feat(api): update via SDK Studio (#40)

.stats.yml

@@ -1,2 +1,2 @@
-configured_endpoints: 6
+configured_endpoints: 7
 openapi_spec_url: https://storage.googleapis.com/stainless-sdk-openapi-specs/groqcloud%2Fgroqcloud-c28de228634e737a173375583a09eef5e0d7fa81fcdf7090d14d194e6ef4fdc5.yml

api.md

@@ -1,3 +1,15 @@
+# Embeddings
+
+Types:
+
+```python
+from groq.types import EmbeddingCreateResponse
+```
+
+Methods:
+
+- <code title="post /openai/v1/embeddings">client.embeddings.<a href="./src/groq/resources/embeddings.py">create</a>(\*\*<a href="src/groq/types/embedding_create_params.py">params</a>) -> <a href="./src/groq/types/embedding_create_response.py">EmbeddingCreateResponse</a></code>
+
 # Chat
 
 ## Completions

src/groq/_client.py

@@ -46,6 +46,7 @@
 
 
 class Groq(SyncAPIClient):
+    embeddings: resources.EmbeddingsResource
     chat: resources.ChatResource
     audio: resources.AudioResource
     models: resources.ModelsResource
@@ -106,6 +107,7 @@ def __init__(
             _strict_response_validation=_strict_response_validation,
         )
 
+        self.embeddings = resources.EmbeddingsResource(self)
         self.chat = resources.ChatResource(self)
         self.audio = resources.AudioResource(self)
         self.models = resources.ModelsResource(self)
@@ -218,6 +220,7 @@ def _make_status_error(
 
 
 class AsyncGroq(AsyncAPIClient):
+    embeddings: resources.AsyncEmbeddingsResource
     chat: resources.AsyncChatResource
     audio: resources.AsyncAudioResource
     models: resources.AsyncModelsResource
@@ -278,6 +281,7 @@ def __init__(
             _strict_response_validation=_strict_response_validation,
         )
 
+        self.embeddings = resources.AsyncEmbeddingsResource(self)
         self.chat = resources.AsyncChatResource(self)
         self.audio = resources.AsyncAudioResource(self)
         self.models = resources.AsyncModelsResource(self)
@@ -391,27 +395,31 @@ def _make_status_error(
 
 class GroqWithRawResponse:
     def __init__(self, client: Groq) -> None:
+        self.embeddings = resources.EmbeddingsResourceWithRawResponse(client.embeddings)
         self.chat = resources.ChatResourceWithRawResponse(client.chat)
         self.audio = resources.AudioResourceWithRawResponse(client.audio)
         self.models = resources.ModelsResourceWithRawResponse(client.models)
 
 
 class AsyncGroqWithRawResponse:
     def __init__(self, client: AsyncGroq) -> None:
+        self.embeddings = resources.AsyncEmbeddingsResourceWithRawResponse(client.embeddings)
         self.chat = resources.AsyncChatResourceWithRawResponse(client.chat)
         self.audio = resources.AsyncAudioResourceWithRawResponse(client.audio)
         self.models = resources.AsyncModelsResourceWithRawResponse(client.models)
 
 
 class GroqWithStreamedResponse:
     def __init__(self, client: Groq) -> None:
+        self.embeddings = resources.EmbeddingsResourceWithStreamingResponse(client.embeddings)
         self.chat = resources.ChatResourceWithStreamingResponse(client.chat)
         self.audio = resources.AudioResourceWithStreamingResponse(client.audio)
         self.models = resources.ModelsResourceWithStreamingResponse(client.models)
 
 
 class AsyncGroqWithStreamedResponse:
     def __init__(self, client: AsyncGroq) -> None:
+        self.embeddings = resources.AsyncEmbeddingsResourceWithStreamingResponse(client.embeddings)
         self.chat = resources.AsyncChatResourceWithStreamingResponse(client.chat)
         self.audio = resources.AsyncAudioResourceWithStreamingResponse(client.audio)
         self.models = resources.AsyncModelsResourceWithStreamingResponse(client.models)

src/groq/resources/__init__.py

@@ -24,8 +24,22 @@
     ModelsResourceWithStreamingResponse,
     AsyncModelsResourceWithStreamingResponse,
 )
+from .embeddings import (
+    EmbeddingsResource,
+    AsyncEmbeddingsResource,
+    EmbeddingsResourceWithRawResponse,
+    AsyncEmbeddingsResourceWithRawResponse,
+    EmbeddingsResourceWithStreamingResponse,
+    AsyncEmbeddingsResourceWithStreamingResponse,
+)
 
