Adding dataset_name argument to pipelines index() method

retrieval-bench/src/retrieval_bench/cli/evaluate.py

@@ -199,15 +199,15 @@ def _run_evaluation(
 
     # Cache-only mode: build corpus embeddings and exit without running queries.
     if cache_only:
-        pipeline.dataset_name = dataset_name
-        pipeline.index(corpus_ids=corpus_ids, corpus_images=corpus_images, corpus_texts=corpus_texts)
+        pipeline.index(
+            corpus_ids=corpus_ids, corpus_images=corpus_images, corpus_texts=corpus_texts, dataset_name=dataset_name
+        )
         print("Corpus embeddings cached. Exiting (--cache-only).")
         return
 
     # Evaluate
     print("\nRunning evaluation...")
     try:
-        pipeline.dataset_name = dataset_name
         trace_run_name_eff = trace_run_name or default_trace_run_name(pipeline)
         results = evaluate_retrieval(
             pipeline=pipeline,

retrieval-bench/src/retrieval_bench/pipeline_evaluation/evaluator.py

@@ -91,7 +91,7 @@ def _filtered_run_for_query(qid: str, run_q: Any) -> Any:
         return out
 
     # Dataset context (for trace directory layout).
-    dataset_name_eff = dataset_name or getattr(pipeline, "dataset_name", None) or "unknown_dataset"
+    dataset_name_eff = dataset_name or "unknown_dataset"
 
     # Trace run name: always enabled; default is <PipelineClass>__<model_short>.
     trace_run_name_eff = trace_run_name or default_trace_run_name(pipeline)

retrieval-bench/src/retrieval_bench/pipelines/agentic.py

@@ -253,6 +253,7 @@ def __init__(
         self.retriever_top_k = int(retriever_top_k)
         self.num_concurrent = max(1, int(num_concurrent))
         self._backend_kwargs = dict(backend_kwargs)
+        self._dataset_name = None
 
         # Resolve os.environ/... convention for base_url.
         if base_url and str(base_url).strip().startswith("os.environ/"):
@@ -288,6 +289,14 @@ def __init__(
             print("Error: CUDA is not available. This pipeline requires a GPU.")
             sys.exit(1)
 
+    @property
+    def dataset_name(self):
+        return self._dataset_name
+
+    @dataset_name.setter
+    def dataset_name(self, value: str):
+        self._dataset_name = value
+
     # -----------------------------------------------------------------------
     # Async query loop
     # -----------------------------------------------------------------------
@@ -525,10 +534,13 @@ async def _process_query(q_idx: int, qid: str, query_text: Any) -> None:
     # Main entry point
     # -----------------------------------------------------------------------
 
-    def index(self, corpus_ids: List[str], corpus_images: List[Any], corpus_texts: List[str]) -> None:
+    def index(
+        self, corpus_ids: List[str], corpus_images: List[Any], corpus_texts: List[str], dataset_name: str = None
+    ) -> None:
         super().index(corpus_ids=corpus_ids, corpus_images=corpus_images, corpus_texts=corpus_texts)
 
-        dataset_name = self.dataset_name
+        if dataset_name is not None:
+            self.dataset_name = dataset_name
         task_key = infer_bright_task_key(dataset_name)
 
         corpus = [{"image": img, "markdown": md} for img, md in zip(corpus_images, corpus_texts)]

retrieval-bench/src/retrieval_bench/pipelines/dense.py

@@ -51,10 +51,21 @@ def __init__(self, *, backend: str, top_k: int = 100, **kwargs: Any) -> None:
             print("Error: CUDA is not available. This pipeline requires a GPU.")
             sys.exit(1)
 
-    def index(self, corpus_ids: List[str], corpus_images: List[Any], corpus_texts: List[str]) -> None:
+    @property
+    def dataset_name(self):
+        return self._dataset_name
+
+    @dataset_name.setter
+    def dataset_name(self, value: str):
+        self._dataset_name = value
+
+    def index(
+        self, corpus_ids: List[str], corpus_images: List[Any], corpus_texts: List[str], dataset_name: str = None
+    ) -> None:
         super().index(corpus_ids=corpus_ids, corpus_images=corpus_images, corpus_texts=corpus_texts)
 
-        dataset_name = self.dataset_name
+        if dataset_name is not None:
+            self.dataset_name = dataset_name
         task_key = infer_bright_task_key(dataset_name)
 
         corpus = [{"image": img, "markdown": md} for img, md in zip(corpus_images, corpus_texts)]