feat(api): update via SDK Studio (#31)

Author: stainless-app[bot]

.stats.yml

@@ -1,2 +1,2 @@
-configured_endpoints: 33
-openapi_spec_url: https://storage.googleapis.com/stainless-sdk-openapi-specs/contextual-ai%2Fsunrise-4ed32c3243ce7a772e55bb1ba204736fc3fb1d712d8ca0eb91bac0c7ac626938.yml
+configured_endpoints: 35
+openapi_spec_url: https://storage.googleapis.com/stainless-sdk-openapi-specs/contextual-ai%2Fsunrise-c0b9cfd71efb976777313fb342d2e31ebeb44b1b3f9bb7ddea971e6b2bc5fd19.yml

api.md

@@ -186,3 +186,27 @@ from contextual.types import LMUnitCreateResponse
 Methods:
 
 - <code title="post /lmunit">client.lmunit.<a href="./src/contextual/resources/lmunit.py">create</a>(\*\*<a href="src/contextual/types/lmunit_create_params.py">params</a>) -> <a href="./src/contextual/types/lmunit_create_response.py">LMUnitCreateResponse</a></code>
+
+# Rerank
+
+Types:
+
+```python
+from contextual.types import RerankCreateResponse
+```
+
+Methods:
+
+- <code title="post /rerank">client.rerank.<a href="./src/contextual/resources/rerank.py">create</a>(\*\*<a href="src/contextual/types/rerank_create_params.py">params</a>) -> <a href="./src/contextual/types/rerank_create_response.py">RerankCreateResponse</a></code>
+
+# Generate
+
+Types:
+
+```python
+from contextual.types import GenerateCreateResponse
+```
+
+Methods:
+
+- <code title="post /generate">client.generate.<a href="./src/contextual/resources/generate.py">create</a>(\*\*<a href="src/contextual/types/generate_create_params.py">params</a>) -> <a href="./src/contextual/types/generate_create_response.py">GenerateCreateResponse</a></code>

src/contextual/_client.py

@@ -24,7 +24,7 @@
     get_async_library,
 )
 from ._version import __version__
-from .resources import lmunit
+from .resources import lmunit, rerank, generate
 from ._streaming import Stream as Stream, AsyncStream as AsyncStream
 from ._exceptions import APIStatusError, ContextualAIError
 from ._base_client import (
@@ -51,6 +51,8 @@ class ContextualAI(SyncAPIClient):
     datastores: datastores.DatastoresResource
     agents: agents.AgentsResource
     lmunit: lmunit.LMUnitResource
+    rerank: rerank.RerankResource
+    generate: generate.GenerateResource
     with_raw_response: ContextualAIWithRawResponse
     with_streaming_response: ContextualAIWithStreamedResponse
 
@@ -111,6 +113,8 @@ def __init__(
         self.datastores = datastores.DatastoresResource(self)
         self.agents = agents.AgentsResource(self)
         self.lmunit = lmunit.LMUnitResource(self)
+        self.rerank = rerank.RerankResource(self)
+        self.generate = generate.GenerateResource(self)
         self.with_raw_response = ContextualAIWithRawResponse(self)
         self.with_streaming_response = ContextualAIWithStreamedResponse(self)
 
@@ -223,6 +227,8 @@ class AsyncContextualAI(AsyncAPIClient):
     datastores: datastores.AsyncDatastoresResource
     agents: agents.AsyncAgentsResource
     lmunit: lmunit.AsyncLMUnitResource
+    rerank: rerank.AsyncRerankResource
+    generate: generate.AsyncGenerateResource
     with_raw_response: AsyncContextualAIWithRawResponse
     with_streaming_response: AsyncContextualAIWithStreamedResponse
 
@@ -283,6 +289,8 @@ def __init__(
         self.datastores = datastores.AsyncDatastoresResource(self)
         self.agents = agents.AsyncAgentsResource(self)
         self.lmunit = lmunit.AsyncLMUnitResource(self)
+        self.rerank = rerank.AsyncRerankResource(self)
+        self.generate = generate.AsyncGenerateResource(self)
         self.with_raw_response = AsyncContextualAIWithRawResponse(self)
         self.with_streaming_response = AsyncContextualAIWithStreamedResponse(self)
 
