div",L,[e(z,{title:"",data:_.value,searchable:"","search-fields":["name"],"create-text":"新建数据处理","create-to":"/data-process/create","row-key":"id","page-size":10},{columns:a(()=>[e(o,{label:"任务ID",prop:"id",align:"center",width:"100"}),e(o,{label:"任务名称",prop:"name",align:"center","show-overflow-tooltip":""}),e(o,{label:"任务状态",align:"center",width:"110"},{default:a(({row:t})=>[e(B,{status:t.status},null,8,["status"])]),_:1}),e(o,{label:"处理类型",align:"center",width:"140"},{default:a(({row:t})=>[t.process_type?(l(),C(h,{key:0,size:"small",type:"info",effect:"plain"},{default:a(()=>[i(n(u[t.process_type]||t.process_type),1)]),_:2},1024)):(l(),m("span",N,"-"))]),_:1}),e(o,{label:"源数据集",align:"center","show-overflow-tooltip":""},{default:a(({row:t})=>[i(n(t.source_dataset||"-"),1)]),_:1}),e(o,{label:"输出数据集",align:"center","show-overflow-tooltip":""},{default:a(({row:t})=>[i(n(t.output_dataset||"-"),1)]),_:1}),e(o,{label:"创建时间",align:"center",width:"190"},{default:a(({row:t})=>[i(n(y(t.create_time)),1)]),_:1})]),actions:a(({row:t})=>[p("div",I,[e(c,{type:"primary",link:"",size:"small",onClick:w=>f(t)},{default:a(()=>[...r[0]||(r[0]=[p("i",{class:"fa fa-file-text-o",style:{"margin-right":"4px"}},null,-1),i("详情 ",-1)])]),_:1},8,["onClick"]),e(c,{type:"danger",link:"",size:"small",onClick:w=>g(t)},{default:a(()=>[...r[1]||(r[1]=[p("i",{class:"fa fa-trash-o",style:{"margin-right":"4px"}},null,-1),i("删除 ",-1)])]),_:1},8,["onClick"])])]),_:1},8,["data"])])}}}),yt=V(P,[["__scopeId","data-v-c0da9336"]]);export{yt as default};

frontend/dist/assets/DataTablePage-CHPswbwi.css

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-@charset "UTF-8";.data-table-page[data-v-e6e41965]{width:100%;height:100%;box-sizing:border-box;display:flex;flex-direction:column;min-height:0}.data-table-page .table-card[data-v-e6e41965]{margin-bottom:0;width:100%;flex:1;display:flex;flex-direction:column;min-height:0;overflow:hidden}.data-table-page .table-toolbar[data-v-e6e41965]{display:flex;align-items:center;justify-content:space-between;margin-bottom:8px;padding:0 12px;flex-wrap:wrap;gap:12px}.data-table-page .table-title[data-v-e6e41965]{font-size:18px;font-weight:600;color:#1e293b;margin:0;letter-spacing:-.3px}.data-table-page .toolbar-actions[data-v-e6e41965]{display:flex;align-items:center;gap:12px}.data-table-page .search-input[data-v-e6e41965]{width:280px}.data-table-page .search-input[data-v-e6e41965] .el-input__wrapper{border-radius:8px;box-shadow:0 0 0 1px #e2e8f0 inset;transition:all .2s}.data-table-page .search-input[data-v-e6e41965] .el-input__wrapper.is-focus{box-shadow:0 0 0 1px var(--primary-color) inset}.data-table-page .batch-bar[data-v-e6e41965]{display:flex;align-items:center;justify-content:space-between;background:#4f46e50d;border:1px solid rgba(79,70,229,.1);border-radius:8px;padding:10px 16px;margin:0 24px 16px}.data-table-page .batch-bar .batch-info[data-v-e6e41965]{font-size:13px;color:var(--primary-color)}.data-table-page .batch-bar .batch-info strong[data-v-e6e41965]{font-weight:600;margin:0 4px}.data-table-page .table-body[data-v-e6e41965]{flex:1;min-height:0}.data-table-page .table-pagination[data-v-e6e41965]{display:flex;justify-content:flex-end;align-items:center;padding:16px 24px;border-top:1px solid #f1f5f9}.data-table-page .table-pagination[data-v-e6e41965] .el-pagination{margin-top:0}.data-table-page[data-v-e6e41965] .el-card__body{display:flex;flex-direction:column;padding:12px 0 0;flex:1;min-height:0}.data-table-page[data-v-e6e41965] .el-table__inner-wrapper:before{display:none}.data-table-page[data-v-e6e41965] .el-table td.el-table__cell{padding:18px 0!important}.data-table-page[data-v-e6e41965] .el-table__cell .cell{line-height:22px;white-space:nowrap}.data-table-page[data-v-e6e41965] .el-table .action-buttons{display:flex;align-items:center;justify-content:center;flex-wrap:nowrap;gap:8px}

frontend/dist/assets/DataTablePage-DzEUb2tf.js

@@ -1 +0,0 @@
-import{E as M}from"./index-h5FkzAP_.js";import{E as Z}from"./index-BNsCyG4A.js";import{E as A}from"./el-card-D86TXgMv.js";import{E as G}from"./el-pagination-C9R_oTJ1.js";import{E as H,a as J}from"./el-table-C7Zx0Bmo.js";import{v as O}from"./directive-B1cwmVOz.js";import{E as b}from"./index-DqJTjrY3.js";import{d as Q,y as f,D as _,c as D,s as p,w as r,o as u,q as s,h as w,e as g,g as v,x as y,aa as k,Z as W,K as X,j as $,v as Y}from"./index-Ds9AETjS.js";import"./el-scrollbar-CF42LNzg.js";import"./el-popper-D1tByNLb.js";/* empty css               */import"./el-select-BD70by95.js";import"./el-tooltip-l0sNRNKZ.js";import"./el-checkbox-pV3o_8iy.js";import{_ as ee}from"./_plugin-vue_export-helper-DlAUqK2U.js";const te={class:"data-table-page"},ae={class:"table-toolbar"},le={class:"table-title"},oe={class:"toolbar-actions"},se={key:0,class:"batch-bar"},ne={class:"batch-info"},re={class:"table-body"},ie={class:"table-pagination"},ce=Q({__name:"DataTablePage",props:{title:{},data:{},loading:{type:Boolean,default:!1},searchable:{type:Boolean,default:!1},searchFields:{},multiSelect:{type:Boolean,default:!1},createText:{},createTo:{},deleteFn:{},rowKey:{default:"id"},emptyText:{default:"暂无数据"},pageSize:{default:10},pageSizes:{default:()=>[10,20,50,100]}},emits:["create","refresh"],setup(l,{expose:F,emit:V}){const a=l,x=V,P=Y(),S=f(),o=f(new Set),d=f(""),m=f(1),h=f(a.pageSize);function z(t){return t[a.rowKey]??t.name??t.id}const T=$(()=>{var e;if(!d.value.trim()||!((e=a.searchFields)!=null&&e.length))return a.data;const t=d.value.toLowerCase().trim();return a.data.filter(i=>a.searchFields.some(n=>String(i[n]??"").toLowerCase().includes(t)))}),I=$(()=>{const t=(m.value-1)*h.value;return T.value.slice(t,t+h.value)});function K(t){o.value=new Set(t.map(z))}async function E(t,e="确定要删除这条记录吗？"){if(a.deleteFn)try{await b.confirm(e,"确认删除",{type:"warning",confirmButtonText:"确定",cancelButtonText:"取消"}),await a.deleteFn(t),x("refresh")}catch{}}async function N(){if(!(o.value.size===0||!a.deleteFn))try{await b.confirm(`确定要删除选中的 ${o.value.size} 条记录吗？`,"批量删除",{type:"warning"});const t=a.data.filter(n=>o.value.has(z(n)));let e=0,i=0;for(const n of t)try{await a.deleteFn(n),e++}catch{i++}o.value.clear(),x("refresh"),i===0?b.alert(`成功删除 ${e} 条记录`,"成功",{type:"success"}):b.alert(`成功 ${e} 条，失败 ${i} 条`,"部分失败",{type:"warning"})}catch{}}function C(){var t;o.value.clear(),(t=S.value)==null||t.clearSelection()}function L(){a.createTo?P.push(a.createTo):x("create")}return _(()=>a.data,()=>{var t;o.value.clear(),m.value=1,(t=S.value)==null||t.clearSelection()}),_(()=>d.value,()=>{m.value=1}),F({handleDelete:E,clearSelection:C}),(t,e)=>{const i=M,n=Z,B=H,R=J,U=G,j=A,q=O;return u(),D("div",te,[p(j,{shadow:"never",class:"table-card"},{default:r(()=>[s("div",ae,[w(t.$slots,"title",{},()=>[s("h2",le,k(l.title),1)],!0),s("div",oe,[l.searchable?(u(),g(i,{key:0,modelValue:d.value,"onUpdate:modelValue":e[0]||(e[0]=c=>d.value=c),placeholder:`搜索${l.title}...`,clearable:"",class:"search-input"},{prefix:r(()=>[...e[3]||(e[3]=[s("i",{class:"fa fa-search"},null,-1)])]),_:1},8,["modelValue","placeholder"])):v("",!0),l.createText?(u(),g(n,{key:1,type:"primary",onClick:L},{default:r(()=>[e[4]||(e[4]=s("i",{class:"fa fa-plus",style:{"margin-right":"4px"}},null,-1)),y(" "+k(l.createText),1)]),_:1})):v("",!0),w(t.$slots,"toolbar-extra",{},void 0,!0)])]),p(W,{name:"el-zoom-in-top"},{default:r(()=>[l.multiSelect&&o.value.size>0?(u(),D("div",se,[s("span",ne,[e[6]||(e[6]=y(" 已选择 ",-1)),s("strong",null,k(o.value.size),1),e[7]||(e[7]=y(" 项 ",-1)),p(n,{link:"",type:"primary",onClick:C},{default:r(()=>[...e[5]||(e[5]=[y("取消选择",-1)])]),_:1})]),p(n,{type:"danger",size:"small",onClick:N},{default:r(()=>[e[8]||(e[8]=s("i",{class:"fa fa-trash",style:{"margin-right":"4px"}},null,-1)),y(" 批量删除 ("+k(o.value.size)+") ",1)]),_:1})])):v("",!0)]),_:1}),s("div",re,[X((u(),g(R,{ref_key:"tableRef",ref:S,data:I.value,"row-key":l.rowKey,height:"100%",style:{width:"100%"},"empty-text":l.emptyText,onSelectionChange:K},{default:r(()=>[l.multiSelect?(u(),g(B,{key:0,type:"selection",width:"50",align:"center","reserve-selection":"",fixed:"left"})):v("",!0),w(t.$slots,"columns",{},void 0,!0),t.$slots.actions?(u(),g(B,{key:1,label:"操作",align:"center",width:"220",fixed:"right"},{default:r(({row:c})=>[w(t.$slots,"actions",{row:c,handleDelete:E},void 0,!0)]),_:3})):v("",!0)]),_:3},8,["data","row-key","empty-text"])),[[q,l.loading]])]),s("div",ie,[p(U,{"current-page":m.value,"onUpdate:currentPage":e[1]||(e[1]=c=>m.value=c),"page-size":h.value,"onUpdate:pageSize":e[2]||(e[2]=c=>h.value=c),background:"",layout:"total, sizes, prev, pager, next, jumper","page-sizes":l.pageSizes,total:T.value.length},null,8,["current-page","page-size","page-sizes","total"])])]),_:3})])}}}),Te=ee(ce,[["__scopeId","data-v-e6e41965"]]);export{Te as D};

