fix(benchmark): 记录实际Embedding空间与逐样本回退信息

backend/app/benchmarks/rag.py

@@ -24,6 +24,7 @@ from app.contracts import (
     SearchRequest,
 )
 from app.retrieval.engine import engine
+from app.retrieval.provenance import capture_embedding
 
 logger = logging.getLogger(__name__)
 
@@ -90,7 +91,9 @@ async def _evaluate_one(
         score_threshold=request.retrieval.score_threshold,
     )
     start = time.perf_counter()
+    embedding = {}
     try:
+        with capture_embedding() as embedding:
             response = await engine.search(search_request)
         latency_ms = (time.perf_counter() - start) * 1000.0
     except Exception as exc:  # 单个样本失败不中断整个 Benchmark
@@ -100,6 +103,7 @@ async def _evaluate_one(
             exc_info=exc,
         )
         return RAGCaseResult(
+            embedding=embedding,
             case_id=case.case_id,
             mode=mode,
             repeat=repeat,
@@ -115,6 +119,7 @@ async def _evaluate_one(
     k = request.retrieval.top_k
 
     return RAGCaseResult(
+        embedding=embedding,
         case_id=case.case_id,
         mode=mode,
         repeat=repeat,

backend/app/benchmarks/service.py

@@ -86,7 +86,8 @@ def _config_snapshot(request: RAGRunRequest, dataset: RAGDataset) -> dict:
         "modes": [m.value for m in request.modes],
         "retrieval": request.retrieval.model_dump(),
         "repeat": request.repeat,
-        "embedding": {
+        "embedding": {"policy": "per_case", "details": "cases[].embedding"},
+        "local_embedding": {
             "model_id": engine.embedding.model_id,
             "version": engine.embedding.version,
             "dim": engine.embedding.dim,

backend/app/contracts.py

@@ -1131,6 +1131,7 @@ class BenchmarkEvent(Contract):
 
 
 class RAGCaseResult(Contract):
+    embedding: dict[str, Any] = Field(default_factory=dict)
     case_id: str
     mode: SearchMode
     repeat: int

backend/app/providers/routing.py

@@ -24,6 +24,7 @@ from app.providers.base import ProviderError
 from app.providers.credentials import CredentialResolver, CredentialStoreError
 from app.providers.registry import ProviderNotFoundError, ProviderRegistry
 from app.retrieval.embedding import EmbeddingProvider, HashEmbeddingProvider
+from app.retrieval.provenance import record_embedding
 
 CAPABILITIES = ("embedding", "transcription", "speaker_matching")
 HTTP_TYPES = {ProviderType.openai_chat, ProviderType.openai_compatible}
@@ -175,7 +176,10 @@ class ModelRoutingService:
         return data, url
 
     async def embed(self, texts: list[str]) -> EmbeddingResult:
-        binding = self.configuration().embedding
+        config = self.configuration()
+        binding = config.embedding
+        record_embedding(route_version=config.version,
+                         requested_route=binding.model_dump() if binding else None)
         reason = None
         if binding and texts:
             try:

backend/app/retrieval/engine.py

@@ -23,6 +23,7 @@ from app.retrieval.embedding import EmbeddingProvider, HashEmbeddingProvider
 from app.retrieval.hybrid import normalize_scores, rrf_fuse
 from app.retrieval.reranker import LexicalReranker, RankedCandidate, RerankerProvider
 from app.retrieval import routed_vectors
+from app.retrieval.provenance import record_embedding
 from app.retrieval.vectorstore import SqliteVecStore, VectorStore
 from app.textutils import make_snippet, match_query
 