@@ -396,27 +404,35 @@ def __init__(self, client: ContextualAI) -> None:
         self.datastores = datastores.DatastoresResourceWithRawResponse(client.datastores)
         self.agents = agents.AgentsResourceWithRawResponse(client.agents)
         self.lmunit = lmunit.LMUnitResourceWithRawResponse(client.lmunit)
+        self.rerank = rerank.RerankResourceWithRawResponse(client.rerank)
+        self.generate = generate.GenerateResourceWithRawResponse(client.generate)
 
 
 class AsyncContextualAIWithRawResponse:
     def __init__(self, client: AsyncContextualAI) -> None:
         self.datastores = datastores.AsyncDatastoresResourceWithRawResponse(client.datastores)
         self.agents = agents.AsyncAgentsResourceWithRawResponse(client.agents)
         self.lmunit = lmunit.AsyncLMUnitResourceWithRawResponse(client.lmunit)
+        self.rerank = rerank.AsyncRerankResourceWithRawResponse(client.rerank)
+        self.generate = generate.AsyncGenerateResourceWithRawResponse(client.generate)
 
 
 class ContextualAIWithStreamedResponse:
     def __init__(self, client: ContextualAI) -> None:
         self.datastores = datastores.DatastoresResourceWithStreamingResponse(client.datastores)
         self.agents = agents.AgentsResourceWithStreamingResponse(client.agents)
         self.lmunit = lmunit.LMUnitResourceWithStreamingResponse(client.lmunit)
+        self.rerank = rerank.RerankResourceWithStreamingResponse(client.rerank)
+        self.generate = generate.GenerateResourceWithStreamingResponse(client.generate)
 
 
 class AsyncContextualAIWithStreamedResponse:
     def __init__(self, client: AsyncContextualAI) -> None:
         self.datastores = datastores.AsyncDatastoresResourceWithStreamingResponse(client.datastores)
         self.agents = agents.AsyncAgentsResourceWithStreamingResponse(client.agents)
         self.lmunit = lmunit.AsyncLMUnitResourceWithStreamingResponse(client.lmunit)
+        self.rerank = rerank.AsyncRerankResourceWithStreamingResponse(client.rerank)
+        self.generate = generate.AsyncGenerateResourceWithStreamingResponse(client.generate)
 
 
 Client = ContextualAI

src/contextual/resources/__init__.py

@@ -16,6 +16,22 @@
     LMUnitResourceWithStreamingResponse,
     AsyncLMUnitResourceWithStreamingResponse,
 )
+from .rerank import (
+    RerankResource,
+    AsyncRerankResource,
+    RerankResourceWithRawResponse,
+    AsyncRerankResourceWithRawResponse,
+    RerankResourceWithStreamingResponse,
+    AsyncRerankResourceWithStreamingResponse,
+)
+from .generate import (
+    GenerateResource,
+    AsyncGenerateResource,
+    GenerateResourceWithRawResponse,
+    AsyncGenerateResourceWithRawResponse,
+    GenerateResourceWithStreamingResponse,
+    AsyncGenerateResourceWithStreamingResponse,
+)
 from .datastores import (
     DatastoresResource,
     AsyncDatastoresResource,
@@ -44,4 +60,16 @@
     "AsyncLMUnitResourceWithRawResponse",
     "LMUnitResourceWithStreamingResponse",
     "AsyncLMUnitResourceWithStreamingResponse",
+    "RerankResource",
+    "AsyncRerankResource",
+    "RerankResourceWithRawResponse",
+    "AsyncRerankResourceWithRawResponse",
+    "RerankResourceWithStreamingResponse",
+    "AsyncRerankResourceWithStreamingResponse",
+    "GenerateResource",
+    "AsyncGenerateResource",
+    "GenerateResourceWithRawResponse",
+    "AsyncGenerateResourceWithRawResponse",
+    "GenerateResourceWithStreamingResponse",
+    "AsyncGenerateResourceWithStreamingResponse",
 ]

src/contextual/resources/agents/datasets/evaluate.py

@@ -81,20 +81,20 @@ def create(
     ) -> CreateDatasetResponse:
         """
         Create a new evaluation `Dataset` for the specified `Agent` using the provided
-        JSONL file. A `Dataset` is a versioned collection of samples conforming to a
-        particular schema, and can be used to store `Evaluation` test-sets and retrieve
-        `Evaluation` results.
+        JSONL or CSV file. A `Dataset` is a versioned collection of samples conforming
+        to a particular schema, and can be used to store `Evaluation` test-sets and
+        retrieve `Evaluation` results.
 