frontend/dist/assets/DataTablePage-nV2v3ghG.css

@@ -0,0 +1 @@
+@charset "UTF-8";.data-table-page[data-v-db8d3b6e]{width:100%;height:100%;box-sizing:border-box;display:flex;flex-direction:column;min-height:0}.data-table-page .table-card[data-v-db8d3b6e]{margin-bottom:0;width:100%;flex:1;display:flex;flex-direction:column;min-height:0;overflow:hidden}.data-table-page .table-toolbar[data-v-db8d3b6e]{display:flex;align-items:center;justify-content:space-between;margin-bottom:8px;padding:0 12px;flex-wrap:wrap;gap:12px}.data-table-page .table-title[data-v-db8d3b6e]{font-size:18px;font-weight:600;color:#1e293b;margin:0;letter-spacing:-.3px}.data-table-page .toolbar-actions[data-v-db8d3b6e]{display:flex;align-items:center;gap:12px}.data-table-page .search-input[data-v-db8d3b6e]{width:280px}.data-table-page .search-input[data-v-db8d3b6e] .el-input__wrapper{border-radius:8px;box-shadow:0 0 0 1px #e2e8f0 inset;transition:all .2s}.data-table-page .search-input[data-v-db8d3b6e] .el-input__wrapper.is-focus{box-shadow:0 0 0 1px var(--primary-color) inset}.data-table-page .batch-bar[data-v-db8d3b6e]{display:flex;align-items:center;justify-content:space-between;background:#4f46e50d;border:1px solid rgba(79,70,229,.1);border-radius:8px;padding:10px 16px;margin:0 24px 16px}.data-table-page .batch-bar .batch-info[data-v-db8d3b6e]{font-size:13px;color:var(--primary-color)}.data-table-page .batch-bar .batch-info strong[data-v-db8d3b6e]{font-weight:600;margin:0 4px}.data-table-page .table-body[data-v-db8d3b6e]{flex:1;min-height:0}.data-table-page .table-pagination[data-v-db8d3b6e]{display:flex;justify-content:flex-end;align-items:center;padding:16px 24px;border-top:1px solid #f1f5f9}.data-table-page .table-pagination[data-v-db8d3b6e] .el-pagination{margin-top:0}.data-table-page[data-v-db8d3b6e] .el-card__body{display:flex;flex-direction:column;padding:12px 0 0;flex:1;min-height:0}.data-table-page[data-v-db8d3b6e] .el-table__inner-wrapper:before{display:none}.data-table-page[data-v-db8d3b6e] .el-table td.el-table__cell{padding:18px 0!important}.data-table-page[data-v-db8d3b6e] .el-table__cell .cell{line-height:22px;white-space:nowrap}.data-table-page[data-v-db8d3b6e] .el-table .action-buttons{display:flex;align-items:center;justify-content:center;flex-wrap:nowrap;gap:8px}

frontend/dist/assets/DatasetListView-CF1w9JzC.js

@@ -1 +0,0 @@
-import{E as z}from"./index-BNsCyG4A.js";import{E}from"./index-ByhS8A1d.js";import{E as T}from"./el-table-C7Zx0Bmo.js";import{d as L,G as A,e as k,w as e,y as f,j as V,o as y,q as r,s as l,x as s,g as $,aa as p,f as b,aY as B,aZ as N,n as w,v as P,A as S}from"./index-Ds9AETjS.js";/* empty css               */import"./el-checkbox-pV3o_8iy.js";import{D as M}from"./DataTablePage-DzEUb2tf.js";import{g as R,d as G,a as I}from"./dataset-BWM6p-mU.js";import{_ as Y}from"./_plugin-vue_export-helper-DlAUqK2U.js";import"./el-scrollbar-CF42LNzg.js";import"./index-BSbCLbaz.js";import"./el-popper-D1tByNLb.js";import"./index-sWKLi7bH.js";import"./_baseClone-CZxxnGCv.js";import"./_baseIteratee-82gB5Yov.js";import"./castArray-BWI4UxBy.js";import"./debounce-D6mJVDYC.js";import"./toNumber-DrayciwT.js";import"./raf-zQ00nuMI.js";import"./omit-BzI1MV_K.js";import"./index-h5FkzAP_.js";import"./index-B0if-AHo.js";import"./el-card-D86TXgMv.js";import"./el-pagination-C9R_oTJ1.js";import"./el-select-BD70by95.js";import"./vnode-CBiWHFw7.js";import"./index-CQlF36Lk.js";import"./scroll-DVmMv0d9.js";import"./clamp-CyhADbdq.js";import"./directive-B1cwmVOz.js";import"./index-DqJTjrY3.js";import"./validator-D1MwuZXj.js";import"./index-jBnYrPdD.js";import"./index-RtwaMutE.js";import"./el-tooltip-l0sNRNKZ.js";const j={class:"capsule-tabs"},q={class:"action-buttons"},O=L({__name:"DatasetListView",setup(U){const C=P(),m=f(!1),c=f([]),n=f("upload"),D=V(()=>n.value==="task"?c.value.filter(i=>i.source==="task"):c.value.filter(i=>i.source!=="task"));async function d(){m.value=!0;try{c.value=await R()||[]}catch{}finally{m.value=!1}}async function _(i){await G(i.id),c.value=c.value.filter(a=>a.id!==i.id),await d(),S.success("删除成功")}function h(i){C.push(`/dataset/${i.id}/preview`)}function x(i){window.open(I(i.id),"_blank")}return A(d),(i,a)=>{const o=T,v=E,u=z;return y(),k(M,{title:"数据集管理",data:D.value,loading:m.value,searchable:"","search-fields":["name","description"],"create-text":n.value==="upload"?"上传数据集":"","create-to":"/dataset/create","delete-fn":_,"row-key":"id",onRefresh:d},{title:e(()=>[r("div",j,[r("button",{class:w(["capsule-tab-item",{active:n.value==="upload"}]),onClick:a[0]||(a[0]=t=>n.value="upload")}," 上传任务 ",2),r("button",{class:w(["capsule-tab-item",{active:n.value==="task"}]),onClick:a[1]||(a[1]=t=>n.value="task")}," 数据任务 ",2)])]),columns:e(()=>[n.value==="task"?(y(),k(o,{key:0,label:"任务ID",prop:"task_id",align:"center",width:"100"})):$("",!0),l(o,{label:"数据集名称",prop:"name",align:"center"}),l(o,{label:"数据类型",align:"center",width:"110"},{default:e(({row:t})=>[l(v,{type:"primary",size:"small"},{default:e(()=>[s(p(b(B)[String(t.type).toLowerCase()]||t.type||"-"),1)]),_:2},1024)]),_:1}),l(o,{label:"存储位置",align:"center",width:"110"},{default:e(({row:t})=>[l(v,{type:"success",size:"small"},{default:e(()=>[s(p(b(N)[t.storage_type]||t.storage_type||"-"),1)]),_:2},1024)]),_:1}),l(o,{label:"大小",align:"center",width:"100"},{default:e(({row:t})=>[s(p(t.size&&t.size!=="0 B"&&t.size!=="0"?t.size:"-"),1)]),_:1}),l(o,{label:"数据条数",align:"center",width:"100"},{default:e(({row:t})=>[s(p(t.count||0),1)]),_:1}),l(o,{label:"描述",align:"center","show-overflow-tooltip":""},{default:e(({row:t})=>[s(p(t.description||"-"),1)]),_:1}),l(o,{label:"创建时间",align:"center",width:"180"},{default:e(({row:t})=>[s(p(t.create_time?new Date(t.create_time).toLocaleString("zh-CN"):"-"),1)]),_:1})]),actions:e(({row:t})=>[r("div",q,[l(u,{type:"primary",link:"",size:"small",onClick:g=>h(t)},{default:e(()=>[...a[2]||(a[2]=[r("i",{class:"fa fa-eye",style:{"margin-right":"4px"}},null,-1),s("详情 ",-1)])]),_:1},8,["onClick"]),l(u,{type:"success",link:"",size:"small",onClick:g=>x(t)},{default:e(()=>[...a[3]||(a[3]=[r("i",{class:"fa fa-download",style:{"margin-right":"4px"}},null,-1),s("下载 ",-1)])]),_:1},8,["onClick"]),l(u,{type:"danger",link:"",size:"small",onClick:g=>_(t)},{default:e(()=>[...a[4]||(a[4]=[r("i",{class:"fa fa-trash-o",style:{"margin-right":"4px"}},null,-1),s("删除 ",-1)])]),_:1},8,["onClick"])])]),_:1},8,["data","loading","create-text"])}}}),Et=Y(O,[["__scopeId","data-v-e406ac6d"]]);export{Et as default};

