feat(api): update via SDK Studio (#68)

stainless-app[bot] · stainless-app[bot] · commit 7b8f9488ff9b · 2025-03-13T02:36:50.000Z
diff --git a/.stats.yml b/.stats.yml
@@ -1,2 +1,2 @@
 configured_endpoints: 46
-openapi_spec_url: https://storage.googleapis.com/stainless-sdk-openapi-specs/contextual-ai%2Fsunrise-194878b194cd507d7c5418ff38cc0fc53441ef618f991990d334b4b75775cd8f.yml
+openapi_spec_url: https://storage.googleapis.com/stainless-sdk-openapi-specs/contextual-ai%2Fsunrise-e20c18e11af76caf98794e68406fc556cf91d13e8e27a3b1d1909a4f8c2d3eb5.yml
diff --git a/src/contextual/resources/generate.py b/src/contextual/resources/generate.py
@@ -82,7 +82,8 @@ def create(
           messages: List of messages in the conversation so far. The last message must be from the
               user.
 
-          model: The version of the Contextual's GLM to use. Currently, we just have "v1".
+          model: The version of the Contextual's GLM to use. Currently, we just have
+              "ctxl-rerank-en-v1-instruct".
 
           avoid_commentary: Flag to indicate whether the model should avoid providing additional commentary
               in responses. Commentary is conversational in nature and does not contain
@@ -188,7 +189,8 @@ async def create(
           messages: List of messages in the conversation so far. The last message must be from the
               user.
 
-          model: The version of the Contextual's GLM to use. Currently, we just have "v1".
+          model: The version of the Contextual's GLM to use. Currently, we just have
+              "ctxl-rerank-en-v1-instruct".
 
           avoid_commentary: Flag to indicate whether the model should avoid providing additional commentary
               in responses. Commentary is conversational in nature and does not contain
diff --git a/src/contextual/resources/rerank.py b/src/contextual/resources/rerank.py
@@ -63,38 +63,30 @@ def create(
         timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
     ) -> RerankCreateResponse:
         """
-        Rank a list of documents according to their relevance to a query and your custom
-        instructions about how to prioritize retrievals. We evaluated the model on
-        instructions for recency, document type, source, and metadata, and it can
-        generalize to other instructions as well.
-
-        The total request cannot exceed 400,000 tokens. The combined length of the
-        query, instruction and any document with its metadata must not exceed 8,000
-        tokens. Email
+        Rank a list of documents according to their relevance to a query.
+
+        The total request cannot exceed 400,000 tokens. The combined length of any
+        document, instruction and the query must not exceed 4,000 tokens. Email
         [rerank-feedback@contextual.ai](mailto:rerank-feedback@contextual.ai) with any
         feedback or questions.
 
         Args:
-          documents: The texts to be reranked according to their relevance to the query and the
-              optional instruction
+          documents: The texts to be reranked according to their relevance to the query
 
-          model: The version of the reranker to use. Currently, we just have
-              "ctxl-rerank-en-v1-instruct".
+          model: The version of the reranker to use. Currently, we just have "v1".
 
           query: The string against which documents will be ranked for relevance
 
-          instruction: Instructions that the reranker references when ranking retrievals. We evaluated
-              the model on instructions for recency, document type, source, and metadata, and
-              it can generalize to other instructions as well. Note that we do not guarantee
-              that the reranker will follow these instructions exactly. Examples: "Prioritize
-              internal sales documents over market analysis reports. More recent documents
-              should be weighted higher. Enterprise portal content supersedes distributor
-              communications." and "Emphasize forecasts from top-tier investment banks. Recent
-              analysis should take precedence. Disregard aggregator sites and favor detailed
-              research notes over news summaries."
+          instruction: Instructions that the reranker references when ranking retrievals. Note that we
+              do not guarantee that the reranker will follow these instructions exactly.
+              Examples: "Prioritize internal sales documents over market analysis reports.
+              More recent documents should be weighted higher. Enterprise portal content
+              supersedes distributor communications." and "Emphasize forecasts from top-tier
+              investment banks. Recent analysis should take precedence. Disregard aggregator
+              sites and favor detailed research notes over news summaries."
 