         Each `Dataset` is versioned and validated against its schema during creation and
         subsequent updates. The provided `Dataset` file must conform to the schema
         defined for the `dataset_type`.
 
-        File schema for `dataset_type` `evaluation_set` is a JSONL or CSV file where
-        each line is one JSON object with the following required keys:
+        File schema for `dataset_type` `evaluation_set` is a CSV file or a JSONL file
+        where each line is one JSON object. The following keys are required:
 
-        - `prompt` (required, `string`): Prompt or question
+        - `prompt` (`string`): Prompt or question
 
-        - `reference` (required, `string`): Required reference or ground truth response
+        - `reference` (`string`): Reference or ground truth response
 
         Args:
           agent_id: Agent ID to associate with the evaluation dataset
@@ -103,7 +103,7 @@ def create(
 
           dataset_type: Type of evaluation dataset which determines its schema and validation rules.
 
-          file: JSONL file containing the evaluation dataset
+          file: JSONL or CSV file containing the evaluation dataset
 
           extra_headers: Send extra headers
 
@@ -228,12 +228,12 @@ def update(
         Create a new version of the dataset by appending content to the `Dataset` and
         validating against its schema.
 
-        File schema for `dataset_type` `evaluation_set` is a JSONL file where each line
-        is one JSON object with the following required keys:
+        File schema for `dataset_type` `evaluation_set` is a CSV file or a JSONL file
+        where each line is one JSON object. The following keys are required:
 
         - `prompt` (`string`): Prompt or question
 
-        - `reference` (`string`): Required reference or ground truth response
+        - `reference` (`string`): Reference or ground truth response
 
         Args:
           agent_id: Agent ID associated with the evaluation dataset
@@ -243,7 +243,7 @@ def update(
           dataset_type: Type of evaluation dataset which determines its schema and validation rules.
               Must match the `dataset_type` used at dataset creation time.
 
-          file: JSONL file containing the entries to append to the evaluation dataset
+          file: JSONL or CSV file containing the entries to append to the evaluation dataset
 
           extra_headers: Send extra headers
 
@@ -459,20 +459,20 @@ async def create(
     ) -> CreateDatasetResponse:
         """
         Create a new evaluation `Dataset` for the specified `Agent` using the provided
-        JSONL file. A `Dataset` is a versioned collection of samples conforming to a
-        particular schema, and can be used to store `Evaluation` test-sets and retrieve
-        `Evaluation` results.
+        JSONL or CSV file. A `Dataset` is a versioned collection of samples conforming
+        to a particular schema, and can be used to store `Evaluation` test-sets and
+        retrieve `Evaluation` results.
 
         Each `Dataset` is versioned and validated against its schema during creation and
         subsequent updates. The provided `Dataset` file must conform to the schema
         defined for the `dataset_type`.
 
-        File schema for `dataset_type` `evaluation_set` is a JSONL or CSV file where
-        each line is one JSON object with the following required keys:
+        File schema for `dataset_type` `evaluation_set` is a CSV file or a JSONL file
+        where each line is one JSON object. The following keys are required:
 
-        - `prompt` (required, `string`): Prompt or question
+        - `prompt` (`string`): Prompt or question
 
-        - `reference` (required, `string`): Required reference or ground truth response
+        - `reference` (`string`): Reference or ground truth response
 
         Args:
           agent_id: Agent ID to associate with the evaluation dataset
@@ -481,7 +481,7 @@ async def create(
 
           dataset_type: Type of evaluation dataset which determines its schema and validation rules.
 
-          file: JSONL file containing the evaluation dataset
+          file: JSONL or CSV file containing the evaluation dataset
 
           extra_headers: Send extra headers
 
@@ -606,12 +606,12 @@ async def update(
         Create a new version of the dataset by appending content to the `Dataset` and
         validating against its schema.
 