@@ -80,6 +81,7 @@ class RetrievalEngine:
                 fts_scores = {h.block_id: -h.bm25 for h in fts_hits}
 
         if request.mode in (SearchMode.vector, SearchMode.hybrid):
+            record_embedding(source="unavailable")
             vec_hits = None
             if (
                 self._routed_defaults is not None
@@ -90,6 +92,8 @@ class RetrievalEngine:
             if vec_hits is None:
                 query_vec = await self.embedding.embed_query(request.query)
                 vec_hits = await self.vector_store.search(query_vec, top_k=recall)
+                record_embedding(source="local", model_id=self.embedding.model_id,
+                                 dimensions=self.embedding.dim, version=self.embedding.version)
             vec_ranked = [v.id for v in vec_hits]
             vec_scores = {v.id: v.score for v in vec_hits}
 

backend/app/retrieval/provenance.py

@@ -0,0 +1,21 @@
+"""Task-local observations of the embedding path actually used by a search."""
+from contextlib import contextmanager
+from contextvars import ContextVar
+
+_observation: ContextVar[dict | None] = ContextVar("embedding_observation", default=None)
+
+
+@contextmanager
+def capture_embedding():
+    result = {"source": "not_used"}
+    token = _observation.set(result)
+    try:
+        yield result
+    finally:
+        _observation.reset(token)
+
+
+def record_embedding(**fields) -> None:
+    result = _observation.get()
+    if result is not None:
+        result.update(fields)

backend/app/retrieval/routed_vectors.py

@@ -20,6 +20,7 @@ from typing import Protocol
 
 from app.database.db import connect, transaction
 from app.retrieval.vectorstore import VectorHit
+from app.retrieval.provenance import record_embedding
 
 logger = logging.getLogger(__name__)
 
@@ -80,6 +81,7 @@ async def embed_remote(texts: list[str]) -> RemoteEmbeddings | None:
             return None
         result = await runtime.embed(texts)
         if result.source != "api":
+            record_embedding(fallback_reason=result.fallback_reason)
             return None
         if not isinstance(result.model_id, str) or not result.model_id or result.model_id == "hash-v1":
             raise ValueError("API embedding needs a distinct space ID")
@@ -94,6 +96,7 @@ async def embed_remote(texts: list[str]) -> RemoteEmbeddings | None:
         )
     except Exception as exc:
         # Avoid logging provider exceptions containing credentials or note text.
+        record_embedding(fallback_reason="REMOTE_EMBEDDING_UNAVAILABLE")
         logger.warning("Remote embedding unavailable (%s); using local index", type(exc).__name__)
         return None
 
@@ -158,6 +161,7 @@ async def search_remote(query: str, *, top_k: int) -> list[VectorHit] | None:
     batch = await embed_remote([query])
     if batch is None:
         return None
+    record_embedding(attempted_space={"model_id": batch.space_id, "dimensions": batch.dimensions})
     try:
         conn = connect()
         try:
@@ -166,6 +170,7 @@ async def search_remote(query: str, *, top_k: int) -> list[VectorHit] | None:
                     "SELECT 1 FROM sqlite_master WHERE type = 'table' AND name = 'routed_block_vectors'"
                 ).fetchone()
                 if exists is None:
+                    record_embedding(fallback_reason="REMOTE_INDEX_MISSING")
                     return None
                 rows = conn.execute(
                     """SELECT b.block_id, r.vector
@@ -184,9 +189,13 @@ async def search_remote(query: str, *, top_k: int) -> list[VectorHit] | None:
                         score = math.fsum(a * b for a, b in zip(batch.vectors[0], vector))
                         yield VectorHit(id=row["block_id"], score=max(0.0, min(1.0, score)))
 
-                return heapq.nlargest(top_k, hits(), key=lambda hit: hit.score)
+                result = heapq.nlargest(top_k, hits(), key=lambda hit: hit.score)
+                record_embedding(source="api", model_id=batch.space_id,
+                                 dimensions=batch.dimensions, fallback_reason=None)
+                return result
         finally:
             conn.close()
     except Exception as exc:
+        record_embedding(fallback_reason="REMOTE_INDEX_UNAVAILABLE")
         logger.debug("Remote vector search unavailable (%s); using local index", type(exc).__name__)
         return None

backend/tests/test_benchmark.py

@@ -221,8 +221,9 @@ def test_config_snapshot_records_index_and_models() -> None:
 
     snapshot = run.config_snapshot
     assert snapshot["index_meta"] is not None
-    assert snapshot["embedding"]["version"]
+    assert snapshot["embedding"]["policy"] == "per_case"
-    assert snapshot["embedding"]["dim"]
+    assert snapshot["local_embedding"]["version"]
+    assert snapshot["local_embedding"]["dim"]
     assert snapshot["reranker"]["version"]
     assert snapshot["retrieval"]["rrf_k"] == 60
 

backend/tests/test_model_routing.py

@@ -150,6 +150,27 @@ def assert_local(rig, result, texts, reason):
     assert rig.embedding.calls == [texts]
 
