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Diffstat (limited to '.venv/lib/python3.12/site-packages/anthropic/resources/messages')
3 files changed, 3303 insertions, 0 deletions
diff --git a/.venv/lib/python3.12/site-packages/anthropic/resources/messages/__init__.py b/.venv/lib/python3.12/site-packages/anthropic/resources/messages/__init__.py new file mode 100644 index 00000000..6e7cf9d9 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/anthropic/resources/messages/__init__.py @@ -0,0 +1,35 @@ +# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details. + +from .batches import ( + Batches, + AsyncBatches, + BatchesWithRawResponse, + AsyncBatchesWithRawResponse, + BatchesWithStreamingResponse, + AsyncBatchesWithStreamingResponse, +) +from .messages import ( + DEPRECATED_MODELS, + Messages, + AsyncMessages, + MessagesWithRawResponse, + AsyncMessagesWithRawResponse, + MessagesWithStreamingResponse, + AsyncMessagesWithStreamingResponse, +) + +__all__ = [ + "Batches", + "AsyncBatches", + "BatchesWithRawResponse", + "AsyncBatchesWithRawResponse", + "BatchesWithStreamingResponse", + "AsyncBatchesWithStreamingResponse", + "Messages", + "AsyncMessages", + "MessagesWithRawResponse", + "AsyncMessagesWithRawResponse", + "MessagesWithStreamingResponse", + "AsyncMessagesWithStreamingResponse", + "DEPRECATED_MODELS", +] diff --git a/.venv/lib/python3.12/site-packages/anthropic/resources/messages/batches.py b/.venv/lib/python3.12/site-packages/anthropic/resources/messages/batches.py new file mode 100644 index 00000000..4ebd8fd4 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/anthropic/resources/messages/batches.py @@ -0,0 +1,717 @@ +# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details. + +from __future__ import annotations + +from typing import Iterable + +import httpx + +from ... import _legacy_response +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_streamed_response_wrapper, async_to_streamed_response_wrapper +from ...pagination import SyncPage, AsyncPage +from ..._exceptions import AnthropicError +from ..._base_client import AsyncPaginator, make_request_options +from ...types.messages import batch_list_params, batch_create_params +from ..._decoders.jsonl import JSONLDecoder, AsyncJSONLDecoder +from ...types.messages.message_batch import MessageBatch +from ...types.messages.deleted_message_batch import DeletedMessageBatch +from ...types.messages.message_batch_individual_response import MessageBatchIndividualResponse + +__all__ = ["Batches", "AsyncBatches"] + + +class Batches(SyncAPIResource): + @cached_property + def with_raw_response(self) -> BatchesWithRawResponse: + """ + This property can be used as a prefix for any HTTP method call to return + the raw response object instead of the parsed content. + + For more information, see https://www.github.com/anthropics/anthropic-sdk-python#accessing-raw-response-data-eg-headers + """ + return BatchesWithRawResponse(self) + + @cached_property + def with_streaming_response(self) -> BatchesWithStreamingResponse: + """ + An alternative to `.with_raw_response` that doesn't eagerly read the response body. + + For more information, see https://www.github.com/anthropics/anthropic-sdk-python#with_streaming_response + """ + return BatchesWithStreamingResponse(self) + + def create( + self, + *, + requests: Iterable[batch_create_params.Request], + # 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, + ) -> MessageBatch: + """ + Send a batch of Message creation requests. + + The Message Batches API can be used to process multiple Messages API requests at + once. Once a Message Batch is created, it begins processing immediately. Batches + can take up to 24 hours to complete. + + Learn more about the Message Batches API in our + [user guide](/en/docs/build-with-claude/batch-processing) + + Args: + requests: List of requests for prompt completion. Each is an individual request to create + a Message. + + 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( + "/v1/messages/batches", + body=maybe_transform({"requests": requests}, batch_create_params.BatchCreateParams), + options=make_request_options( + extra_headers=extra_headers, extra_query=extra_query, extra_body=extra_body, timeout=timeout + ), + cast_to=MessageBatch, + ) + + def retrieve( + self, + message_batch_id: str, + *, + # 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, + ) -> MessageBatch: + """This endpoint is idempotent and can be used to poll for Message Batch + completion. + + To access the results of a Message Batch, make a request to the + `results_url` field in the response. + + Learn more about the Message Batches API in our + [user guide](/en/docs/build-with-claude/batch-processing) + + Args: + message_batch_id: ID of the Message Batch. + + 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 + """ + if not message_batch_id: + raise ValueError(f"Expected a non-empty value for `message_batch_id` but received {message_batch_id!r}") + return self._get( + f"/v1/messages/batches/{message_batch_id}", + options=make_request_options( + extra_headers=extra_headers, extra_query=extra_query, extra_body=extra_body, timeout=timeout + ), + cast_to=MessageBatch, + ) + + def list( + self, + *, + after_id: str | NotGiven = NOT_GIVEN, + before_id: str | NotGiven = NOT_GIVEN, + limit: int | 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, + ) -> SyncPage[MessageBatch]: + """List all Message Batches within a Workspace. + + Most recently created batches are + returned first. + + Learn more about the Message Batches API in our + [user guide](/en/docs/build-with-claude/batch-processing) + + Args: + after_id: ID of the object to use as a cursor for pagination. When provided, returns the + page of results immediately after this object. + + before_id: ID of the object to use as a cursor for pagination. When provided, returns the + page of results immediately before this object. + + limit: Number of items to return per page. + + Defaults to `20`. Ranges from `1` to `1000`. + + 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._get_api_list( + "/v1/messages/batches", + page=SyncPage[MessageBatch], + options=make_request_options( + extra_headers=extra_headers, + extra_query=extra_query, + extra_body=extra_body, + timeout=timeout, + query=maybe_transform( + { + "after_id": after_id, + "before_id": before_id, + "limit": limit, + }, + batch_list_params.BatchListParams, + ), + ), + model=MessageBatch, + ) + + def delete( + self, + message_batch_id: str, + *, + # 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, + ) -> DeletedMessageBatch: + """ + Delete a Message Batch. + + Message Batches can only be deleted once they've finished processing. If you'd + like to delete an in-progress batch, you must first cancel it. + + Learn more about the Message Batches API in our + [user guide](/en/docs/build-with-claude/batch-processing) + + Args: + message_batch_id: ID of the Message Batch. + + 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 + """ + if not message_batch_id: + raise ValueError(f"Expected a non-empty value for `message_batch_id` but received {message_batch_id!r}") + return self._delete( + f"/v1/messages/batches/{message_batch_id}", + options=make_request_options( + extra_headers=extra_headers, extra_query=extra_query, extra_body=extra_body, timeout=timeout + ), + cast_to=DeletedMessageBatch, + ) + + def cancel( + self, + message_batch_id: str, + *, + # 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, + ) -> MessageBatch: + """Batches may be canceled any time before processing ends. + + Once cancellation is + initiated, the batch enters a `canceling` state, at which time the system may + complete any in-progress, non-interruptible requests before finalizing + cancellation. + + The number of canceled requests is specified in `request_counts`. To determine + which requests were canceled, check the individual results within the batch. + Note that cancellation may not result in any canceled requests if they were + non-interruptible. + + Learn more about the Message Batches API in our + [user guide](/en/docs/build-with-claude/batch-processing) + + Args: + message_batch_id: ID of the Message Batch. + + 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 + """ + if not message_batch_id: + raise ValueError(f"Expected a non-empty value for `message_batch_id` but received {message_batch_id!r}") + return self._post( + f"/v1/messages/batches/{message_batch_id}/cancel", + options=make_request_options( + extra_headers=extra_headers, extra_query=extra_query, extra_body=extra_body, timeout=timeout + ), + cast_to=MessageBatch, + ) + + def results( + self, + message_batch_id: str, + *, + # 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, + ) -> JSONLDecoder[MessageBatchIndividualResponse]: + """ + Streams the results of a Message Batch as a `.jsonl` file. + + Each line in the file is a JSON object containing the result of a single request + in the Message Batch. Results are not guaranteed to be in the same order as + requests. Use the `custom_id` field to match results to requests. + + Learn more about the Message Batches API in our + [user guide](/en/docs/build-with-claude/batch-processing) + + Args: + message_batch_id: ID of the Message Batch. + + 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 + """ + if not message_batch_id: + raise ValueError(f"Expected a non-empty value for `message_batch_id` but received {message_batch_id!r}") + + batch = self.retrieve(message_batch_id=message_batch_id) + if not batch.results_url: + raise AnthropicError( + f"No `results_url` for the given batch; Has it finished processing? {batch.processing_status}" + ) + + extra_headers = {"Accept": "application/binary", **(extra_headers or {})} + return self._get( + batch.results_url, + options=make_request_options( + extra_headers=extra_headers, extra_query=extra_query, extra_body=extra_body, timeout=timeout + ), + cast_to=JSONLDecoder[MessageBatchIndividualResponse], + stream=True, + ) + + +class AsyncBatches(AsyncAPIResource): + @cached_property + def with_raw_response(self) -> AsyncBatchesWithRawResponse: + """ + This property can be used as a prefix for any HTTP method call to return + the raw response object instead of the parsed content. + + For more information, see https://www.github.com/anthropics/anthropic-sdk-python#accessing-raw-response-data-eg-headers + """ + return AsyncBatchesWithRawResponse(self) + + @cached_property + def with_streaming_response(self) -> AsyncBatchesWithStreamingResponse: + """ + An alternative to `.with_raw_response` that doesn't eagerly read the response body. + + For more information, see https://www.github.com/anthropics/anthropic-sdk-python#with_streaming_response + """ + return