frontend/dist/assets/DatasetListView-CwP8x_GJ.js

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+import{E as z}from"./index-DxH5-_w6.js";import{E}from"./el-tag-C2d1Zfbn.js";import{E as T}from"./el-table-B6qMxzT4.js";import{d as L,G as A,e as k,w as e,y as f,j as V,o as y,q as r,s as l,x as s,g as $,aa as p,f as b,aY as B,aZ as N,n as w,v as P,A as S}from"./index-D5veDkuG.js";import"./el-checkbox-D6iLoXkw.js";import{D as M}from"./DataTablePage-C2qtw8vm.js";import{g as R,d as G,a as I}from"./dataset-BvcQX9QF.js";import{_ as Y}from"./_plugin-vue_export-helper-DlAUqK2U.js";import"./el-scrollbar-GFOwBTUz.js";import"./index-BT1A2I5c.js";import"./el-popper-DDJTxf27.js";import"./index-DmAaj_YO.js";import"./_baseClone-BuqHm105.js";import"./_baseIteratee-N-G7GeYE.js";import"./castArray-BDIbf8ih.js";import"./debounce-D3zKL4rk.js";import"./toNumber-a8w_gs0u.js";import"./raf-Dsox5IbR.js";import"./omit-DxQEBv1Z.js";import"./index-DbBHj60R.js";import"./index-DDY6aSpq.js";import"./el-card-DzhpMhms.js";import"./el-pagination-DeOi3r5c.js";import"./el-select-BQaRHu3V.js";import"./vnode-CYZHqsIy.js";import"./index-DbuKXJ7V.js";import"./scroll-DswMXu1y.js";import"./clamp-Ddg8_Yc2.js";import"./directive-BBfPDwlQ.js";import"./index-BMWxqs3c.js";import"./validator-CddYkkyy.js";import"./index-fqcbYM03.js";import"./index-BNyn2v9n.js";import"./el-tooltip-l0sNRNKZ.js";const j={class:"capsule-tabs"},q={class:"action-buttons"},O=L({__name:"DatasetListView",setup(U){const C=P(),d=f(!1),c=f([]),n=f("upload"),D=V(()=>n.value==="task"?c.value.filter(i=>i.source==="task"):c.value.filter(i=>i.source!=="task"));async function m(){d.value=!0;try{c.value=await R()||[]}catch{}finally{d.value=!1}}async function _(i){await G(i.id),c.value=c.value.filter(a=>a.id!==i.id),await m(),S.success("删除成功")}function h(i){C.push(`/dataset/${i.id}/preview`)}function x(i){window.open(I(i.id),"_blank")}return A(m),(i,a)=>{const o=T,v=E,u=z;return y(),k(M,{title:"数据集管理",data:D.value,loading:d.value,searchable:"","search-fields":["name","description"],"create-text":n.value==="upload"?"上传数据集":"","create-to":"/dataset/create","delete-fn":_,"row-key":"id",onRefresh:m},{title:e(()=>[r("div",j,[r("button",{class:w(["capsule-tab-item",{active:n.value==="upload"}]),onClick:a[0]||(a[0]=t=>n.value="upload")}," 上传任务 ",2),r("button",{class:w(["capsule-tab-item",{active:n.value==="task"}]),onClick:a[1]||(a[1]=t=>n.value="task")}," 数据任务 ",2)])]),columns:e(()=>[n.value==="task"?(y(),k(o,{key:0,label:"任务ID",prop:"task_id",align:"center",width:"100"})):$("",!0),l(o,{label:"数据集名称",prop:"name",align:"center"}),l(o,{label:"数据类型",align:"center",width:"110"},{default:e(({row:t})=>[l(v,{type:"primary",size:"small"},{default:e(()=>[s(p(b(B)[String(t.type).toLowerCase()]||t.type||"-"),1)]),_:2},1024)]),_:1}),l(o,{label:"存储位置",align:"center",width:"110"},{default:e(({row:t})=>[l(v,{type:"success",size:"small"},{default:e(()=>[s(p(b(N)[t.storage_type]||t.storage_type||"-"),1)]),_:2},1024)]),_:1}),l(o,{label:"大小",align:"center",width:"100"},{default:e(({row:t})=>[s(p(t.size&&t.size!=="0 B"&&t.size!=="0"?t.size:"-"),1)]),_:1}),l(o,{label:"数据条数",align:"center",width:"100"},{default:e(({row:t})=>[s(p(t.count||0),1)]),_:1}),l(o,{label:"描述",align:"center","show-overflow-tooltip":""},{default:e(({row:t})=>[s(p(t.description||"-"),1)]),_:1}),l(o,{label:"创建时间",align:"center",width:"180"},{default:e(({row:t})=>[s(p(t.create_time?new Date(t.create_time).toLocaleString("zh-CN"):"-"),1)]),_:1})]),actions:e(({row:t})=>[r("div",q,[l(u,{type:"primary",link:"",size:"small",onClick:g=>h(t)},{default:e(()=>[...a[2]||(a[2]=[r("i",{class:"fa fa-eye",style:{"margin-right":"4px"}},null,-1),s("详情 ",-1)])]),_:1},8,["onClick"]),l(u,{type:"success",link:"",size:"small",onClick:g=>x(t)},{default:e(()=>[...a[3]||(a[3]=[r("i",{class:"fa fa-download",style:{"margin-right":"4px"}},null,-1),s("下载 ",-1)])]),_:1},8,["onClick"]),l(u,{type:"danger",link:"",size:"small",onClick:g=>_(t)},{default:e(()=>[...a[4]||(a[4]=[r("i",{class:"fa fa-trash-o",style:{"margin-right":"4px"}},null,-1),s("删除 ",-1)])]),_:1},8,["onClick"])])]),_:1},8,["data","loading","create-text"])}}}),zt=Y(O,[["__scopeId","data-v-e406ac6d"]]);export{zt as default};

