feat(evidence): evaluate retrieval with a small fixture

harness/tht/cli/preprocess_cmd.py

@@ -28,6 +28,53 @@ def _evidence_json_context(config: Path):
     return _load_config_or_exit(config)
 
 
+def _evaluation_workspace_root(cfg) -> Path:
+    evidence = cfg.evidence
+    if evidence is None:
+        raise RuntimeError("Evidence evaluation fixture is unavailable")
+    if evidence.source_root is not None:
+        return evidence.source_root
+    filesystem_roots = [source.root for source in evidence.sources if source.type == "filesystem"]
+    if len(filesystem_roots) != 1:
+        raise RuntimeError("Evidence evaluation requires one filesystem workspace source")
+    root = filesystem_roots[0]
+    if root.name == "curated":
+        root = root.parent
+    if root.name == "evidence":
+        return root.parent
+    return root
+
+
+def _candidate_evaluator(cfg, *, vector_store, embedder):
+    """Bind candidate publication to the same read-only retrieval evaluator as the CLI."""
+    from tht.evidence.canonical import load_curated_tree
+    from tht.evidence.evaluation import evaluate_retrieval, load_evaluation_fixture
+
+    workspace_root = _evaluation_workspace_root(cfg)
+    language = _bm25_language(cfg.language)
+
+    def evaluate(manifest):
+        document_generations = manifest.metadata.get("document_generations")
+        if not isinstance(document_generations, dict):
+            raise TypeError("candidate Evidence generation is invalid")
+        return evaluate_retrieval(
+            load_evaluation_fixture(workspace_root / "evidence" / "evaluation.yaml"),
+            workspace_revision=cfg._workspace_revision,
+            vector_generation=manifest.vector_generation,
+            document_generations=document_generations,
+            workspace_id=cfg._workspace_id,
+            language=language,
+            searcher=vector_store,
+            embedder=embedder,
+            expected_kinds={
+                evidence.id: evidence.kind
+                for evidence in load_curated_tree(workspace_root / "evidence" / "curated")
+            },
+        )
+
+    return evaluate
+
+
 def _evidence_json_payload(cfg, payload: dict, *, code: str, error: str | None = None) -> dict:
     value = {
         **payload,
@@ -112,15 +159,18 @@ def run_from_config(config: Path, *, dry_run: bool = False, resume: str | None =
     if cfg.embeddings is None:
         raise RuntimeError("embeddings are not configured")
     corpus_root = cfg.paths.artifacts.parent / "corpus"
+    vector_store = build_vector_store(cfg, require_write=True)
+    embedder = make_embedder(cfg.embeddings)
     pipeline = build_preprocessing_pipeline(
         store=CorpusStore(corpus_root), sources=build_sources(cfg.evidence),
-        embedder=make_embedder(cfg.embeddings),
-        vector_store=build_vector_store(cfg, require_write=True),
+        embedder=embedder,
+        vector_store=vector_store,
         embedding_model=cfg.embeddings.model, embedding_dimensions=cfg.embeddings.dim,
         chunk_policy=ChunkPolicy(version="chunk-v1", max_chars=cfg.vector.max_chunk_chars),
         pipeline_version="evidence-v1",
         retain_published_generations=cfg.vector.retain_published_generations,
         sparse_language=_bm25_language(cfg.language),
+        candidate_evaluator=_candidate_evaluator(cfg, vector_store=vector_store, embedder=embedder),
     )
     def fingerprint(value: str) -> str:
         return "sha256:" + hashlib.sha256(value.encode()).hexdigest()