AsyncBatchesWithStreamingResponse(self) + + async def create( + self, + *, + requests: Iterable[batch_create_params.Request], + # 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, + ) -> MessageBatch: + """ + Send a batch of Message creation requests. + + The Message Batches API can be used to process multiple Messages API requests at + once. Once a Message Batch is created, it begins processing immediately. Batches + can take up to 24 hours to complete. + + Learn more about the Message Batches API in our + [user guide](/en/docs/build-with-claude/batch-processing) + + Args: + requests: List of requests for prompt completion. Each is an individual request to create + a Message. + + 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( + "/v1/messages/batches", + body=await async_maybe_transform({"requests": requests}, batch_create_params.BatchCreateParams), + options=make_request_options( + extra_headers=extra_headers, extra_query=extra_query, extra_body=extra_body, timeout=timeout + ), + cast_to=MessageBatch, + ) + + async def retrieve( + self, + message_batch_id: str, + *, + # 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, + ) -> MessageBatch: + """This endpoint is idempotent and can be used to poll for Message Batch + completion. + + To access the results of a Message Batch, make a request to the + `results_url` field in the response. + + Learn more about the Message Batches API in our + [user guide](/en/docs/build-with-claude/batch-processing) + + Args: + message_batch_id: ID of the Message Batch. + + 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 + """ + if not message_batch_id: + raise ValueError(f"Expected a non-empty value for `message_batch_id` but received {message_batch_id!r}") + return await self._get( + f"/v1/messages/batches/{message_batch_id}", + options=make_request_options( + extra_headers=extra_headers, extra_query=extra_query, extra_body=extra_body, timeout=timeout + ), + cast_to=MessageBatch, + ) + + def list( + self, + *, + after_id: str | NotGiven = NOT_GIVEN, + before_id: str | NotGiven = NOT_GIVEN, + limit: int | 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, + ) -> AsyncPaginator[MessageBatch, AsyncPage[MessageBatch]]: + """List all Message Batches within a Workspace. + + Most recently created batches are + returned first. + + Learn more about the Message Batches API in our + [user guide](/en/docs/build-with-claude/batch-processing) + + Args: + after_id: ID of the object to use as a cursor for pagination. When provided, returns the + page of results immediately after this object. + + before_id: ID of the object to use as a cursor for pagination. When provided, returns the + page of results immediately before this object. + + limit: Number of items to return per page. + + Defaults to `20`. Ranges from `1` to `1000`. + + 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._get_api_list( + "/v1/messages/batches", + page=AsyncPage[MessageBatch], + options=make_request_options( + extra_headers=extra_headers, + extra_query=extra_query, + extra_body=extra_body, + timeout=timeout, + query=maybe_transform( + { + "after_id": after_id, + "before_id": before_id, + "limit": limit, + }, + batch_list_params.BatchListParams, + ), + ), + model=MessageBatch, + ) + + async def delete( + self, + message_batch_id: str, + *, + # 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, + ) -> DeletedMessageBatch: + """ + Delete a Message Batch. + + Message Batches can only be deleted once they've finished processing. If you'd + like to delete an in-progress batch, you must first cancel it. + + Learn more about the Message Batches API in our + [user guide](/en/docs/build-with-claude/batch-processing) + + Args: + message_batch_id: ID of the Message Batch. + + 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 + """ + if not message_batch_id: + raise ValueError(f"Expected a non-empty value for `message_batch_id` but received {message_batch_id!r}") + return await self._delete( + f"/v1/messages/batches/{message_batch_id}", + options=make_request_options( + extra_headers=extra_headers, extra_query=extra_query, extra_body=extra_body, timeout=timeout + ), + cast_to=DeletedMessageBatch, + ) + + async def cancel( + self, + message_batch_id: str, + *, + # 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, + ) -> MessageBatch: + """Batches may be canceled any time before processing ends. + + Once cancellation is + initiated, the batch enters a `canceling` state, at which time the system may + complete any in-progress, non-interruptible requests before finalizing + cancellation. + + The number of canceled requests is specified in `request_counts`. To determine + which requests were canceled, check the individual results within the batch. + Note that cancellation may not result in any canceled requests if they were + non-interruptible. + + Learn more about the Message Batches API in our + [user guide](/en/docs/build-with-claude/batch-processing) + + Args: + message_batch_id: ID of the Message Batch. + + 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 + """ + if not message_batch_id: + raise ValueError(f"Expected a non-empty value for `message_batch_id` but received {message_batch_id!r}") + return await self._post( + f"/v1/messages/batches/{message_batch_id}/cancel", + options=make_request_options( + extra_headers=extra_headers, extra_query=extra_query, extra_body=extra_body, timeout=timeout + ), + cast_to=MessageBatch, + ) + + async def results( + self, + message_batch_id: str, + *, + # 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, + ) -> AsyncJSONLDecoder[MessageBatchIndividualResponse]: + """ + Streams the results of a Message Batch as a `.jsonl` file. + + Each line in the file is a JSON object containing the result of a single request + in the Message Batch. Results are not guaranteed to be in the same order as + requests. Use the `custom_id` field to match results to requests. + + Learn more about the Message Batches API in our + [user guide](/en/docs/build-with-claude/batch-processing) + + Args: + message_batch_id: ID of the Message Batch. + + 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 + """ + if not message_batch_id: + raise ValueError(f"Expected a non-empty value for `message_batch_id` but received {message_batch_id!r}") + + batch = await self.retrieve(message_batch_id=message_batch_id) + if not batch.results_url: + raise AnthropicError( + f"No `results_url` for the given batch; Has it finished processing? {batch.processing_status}" + ) + + extra_headers = {"Accept": "application/binary", **(extra_headers or {})} + return await self._get( + batch.results_url, + options=make_request_options( + extra_headers=extra_headers, extra_query=extra_query, extra_body=extra_body, timeout=timeout + ), + cast_to=AsyncJSONLDecoder[MessageBatchIndividualResponse], + stream=True, + ) + + +class BatchesWithRawResponse: + def __init__(self, batches: Batches) -> None: + self._batches = batches + + self.create = _legacy_response.to_raw_response_wrapper( + batches.create, + ) + self.retrieve = _legacy_response.to_raw_response_wrapper( + batches.retrieve, + ) + self.list = _legacy_response.to_raw_response_wrapper( + batches.list, + ) + self.delete = _legacy_response.to_raw_response_wrapper( + batches.delete, + ) + self.cancel = _legacy_response.to_raw_response_wrapper( + batches.cancel, + ) + + +class AsyncBatchesWithRawResponse: + def __init__(self, batches: AsyncBatches) -> None: + self._batches = batches + + self.create = _legacy_response.async_to_raw_response_wrapper( + batches.create, + ) + self.retrieve = _legacy_response.async_to_raw_response_wrapper( + batches.retrieve, + ) + self.list = _legacy_response.async_to_raw_response_wrapper( + batches.list, + ) + self.delete = _legacy_response.async_to_raw_response_wrapper( + batches.delete, + ) + self.cancel = _legacy_response.async_to_raw_response_wrapper( + batches.cancel, + ) + + +class BatchesWithStreamingResponse: + def __init__(self, batches: Batches) -> None: + self._batches = batches + + self.create = to_streamed_response_wrapper( + batches.create, + ) + self.retrieve = to_streamed_response_wrapper( + batches.retrieve, + ) + self.list = to_streamed_response_wrapper( + batches.list, + ) + self.delete = to_streamed_response_wrapper( + batches.delete, + ) + self.cancel = to_streamed_response_wrapper( + batches.cancel, + ) + + +class AsyncBatchesWithStreamingResponse: + def __init__(self, batches: AsyncBatches) -> None: + self._batches = batches + + self.create = async_to_streamed_response_wrapper( + batches.create, + ) + self.retrieve = async_to_streamed_response_wrapper( + batches.retrieve, + ) + self.list = async_to_streamed_response_wrapper( + batches.list, + ) + self.delete = async_to_streamed_response_wrapper( + batches.delete, + ) + self.cancel = async_to_streamed_response_wrapper( + batches.cancel, + ) diff --git a/.venv/lib/python3.12/site-packages/anthropic/resources/messages/messages.py b/.venv/lib/python3.12/site-packages/anthropic/resources/messages/messages.py new file mode 100644 index 00000000..70bceb7f --- /dev/null +++ b/.venv/lib/python3.12/site-packages/anthropic/resources/messages/messages.py @@ -0,0 +1,2551 @@ +# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details. + +from __future__ import annotations + +import warnings +from typing import List, Union, Iterable +from functools import partial +from typing_extensions import Literal, overload + +import httpx + +from ... import _legacy_response +from ...types import ( + ThinkingConfigParam, + message_create_params, + message_count_tokens_params, +) +from .batches import ( + Batches, + AsyncBatches, + BatchesWithRawResponse, + AsyncBatchesWithRawResponse, + BatchesWithStreamingResponse, + AsyncBatchesWithStreamingResponse, +) +from ..._types import NOT_GIVEN, Body, Query, Headers, NotGiven +from ..._utils import ( + is_given, + required_args, + maybe_transform, + async_maybe_transform, +) +from ..._compat import cached_property +from ..._resource import SyncAPIResource, AsyncAPIResource +from ..._response import to_streamed_response_wrapper, async_to_streamed_response_wrapper +from ..._constants import DEFAULT_TIMEOUT +from ..._streaming import Stream, AsyncStream +from ..._base_client import make_request_options +from ...lib.streaming import MessageStreamManager, AsyncMessageStreamManager +from ...types.message import Message +from ...types.model_param import ModelParam +from ...types.message_param import MessageParam +from ...types.metadata_param import MetadataParam +from ...types.text_block_param import TextBlockParam +from ...types.tool_union_param import ToolUnionParam +from ...types.tool_choice_param import ToolChoiceParam +from ...types.message_tokens_count