# This file was auto-generated by Fern from our API Definition. import typing from ..core.client_wrapper import AsyncClientWrapper, SyncClientWrapper from ..core.request_options import RequestOptions from ..requests.chat_completion_request_message import ChatCompletionRequestMessageParams from ..requests.chat_completion_tool import ChatCompletionToolParams from ..requests.stop_configuration import StopConfigurationParams from ..requests.tool_choice_option import ToolChoiceOptionParams from ..types.chat_completion_chunk import ChatCompletionChunk from ..types.create_chat_completion_response import CreateChatCompletionResponse from ..types.reasoning_effort import ReasoningEffort from ..types.sarvam_model_ids import SarvamModelIds from .raw_client import AsyncRawChatClient, RawChatClient # this is used as the default value for optional parameters OMIT = typing.cast(typing.Any, ...) class ChatClient: def __init__(self, *, client_wrapper: SyncClientWrapper): self._raw_client = RawChatClient(client_wrapper=client_wrapper) @property def with_raw_response(self) -> RawChatClient: """ Retrieves a raw implementation of this client that returns raw responses. Returns ------- RawChatClient """ return self._raw_client @typing.overload def completions( self, *, messages: typing.Sequence[ChatCompletionRequestMessageParams], model: SarvamModelIds, temperature: typing.Optional[float] = ..., top_p: typing.Optional[float] = ..., reasoning_effort: typing.Optional[ReasoningEffort] = ..., max_tokens: typing.Optional[int] = ..., stream: typing.Literal[True], stop: typing.Optional[StopConfigurationParams] = ..., n: typing.Optional[int] = ..., seed: typing.Optional[int] = ..., frequency_penalty: typing.Optional[float] = ..., presence_penalty: typing.Optional[float] = ..., wiki_grounding: typing.Optional[bool] = ..., tools: typing.Optional[typing.Sequence[ChatCompletionToolParams]] = ..., tool_choice: typing.Optional[ToolChoiceOptionParams] = ..., request_options: typing.Optional[RequestOptions] = ..., ) -> typing.Iterator[ChatCompletionChunk]: ... @typing.overload def completions( self, *, messages: typing.Sequence[ChatCompletionRequestMessageParams], model: SarvamModelIds, temperature: typing.Optional[float] = ..., top_p: typing.Optional[float] = ..., reasoning_effort: typing.Optional[ReasoningEffort] = ..., max_tokens: typing.Optional[int] = ..., stream: typing.Optional[typing.Literal[False]] = ..., stop: typing.Optional[StopConfigurationParams] = ..., n: typing.Optional[int] = ..., seed: typing.Optional[int] = ..., frequency_penalty: typing.Optional[float] = ..., presence_penalty: typing.Optional[float] = ..., wiki_grounding: typing.Optional[bool] = ..., tools: typing.Optional[typing.Sequence[ChatCompletionToolParams]] = ..., tool_choice: typing.Optional[ToolChoiceOptionParams] = ..., request_options: typing.Optional[RequestOptions] = ..., ) -> CreateChatCompletionResponse: ... def completions( self, *, messages: typing.Sequence[ChatCompletionRequestMessageParams], model: SarvamModelIds, temperature: typing.Optional[float] = OMIT, top_p: typing.Optional[float] = OMIT, reasoning_effort: typing.Optional[ReasoningEffort] = OMIT, max_tokens: typing.Optional[int] = OMIT, stream: typing.Optional[bool] = OMIT, stop: typing.Optional[StopConfigurationParams] = OMIT, n: typing.Optional[int] = OMIT, seed: typing.Optional[int] = OMIT, frequency_penalty: typing.Optional[float] = OMIT, presence_penalty: typing.Optional[float] = OMIT, wiki_grounding: typing.Optional[bool] = OMIT, tools: typing.Optional[typing.Sequence[ChatCompletionToolParams]] = OMIT, tool_choice: typing.Optional[ToolChoiceOptionParams] = OMIT, request_options: typing.Optional[RequestOptions] = None, ) -> typing.Union[CreateChatCompletionResponse, typing.Iterator[ChatCompletionChunk]]: """ Parameters ---------- messages : typing.Sequence[ChatCompletionRequestMessageParams] A list of messages comprising the conversation so far. model : SarvamModelIds Model ID used to generate the response, like `sarvam-m`. temperature : typing.Optional[float] What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic. We generally recommend altering this or `top_p` but not both. top_p : typing.Optional[float] An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered. We generally recommend altering this or `temperature` but not both. reasoning_effort : typing.Optional[ReasoningEffort] The effort to use for reasoning max_tokens : typing.Optional[int] The maximum number of tokens that can be generated in the chat completion. stream : typing.Optional[bool] If set to true, the model response data will be streamed to the client as it is generated using [server-sent events](https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events/Using_server-sent_events#Event_stream_format). When