frontend/dist/assets/DimensionCreateView-dON_Mrtf.js

@@ -0,0 +1 @@
+import{a as k,E as M}from"./el-form-item-rya6onmd.js";import{E as q}from"./index-DbBHj60R.js";import{E as R}from"./index-DxH5-_w6.js";import{d as S,G as B,e as F,w as i,j as y,y as _,z as I,o as O,s as r,f as m,aB as P,x as h,aa as j,A as x,v as A,ac as L}from"./index-D5veDkuG.js";import{P as N}from"./PageCard-DFGMy1ik.js";import{E as T,D as U}from"./DimensionFormFields-CNtVeJ2J.js";import{b as z,u as G,c as H}from"./eval-DOmZ1wrg.js";import{g as J}from"./model-Do_d1nlo.js";import"./castArray-BDIbf8ih.js";import"./_baseClone-BuqHm105.js";import"./raf-Dsox5IbR.js";import"./index-DDY6aSpq.js";import"./index-BT1A2I5c.js";import"./el-card-DzhpMhms.js";import"./_plugin-vue_export-helper-DlAUqK2U.js";import"./el-select-BQaRHu3V.js";import"./index-DmAaj_YO.js";import"./vnode-CYZHqsIy.js";import"./el-popper-DDJTxf27.js";import"./el-scrollbar-GFOwBTUz.js";import"./index-DbuKXJ7V.js";import"./el-tag-C2d1Zfbn.js";import"./scroll-DswMXu1y.js";import"./clamp-Ddg8_Yc2.js";import"./toNumber-a8w_gs0u.js";import"./_baseIteratee-N-G7GeYE.js";import"./el-checkbox-D6iLoXkw.js";import"./omit-DxQEBv1Z.js";import"./el-radio-group-HDdMmxmO.js";import"./el-input-number-Cs1MXSA_.js";import"./index-APLq0PKf.js";import"./el-slider-pAA0AhdE.js";import"./debounce-D3zKL4rk.js";import"./el-switch-DCvDahA2.js";import"./validator-CddYkkyy.js";import"./el-tooltip-l0sNRNKZ.js";/* empty css                          */const Se=S({__name:"DimensionCreateView",setup(K){const c=L(),f=A(),n=_(),p=_(!1),u=y(()=>!!c.params.id),s=y(()=>c.params.id),d=_([]);let e=I({name:"",type:"",description:"",eval_model:"",eval_method:"",eval_prompt:"",is_active:!0,is_default:!1,bleu_n:1,output_precision:3,score_min:0,score_max:5,pass_threshold:3});const b={name:[{required:!0,message:"请输入维度名称",trigger:"blur"},{max:50,message:"不超过 50 字符",trigger:"blur"}],type:[{required:!0,message:"请选择指标类型",trigger:"change"}],eval_model:[{required:!0,message:"请选择大模型",trigger:"change"}],eval_method:[{required:!0,message:"请选择评估方式",trigger:"change"}],eval_prompt:[{required:!0,message:"请填写评估 Prompt",trigger:"blur"}]};async function w(){var o,t;if(s.value)try{const a=await z(s.value);Object.assign(e,{name:a.name||"",type:a.type||"",description:a.description||"",eval_model:a.eval_model||"",eval_method:a.eval_method||(a.type?(t=(o=T[a.type])==null?void 0:o[0])==null?void 0:t.value:""),eval_prompt:a.eval_prompt||"",is_active:a.is_active!==!1,is_default:!!a.is_default,bleu_n:a.bleu_n??1,output_precision:a.output_precision??3,score_min:a.score_min??0,score_max:a.score_max??5,pass_threshold:a.pass_threshold??3})}catch{}}async function E(){try{const o=await J()||[];d.value=o.filter(t=>t.purpose==="evaluation")}catch{d.value=[]}}async function D(){n.value&&await n.value.validate(async o=>{if(o){p.value=!0;try{const t={name:e.name,type:e.type,description:e.description,eval_model:e.type==="text_similarity"?null:e.eval_model,eval_method:e.eval_method,eval_prompt:e.type==="text_similarity"?null:e.eval_prompt,is_active:e.is_active,is_default:e.is_default,create_time:new Date().toISOString()};e.type==="text_similarity"&&(t.bleu_n=e.bleu_n,t.output_precision=e.output_precision),e.type==="metric"&&(t.score_min=e.score_min,t.score_max=e.score_max,t.pass_threshold=e.pass_threshold),u.value&&s.value?(await G(s.value,t),x.success("更新成功")):(await H(t),x.success("创建成功")),f.push("/model-eval")}catch{}finally{p.value=!1}}})}function V(){f.back()}return B(()=>{E(),w()}),(o,t)=>{const a=q,v=k,g=R,C=M;return O(),F(N,{title:u.value?"编辑评测维度":"添加评测维度"},{default:i(()=>[r(C,{ref_key:"formRef",ref:n,model:m(e),rules:b,"label-width":"130px",style:{"max-width":"760px"}},{default:i(()=>[r(v,{label:"维度名称",prop:"name"},{default:i(()=>[r(a,{modelValue:m(e).name,"onUpdate:modelValue":t[0]||(t[0]=l=>m(e).name=l),placeholder:"请输入维度名称",maxlength:"50","show-word-limit":""},null,8,["modelValue"])]),_:1}),r(U,{modelValue:m(e),"onUpdate:modelValue":t[1]||(t[1]=l=>P(e)?e.value=l:e=l),"eval-models":d.value},null,8,["modelValue","eval-models"]),r(v,null,{default:i(()=>[r(g,{type:"primary",loading:p.value,onClick:D},{default:i(()=>[h(j(u.value?"保存":"创建"),1)]),_:1},8,["loading"]),r(g,{onClick:V},{default:i(()=>[...t[2]||(t[2]=[h("取消",-1)])]),_:1})]),_:1})]),_:1},8,["model"])]),_:1},8,["title"])}}});export{Se as default};

frontend/dist/assets/DimensionCreateView-l0omXzHp.js

@@ -1 +0,0 @@
-import{a as k,E as M}from"./el-form-item-Bq0n_p5a.js";import{E as q}from"./index-h5FkzAP_.js";import{E as R}from"./index-BNsCyG4A.js";import{d as S,G as B,e as F,w as i,j as y,y as _,z as I,o as O,s as r,f as m,aB as P,x as h,aa as j,A as x,v as A,ac as L}from"./index-Ds9AETjS.js";import{P as N}from"./PageCard-CV3p14-x.js";import{E as T,D as U}from"./DimensionFormFields-Bvlt4Km3.js";import{b as z,u as G,c as H}from"./eval-RqHG_SzR.js";import{g as J}from"./model-cWd9CDtT.js";import"./castArray-BWI4UxBy.js";import"./_baseClone-CZxxnGCv.js";import"./raf-zQ00nuMI.js";import"./index-B0if-AHo.js";import"./index-BSbCLbaz.js";import"./el-card-D86TXgMv.js";import"./_plugin-vue_export-helper-DlAUqK2U.js";import"./el-select-BD70by95.js";import"./index-sWKLi7bH.js";import"./vnode-CBiWHFw7.js";import"./el-popper-D1tByNLb.js";import"./el-scrollbar-CF42LNzg.js";import"./index-CQlF36Lk.js";import"./index-ByhS8A1d.js";import"./scroll-DVmMv0d9.js";import"./clamp-CyhADbdq.js";import"./toNumber-DrayciwT.js";import"./_baseIteratee-82gB5Yov.js";import"./el-checkbox-pV3o_8iy.js";import"./omit-BzI1MV_K.js";import"./el-radio-group-DxV9SE4w.js";import"./el-input-number-Bg2C0S8w.js";import"./index-2LOSO4Pw.js";import"./el-slider-mZJHOhL2.js";import"./debounce-D6mJVDYC.js";import"./el-switch-DflKYtBf.js";import"./validator-D1MwuZXj.js";import"./el-tooltip-l0sNRNKZ.js";/* empty css                          *//* empty css               */const Be=S({__name:"DimensionCreateView",setup(K){const c=L(),f=A(),n=_(),p=_(!1),u=y(()=>!!c.params.id),s=y(()=>c.params.id),d=_([]);let e=I({name:"",type:"",description:"",eval_model:"",eval_method:"",eval_prompt:"",is_active:!0,is_default:!1,bleu_n:1,output_precision:3,score_min:0,score_max:5,pass_threshold:3});const b={name:[{required:!0,message:"请输入维度名称",trigger:"blur"},{max:50,message:"不超过 50 字符",trigger:"blur"}],type:[{required:!0,message:"请选择指标类型",trigger:"change"}],eval_model:[{required:!0,message:"请选择大模型",trigger:"change"}],eval_method:[{required:!0,message:"请选择评估方式",trigger:"change"}],eval_prompt:[{required:!0,message:"请填写评估 Prompt",trigger:"blur"}]};async function w(){var o,t;if(s.value)try{const a=await z(s.value);Object.assign(e,{name:a.name||"",type:a.type||"",description:a.description||"",eval_model:a.eval_model||"",eval_method:a.eval_method||(a.type?(t=(o=T[a.type])==null?void 0:o[0])==null?void 0:t.value:""),eval_prompt:a.eval_prompt||"",is_active:a.is_active!==!1,is_default:!!a.is_default,bleu_n:a.bleu_n??1,output_precision:a.output_precision??3,score_min:a.score_min??0,score_max:a.score_max??5,pass_threshold:a.pass_threshold??3})}catch{}}async function E(){try{const o=await J()||[];d.value=o.filter(t=>t.purpose==="evaluation")}catch{d.value=[]}}async function D(){n.value&&await n.value.validate(async o=>{if(o){p.value=!0;try{const t={name:e.name,type:e.type,description:e.description,eval_model:e.type==="text_similarity"?null:e.eval_model,eval_method:e.eval_method,eval_prompt:e.type==="text_similarity"?null:e.eval_prompt,is_active:e.is_active,is_default:e.is_default,create_time:new Date().toISOString()};e.type==="text_similarity"&&(t.bleu_n=e.bleu_n,t.output_precision=e.output_precision),e.type==="metric"&&(t.score_min=e.score_min,t.score_max=e.score_max,t.pass_threshold=e.pass_threshold),u.value&&s.value?(await G(s.value,t),x.success("更新成功")):(await H(t),x.success("创建成功")),f.push("/model-eval")}catch{}finally{p.value=!1}}})}function V(){f.back()}return B(()=>{E(),w()}),(o,t)=>{const a=q,v=k,g=R,C=M;return O(),F(N,{title:u.value?"编辑评测维度":"添加评测维度"},{default:i(()=>[r(C,{ref_key:"formRef",ref:n,model:m(e),rules:b,"label-width":"130px",style:{"max-width":"760px"}},{default:i(()=>[r(v,{label:"维度名称",prop:"name"},{default:i(()=>[r(a,{modelValue:m(e).name,"onUpdate:modelValue":t[0]||(t[0]=l=>m(e).name=l),placeholder:"请输入维度名称",maxlength:"50","show-word-limit":""},null,8,["modelValue"])]),_:1}),r(U,{modelValue:m(e),"onUpdate:modelValue":t[1]||(t[1]=l=>P(e)?e.value=l:e=l),"eval-models":d.value},null,8,["modelValue","eval-models"]),r(v,null,{default:i(()=>[r(g,{type:"primary",loading:p.value,onClick:D},{default:i(()=>[h(j(u.value?"保存":"创建"),1)]),_:1},8,["loading"]),r(g,{onClick:V},{default:i(()=>[...t[2]||(t[2]=[h("取消",-1)])]),_:1})]),_:1})]),_:1},8,["model"])]),_:1},8,["title"])}}});export{Be as default};

frontend/dist/assets/EvalView--2TVFV5G.css

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frontend/dist/assets/EvalView-CB5BSC8Y.css