import MessageTokensCount +from ...types.thinking_config_param import ThinkingConfigParam +from ...types.raw_message_stream_event import RawMessageStreamEvent +from ...types.message_count_tokens_tool_param import MessageCountTokensToolParam + +__all__ = ["Messages", "AsyncMessages"] + + +DEPRECATED_MODELS = { + "claude-1.3": "November 6th, 2024", + "claude-1.3-100k": "November 6th, 2024", + "claude-instant-1.1": "November 6th, 2024", + "claude-instant-1.1-100k": "November 6th, 2024", + "claude-instant-1.2": "November 6th, 2024", + "claude-3-sonnet-20240229": "July 21st, 2025", + "claude-2.1": "July 21st, 2025", + "claude-2.0": "July 21st, 2025", +} + + +class Messages(SyncAPIResource): + @cached_property + def batches(self) -> Batches: + return Batches(self._client) + + @cached_property + def with_raw_response(self) -> MessagesWithRawResponse: + """ + This property can be used as a prefix for any HTTP method call to return + the raw response object instead of the parsed content. + + For more information, see https://www.github.com/anthropics/anthropic-sdk-python#accessing-raw-response-data-eg-headers + """ + return MessagesWithRawResponse(self) + + @cached_property + def with_streaming_response(self) -> MessagesWithStreamingResponse: + """ + An alternative to `.with_raw_response` that doesn't eagerly read the response body. + + For more information, see https://www.github.com/anthropics/anthropic-sdk-python#with_streaming_response + """ + return MessagesWithStreamingResponse(self) + + @overload + def create( + self, + *, + max_tokens: int, + messages: Iterable[MessageParam], + model: ModelParam, + metadata: MetadataParam | NotGiven = NOT_GIVEN, + stop_sequences: List[str] | NotGiven = NOT_GIVEN, + stream: Literal[False] | NotGiven = NOT_GIVEN, + system: Union[str, Iterable[TextBlockParam]] | NotGiven = NOT_GIVEN, + temperature: float | NotGiven = NOT_GIVEN, + thinking: ThinkingConfigParam | NotGiven = NOT_GIVEN, + tool_choice: ToolChoiceParam | NotGiven = NOT_GIVEN, + tools: Iterable[ToolUnionParam] | NotGiven = NOT_GIVEN, + top_k: int | NotGiven = NOT_GIVEN, + top_p: float | 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, + ) -> Message: + """ + Send a structured list of input messages with text and/or image content, and the + model will generate the next message in the conversation. + + The Messages API can be used for either single queries or stateless multi-turn + conversations. + + Learn more about the Messages API in our [user guide](/en/docs/initial-setup) + + Args: + max_tokens: The maximum number of tokens to generate before stopping. + + Note that our models may stop _before_ reaching this maximum. This parameter + only specifies the absolute maximum number of tokens to generate. + + Different models have different maximum values for this parameter. See + [models](https://docs.anthropic.com/en/docs/models-overview) for details. + + messages: Input messages. + + Our models are trained to operate on alternating `user` and `assistant` + conversational turns. When creating a new `Message`, you specify the prior + conversational turns with the `messages` parameter, and the model then generates + the next `Message` in the conversation. Consecutive `user` or `assistant` turns + in your request will be combined into a single turn. + + Each input message must be an object with a `role` and `content`. You can + specify a single `user`-role message, or you can include multiple `user` and + `assistant` messages. + + If the final message uses the `assistant` role, the response content will + continue immediately from the content in that message. This can be used to + constrain part of the model's response. + + Example with a single `user` message: + + ```json + [{ "role": "user", "content": "Hello, Claude" }] + ``` + + Example with multiple conversational turns: + + ```json + [ + { "role": "user", "content": "Hello there." }, + { "role": "assistant", "content": "Hi, I'm Claude. How can I help you?" }, + { "role": "user", "content": "Can you explain LLMs in plain English?" } + ] + ``` + + Example with a partially-filled response from Claude: + + ```json + [ + { + "role": "user", + "content": "What's the Greek name for Sun? (A) Sol (B) Helios (C) Sun" + }, + { "role": "assistant", "content": "The best answer is (" } + ] + ``` + + Each input message `content` may be either a single `string` or an array of + content blocks, where each block has a specific `type`. Using a `string` for + `content` is shorthand for an array of one content block of type `"text"`. The + following input messages are equivalent: + + ```json + { "role": "user", "content": "Hello, Claude" } + ``` + + ```json + { "role": "user", "content": [{ "type": "text", "text": "Hello, Claude" }] } + ``` + + Starting with Claude 3 models, you can also send image content blocks: + + ```json + { + "role": "user", + "content": [ + { + "type": "image", + "source": { + "type": "base64", + "media_type": "image/jpeg", + "data": "/9j/4AAQSkZJRg..." + } + }, + { "type": "text", "text": "What is in this image?" } + ] + } + ``` + + We currently support the `base64` source type for images, and the `image/jpeg`, + `image/png`, `image/gif`, and `image/webp` media types. + + See [examples](https://docs.anthropic.com/en/api/messages-examples#vision) for + more input examples. + + Note that if you want to include a + [system prompt](https://docs.anthropic.com/en/docs/system-prompts), you can use + the top-level `system` parameter — there is no `"system"` role for input + messages in the Messages API. + + model: The model that will complete your prompt.