Merge branch 'main' of github.com:ginkgobioworks/ginkgo-ai-client int…

…o zulko/batched-batches

ginkgo_ai_client/client.py

@@ -41,6 +41,8 @@ def __init__(
         self.cause = cause
         self.query = query
         self.result_url = result_url
+        message = self._format_error_message()
+        super().__init__(message)
 
     def _format_error_message(self):
         cause_str = f"{self.cause.__class__.__name__}: {self.cause}"

ginkgo_ai_client/queries.py

@@ -4,11 +4,26 @@
 from abc import ABC, abstractmethod
 
 import pydantic
+
 from ginkgo_ai_client.utils import fasta_sequence_iterator, IteratorWithLength
 
 
 class QueryBase(pydantic.BaseModel, ABC):
-    query_name: Optional[str] = None
+    """Base class for all queries. It's functions are:
+    - Specify the mandatory class methods `to_request_params` and `parse_response`
+    - Provide a better error message when a user forgets to use named arguments only.
+      Without that tweak, the default error message from pydantic is very technical
+      and confusing to new users.
+    """
+
+    def __new__(cls, *args, **kwargs):
+        if args:
+            raise TypeError(
+                f"Invalid initialization: {cls.__name__} does not accept unnamed "
+                f"arguments. Please name all inputs, for instance "
+                f"`{cls.__name__}(field_name=value, other_field=value, ...)`."
+            )
+        return super().__new__(cls)
 
     @abstractmethod
     def to_request_params(self) -> Dict:

test/test_query_creation.py

@@ -0,0 +1,13 @@
+import pytest
+import re
+from ginkgo_ai_client.queries import MeanEmbeddingQuery
+
+
+def test_that_forgetting_to_name_arguments_raises_the_better_error_message():
+    expected_error_message = re.escape(
+        "Invalid initialization: MeanEmbeddingQuery does not accept unnamed arguments. "
+        "Please name all inputs, for instance "
+        "`MeanEmbeddingQuery(field_name=value, other_field=value, ...)`."
+    )
+    with pytest.raises(TypeError, match=expected_error_message):
+        MeanEmbeddingQuery("MLLK<mask>P", model="ginkgo-aa0-650M")