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frontend/dist/assets/EvalView-CPiVytgX.js

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frontend/dist/assets/EvalView-Eldo4kFi.js

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frontend/dist/assets/GuideView-CPXL-GBe.js

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frontend/dist/assets/InferenceCreateView-D9JfMwNe.js

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frontend/dist/assets/InferenceCreateView-DEMDPZG-.js

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frontend/dist/assets/LoginView-w9agRGlh.js

@@ -1 +1 @@
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frontend/dist/assets/MergeWeightsView-D-_UJ_G8.js

@@ -0,0 +1 @@
+import{E as A}from"./el-alert-DhjRMRnp.js";import{a as N,E as T}from"./el-form-item-rya6onmd.js";import{E as B}from"./index-DbBHj60R.js";import{E as I}from"./index-DxH5-_w6.js";import{E as R}from"./el-tag-C2d1Zfbn.js";import{d as P,z as q,G as D,e as d,w as o,j as y,y as E,o as n,s as t,f as F,ar as U,x as m,aa as W,c as j,g as z,A as M,v as G,ac as H}from"./index-D5veDkuG.js";import{P as L}from"./PageCard-DFGMy1ik.js";import{a as O,m as S}from"./model-Do_d1nlo.js";import{_ as J}from"./_plugin-vue_export-helper-DlAUqK2U.js";import"./vnode-CYZHqsIy.js";import"./castArray-BDIbf8ih.js";import"./_baseClone-BuqHm105.js";import"./raf-Dsox5IbR.js";import"./index-DDY6aSpq.js";import"./index-BT1A2I5c.js";import"./el-card-DzhpMhms.js";const K={key:0,class:"model-status"},Q=P({__name:"MergeWeightsView",setup(X){const v=H(),b=G(),s=E(!1),u=y(()=>v.query.model||""),w=y(()=>v.query.method||"lora"),i=E([]),_=y(()=>i.value.find(l=>l.name===u.value)),a=q({model_name:u.value,train_method:w.value,base_model_path:""});async function V(){try{const l=await O();i.value=(l==null?void 0:l.models)||[];const e=i.value.find(p=>p.name===u.value);a.base_model_path=(e==null?void 0:e.base_model_path)||""}catch{}}async function k(){if(!a.model_name||!a.base_model_path){M.warning("缺少模型信息");return}s.value=!0;try{await S({model_name:a.model_name,train_method:a.train_method,base_model_path:a.base_model_path}),M.success("合并成功"),b.push("/model-manage")}catch{}finally{s.value=!1}}function x(){b.back()}return D(V),(l,e)=>{const p=A,f=B,r=N,h=I,C=T,c=R;return n(),d(L,{title:"合并权重"},{default:o(()=>[t(p,{type:"info",closable:!1,"show-icon":"",title:"将 LoRA 适配器权重合并到基座模型，合并后可直接用于推理部署。",style:{"margin-bottom":"20px"}}),t(C,{"label-width":"120px",style:{"max-width":"600px"}},{default:o(()=>[t(r,{label:"模型名称"},{default:o(()=>[t(f,{modelValue:a.model_name,"onUpdate:modelValue":e[0]||(e[0]=g=>a.model_name=g),disabled:""},null,8,["modelValue"])]),_:1}),t(r,{label:"训练方法"},{default:o(()=>[t(f,{"model-value":F(U)[a.train_method]||a.train_method,disabled:""},null,8,["model-value"])]),_:1}),t(r,{label:"基座模型路径"},{default:o(()=>[t(f,{modelValue:a.base_model_path,"onUpdate:modelValue":e[1]||(e[1]=g=>a.base_model_path=g),placeholder:"基座模型路径"},null,8,["modelValue"])]),_:1}),t(r,null,{default:o(()=>[t(h,{type:"primary",loading:s.value,onClick:k},{default:o(()=>[m(W(s.value?"合并中...":"开始合并"),1)]),_:1},8,["loading"]),t(h,{onClick:x},{default:o(()=>[...e[2]||(e[2]=[m("取消",-1)])]),_:1})]),_:1})]),_:1}),_.value?(n(),j("div",K,[_.value.merging?(n(),d(c,{key:0,type:"warning"},{default:o(()=>[...e[3]||(e[3]=[m("合并中",-1)])]),_:1})):_.value.merged?(n(),d(c,{key:1,type:"success"},{default:o(()=>[...e[4]||(e[4]=[m("已合并",-1)])]),_:1})):(n(),d(c,{key:2,type:"info"},{default:o(()=>[...e[5]||(e[5]=[m("未合并",-1)])]),_:1}))])):z("",!0)]),_:1})}}}),pe=J(Q,[["__scopeId","data-v-2e30aa99"]]);export{pe as default};

frontend/dist/assets/MergeWeightsView-lUYxd1a_.js

@@ -1 +0,0 @@
-import{E as A}from"./el-alert-CoSdHkeK.js";import{a as N,E as T}from"./el-form-item-Bq0n_p5a.js";import{E as B}from"./index-h5FkzAP_.js";import{E as I}from"./index-BNsCyG4A.js";import{E as R}from"./index-ByhS8A1d.js";import{d as P,z as q,G as D,e as d,w as o,j as y,y as E,o as n,s as t,f as F,ar as U,x as m,aa as W,c as j,g as z,A as M,v as G,ac as H}from"./index-Ds9AETjS.js";/* empty css               */import{P as L}from"./PageCard-CV3p14-x.js";import{a as O,m as S}from"./model-cWd9CDtT.js";import{_ as J}from"./_plugin-vue_export-helper-DlAUqK2U.js";import"./vnode-CBiWHFw7.js";import"./castArray-BWI4UxBy.js";import"./_baseClone-CZxxnGCv.js";import"./raf-zQ00nuMI.js";import"./index-B0if-AHo.js";import"./index-BSbCLbaz.js";import"./el-card-D86TXgMv.js";const K={key:0,class:"model-status"},Q=P({__name:"MergeWeightsView",setup(X){const v=H(),b=G(),s=E(!1),u=y(()=>v.query.model||""),w=y(()=>v.query.method||"lora"),i=E([]),_=y(()=>i.value.find(l=>l.name===u.value)),a=q({model_name:u.value,train_method:w.value,base_model_path:""});async function V(){try{const l=await O();i.value=(l==null?void 0:l.models)||[];const e=i.value.find(p=>p.name===u.value);a.base_model_path=(e==null?void 0:e.base_model_path)||""}catch{}}async function k(){if(!a.model_name||!a.base_model_path){M.warning("缺少模型信息");return}s.value=!0;try{await S({model_name:a.model_name,train_method:a.train_method,base_model_path:a.base_model_path}),M.success("合并成功"),b.push("/model-manage")}catch{}finally{s.value=!1}}function x(){b.back()}return D(V),(l,e)=>{const p=A,f=B,r=N,h=I,C=T,c=R;return n(),d(L,{title:"合并权重"},{default:o(()=>[t(p,{type:"info",closable:!1,"show-icon":"",title:"将 LoRA 适配器权重合并到基座模型，合并后可直接用于推理部署。",style:{"margin-bottom":"20px"}}),t(C,{"label-width":"120px",style:{"max-width":"600px"}},{default:o(()=>[t(r,{label:"模型名称"},{default:o(()=>[t(f,{modelValue:a.model_name,"onUpdate:modelValue":e[0]||(e[0]=g=>a.model_name=g),disabled:""},null,8,["modelValue"])]),_:1}),t(r,{label:"训练方法"},{default:o(()=>[t(f,{"model-value":F(U)[a.train_method]||a.train_method,disabled:""},null,8,["model-value"])]),_:1}),t(r,{label:"基座模型路径"},{default:o(()=>[t(f,{modelValue:a.base_model_path,"onUpdate:modelValue":e[1]||(e[1]=g=>a.base_model_path=g),placeholder:"基座模型路径"},null,8,["modelValue"])]),_:1}),t(r,null,{default:o(()=>[t(h,{type:"primary",loading:s.value,onClick:k},{default:o(()=>[m(W(s.value?"合并中...":"开始合并"),1)]),_:1},8,["loading"]),t(h,{onClick:x},{default:o(()=>[...e[2]||(e[2]=[m("取消",-1)])]),_:1})]),_:1})]),_:1}),_.value?(n(),j("div",K,[_.value.merging?(n(),d(c,{key:0,type:"warning"},{default:o(()=>[...e[3]||(e[3]=[m("合并中",-1)])]),_:1})):_.value.merged?(n(),d(c,{key:1,type:"success"},{default:o(()=>[...e[4]||(e[4]=[m("已合并",-1)])]),_:1})):(n(),d(c,{key:2,type:"info"},{default:o(()=>[...e[5]||(e[5]=[m("未合并",-1)])]),_:1}))])):z("",!0)]),_:1})}}}),fe=J(Q,[["__scopeId","data-v-2e30aa99"]]);export{fe as default};