 __all__ = [
+    "EmbeddingsResource",
+    "AsyncEmbeddingsResource",
+    "EmbeddingsResourceWithRawResponse",
+    "AsyncEmbeddingsResourceWithRawResponse",
+    "EmbeddingsResourceWithStreamingResponse",
+    "AsyncEmbeddingsResourceWithStreamingResponse",
     "ChatResource",
     "AsyncChatResource",
     "ChatResourceWithRawResponse",

src/groq/resources/embeddings.py

@@ -0,0 +1,203 @@
+# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
+
+from __future__ import annotations
+
+from typing import List, Union, Optional
+from typing_extensions import Literal
+
+import httpx
+
+from ..types import embedding_create_params
+from .._types import NOT_GIVEN, Body, Query, Headers, NotGiven
+from .._utils import (
+    maybe_transform,
+    async_maybe_transform,
+)
+from .._compat import cached_property
+from .._resource import SyncAPIResource, AsyncAPIResource
+from .._response import (
+    to_raw_response_wrapper,
+    to_streamed_response_wrapper,
+    async_to_raw_response_wrapper,
+    async_to_streamed_response_wrapper,
+)
+from .._base_client import (
+    make_request_options,
+)
+from ..types.embedding_create_response import EmbeddingCreateResponse
+
+__all__ = ["EmbeddingsResource", "AsyncEmbeddingsResource"]
+
+
+class EmbeddingsResource(SyncAPIResource):
+    @cached_property
+    def with_raw_response(self) -> EmbeddingsResourceWithRawResponse:
+        return EmbeddingsResourceWithRawResponse(self)
+
+    @cached_property
+    def with_streaming_response(self) -> EmbeddingsResourceWithStreamingResponse:
+        return EmbeddingsResourceWithStreamingResponse(self)
+
+    def create(
+        self,
+        *,
+        input: Union[str, List[str]],
+        model: str,
+        dimensions: int | NotGiven = NOT_GIVEN,
+        encoding_format: Literal["float", "base64"] | NotGiven = NOT_GIVEN,
+        user: Optional[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,
+        extra_query: Query | None = None,
+        extra_body: Body | None = None,
+        timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
+    ) -> EmbeddingCreateResponse:
+        """
+        Creates an embedding vector representing the input text.
+
+        Args:
+          input: Input text to embed, encoded as a string or array of tokens. To embed multiple
+              inputs in a single request, pass an array of strings or array of token arrays.
+              The input must not exceed the max input tokens for the model, cannot be an empty
+              string, and any array must be 2048 dimensions or less.
+
+          model: ID of the model to use.
+
+          dimensions: The number of dimensions to return the embeddings in.
+
+          encoding_format: The format to return the embeddings in.
+
+          user: A unique identifier representing your end-user, which can help us monitor and
+              detect abuse.
+
+          extra_headers: Send extra headers
+
+          extra_query: Add additional query parameters to the request
+
+          extra_body: Add additional JSON properties to the request
+
+          timeout: Override the client-level default timeout for this request, in seconds
+        """
+        return self._post(
+            "/openai/v1/embeddings",
+            body=maybe_transform(
+                {
+                    "input": input,
+                    "model": model,
+                    "dimensions": dimensions,
+                    "encoding_format": encoding_format,
+                    "user": user,
+                },
+                embedding_create_params.EmbeddingCreateParams,
+            ),
+            options=make_request_options(
+                extra_headers=extra_headers, extra_query=extra_query, extra_body=extra_body, timeout=timeout
+            ),
+            cast_to=EmbeddingCreateResponse,
+        )
+
+
+class AsyncEmbeddingsResource(AsyncAPIResource):
+    @cached_property
+    def with_raw_response(self) -> AsyncEmbeddingsResourceWithRawResponse:
+        return AsyncEmbeddingsResourceWithRawResponse(self)
+
+    @cached_property
+    def with_streaming_response(self) -> AsyncEmbeddingsResourceWithStreamingResponse:
+        return AsyncEmbeddingsResourceWithStreamingResponse(self)
+
+    async def create(
+        self,
+        *,
+        input: Union[str, List[str]],
+        model: str,
+        dimensions: int | NotGiven = NOT_GIVEN,
+        encoding_format: Literal["float", "base64"] | NotGiven = NOT_GIVEN,
+        user: Optional[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,
+        extra_query: Query | None = None,
+        extra_body: Body | None = None,
+        timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
+    ) -> EmbeddingCreateResponse:
+        """
+        Creates an embedding vector representing the input text.
+
+        Args:
+          input: Input text to embed, encoded as a string or array of tokens. To embed multiple
+              inputs in a single request, pass an array of strings or array of token arrays.
+              The input must not exceed the max input tokens for the model, cannot be an empty
+              string, and any array must be 2048 dimensions or less.
+
+          model: ID of the model to use.
+
+          dimensions: The number of dimensions to return the embeddings in.
+
+          encoding_format: The format to return the embeddings in.
+
+          user: A unique identifier representing your end-user, which can help us monitor and
+              detect abuse.
+
+          extra_headers: Send extra headers
+
+          extra_query: Add additional query parameters to the request
+
+          extra_body: Add additional JSON properties to the request
+
+          timeout: Override the client-level default timeout for this request, in seconds
+        """
+        return await self._post(
+            "/openai/v1/embeddings",
+            body=await async_maybe_transform(
+                {
+                    "input": input,
+                    "model": model,
+                    "dimensions": dimensions,
+                    "encoding_format": encoding_format,
+                    "user": user,
+                },
+                embedding_create_params.EmbeddingCreateParams,
+            ),
+            options=make_request_options(
+                extra_headers=extra_headers, extra_query=extra_query, extra_body=extra_body, timeout=timeout
+            ),
+            cast_to=EmbeddingCreateResponse,
+        )
+
+
+class EmbeddingsResourceWithRawResponse:
+    def __init__(self, embeddings: EmbeddingsResource) -> None:
+        self._embeddings = embeddings
+
+        self.create = to_raw_response_wrapper(
+            embeddings.create,
+        )
+
+
+class AsyncEmbeddingsResourceWithRawResponse:
+    def __init__(self, embeddings: AsyncEmbeddingsResource) -> None:
+        self._embeddings = embeddings
+
+        self.create = async_to_raw_response_wrapper(
+            embeddings.create,
+        )
+
+
+class EmbeddingsResourceWithStreamingResponse:
+    def __init__(self, embeddings: EmbeddingsResource) -> None:
+        self._embeddings = embeddings
+
+        self.create = to_streamed_response_wrapper(
+            embeddings.create,
+        )
+
+
+class AsyncEmbeddingsResourceWithStreamingResponse:
+    def __init__(self, embeddings: AsyncEmbeddingsResource) -> None:
+        self._embeddings = embeddings
+
+        self.create = async_to_streamed_response_wrapper(
+            embeddings.create,
+        )

