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-5033f9217ca384041df97464049b6ed07fd6d6dc47d76db59246fa67edec9ee5.yml
-openapi_spec_hash: ed015407e5771e22d0b48721f6f88e11
+openapi_spec_url: https://storage.googleapis.com/stainless-sdk-openapi-specs/isaacus%2Fisaacus-84e454eb280ff5e816abbb592ea590979e180df5c49af8cbe9bec81efb847f81.yml
+openapi_spec_hash: 9eaef1b127c6dc6ff23ea1367d42459a
 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"]] | Omit = omit,
+        overflow_strategy: Optional[Literal["auto", "drop_end", "chunk"]] | 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,
@@ -71,9 +71,15 @@ def create(
 
           overflow_strategy: The strategy for handling content exceeding the model's maximum input length.
 
-              `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.
+              `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.
 
               `drop_end` drops tokens from the end of input exceeding the model's maximum
               input length.
@@ -131,7 +137,7 @@ async def create(
         *,
         model: Literal["kanon-2-enricher"],
         texts: Union[SequenceNotStr[str], str],
-        overflow_strategy: Optional[Literal["auto", "drop_end"]] | Omit = omit,
+        overflow_strategy: Optional[Literal["auto", "drop_end", "chunk"]] | 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,
@@ -153,9 +159,15 @@ async def create(
 
           overflow_strategy: The strategy for handling content exceeding the model's maximum input length.
 
-              `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.
+              `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.
 
               `drop_end` drops tokens from the end of input exceeding the model's maximum
               input length.

src/isaacus/types/enrichment_create_params.py

@@ -25,12 +25,18 @@ class EnrichmentCreateParams(TypedDict, total=False):
     No more than 8 texts can be enriched in a single request.
     """
 
-    overflow_strategy: Optional[Literal["auto", "drop_end"]]
+    overflow_strategy: Optional[Literal["auto", "drop_end", "chunk"]]
     """The strategy for handling content exceeding the model's maximum input length.
 
-    `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.
+    `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.
 
     `drop_end` drops tokens from the end of input exceeding the model's maximum
     input length.

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=None,
+            overflow_strategy="auto",
         )
         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=None,
+            overflow_strategy="auto",
         )
         assert_matches_type(EnrichmentResponse, enrichment, path=["response"])