frontend/dist/assets/ModelManageView-B6vWdbQY.css

@@ -0,0 +1 @@
+.model-manage-page[data-v-cd3c82d5]{height:100%}.capsule-tabs[data-v-cd3c82d5]{display:flex;gap:2px;padding:3px;border:1px solid #e2e8f0;border-radius:8px;background:#f1f5f9}.capsule-tab-item[data-v-cd3c82d5]{border:0;border-radius:6px;background:transparent;padding:6px 20px;color:#64748b;font-size:13px;font-weight:500;cursor:pointer}.capsule-tab-item.active[data-v-cd3c82d5]{background:#fff;color:#4f46e5;box-shadow:0 1px 3px #0000000f;font-weight:600}.action-buttons[data-v-cd3c82d5]{display:flex;justify-content:center;gap:8px}.runtime-panel[data-v-cd3c82d5]{display:grid;gap:16px;padding:16px 24px;background:#f8fafc}.panel-title[data-v-cd3c82d5]{display:flex;justify-content:space-between;align-items:center;margin-bottom:8px;color:#1f2937}

frontend/dist/assets/ModelManageView-SqczPXXo.css

@@ -1 +0,0 @@
-@charset "UTF-8";.capsule-tabs[data-v-4609d016]{display:flex;background:#f1f5f9;padding:3px;border-radius:8px;gap:2px;border:1px solid #e2e8f0}.capsule-tab-item[data-v-4609d016]{border:0;background:transparent;padding:6px 20px;font-size:13px;font-weight:500;color:#64748b;cursor:pointer;border-radius:6px;transition:all .2s ease;outline:none}.capsule-tab-item[data-v-4609d016]:hover{color:#1e293b}.capsule-tab-item.active[data-v-4609d016]{background:#fff;color:#4f46e5;box-shadow:0 1px 3px #0000000f,0 1px 2px #0000000a;font-weight:600}.action-buttons[data-v-4609d016]{display:flex;justify-content:center;gap:8px}

frontend/dist/assets/ModelStatusTag.vue_vue_type_script_setup_true_lang-CDqn2Ul4.js

@@ -0,0 +1 @@
+import{E as o}from"./el-tag-C2d1Zfbn.js";import{d as p,e as n,w as r,j as c,o as l,x as u,aa as m}from"./index-D5veDkuG.js";import{s as _,a as i}from"./status-Dl1fykxa.js";const T=p({__name:"ModelStatusTag",props:{status:{}},setup(e){const t=e,a=c(()=>({text:i(t.status),type:_(t.status)}));return(f,g)=>{const s=o;return l(),n(s,{type:a.value.type,size:"small",effect:"light"},{default:r(()=>[u(m(a.value.text),1)]),_:1},8,["type"])}}});export{T as _};

frontend/dist/assets/ModelStatusTag.vue_vue_type_script_setup_true_lang-CHgevs5i.js

@@ -1 +0,0 @@
-import{E as r}from"./index-ByhS8A1d.js";import{d as o,e as c,w as p,j as u,o as i,x as d,aa as l}from"./index-Ds9AETjS.js";/* empty css               */const m=o({__name:"ModelStatusTag",props:{status:{}},setup(a){const s=a,t=u(()=>{const e=s.status||"";switch(e){case"running":case"ready":case"loaded":return{text:e==="ready"||e==="loaded"?"已就绪":"运行中",type:"success"};case"pending":return{text:"等待中",type:"info"};case"failed":return{text:"失败",type:"danger"};case"starting":case"loading":return{text:"加载中",type:"warning"};case"completed":return{text:"已完成",type:"success"};case"stopped":return{text:"已停止",type:"info"};default:return{text:e||"未知",type:"info"}}});return(e,f)=>{const n=r;return i(),c(n,{type:t.value.type,size:"small",effect:"light"},{default:p(()=>[d(l(t.value.text),1)]),_:1},8,["type"])}}});export{m as _};

frontend/dist/assets/PageCard-DFGMy1ik.js

@@ -1 +1 @@
-import{E as i}from"./el-card-D86TXgMv.js";import{d as n,e as u,w as p,o as a,c as s,h as o,g as r,q as d,aa as l}from"./index-Ds9AETjS.js";import{_}from"./_plugin-vue_export-helper-DlAUqK2U.js";const h={key:0,class:"page-card-header"},m={class:"page-card-title"},f={key:0,class:"page-card-subtitle"},g={key:0,class:"page-card-extra"},v={class:"page-card-body"},b=n({__name:"PageCard",props:{title:{},subtitle:{},bordered:{type:Boolean,default:!1}},setup(t){return(e,k)=>{const c=i;return a(),u(c,{shadow:"never",class:"page-card"},{default:p(()=>[t.title||e.$slots.header?(a(),s("div",h,[o(e.$slots,"header",{},()=>[d("div",null,[d("h2",m,l(t.title),1),t.subtitle?(a(),s("p",f,l(t.subtitle),1)):r("",!0)])],!0),e.$slots.extra?(a(),s("div",g,[o(e.$slots,"extra",{},void 0,!0)])):r("",!0)])):r("",!0),d("div",v,[o(e.$slots,"default",{},void 0,!0)])]),_:3})}}}),$=_(b,[["__scopeId","data-v-05c37e8a"]]);export{$ as P};
+import{E as i}from"./el-card-DzhpMhms.js";import{d as n,e as u,w as p,o as a,c as s,h as o,g as r,q as d,aa as l}from"./index-D5veDkuG.js";import{_}from"./_plugin-vue_export-helper-DlAUqK2U.js";const h={key:0,class:"page-card-header"},m={class:"page-card-title"},f={key:0,class:"page-card-subtitle"},g={key:0,class:"page-card-extra"},v={class:"page-card-body"},b=n({__name:"PageCard",props:{title:{},subtitle:{},bordered:{type:Boolean,default:!1}},setup(t){return(e,k)=>{const c=i;return a(),u(c,{shadow:"never",class:"page-card"},{default:p(()=>[t.title||e.$slots.header?(a(),s("div",h,[o(e.$slots,"header",{},()=>[d("div",null,[d("h2",m,l(t.title),1),t.subtitle?(a(),s("p",f,l(t.subtitle),1)):r("",!0)])],!0),e.$slots.extra?(a(),s("div",g,[o(e.$slots,"extra",{},void 0,!0)])):r("",!0)])):r("",!0),d("div",v,[o(e.$slots,"default",{},void 0,!0)])]),_:3})}}}),$=_(b,[["__scopeId","data-v-05c37e8a"]]);export{$ as P};

frontend/dist/assets/PermissionDeniedView-D-L3VoaK.js

@@ -1 +1 @@
-import{_ as o}from"./_plugin-vue_export-helper-DlAUqK2U.js";import{o as c,c as t,q as s}from"./index-Ds9AETjS.js";const n={},r={class:"simple-page"};function a(i,e){return c(),t("section",r,[...e[0]||(e[0]=[s("h1",null,"无权访问",-1),s("p",null,"当前账号没有访问该页面的权限，请联系管理员调整角色或页面权限。",-1)])])}const p=o(n,[["render",a],["__scopeId","data-v-cc370c43"]]);export{p as default};
+import{_ as o}from"./_plugin-vue_export-helper-DlAUqK2U.js";import{o as c,c as t,q as s}from"./index-D5veDkuG.js";const n={},r={class:"simple-page"};function a(i,e){return c(),t("section",r,[...e[0]||(e[0]=[s("h1",null,"无权访问",-1),s("p",null,"当前账号没有访问该页面的权限，请联系管理员调整角色或页面权限。",-1)])])}const p=o(n,[["render",a],["__scopeId","data-v-cc370c43"]]);export{p as default};