true, returns an Iterator[ChatCompletionChunk] instead of CreateChatCompletionResponse. stop : typing.Optional[StopConfigurationParams] n : typing.Optional[int] How many chat completion choices to generate for each input message. Note that you will be charged based on the number of generated tokens across all of the choices. Keep `n` as `1` to minimize costs. seed : typing.Optional[int] This feature is in Beta. If specified, our system will make a best effort to sample deterministically, such that repeated requests with the same `seed` and parameters should return the same result. Determinism is not guaranteed, and you should refer to the `system_fingerprint` response parameter to monitor changes in the backend. frequency_penalty : typing.Optional[float] Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim. presence_penalty : typing.Optional[float] Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics. wiki_grounding : typing.Optional[bool] If set to true, the model response will be wiki grounded. tools : typing.Optional[typing.Sequence[ChatCompletionToolParams]] A list of tools the model may call. Currently, only functions are supported as a tool. tool_choice : typing.Optional[ToolChoiceOptionParams] Controls which (if any) tool is called by the model. request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- CreateChatCompletionResponse or Iterator[ChatCompletionChunk] When stream=False (default): CreateChatCompletionResponse. When stream=True: Iterator yielding ChatCompletionChunk objects. Examples -------- from sarvamai import SarvamAI client = SarvamAI( api_subscription_key="YOUR_API_SUBSCRIPTION_KEY", ) # Non-streaming response = client.chat.completions( messages=[{"role": "user", "content": "Hello"}], model="sarvam-m", ) # Streaming for chunk in client.chat.completions( messages=[{"role": "user", "content": "Hello"}], model="sarvam-m", stream=True, ): print(chunk) """ if stream is True: return self._raw_client.completions( messages=messages, model=model, temperature=temperature, top_p=top_p, reasoning_effort=reasoning_effort, max_tokens=max_tokens, stream=True, stop=stop, n=n, seed=seed, frequency_penalty=frequency_penalty, presence_penalty=presence_penalty, wiki_grounding=wiki_grounding, tools=tools, tool_choice=tool_choice, request_options=request_options, ) _response = self._raw_client.completions( messages=messages, model=model, temperature=temperature, top_p=top_p, reasoning_effort=reasoning_effort, max_tokens=max_tokens, stream=stream, stop=stop, n=n, seed=seed, frequency_penalty=frequency_penalty, presence_penalty=presence_penalty, wiki_grounding=wiki_grounding, tools=tools, tool_choice=tool_choice, request_options=request_options, ) return _response.data class AsyncChatClient: def __init__(self, *, client_wrapper: AsyncClientWrapper): self._raw_client = AsyncRawChatClient(client_wrapper=client_wrapper) @property def with_raw_response(self) -> AsyncRawChatClient: """ Retrieves a raw implementation of this client that returns raw responses. Returns ------- AsyncRawChatClient """ return self._raw_client @typing.overload async def completions( self, *, messages: typing.Sequence[ChatCompletionRequestMessageParams], model: SarvamModelIds, temperature: typing.Optional[float] = ..., top_p: typing.Optional[float] = ..., reasoning_effort: typing.Optional[ReasoningEffort] = ..., max_tokens: typing.Optional[int] = ..., stream: typing.Literal[True], stop: typing.Optional[StopConfigurationParams] = ..., n: typing.Optional[int] = ..., seed: typing.Optional[int] = ..., frequency_penalty: typing.Optional[float] = ..., presence_penalty: typing.Optional[float] = ..., wiki_grounding: typing.Optional[bool] = ..., tools: typing.Optional[typing.Sequence[ChatCompletionToolParams]] = ..., tool_choice: typing.Optional[ToolChoiceOptionParams] = ..., request_options: typing.Optional[RequestOptions] = ..., ) -> typing.AsyncIterator[ChatCompletionChunk]: ... @typing.overload async def completions( self, *, messages: typing.Sequence[ChatCompletionRequestMessageParams], model: SarvamModelIds, temperature: typing.Optional[float] = ..., top_p: typing.Optional[float] = ..., reasoning_effort: typing.Optional[ReasoningEffort] = ..., max_tokens: typing.Optional[int] = ..., stream: typing.Optional[typing.Literal[False]] = ..., stop: typing.Optional[StopConfigurationParams] = ..., n: typing.Optional[int] = ..., seed: typing.Optional[int] = ..., frequency_penalty: typing.Optional[float] = ..., presence_penalty: typing.Optional[float] = ..., wiki_grounding: typing.Optional[bool] = ..., tools: typing.Optional[typing.Sequence[ChatCompletionToolParams]] = ..., tool_choice: typing.Optional[ToolChoiceOptionParams] = ..., request_options: typing.Optional[RequestOptions] = ..., ) -> CreateChatCompletionResponse: ... async def completions( self, *, messages: typing.Sequence[ChatCompletionRequestMessageParams], model: SarvamModelIds, temperature: typing.Optional[float] = OMIT, top_p: typing.Optional[float] = OMIT, reasoning_effort: typing.Optional[ReasoningEffort] = OMIT, max_tokens: typing.Optional[int] = OMIT, stream: typing.Optional[bool] = OMIT, stop: typing.Optional[StopConfigurationParams] = OMIT, n: typing.Optional[int] = OMIT, seed: typing.Optional[int] = OMIT, frequency_penalty: typing.Optional[float] = OMIT, presence_penalty: typing.Optional[float] = OMIT, wiki_grounding: typing.Optional[bool] = OMIT, tools: typing.Optional[typing.Sequence[ChatCompletionToolParams]] = OMIT, tool_choice: typing.Optional[ToolChoiceOptionParams] = OMIT, request_options: typing.Optional[RequestOptions] = None, ) -> typing.Union[CreateChatCompletionResponse, typing.AsyncIterator[ChatCompletionChunk]]: """ Parameters ---------- messages : typing.Sequence[ChatCompletionRequestMessageParams] A list of messages comprising the conversation so far. model : SarvamModelIds Model ID used to generate the response, like `sarvam-m`. temperature : typing.Optional[float] What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic. We generally recommend altering this or `top_p` but not both. top_p : typing.Optional[float] An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered. We generally recommend altering this or `temperature` but not both. reasoning_effort : typing.Optional[ReasoningEffort] The effort to use for reasoning max_tokens : typing.Optional[int] The maximum number of tokens that can be generated in the chat completion. stream : typing.Optional[bool] If set to true, the model response data will be streamed to the client as it is generated using [server-sent events](https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events/Using_server-sent_events#Event_stream_format). When true, returns an AsyncIterator[ChatCompletionChunk] instead of CreateChatCompletionResponse. stop : typing.Optional[StopConfigurationParams] n : typing.Optional[int] How many chat completion choices to generate for each input message. Note that you will be charged based on the number of generated tokens across all of the choices. Keep `n` as `1` to minimize costs. seed : typing.Optional[int] This feature is in Beta. If specified, our system will make a best effort to sample deterministically, such that repeated requests with the same `seed` and parameters should return the same result. Determinism is not guaranteed, and you should refer to the `system_fingerprint` response parameter to monitor changes in the backend. frequency_penalty : typing.Optional[float] Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim. presence_penalty : typing.Optional[float] Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics. wiki_grounding : typing.Optional[bool] If set to true, the model response will be wiki grounded. tools : typing.Optional[typing.Sequence[ChatCompletionToolParams]] A list of tools the model may call. Currently, only functions are supported as a tool. tool_choice : typing.Optional[ToolChoiceOptionParams] Controls which (if any) tool is called by the model. request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- CreateChatCompletionResponse or AsyncIterator[ChatCompletionChunk] When stream=False (default): CreateChatCompletionResponse. When stream=True: AsyncIterator yielding ChatCompletionChunk objects. Examples -------- import asyncio from sarvamai import AsyncSarvamAI client = AsyncSarvamAI( api_subscription_key="YOUR_API_SUBSCRIPTION_KEY", ) async def main() -> None: # Non-streaming response = await client.chat.completions( messages=[{"role": "user", "content": "Hello"}], model="sarvam-m", ) # Streaming async for chunk in client.chat.completions( messages=[{"role": "user", "content": "Hello"}], model="sarvam-m", stream=True, ): print(chunk) asyncio.run(main()) """ if stream is True: return await self._raw_client.completions( messages=messages, model=model, temperature=temperature, top_p=top_p, reasoning_effort=reasoning_effort, max_tokens=max_tokens, stream=True, stop=stop, n=n, seed=seed, frequency_penalty=frequency_penalty, presence_penalty=presence_penalty, wiki_grounding=wiki_grounding, tools=tools, tool_choice=tool_choice, request_options=request_options, ) _response = await self._raw_client.completions( messages=messages, model=model, temperature=temperature, top_p=top_p, reasoning_effort=reasoning_effort, max_tokens=max_tokens, stream=stream, stop=stop, n=n, seed=seed, frequency_penalty=frequency_penalty, presence_penalty=presence_penalty, wiki_grounding=wiki_grounding, tools=tools, tool_choice=tool_choice, request_options=request_options, ) return _response.data