           metadata: Metadata for documents being passed to the reranker. Must be the same length as
-              the documents list. If a document does not have metadata, add an empty string.
+              the documents list.
 
           top_n: The number of top-ranked results to return
 
@@ -163,38 +155,30 @@ async def create(
         timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
     ) -> RerankCreateResponse:
         """
-        Rank a list of documents according to their relevance to a query and your custom
-        instructions about how to prioritize retrievals. We evaluated the model on
-        instructions for recency, document type, source, and metadata, and it can
-        generalize to other instructions as well.
-
-        The total request cannot exceed 400,000 tokens. The combined length of the
-        query, instruction and any document with its metadata must not exceed 8,000
-        tokens. Email
+        Rank a list of documents according to their relevance to a query.
+
+        The total request cannot exceed 400,000 tokens. The combined length of any
+        document, instruction and the query must not exceed 4,000 tokens. Email
         [rerank-feedback@contextual.ai](mailto:rerank-feedback@contextual.ai) with any
         feedback or questions.
 
         Args:
-          documents: The texts to be reranked according to their relevance to the query and the
-              optional instruction
+          documents: The texts to be reranked according to their relevance to the query
 
-          model: The version of the reranker to use. Currently, we just have
-              "ctxl-rerank-en-v1-instruct".
+          model: The version of the reranker to use. Currently, we just have "v1".
 
           query: The string against which documents will be ranked for relevance
 
-          instruction: Instructions that the reranker references when ranking retrievals. We evaluated
-              the model on instructions for recency, document type, source, and metadata, and
-              it can generalize to other instructions as well. Note that we do not guarantee
-              that the reranker will follow these instructions exactly. Examples: "Prioritize
-              internal sales documents over market analysis reports. More recent documents
-              should be weighted higher. Enterprise portal content supersedes distributor
-              communications." and "Emphasize forecasts from top-tier investment banks. Recent
-              analysis should take precedence. Disregard aggregator sites and favor detailed
-              research notes over news summaries."
+          instruction: Instructions that the reranker references when ranking retrievals. Note that we
+              do not guarantee that the reranker will follow these instructions exactly.
+              Examples: "Prioritize internal sales documents over market analysis reports.
+              More recent documents should be weighted higher. Enterprise portal content
+              supersedes distributor communications." and "Emphasize forecasts from top-tier
+              investment banks. Recent analysis should take precedence. Disregard aggregator
+              sites and favor detailed research notes over news summaries."
 
           metadata: Metadata for documents being passed to the reranker. Must be the same length as
-              the documents list. If a document does not have metadata, add an empty string.
+              the documents list.
 
           top_n: The number of top-ranked results to return
 
diff --git a/src/contextual/types/agents/query_create_params.py b/src/contextual/types/agents/query_create_params.py
@@ -3,7 +3,10 @@
 from __future__ import annotations
 
 from typing import List, Union, Iterable
-from typing_extensions import Literal, Required, TypeAlias, TypedDict
+from datetime import datetime
+from typing_extensions import Literal, Required, Annotated, TypeAlias, TypedDict
+
+from ..._utils import PropertyInfo
 
 __all__ = ["QueryCreateParams", "Message", "DocumentsFilters", "DocumentsFiltersBaseMetadataFilter"]
 
@@ -104,7 +107,10 @@ class DocumentsFiltersBaseMetadataFilter(TypedDict, total=False):
     ]
     """Operator to be used for the filter."""
 
-    value: Union[str, float, bool, List[Union[str, float, bool]], None]
+    value: Annotated[
+        Union[str, Union[str, datetime], float, bool, Iterable[object], List[Union[str, float, bool]]],
+        PropertyInfo(format="iso8601"),
+    ]
     """The value to be searched for in the field.
 