frontend/dist/assets/ToolCreateView-B1IDAgEB.js

@@ -1 +1 @@
-import{a as y,E as h}from"./el-form-item-Bq0n_p5a.js";import{E as B}from"./index-h5FkzAP_.js";import{E as R}from"./index-BNsCyG4A.js";import{d as S,G as I,e as N,w as l,j as g,z as O,o as m,s as o,q as v,c as x,ad as U,n as V,f as q,bx as F,M as L,x as b,aa as M,A as w,y as j,v as z,ac as D}from"./index-Ds9AETjS.js";import{P}from"./PageCard-CV3p14-x.js";import{u as A}from"./tools-s3Et6p-2.js";import{_ as G}from"./_plugin-vue_export-helper-DlAUqK2U.js";import"./castArray-BWI4UxBy.js";import"./_baseClone-CZxxnGCv.js";import"./raf-zQ00nuMI.js";import"./index-B0if-AHo.js";import"./index-BSbCLbaz.js";import"./el-card-D86TXgMv.js";const $={class:"icon-grid"},H=["onClick"],J=S({__name:"ToolCreateView",setup(K){const d=D(),p=z(),n=A(),i=j(),s=g(()=>d.params.id!=null),c=g(()=>s.value?n.getTool(d.params.id):void 0),e=O({id:"",name:"",description:"",url:"",icon:"fa-cog"}),C={name:[{required:!0,message:"请输入工具名称",trigger:"blur"},{max:30,message:"不超过 30 字符",trigger:"blur"}],url:[{required:!0,message:"请输入跳转地址",trigger:"blur"}]};function k(){i.value&&i.value.validate(f=>{f&&(s.value?(n.updateTool(e.id,{...e}),w.success("更新成功")):(n.addTool({...e,id:"custom_"+Date.now()}),w.success("添加成功")),p.push("/tools"))})}function E(){p.back()}return I(()=>{c.value&&Object.assign(e,c.value)}),(f,a)=>{const u=B,r=y,_=R,T=h;return m(),N(P,{title:s.value?"编辑自定义工具":"添加自定义工具"},{default:l(()=>[o(T,{ref_key:"formRef",ref:i,model:e,rules:C,"label-width":"100px",style:{"max-width":"600px"}},{default:l(()=>[o(r,{label:"工具名称",prop:"name"},{default:l(()=>[o(u,{modelValue:e.name,"onUpdate:modelValue":a[0]||(a[0]=t=>e.name=t),placeholder:"请输入工具名称",maxlength:"30","show-word-limit":""},null,8,["modelValue"])]),_:1}),o(r,{label:"工具描述"},{default:l(()=>[o(u,{modelValue:e.description,"onUpdate:modelValue":a[1]||(a[1]=t=>e.description=t),type:"textarea",rows:2,maxlength:"100","show-word-limit":""},null,8,["modelValue"])]),_:1}),o(r,{label:"跳转地址",prop:"url"},{default:l(()=>[o(u,{modelValue:e.url,"onUpdate:modelValue":a[2]||(a[2]=t=>e.url=t),placeholder:"相对路径或完整 URL"},null,8,["modelValue"])]),_:1}),o(r,{label:"图标"},{default:l(()=>[v("div",$,[(m(!0),x(L,null,U(q(F),t=>(m(),x("div",{key:t,class:V(["icon-option",{selected:e.icon===t}]),onClick:Q=>e.icon=t},[v("i",{class:V(["fa",t])},null,2)],10,H))),128))])]),_:1}),o(r,null,{default:l(()=>[o(_,{type:"primary",onClick:k},{default:l(()=>[b(M(s.value?"保存":"添加"),1)]),_:1}),o(_,{onClick:E},{default:l(()=>[...a[3]||(a[3]=[b("取消",-1)])]),_:1})]),_:1})]),_:1},8,["model"])]),_:1},8,["title"])}}}),ue=G(J,[["__scopeId","data-v-9116c5de"]]);export{ue as default};
+import{a as y,E as h}from"./el-form-item-rya6onmd.js";import{E as B}from"./index-DbBHj60R.js";import{E as R}from"./index-DxH5-_w6.js";import{d as S,G as I,e as N,w as l,j as g,z as O,o as m,s as o,q as v,c as x,ad as U,n as V,f as q,bx as F,M as L,x as b,aa as M,A as w,y as j,v as z,ac as D}from"./index-D5veDkuG.js";import{P}from"./PageCard-DFGMy1ik.js";import{u as A}from"./tools-niY0Ru_Q.js";import{_ as G}from"./_plugin-vue_export-helper-DlAUqK2U.js";import"./castArray-BDIbf8ih.js";import"./_baseClone-BuqHm105.js";import"./raf-Dsox5IbR.js";import"./index-DDY6aSpq.js";import"./index-BT1A2I5c.js";import"./el-card-DzhpMhms.js";const $={class:"icon-grid"},H=["onClick"],J=S({__name:"ToolCreateView",setup(K){const d=D(),p=z(),n=A(),i=j(),s=g(()=>d.params.id!=null),c=g(()=>s.value?n.getTool(d.params.id):void 0),e=O({id:"",name:"",description:"",url:"",icon:"fa-cog"}),C={name:[{required:!0,message:"请输入工具名称",trigger:"blur"},{max:30,message:"不超过 30 字符",trigger:"blur"}],url:[{required:!0,message:"请输入跳转地址",trigger:"blur"}]};function k(){i.value&&i.value.validate(f=>{f&&(s.value?(n.updateTool(e.id,{...e}),w.success("更新成功")):(n.addTool({...e,id:"custom_"+Date.now()}),w.success("添加成功")),p.push("/tools"))})}function E(){p.back()}return I(()=>{c.value&&Object.assign(e,c.value)}),(f,a)=>{const u=B,r=y,_=R,T=h;return m(),N(P,{title:s.value?"编辑自定义工具":"添加自定义工具"},{default:l(()=>[o(T,{ref_key:"formRef",ref:i,model:e,rules:C,"label-width":"100px",style:{"max-width":"600px"}},{default:l(()=>[o(r,{label:"工具名称",prop:"name"},{default:l(()=>[o(u,{modelValue:e.name,"onUpdate:modelValue":a[0]||(a[0]=t=>e.name=t),placeholder:"请输入工具名称",maxlength:"30","show-word-limit":""},null,8,["modelValue"])]),_:1}),o(r,{label:"工具描述"},{default:l(()=>[o(u,{modelValue:e.description,"onUpdate:modelValue":a[1]||(a[1]=t=>e.description=t),type:"textarea",rows:2,maxlength:"100","show-word-limit":""},null,8,["modelValue"])]),_:1}),o(r,{label:"跳转地址",prop:"url"},{default:l(()=>[o(u,{modelValue:e.url,"onUpdate:modelValue":a[2]||(a[2]=t=>e.url=t),placeholder:"相对路径或完整 URL"},null,8,["modelValue"])]),_:1}),o(r,{label:"图标"},{default:l(()=>[v("div",$,[(m(!0),x(L,null,U(q(F),t=>(m(),x("div",{key:t,class:V(["icon-option",{selected:e.icon===t}]),onClick:Q=>e.icon=t},[v("i",{class:V(["fa",t])},null,2)],10,H))),128))])]),_:1}),o(r,null,{default:l(()=>[o(_,{type:"primary",onClick:k},{default:l(()=>[b(M(s.value?"保存":"添加"),1)]),_:1}),o(_,{onClick:E},{default:l(()=>[...a[3]||(a[3]=[b("取消",-1)])]),_:1})]),_:1})]),_:1},8,["model"])]),_:1},8,["title"])}}}),ue=G(J,[["__scopeId","data-v-9116c5de"]]);export{ue as default};

frontend/dist/assets/UserCreateView-D_u7YOkp.js

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+import{E as y,a as x}from"./el-form-item-rya6onmd.js";import{E as k}from"./index-DbBHj60R.js";import{E as U}from"./index-DxH5-_w6.js";import{E as C,a as B}from"./el-select-BQaRHu3V.js";import{a as F,E as I}from"./el-checkbox-D6iLoXkw.js";import{E as N,b as R}from"./el-radio-group-HDdMmxmO.js";import{d as G,c as _,q as M,s as o,w as l,o as f,x as r,M as O,ad as S,aa as q,f as z,v as A,y as D,z as L,db as T,A as $}from"./index-D5veDkuG.js";/* empty css                          */import"./el-scrollbar-GFOwBTUz.js";import"./el-popper-DDJTxf27.js";import"./el-tag-C2d1Zfbn.js";import{_ as j}from"./_plugin-vue_export-helper-DlAUqK2U.js";import"./castArray-BDIbf8ih.js";import"./_baseClone-BuqHm105.js";import"./raf-Dsox5IbR.js";import"./index-DDY6aSpq.js";import"./index-BT1A2I5c.js";import"./index-DmAaj_YO.js";import"./vnode-CYZHqsIy.js";import"./index-DbuKXJ7V.js";import"./scroll-DswMXu1y.js";import"./clamp-Ddg8_Yc2.js";import"./toNumber-a8w_gs0u.js";import"./_baseIteratee-N-G7GeYE.js";import"./omit-DxQEBv1Z.js";const H={class:"user-create"},J=G({__name:"UserCreateView",setup(K){const d=A(),n=D(!1),a=L({username:"",display_name:"",password:"platform123",role:"viewer",status:"active",permissions:["dashboard"]}),b=["dashboard","fine-tune","model-eval","model-inference","model-manage","dataset","data-process","data-convert","compute","hardware","logs","user-settings"];async function V(){n.value=!0;try{await T(a),$.success("用户已创建"),d.push("/user-settings")}finally{n.value=!1}}return(P,e)=>{const m=k,s=x,u=B,v=C,p=R,c=N,E=I,w=F,i=U,g=y;return f(),_("section",H,[e[11]||(e[11]=M("h1",null,"创建用户",-1)),o(g,{model:a,"label-width":"110px",class:"user-form"},{default:l(()=>[o(s,{label:"账号"},{default:l(()=>[o(m,{modelValue:a.username,"onUpdate:modelValue":e[0]||(e[0]=t=>a.username=t)},null,8,["modelValue"])]),_:1}),o(s,{label:"显示名称"},{default:l(()=>[o(m,{modelValue:a.display_name,"onUpdate:modelValue":e[1]||(e[1]=t=>a.display_name=t)},null,8,["modelValue"])]),_:1}),o(s,{label:"初始密码"},{default:l(()=>[o(m,{modelValue:a.password,"onUpdate:modelValue":e[2]||(e[2]=t=>a.password=t),type:"password","show-password":""},null,8,["modelValue"])]),_:1}),o(s,{label:"角色"},{default:l(()=>[o(v,{modelValue:a.role,"onUpdate:modelValue":e[3]||(e[3]=t=>a.role=t)},{default:l(()=>[o(u,{label:"管理员",value:"admin"}),o(u,{label:"操作员",value:"operator"}),o(u,{label:"观察员",value:"viewer"})]),_:1},8,["modelValue"])]),_:1}),o(s,{label:"状态"},{default:l(()=>[o(c,{modelValue:a.status,"onUpdate:modelValue":e[4]||(e[4]=t=>a.status=t)},{default:l(()=>[o(p,{value:"active"},{default:l(()=>[...e[7]||(e[7]=[r("启用",-1)])]),_:1}),o(p,{value:"disabled"},{default:l(()=>[...e[8]||(e[8]=[r("禁用",-1)])]),_:1})]),_:1},8,["modelValue"])]),_:1}),o(s,{label:"页面权限"},{default:l(()=>[o(w,{modelValue:a.permissions,"onUpdate:modelValue":e[5]||(e[5]=t=>a.permissions=t)},{default:l(()=>[(f(),_(O,null,S(b,t=>o(E,{key:t,value:t},{default:l(()=>[r(q(t),1)]),_:2},1032,["value"])),64))]),_:1},8,["modelValue"])]),_:1}),o(s,null,{default:l(()=>[o(i,{onClick:e[6]||(e[6]=t=>z(d).back())},{default:l(()=>[...e[9]||(e[9]=[r("返回",-1)])]),_:1}),o(i,{type:"primary",loading:n.value,onClick:V},{default:l(()=>[...e[10]||(e[10]=[r("保存",-1)])]),_:1},8,["loading"])]),_:1})]),_:1},8,["model"])])}}}),Ee=j(J,[["__scopeId","data-v-57777381"]]);export{Ee as default};

frontend/dist/assets/UserCreateView-H9YbwuR2.js

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frontend/dist/assets/UserPermissionView-XeRSr4FE.js