 
+def test_embedding_observation_keeps_request_binding_when_config_changes(rig):
+    from app.retrieval.provenance import capture_embedding
+    initial = bind(rig, model="original-model")
+
+    def handler(request):
+        assert json.loads(request.content)["model"] == "original-model"
+        bind(rig, model="next-model")
+        return response({"data": [{"index": 0, "embedding": [1, 0, 0]}]})
+
+    rig.http.handler = handler
+    with capture_embedding() as observation:
+        result = run(rig.service.embed(["query"]))
+    assert result.source == "api"
+    assert observation["route_version"] == initial.config.version
+    assert observation["requested_route"]["model"] == "original-model"
+    assert observation["requested_route"]["provider_id"] == "test-provider"
+    assert rig.service.configuration().embedding.model == "next-model"
+    assert rig.credentials.value not in json.dumps(observation)
+    assert "credential_id" not in json.dumps(observation)
+
+
 @pytest.fixture
 def audio(tmp_path):
     source, reference = tmp_path / "audio.wav", tmp_path / "reference.wav"

backend/tests/test_routed_retrieval.py

@@ -66,6 +66,83 @@ async def seed():
     return apple, banana
 
 
+@pytest.mark.parametrize("outcome", ["api", "api_failure", "missing_space"])
+def test_benchmark_reports_actual_embedding_and_fallback(runtime, outcome):
+    from app.benchmarks import service
+    from app.contracts import RAGRunRequest
+
+    async def scenario():
+        apple, banana = await seed()
+        if outcome == "api_failure":
+            runtime.result_override = SimpleNamespace(source="local", fallback_reason="PROVIDER_TIMEOUT")
+        elif outcome == "missing_space":
+            runtime.model_id = "space-without-index"
+        directory = get_settings().benchmark_datasets_path
+        directory.mkdir(parents=True, exist_ok=True)
+        (directory / "routing.json").write_text(json.dumps({
+            "dataset_id": "routing", "kind": "rag", "version": "1",
+            "cases": [{"case_id": "query", "query": "apple", "expected_note_ids": [banana.note_id]}],
+        }), encoding="utf-8")
+        run = await service.create_rag_run(RAGRunRequest(
+            dataset_id="routing", modes=[SearchMode.fts, SearchMode.vector],
+        ))
+        await service.wait_for_run(run.run_id)
+        report = service.get_report(run.run_id)
+        assert report.config_snapshot["embedding"]["policy"] == "per_case"
+        fts, vector = report.cases
+        assert fts.embedding == {"source": "not_used"}
+        if outcome == "api":
+            assert vector.embedding["source"] == "api"
+            assert vector.embedding["model_id"] == "space-a"
+            assert vector.embedding["dimensions"] == 3
+            assert vector.retrieved_note_ids[0] == banana.note_id
+        else:
+            assert vector.embedding["source"] == "local"
+            assert vector.embedding["model_id"] == "hash-v1"
+            assert vector.embedding["dimensions"] == 128
+            assert vector.retrieved_note_ids[0] == apple.note_id
+            if outcome == "api_failure":
+                assert vector.embedding["fallback_reason"] == "PROVIDER_TIMEOUT"
+            if outcome == "missing_space":
+                assert vector.embedding["fallback_reason"] == "REMOTE_INDEX_UNAVAILABLE"
+                assert vector.embedding["attempted_space"]["model_id"] == "space-without-index"
+        events = service.get_events(run.run_id)
+        case_events = [e for e in events if e.event.value == "CaseCompleted"]
+        assert case_events[-1].data["embedding"] == vector.embedding
+
+    asyncio.run(scenario())
+
+
+def test_embedding_observations_are_isolated_between_concurrent_searches(runtime, monkeypatch):
+    from app.retrieval.provenance import capture_embedding
+
+    async def scenario():
+        await seed()
+        original = runtime.embed
+
+        async def embed(texts):
+            await asyncio.sleep(0)
+            if texts == ["offline"]:
+                raise RuntimeError("private upstream details")
+            return await original(texts)
+
+        monkeypatch.setattr(runtime, "embed", embed)
+
+        async def query(text):
+            with capture_embedding() as observation:
+                await engine.search(SearchRequest(query=text, mode=SearchMode.vector))
+            return observation
+
+        remote, local, another = await asyncio.gather(query("apple"), query("offline"), query("apple"))
+        assert remote["source"] == another["source"] == "api"
+        assert local["source"] == "local"
+        assert local["fallback_reason"] == "REMOTE_EMBEDDING_UNAVAILABLE"
+        assert "fallback_reason" not in remote or remote["fallback_reason"] is None
+        assert "private upstream" not in json.dumps(local)
+
+    asyncio.run(scenario())
+
+
 @pytest.mark.parametrize("failure", ["cancel", "write"])
 def test_rebuild_failure_preserves_concurrent_configuration_and_all_indexes(runtime, monkeypatch, failure):
     from app.container import container

