dataset accept file uploads

llama_stack/apis/evals/client.py

@@ -12,9 +12,28 @@ import httpx
 from termcolor import cprint
 
 from .evals import *  # noqa: F403
+import base64
+import mimetypes
+import os
+
 from ..datasets.client import DatasetsClient
 
 
+def data_url_from_file(file_path: str) -> str:
+    if not os.path.exists(file_path):
+        raise FileNotFoundError(f"File not found: {file_path}")
+
+    with open(file_path, "rb") as file:
+        file_content = file.read()
+
+    base64_content = base64.b64encode(file_content).decode("utf-8")
+    mime_type, _ = mimetypes.guess_type(file_path)
+
+    data_url = f"data:{mime_type};base64,{base64_content}"
+
+    return data_url
+
+
 class EvaluationClient(Evals):
     def __init__(self, base_url: str):
         self.base_url = base_url
@@ -70,9 +89,8 @@ class EvaluationClient(Evals):
             return EvaluateResponse(**response.json())
 
 
-async def run_main(host: str, port: int):
+async def run_main(host: str, port: int, eval_dataset_path: str = ""):
     client = EvaluationClient(f"http://{host}:{port}")
-
     dataset_client = DatasetsClient(f"http://{host}:{port}")
 
     # Full Eval Task
@@ -114,10 +132,19 @@ async def run_main(host: str, port: int):
     )
     cprint(response, "cyan")
 
+    response = await dataset_client.create_dataset(
+        dataset_def=CustomDatasetDef(
+            identifier="rag-evals",
+            url=data_url_from_file(eval_dataset_path),
+        )
+    )
+    cprint(response, "cyan")
+
     # 2. run evals on the registered dataset
     response = await client.run_scorer(
         dataset_config=EvaluateDatasetConfig(
-            dataset_identifier="Llama-3.1-8B-Instruct-evals__mmlu_pro__details",
+            dataset_identifier="rag-evals",
+            # dataset_identifier="Llama-3.1-8B-Instruct-evals__mmlu_pro__details",
             row_limit=10,
         ),
         eval_scoring_config=EvaluateScoringConfig(
@@ -141,8 +168,8 @@ async def run_main(host: str, port: int):
     # )
 
 
-def main(host: str, port: int):
-    asyncio.run(run_main(host, port))
+def main(host: str, port: int, eval_dataset_path: str = ""):
+    asyncio.run(run_main(host, port, eval_dataset_path))
 
 
 if __name__ == "__main__":

llama_stack/distribution/registry/datasets/dataset_wrappers.py

@@ -3,10 +3,13 @@
 #
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
+import io
+
 import pandas
 from datasets import Dataset, load_dataset
 
 from llama_stack.apis.datasets import *  # noqa: F403
+from llama_stack.providers.utils.memory.vector_store import parse_data_url
 
 
 class CustomDataset(BaseDataset[DictSample]):
@@ -37,11 +40,31 @@ class CustomDataset(BaseDataset[DictSample]):
         if self.dataset:
             return
 
-        # TODO: better support w/ data url
+        # TODO: more robust support w/ data url
         if self.config.url.endswith(".csv"):
             df = pandas.read_csv(self.config.url)
         elif self.config.url.endswith(".xlsx"):
             df = pandas.read_excel(self.config.url)
+        elif self.config.url.startswith("data:"):
+            parts = parse_data_url(self.config.url)
+            data = parts["data"]
+            if parts["is_base64"]:
+                data = base64.b64decode(data)
+            else:
+                data = unquote(data)
+                encoding = parts["encoding"] or "utf-8"
+                data = data.encode(encoding)
+
+            mime_type = parts["mimetype"]
+            mime_category = mime_type.split("/")[0]
+            data_bytes = io.BytesIO(data)
+
+            if mime_category == "text":
+                df = pandas.read_csv(data_bytes)
+            else:
+                df = pandas.read_excel(data_bytes)
+        else:
+            raise ValueError(f"Unsupported file type: {self.config.url}")
 
         if n_samples is not None:
             df = df.sample(n=n_samples)

llama_stack/providers/impls/meta_reference/evals/tasks/run_scoring_task.py

@@ -11,7 +11,6 @@ from llama_stack.providers.impls.meta_reference.evals.scorer.basic_scorers impor
 
 from llama_stack.apis.evals import *  # noqa: F403
 from llama_stack.apis.inference import *  # noqa: F403
-from termcolor import cprint
 
 
 class RunScoringTask(BaseTask):
@@ -62,9 +61,8 @@ class RunScoringTask(BaseTask):
         dataset.load(n_samples=dataset_config.row_limit)
         print(f"Running on {len(dataset)} samples")
 
-        # transform dataset into
+        # transform dataset into List[ScorerInputSample]
         postprocessed = self.transform_score_input_sample(dataset)
-        cprint(postprocessed, "blue")
 
         # F3 - scorer
         scorer_config_list = eval_scoring_config.scorer_config_list

llama_stack/providers/registry/evals.py

@@ -22,6 +22,7 @@ def available_providers() -> List[ProviderSpec]:
                 "datasets",
                 "numpy",
                 "autoevals",
+                "openpyxl",
             ],
             module="llama_stack.providers.impls.meta_reference.evals",
             config_class="llama_stack.providers.impls.meta_reference.evals.MetaReferenceEvalsImplConfig",