\n\nSee + [models](https://docs.anthropic.com/en/docs/models-overview) for additional + details and options. + + metadata: An object describing metadata about the request. + + stop_sequences: Custom text sequences that will cause the model to stop generating. + + Our models will normally stop when they have naturally completed their turn, + which will result in a response `stop_reason` of `"end_turn"`. + + If you want the model to stop generating when it encounters custom strings of + text, you can use the `stop_sequences` parameter. If the model encounters one of + the custom sequences, the response `stop_reason` value will be `"stop_sequence"` + and the response `stop_sequence` value will contain the matched stop sequence. + + stream: Whether to incrementally stream the response using server-sent events. + + See [streaming](https://docs.anthropic.com/en/api/messages-streaming) for + details. + + system: System prompt. + + A system prompt is a way of providing context and instructions to Claude, such + as specifying a particular goal or role. See our + [guide to system prompts](https://docs.anthropic.com/en/docs/system-prompts). + + temperature: Amount of randomness injected into the response. + + Defaults to `1.0`. Ranges from `0.0` to `1.0`. Use `temperature` closer to `0.0` + for analytical / multiple choice, and closer to `1.0` for creative and + generative tasks. + + Note that even with `temperature` of `0.0`, the results will not be fully + deterministic. + + thinking: Configuration for enabling Claude's extended thinking. + + When enabled, responses include `thinking` content blocks showing Claude's + thinking process before the final answer. Requires a minimum budget of 1,024 + tokens and counts towards your `max_tokens` limit. + + See + [extended thinking](https://docs.anthropic.com/en/docs/build-with-claude/extended-thinking) + for details. + + tool_choice: How the model should use the provided tools. The model can use a specific tool, + any available tool, decide by itself, or not use tools at all. + + tools: Definitions of tools that the model may use. + + If you include `tools` in your API request, the model may return `tool_use` + content blocks that represent the model's use of those tools. You can then run + those tools using the tool input generated by the model and then optionally + return results back to the model using `tool_result` content blocks. + + Each tool definition includes: + + - `name`: Name of the tool. + - `description`: Optional, but strongly-recommended description of the tool. + - `input_schema`: [JSON schema](https://json-schema.org/draft/2020-12) for the + tool `input` shape that the model will produce in `tool_use` output content + blocks. + + For example, if you defined `tools` as: + + ```json + [ + { + "name": "get_stock_price", + "description": "Get the current stock price for a given ticker symbol.", + "input_schema": { + "type": "object", + "properties": { + "ticker": { + "type": "string", + "description": "The stock ticker symbol, e.g. AAPL for Apple Inc." + } + }, + "required": ["ticker"] + } + } + ] + ``` + + And then asked the model "What's the S&P 500 at today?", the model might produce + `tool_use` content blocks in the response like this: + + ```json + [ + { + "type": "tool_use", + "id": "toolu_01D7FLrfh4GYq7yT1ULFeyMV", + "name": "get_stock_price", + "input": { "ticker": "^GSPC" } + } + ] + ``` + + You might then run your `get_stock_price` tool with `{"ticker": "^GSPC"}` as an + input, and return the following back to the model in a subsequent `user` + message: + + ```json + [ + { + "type": "tool_result", + "tool_use_id": "toolu_01D7FLrfh4GYq7yT1ULFeyMV", + "content": "259.75 USD" + } + ] + ``` + + Tools can be used for workflows that include running client-side tools and + functions, or more generally whenever you want the model to produce a particular + JSON structure of output. + + See our [guide](https://docs.anthropic.com/en/docs/tool-use) for more details. + + top_k: Only sample from the top K options for each subsequent token. + + Used to remove "long tail" low probability responses. + [Learn more technical details here](https://towardsdatascience.com/how-to-sample-from-language-models-682bceb97277). + + Recommended for advanced use cases only. You usually only need to use + `temperature`. + + top_p: Use nucleus sampling. + + In nucleus sampling, we compute the cumulative distribution over all the options + for each subsequent token in decreasing probability order and cut it off once it + reaches a particular probability specified by `top_p`. You should either alter + `temperature` or `top_p`, but not both. + + Recommended for advanced use cases only. You usually only need to use + `temperature`. + + 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 + """ + ... + + @overload + def create( + self, + *, + max_tokens: int, + messages: Iterable[MessageParam], + model: ModelParam, + stream: Literal[True], + metadata: MetadataParam | NotGiven = NOT_GIVEN, + stop_sequences: List[str] | NotGiven = NOT_GIVEN, + system: Union[str, Iterable[TextBlockParam]] | NotGiven = NOT_GIVEN, + temperature: float | NotGiven = NOT_GIVEN, + thinking: ThinkingConfigParam | NotGiven = NOT_GIVEN, + tool_choice: ToolChoiceParam | NotGiven = NOT_GIVEN, + tools: Iterable[ToolUnionParam] | NotGiven = NOT_GIVEN, + top_k: int | NotGiven = NOT_GIVEN, + top_p: float | 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, + ) -> Stream[RawMessageStreamEvent]: + """ + Send a structured list of input messages with text and/or image content, and the + model will generate the next message in the conversation. + + The Messages API can be used for either single queries or stateless multi-turn + conversations. + + Learn more about the Messages API in our [user guide](/en/docs/initial-setup) + + Args: + max_tokens: The maximum number of tokens to generate before stopping. + + Note that our models may stop _before_ reaching this maximum. This parameter + only specifies the absolute maximum number of tokens to generate. + + Different models have different maximum values for this parameter. See + [models](https://docs.anthropic.com/en/docs/models-overview) for details. + + messages: Input messages. + + Our models are trained to operate on alternating `user` and `assistant` + conversational turns. When creating a new `Message`, you specify the prior + conversational turns with the `messages` parameter, and the model then generates + the next `Message` in the conversation. Consecutive `user` or `assistant` turns + in your request will be combined into a single turn. + + Each input message must be an object with a `role` and `content`. You can + specify a single `user`-role message, or you can include multiple `user` and + `assistant` messages. + + If the final message uses the `assistant` role, the response content will + continue immediately from the content in that message. This can be used to + constrain part of the model's response. + + Example with a single `user` message: + + ```json + [{ "role": "user", "content": "Hello, Claude" }] + ``` + + Example with multiple conversational turns: + + ```json + [ + { "role": "user", "content": "Hello there." }, + { "role": "assistant", "content": "Hi, I'm Claude. How can I help you?" }, + { "role": "user", "content": "Can you explain LLMs in plain English?" } + ] + ``` + + Example with a partially-filled response from Claude: + + ```json + [ + { + "role": "user", + "content": "What's the Greek name for Sun? (A) Sol (B) Helios (C) Sun" + }, + { "role": "assistant", "content": "The best answer is (" } + ] + ``` + + Each input message `content` may be either a single `string` or an array of + content blocks, where each block has a specific `type`. Using a `string` for + `content` is shorthand for an array of one content block of type `"text"`. The + following input messages are equivalent: + + ```json + { "role": "user", "content": "Hello, Claude" } + ``` + + ```json + { "role": "user", "content": [{ "type": "text", "text": "Hello, Claude" }] } + ``` + + Starting with Claude 3 models, you can also send image content blocks: + + ```json + { + "role": "user", + "content": [ + { + "type": "image", + "source": { + "type": "base64", + "media_type": "image/jpeg", + "data": "/9j/4AAQSkZJRg..." + } + }, + { "type": "text", "text": "What is in this image?" } + ] + } + ``` + + We currently support the `base64` source type for images, and the `image/jpeg`, + `image/png`, `image/gif`, and `image/webp` media types. + + See [examples](https://docs.anthropic.com/en/api/messages-examples#vision) for + more input examples. + + Note that if you want to include a + [system prompt](https://docs.anthropic.com/en/docs/system-prompts), you can use + the top-level `system` parameter — there is no `"system"` role for input + messages in the Messages API. + + model: The model that will complete your prompt.\n\nSee + [models](https://docs.anthropic.com/en/docs/models-overview) for additional + details and options. + + stream: Whether to incrementally stream the response using server-sent events. + + See [streaming](https://docs.anthropic.com/en/api/messages-streaming) for + details. + + metadata: An object describing metadata about the request. + + stop_sequences: Custom text sequences that will cause the model to stop generating. + + Our models will normally stop when they have naturally completed their turn, + which will result in a response `stop_reason` of `"end_turn"`. + + If you want the model to stop generating when it encounters custom strings of + text, you can use the `stop_sequences` parameter. If the model encounters one of + the custom sequences, the response `stop_reason` value will be `"stop_sequence"` + and the response `stop_sequence` value will contain the matched stop sequence. + + system: System prompt. + + A system prompt is a way of providing context and instructions to Claude, such + as specifying a particular goal or role. See our + [guide to system prompts](https://docs.anthropic.com/en/docs/system-prompts). + + temperature: Amount of randomness injected into the response. + + Defaults to `1.0`. Ranges from `0.0` to `1.0`. Use `temperature` closer to `0.0` + for analytical / multiple choice, and closer to `1.0` for creative and + generative tasks. + + Note that even with `temperature` of `0.0`, the results will not be fully + deterministic. + + thinking: Configuration for enabling Claude's extended thinking. + + When enabled, responses