-        File schema for `dataset_type` `evaluation_set` is a JSONL file where each line
-        is one JSON object with the following required keys:
+        File schema for `dataset_type` `evaluation_set` is a CSV file or a JSONL file
+        where each line is one JSON object. The following keys are required:
 
         - `prompt` (`string`): Prompt or question
 
-        - `reference` (`string`): Required reference or ground truth response
+        - `reference` (`string`): Reference or ground truth response
 
         Args:
           agent_id: Agent ID associated with the evaluation dataset
@@ -621,7 +621,7 @@ async def update(
           dataset_type: Type of evaluation dataset which determines its schema and validation rules.
               Must match the `dataset_type` used at dataset creation time.
 
-          file: JSONL file containing the entries to append to the evaluation dataset
+          file: JSONL or CSV file containing the entries to append to the evaluation dataset
 
           extra_headers: Send extra headers
 

src/contextual/resources/agents/evaluate/evaluate.py

@@ -2,7 +2,7 @@
 
 from __future__ import annotations
 
-from typing import List, Mapping, Optional, cast
+from typing import List, Mapping, cast
 from typing_extensions import Literal
 
 import httpx
@@ -68,7 +68,7 @@ def create(
         metrics: List[Literal["equivalence", "groundedness"]],
         evalset_file: FileTypes | NotGiven = NOT_GIVEN,
         evalset_name: str | NotGiven = NOT_GIVEN,
-        llm_model_id: Optional[str] | NotGiven = NOT_GIVEN,
+        llm_model_id: str | NotGiven = NOT_GIVEN,
         # 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,
@@ -175,7 +175,7 @@ async def create(
         metrics: List[Literal["equivalence", "groundedness"]],
         evalset_file: FileTypes | NotGiven = NOT_GIVEN,
         evalset_name: str | NotGiven = NOT_GIVEN,
-        llm_model_id: Optional[str] | NotGiven = NOT_GIVEN,
+        llm_model_id: str | NotGiven = NOT_GIVEN,
         # 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,

src/contextual/resources/agents/tune/tune.py

@@ -111,21 +111,21 @@ def create(
               JSON object represents a single training example. The four required fields are
               `guideline`, `prompt`, `reference`, and `knowledge`.
 
-              - `knowledge` (`list[str]`): Knowledge or retrievals used to generate the
-                reference response, as a list of string text chunks
+              - `knowledge` (`list[str]`): Retrieved knowledge used to generate the reference
+                answer. `knowledge` is a list of retrieved text chunks.
 
-              - `reference` field should be the model's response to the prompt.
+              - `reference` (`str`): The gold-standard answer to the prompt.
 
-              - `guideline` (`str): Guidelines or criteria for model output
+              - `guideline` (`str`): Guidelines for model output.
 
-              - `prompt` (required, `string`): Prompt or question model should respond to.
+              - `prompt` (`str`): Question for the model to respond to.
 
               Example:
 
               ```json
               [
                 {
-                  "guideline": "The response should be accurate.",
+                  "guideline": "The answer should be accurate.",
                   "prompt": "What was last quarter's revenue?",
                   "reference": "According to recent reports, the Q3 revenue was $1.2 million, a 0.1 million increase from Q2.",
                   "knowledge": [
@@ -244,21 +244,21 @@ async def create(
               JSON object represents a single training example. The four required fields are
               `guideline`, `prompt`, `reference`, and `knowledge`.
 
-              - `knowledge` (`list[str]`): Knowledge or retrievals used to generate the
-                reference response, as a list of string text chunks
+              - `knowledge` (`list[str]`): Retrieved knowledge used to generate the reference
+                answer. `knowledge` is a list of retrieved text chunks.
 
-              - `reference` field should be the model's response to the prompt.
+              - `reference` (`str`): The gold-standard answer to the prompt.
 
-              - `guideline` (`str): Guidelines or criteria for model output
+              - `guideline` (`str`): Guidelines for model output.
 
-              - `prompt` (required, `string`): Prompt or question model should respond to.
+              - `prompt` (`str`): Question for the model to respond to.
 
               Example:
 
               ```json
               [
                 {
-                  "guideline": "The response should be accurate.",
+                  "guideline": "The answer should be accurate.",
                   "prompt": "What was last quarter's revenue?",
                   "reference": "According to recent reports, the Q3 revenue was $1.2 million, a 0.1 million increase from Q2.",
                   "knowledge": [