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frontend/dist/assets/UserSettingsView-CF5QiqZ2.js

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-import{E as v}from"./index-BNsCyG4A.js";import{a as w,E as b}from"./el-table-C7Zx0Bmo.js";import{v as g}from"./directive-B1cwmVOz.js";import{d as h,G as y,K as E,c as x,q as a,s as t,w as r,y as p,da as V,o as B,x as m,aa as C}from"./index-Ds9AETjS.js";import"./el-scrollbar-CF42LNzg.js";import"./el-popper-D1tByNLb.js";import"./el-tooltip-l0sNRNKZ.js";import"./el-checkbox-pV3o_8iy.js";/* empty css               */import{_ as U}from"./_plugin-vue_export-helper-DlAUqK2U.js";import"./_baseClone-CZxxnGCv.js";import"./index-BSbCLbaz.js";import"./_baseIteratee-82gB5Yov.js";import"./castArray-BWI4UxBy.js";import"./debounce-D6mJVDYC.js";import"./toNumber-DrayciwT.js";import"./index-sWKLi7bH.js";import"./raf-zQ00nuMI.js";import"./omit-BzI1MV_K.js";const k={class:"user-settings"},N={class:"page-header"},S=h({__name:"UserSettingsView",setup(T){const s=p(!1),i=p([]);async function d(){s.value=!0;try{i.value=await V()}finally{s.value=!1}}return y(d),(u,e)=>{const _=v,o=b,c=w,f=g;return E((B(),x("section",k,[a("header",N,[e[2]||(e[2]=a("div",null,[a("h1",null,"用户设置"),a("p",null,"管理平台账号、角色状态和页面权限。")],-1)),t(_,{type:"primary",onClick:e[0]||(e[0]=l=>u.$router.push("/user-settings/create"))},{default:r(()=>[...e[1]||(e[1]=[m("创建用户",-1)])]),_:1})]),t(c,{data:i.value},{default:r(()=>[t(o,{prop:"username",label:"账号","min-width":"140"}),t(o,{prop:"display_name",label:"显示名称","min-width":"160"}),t(o,{prop:"role",label:"角色",width:"120"}),t(o,{prop:"status",label:"状态",width:"120"}),t(o,{label:"权限数",width:"120"},{default:r(({row:l})=>{var n;return[m(C(((n=l.permissions)==null?void 0:n.length)||0),1)]}),_:1}),t(o,{prop:"create_time",label:"创建时间","min-width":"180"})]),_:1},8,["data"])])),[[f,s.value]])}}}),X=U(S,[["__scopeId","data-v-16caa8c5"]]);export{X as default};

frontend/dist/assets/UserSettingsView-D3-QhDTj.js

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+import{E as w}from"./index-DxH5-_w6.js";import{E as y}from"./el-tag-C2d1Zfbn.js";import{a as h,E}from"./el-table-B6qMxzT4.js";import{v as T}from"./directive-BBfPDwlQ.js";import{d as x,G as V,K as B,c as C,q as r,s as t,w as o,y as m,da as U,o as k,x as i,f as u,aa as d}from"./index-D5veDkuG.js";import"./el-scrollbar-GFOwBTUz.js";import"./el-popper-DDJTxf27.js";import"./el-tooltip-l0sNRNKZ.js";import"./el-checkbox-D6iLoXkw.js";import{s as N,a as S}from"./status-Dl1fykxa.js";import{_ as D}from"./_plugin-vue_export-helper-DlAUqK2U.js";import"./_baseClone-BuqHm105.js";import"./index-BT1A2I5c.js";import"./_baseIteratee-N-G7GeYE.js";import"./castArray-BDIbf8ih.js";import"./debounce-D3zKL4rk.js";import"./toNumber-a8w_gs0u.js";import"./index-DmAaj_YO.js";import"./raf-Dsox5IbR.js";import"./omit-DxQEBv1Z.js";const L={class:"user-settings"},$={class:"page-header"},q=x({__name:"UserSettingsView",setup(z){const l=m(!1),n=m([]);async function _(){l.value=!0;try{n.value=await U()}finally{l.value=!1}}return V(_),(c,e)=>{const f=w,a=E,g=y,b=h,v=T;return B((k(),C("section",L,[r("header",$,[e[2]||(e[2]=r("div",null,[r("h1",null,"用户设置"),r("p",null,"管理平台账号、角色状态和页面权限。")],-1)),t(f,{type:"primary",onClick:e[0]||(e[0]=s=>c.$router.push("/user-settings/create"))},{default:o(()=>[...e[1]||(e[1]=[i("创建用户",-1)])]),_:1})]),t(b,{data:n.value},{default:o(()=>[t(a,{prop:"username",label:"账号","min-width":"140"}),t(a,{prop:"display_name",label:"显示名称","min-width":"160"}),t(a,{prop:"role",label:"角色",width:"120"}),t(a,{label:"状态",width:"120"},{default:o(({row:s})=>[t(g,{type:u(N)(s.status),size:"small"},{default:o(()=>[i(d(u(S)(s.status)),1)]),_:2},1032,["type"])]),_:1}),t(a,{label:"权限数",width:"120"},{default:o(({row:s})=>{var p;return[i(d(((p=s.permissions)==null?void 0:p.length)||0),1)]}),_:1}),t(a,{prop:"create_time",label:"创建时间","min-width":"180"})]),_:1},8,["data"])])),[[v,l.value]])}}}),st=D(q,[["__scopeId","data-v-3c7eb527"]]);export{st as default};

frontend/dist/assets/UserSettingsView-D5LgrnVq.css

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+.user-settings[data-v-3c7eb527]{padding:24px}.page-header[data-v-3c7eb527]{display:flex;justify-content:space-between;gap:16px;margin-bottom:18px}.page-header h1[data-v-3c7eb527]{margin:0;font-size:24px}.page-header p[data-v-3c7eb527]{margin:8px 0 0;color:#64748b}

frontend/dist/assets/UserSettingsView-DKVS0cYk.css

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-.user-settings[data-v-16caa8c5]{padding:24px}.page-header[data-v-16caa8c5]{display:flex;justify-content:space-between;gap:16px;margin-bottom:18px}.page-header h1[data-v-16caa8c5]{margin:0;font-size:24px}.page-header p[data-v-16caa8c5]{margin:8px 0 0;color:#64748b}

frontend/dist/assets/_baseClone-BuqHm105.js

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frontend/dist/assets/_baseIteratee-N-G7GeYE.js

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frontend/dist/assets/castArray-BDIbf8ih.js

@@ -1 +1 @@
-import{ce as a}from"./index-Ds9AETjS.js";function e(){if(!arguments.length)return[];var r=arguments[0];return a(r)?r:[r]}export{e as c};
+import{ce as a}from"./index-D5veDkuG.js";function e(){if(!arguments.length)return[];var r=arguments[0];return a(r)?r:[r]}export{e as c};

frontend/dist/assets/clamp-Ddg8_Yc2.js

@@ -1 +1 @@
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+import{t as n}from"./toNumber-a8w_gs0u.js";function t(d,f,i){return d===d&&(i!==void 0&&(d=d<=i?d:i),f!==void 0&&(d=d>=f?d:f)),d}function c(d,f,i){return i===void 0&&(i=f,f=void 0),i!==void 0&&(i=n(i),i=i===i?i:0),f!==void 0&&(f=n(f),f=f===f?f:0),t(n(d),f,i)}export{c};

frontend/dist/assets/compare-DQvbhHeE.js

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+import{aF as n,aG as t,aI as l}from"./index-D5veDkuG.js";const p=()=>n("/model-compare"),i=e=>n(`/model-compare/${e}`),u=e=>l(`/model-compare/${e}`),h=e=>t("/model-compare/stop-by-pid",{pid:e}),g=e=>t(`/model-compare/${e}/load`),C=e=>t(`/model-compare/${e}/unload`),y=async e=>{const a=await t("/model-compare/stream-chat",e);return{ok:!0,body:{getReader:()=>{const o=(a==null?void 0:a.response)||"",r=new TextEncoder,s=[o.slice(0,o.length/3),o.slice(o.length/3,2*o.length/3),o.slice(2*o.length/3)];let c=0;return{read:async()=>c>=s.length?{done:!0,value:void 0}:{done:!1,value:r.encode(s[c++])}}}}}},v=e=>t("/model-compare/chat-with-port",e);export{i as a,y as b,v as c,u as d,p as g,g as l,h as s,C as u};