     In case of exists operator, it is not needed.
diff --git a/src/contextual/types/datastores/composite_metadata_filter_param.py b/src/contextual/types/datastores/composite_metadata_filter_param.py
@@ -3,8 +3,10 @@
 from __future__ import annotations
 
 from typing import TYPE_CHECKING, List, Union, Iterable, Optional
-from typing_extensions import Literal, Required, TypeAlias, TypedDict, TypeAliasType
+from datetime import datetime
+from typing_extensions import Literal, Required, Annotated, TypeAlias, TypedDict, TypeAliasType
 
+from ..._utils import PropertyInfo
 from ..._compat import PYDANTIC_V2
 
 __all__ = ["CompositeMetadataFilterParam", "Filter", "FilterBaseMetadataFilter"]
@@ -19,7 +21,10 @@ class FilterBaseMetadataFilter(TypedDict, total=False):
     ]
     """Operator to be used for the filter."""
 
-    value: Union[str, float, bool, List[Union[str, float, bool]], None]
+    value: Annotated[
+        Union[str, Union[str, datetime], float, bool, Iterable[object], List[Union[str, float, bool]]],
+        PropertyInfo(format="iso8601"),
+    ]
     """The value to be searched for in the field.
 
     In case of exists operator, it is not needed.
diff --git a/src/contextual/types/generate_create_params.py b/src/contextual/types/generate_create_params.py
@@ -19,7 +19,10 @@ class GenerateCreateParams(TypedDict, total=False):
     """
 
     model: Required[str]
-    """The version of the Contextual's GLM to use. Currently, we just have "v1"."""
+    """The version of the Contextual's GLM to use.
+
+    Currently, we just have "ctxl-rerank-en-v1-instruct".
+    """
 
     avoid_commentary: bool
     """
diff --git a/src/contextual/types/rerank_create_params.py b/src/contextual/types/rerank_create_params.py
@@ -10,38 +10,29 @@
 
 class RerankCreateParams(TypedDict, total=False):
     documents: Required[List[str]]
-    """
-    The texts to be reranked according to their relevance to the query and the
-    optional instruction
-    """
+    """The texts to be reranked according to their relevance to the query"""
 
     model: Required[str]
-    """The version of the reranker to use.
-
-    Currently, we just have "ctxl-rerank-en-v1-instruct".
-    """
+    """The version of the reranker to use. Currently, we just have "v1"."""
 
     query: Required[str]
     """The string against which documents will be ranked for relevance"""
 
     instruction: str
     """Instructions that the reranker references when ranking retrievals.
 
-    We evaluated the model on instructions for recency, document type, source, and
-    metadata, and it can generalize to other instructions as well. Note that we do
-    not guarantee that the reranker will follow these instructions exactly.
-    Examples: "Prioritize internal sales documents over market analysis reports.
-    More recent documents should be weighted higher. Enterprise portal content
-    supersedes distributor communications." and "Emphasize forecasts from top-tier
-    investment banks. Recent analysis should take precedence. Disregard aggregator
-    sites and favor detailed research notes over news summaries."
+    Note that we do not guarantee that the reranker will follow these instructions
+    exactly. Examples: "Prioritize internal sales documents over market analysis
+    reports. More recent documents should be weighted higher. Enterprise portal
+    content supersedes distributor communications." and "Emphasize forecasts from
+    top-tier investment banks. Recent analysis should take precedence. Disregard
+    aggregator sites and favor detailed research notes over news summaries."
     """
 
     metadata: List[str]
     """Metadata for documents being passed to the reranker.
 
-    Must be the same length as the documents list. If a document does not have
-    metadata, add an empty string.
+    Must be the same length as the documents list.
     """
 
     top_n: int

Original file line number	Diff line number	Diff line change
`@@ -1,2 +1,2 @@`
`1`	`1`	`configured_endpoints: 46`
`2`		`-openapi_spec_url: https://storage.googleapis.com/stainless-sdk-openapi-specs/contextual-ai%2Fsunrise-194878b194cd507d7c5418ff38cc0fc53441ef618f991990d334b4b75775cd8f.yml`
	`2`	`+openapi_spec_url: https://storage.googleapis.com/stainless-sdk-openapi-specs/contextual-ai%2Fsunrise-e20c18e11af76caf98794e68406fc556cf91d13e8e27a3b1d1909a4f8c2d3eb5.yml`