src/groq/types/__init__.py

@@ -5,3 +5,5 @@
 from .model import Model as Model
 from .model_list import ModelList as ModelList
 from .translation import Translation as Translation
+from .embedding_create_params import EmbeddingCreateParams as EmbeddingCreateParams
+from .embedding_create_response import EmbeddingCreateResponse as EmbeddingCreateResponse

src/groq/types/embedding_create_params.py

@@ -0,0 +1,33 @@
+# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
+
+from __future__ import annotations
+
+from typing import List, Union, Optional
+from typing_extensions import Literal, Required, TypedDict
+
+__all__ = ["EmbeddingCreateParams"]
+
+
+class EmbeddingCreateParams(TypedDict, total=False):
+    input: Required[Union[str, List[str]]]
+    """Input text to embed, encoded as a string or array of tokens.
+
+    To embed multiple inputs in a single request, pass an array of strings or array
+    of token arrays. The input must not exceed the max input tokens for the model,
+    cannot be an empty string, and any array must be 2048 dimensions or less.
+    """
+
+    model: Required[str]
+    """ID of the model to use."""
+
+    dimensions: int
+    """The number of dimensions to return the embeddings in."""
+
+    encoding_format: Literal["float", "base64"]
+    """The format to return the embeddings in."""
+
+    user: Optional[str]
+    """
+    A unique identifier representing your end-user, which can help us monitor and
+    detect abuse.
+    """

src/groq/types/embedding_create_response.py

@@ -0,0 +1,45 @@
+# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
+
+from typing import List, Union
+from typing_extensions import Literal
+
+from .._models import BaseModel
+
+__all__ = ["EmbeddingCreateResponse", "Data", "Usage"]
+
+
+class Data(BaseModel):
+    embedding: Union[List[float], str]
+    """The embedding vector, which is a list of floats.
+
+    The length of vector depends on the model as listed in the
+    [embedding guide](/docs/guides/embeddings).
+    """
+
+    index: int
+    """The index of the embedding in the list of embeddings."""
+
+    object: Literal["embedding"]
+    """The object type, which is always "embedding"."""
+
+
+class Usage(BaseModel):
+    prompt_tokens: int
+    """The number of tokens used by the prompt."""
+
+    total_tokens: int
+    """The total number of tokens used by the request."""
+
+
+class EmbeddingCreateResponse(BaseModel):
+    data: List[Data]
+    """The list of embeddings generated by the model."""
+
+    model: str
+    """The name of the model used to generate the embedding."""
+
+    object: Literal["list"]
+    """The object type, which is always "list"."""
+
+    usage: Usage
+    """The usage information for the request."""