docs/contracts/第二阶段接口契约-开发版.md

@@ -1567,3 +1567,7 @@ frontend/src/
 ```
 
 目录调整应按实际代码规模渐进进行。Router 只做参数接收和错误映射，状态机、第三方 SDK 与文件处理继续放在 Service/Adapter 层。
+
+### Benchmark Embedding 运行归属（阶段 E 集成修复）
+
+`config_snapshot.local_embedding` 仅表示本地基线；`config_snapshot.embedding` 为 `{ "policy": "per_case", "details": "cases[].embedding" }`。报告与 CaseCompleted 事件的逐样本 `embedding` 包含实际 source（api/local/not_used/unavailable）、model_id、dimensions，以及可选 version、fallback_reason、requested_route、route_version、attempted_space。requested_route 仅含提供商引用、模型、相对端点和维度，不包含 API Key 或凭据引用。FTS 不使用 Embedding，标记 not_used；远程失败或索引不完整回退时记录实际本地模型及原因。

docs/development/Benchmark开发说明.md

@@ -64,7 +64,9 @@ BENCHMARK_CASE_EVALUATION_FAILED
 
 ## 配置快照
 
-报告与运行记录保存 `config_snapshot`：dataset hash/version、modes、retrieval 参数、Embedding model/version/dim、Reranker、索引元数据、App 版本与环境、Python 版本，保证不同实验结果可复现。
+报告与运行记录保存 `config_snapshot`：dataset hash/version、modes、retrieval 参数、Reranker、索引元数据、App 版本与环境、Python 版本。`local_embedding` 记录本地基线 model/version/dim；`embedding.policy = per_case` 表示实际来源以逐样本结果为准，不能把本地基线当作本次使用的模型。
+
+每个 `RAGCaseResult.embedding`（同时出现在报告 cases 和 CaseCompleted SSE 中）记录 `source`（api/local/not_used/unavailable）、实际 `model_id` 空间标识、`dimensions`、本地 `version`、`fallback_reason`。远程路由还记录请求时的 `route_version` 和 `requested_route`（provider_id/model/endpoint/dimensions，不含凭据）、成功生成查询向量后的 `attempted_space`。FTS 标记 not_used；调用失败而未完成向量检索时标记 unavailable。API 不可用或远程索引缺失时，实际模型仍记录最终使用的本地基线。配置允许在样本间改变，逐样本记录对应实际调用；汇总指标可能包含多种空间，比较实验时需检查 cases。记录使用任务局部上下文隔离，并发评测不会相互覆盖。
 
 ## 测试