fix(api): typo in docs

Author: stainless-app[bot]

.stats.yml

@@ -1,4 +1,4 @@
 configured_endpoints: 5
-openapi_spec_url: https://storage.googleapis.com/stainless-sdk-openapi-specs/isaacus%2Fisaacus-854d1a74fd0240b79b6a7902200adf22b85d5cb67710abe7c0177b4f8801157f.yml
-openapi_spec_hash: 9a141dbe42dfb83a674e69441888776f
+openapi_spec_url: https://storage.googleapis.com/stainless-sdk-openapi-specs/isaacus%2Fisaacus-5033f9217ca384041df97464049b6ed07fd6d6dc47d76db59246fa67edec9ee5.yml
+openapi_spec_hash: ed015407e5771e22d0b48721f6f88e11
 config_hash: 9040e7359f066240ad536041fb2c5185

src/isaacus/resources/enrichments.py

@@ -49,7 +49,7 @@ def create(
         *,
         model: Literal["kanon-2-enricher"],
         texts: Union[SequenceNotStr[str], str],
-        overflow_strategy: Optional[Literal["auto", "drop_end", "chunk"]] | Omit = omit,
+        overflow_strategy: Optional[Literal["auto", "drop_end"]] | Omit = omit,
         # Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
         # The extra values given here take precedence over values defined on the client or passed to this method.
         extra_headers: Headers | None = None,
@@ -71,15 +71,9 @@ def create(
 
           overflow_strategy: The strategy for handling content exceeding the model's maximum input length.
 
-              `auto`, which is the recommended setting, currently behaves the same as `chunk`,
-              which intelligently breaks the input up into smaller chunks and then stitches
-              the results back together into a single prediction. In the future `auto` may
-              implement even more sophisticated strategies for handling long contexts such as
-              leveraging chunk overlap and/or a specialized stitching model.
-
-              `chunk` breaks the input up into smaller chunks that fit within the model's
-              context window and then intelligently merges the results into a single
-              prediction at the cost of a minor accuracy drop.
+              `auto` currently behaves the same as `drop_end`, dropping excess tokens from the
+              end of input. In the future, `auto` may implement more sophisticated strategies
+              such as chunking and context-aware stitching.
 
               `drop_end` drops tokens from the end of input exceeding the model's maximum
               input length.
@@ -137,7 +131,7 @@ async def create(
         *,
         model: Literal["kanon-2-enricher"],
         texts: Union[SequenceNotStr[str], str],
-        overflow_strategy: Optional[Literal["auto", "drop_end", "chunk"]] | Omit = omit,
+        overflow_strategy: Optional[Literal["auto", "drop_end"]] | Omit = omit,
         # Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
         # The extra values given here take precedence over values defined on the client or passed to this method.
         extra_headers: Headers | None = None,
@@ -159,15 +153,9 @@ async def create(
 
           overflow_strategy: The strategy for handling content exceeding the model's maximum input length.
 
-              `auto`, which is the recommended setting, currently behaves the same as `chunk`,
-              which intelligently breaks the input up into smaller chunks and then stitches
-              the results back together into a single prediction. In the future `auto` may
-              implement even more sophisticated strategies for handling long contexts such as
-              leveraging chunk overlap and/or a specialized stitching model.
-
-              `chunk` breaks the input up into smaller chunks that fit within the model's
-              context window and then intelligently merges the results into a single
-              prediction at the cost of a minor accuracy drop.
+              `auto` currently behaves the same as `drop_end`, dropping excess tokens from the
+              end of input. In the future, `auto` may implement more sophisticated strategies
+              such as chunking and context-aware stitching.
 
               `drop_end` drops tokens from the end of input exceeding the model's maximum
               input length.

src/isaacus/types/enrichment_create_params.py

@@ -25,18 +25,12 @@ class EnrichmentCreateParams(TypedDict, total=False):
     No more than 8 texts can be enriched in a single request.
     """
 
-    overflow_strategy: Optional[Literal["auto", "drop_end", "chunk"]]
+    overflow_strategy: Optional[Literal["auto", "drop_end"]]
     """The strategy for handling content exceeding the model's maximum input length.
 
-    `auto`, which is the recommended setting, currently behaves the same as `chunk`,
-    which intelligently breaks the input up into smaller chunks and then stitches
-    the results back together into a single prediction. In the future `auto` may
-    implement even more sophisticated strategies for handling long contexts such as
-    leveraging chunk overlap and/or a specialized stitching model.
-
-    `chunk` breaks the input up into smaller chunks that fit within the model's
-    context window and then intelligently merges the results into a single
-    prediction at the cost of a minor accuracy drop.
+    `auto` currently behaves the same as `drop_end`, dropping excess tokens from the
+    end of input. In the future, `auto` may implement more sophisticated strategies
+    such as chunking and context-aware stitching.
 
     `drop_end` drops tokens from the end of input exceeding the model's maximum
     input length.

src/isaacus/types/enrichment_response.py

@@ -20,7 +20,7 @@ class Result(BaseModel):
     document: Document
     """
     The document enriched into version 1.0.0 of the Isaacus Legal Graph Schema
-    (IGLS).
+    (ILGS).
 
     All spans in an enriched document graph are indexed into the Unicode code point
     space of a source document.

src/isaacus/types/ilgs/v1/document.py

@@ -23,7 +23,7 @@
 
 class Document(BaseModel):
     """
-    The document enriched into version 1.0.0 of the Isaacus Legal Graph Schema (IGLS).
+    The document enriched into version 1.0.0 of the Isaacus Legal Graph Schema (ILGS).
 
     All spans in an enriched document graph are indexed into the Unicode code point space of a source document.
 

tests/api_resources/test_enrichments.py

@@ -32,7 +32,7 @@ def test_method_create_with_all_params(self, client: Isaacus) -> None:
         enrichment = client.enrichments.create(
             model="kanon-2-enricher",
             texts=['1.5 You (the "User") agree to be bound by these Terms.'],
-            overflow_strategy="auto",
+            overflow_strategy=None,
         )
         assert_matches_type(EnrichmentResponse, enrichment, path=["response"])
 
@@ -85,7 +85,7 @@ async def test_method_create_with_all_params(self, async_client: AsyncIsaacus) -
         enrichment = await async_client.enrichments.create(
             model="kanon-2-enricher",
             texts=['1.5 You (the "User") agree to be bound by these Terms.'],
-            overflow_strategy="auto",
+            overflow_strategy=None,
         )
         assert_matches_type(EnrichmentResponse, enrichment, path=["response"])