include `thinking` content blocks showing Claude's + thinking process before the final answer. Requires a minimum budget of 1,024 + tokens and counts towards your `max_tokens` limit. + + See + [extended thinking](https://docs.anthropic.com/en/docs/build-with-claude/extended-thinking) + for details. + + tool_choice: How the model should use the provided tools. The model can use a specific tool, + any available tool, decide by itself, or not use tools at all. + + tools: Definitions of tools that the model may use. + + If you include `tools` in your API request, the model may return `tool_use` + content blocks that represent the model's use of those tools. You can then run + those tools using the tool input generated by the model and then optionally + return results back to the model using `tool_result` content blocks. + + Each tool definition includes: + + - `name`: Name of the tool. + - `description`: Optional, but strongly-recommended description of the tool. + - `input_schema`: [JSON schema](https://json-schema.org/draft/2020-12) for the + tool `input` shape that the model will produce in `tool_use` output content + blocks. + + For example, if you defined `tools` as: + + ```json + [ + { + "name": "get_stock_price", + "description": "Get the current stock price for a given ticker symbol.", + "input_schema": { + "type": "object", + "properties": { + "ticker": { + "type": "string", + "description": "The stock ticker symbol, e.g. AAPL for Apple Inc." + } + }, + "required": ["ticker"] + } + } + ] + ``` + + And then asked the model "What's the S&P 500 at today?", the model might produce + `tool_use` content blocks in the response like this: + + ```json + [ + { + "type": "tool_use", + "id": "toolu_01D7FLrfh4GYq7yT1ULFeyMV", + "name": "get_stock_price", + "input": { "ticker": "^GSPC" } + } + ] + ``` + + You might then run your `get_stock_price` tool with `{"ticker": "^GSPC"}` as an + input, and return the following back to the model in a subsequent `user` + message: + + ```json + [ + { + "type": "tool_result", + "tool_use_id": "toolu_01D7FLrfh4GYq7yT1ULFeyMV", + "content": "259.75 USD" + } + ] + ``` + + Tools can be used for workflows that include running client-side tools and + functions, or more generally whenever you want the model to produce a particular + JSON structure of output. + + See our [guide](https://docs.anthropic.com/en/docs/tool-use) for more details. + + top_k: Only sample from the top K options for each subsequent token. + + Used to remove "long tail" low probability responses. + [Learn more technical details here](https://towardsdatascience.com/how-to-sample-from-language-models-682bceb97277). + + Recommended for advanced use cases only. You usually only need to use + `temperature`. + + top_p: Use nucleus sampling. + + In nucleus sampling, we compute the cumulative distribution over all the options + for each subsequent token in decreasing probability order and cut it off once it + reaches a particular probability specified by `top_p`. You should either alter + `temperature` or `top_p`, but not both. + + Recommended for advanced use cases only. You usually only need to use + `temperature`. + + 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 + """ + ... + + @overload + def create( + self, + *, + max_tokens: int, + messages: Iterable[MessageParam], + model: ModelParam, + stream: bool, + metadata: MetadataParam | NotGiven = NOT_GIVEN, + stop_sequences: List[str] | NotGiven = NOT_GIVEN, + system: Union[str, Iterable[TextBlockParam]] | NotGiven = NOT_GIVEN, + temperature: float | NotGiven = NOT_GIVEN, + thinking: ThinkingConfigParam | NotGiven = NOT_GIVEN, + tool_choice: ToolChoiceParam | NotGiven = NOT_GIVEN, + tools: Iterable[ToolUnionParam] | NotGiven = NOT_GIVEN, + top_k: int | NotGiven = NOT_GIVEN, + top_p: float | 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, + ) -> Message | Stream[RawMessageStreamEvent]: + """ + Send a structured list of input messages with text and/or image content, and the + model will generate the next message in the conversation. + + The Messages API can be used for either single queries or stateless multi-turn + conversations. + + Learn more about the Messages API in our [user guide](/en/docs/initial-setup) + + Args: + max_tokens: The maximum number of tokens to generate before stopping. + + Note that our models may stop _before_ reaching this maximum. This parameter + only specifies the absolute maximum number of tokens to generate. + + Different models have different maximum values for this parameter. See + [models](https://docs.anthropic.com/en/docs/models-overview) for details. + + messages: Input messages. + + Our models are trained to operate on alternating `user` and `assistant` + conversational turns. When creating a new `Message`, you specify the prior + conversational turns with the `messages` parameter, and the model then generates + the next `Message` in the conversation. Consecutive `user` or `assistant` turns + in your request will be combined into a single turn. + + Each input message must be an object with a `role` and `content`. You can + specify a single `user`-role message, or you can include multiple `user` and + `assistant` messages. + + If the final message uses the `assistant` role, the response content will + continue immediately from the content in that message. This can be used to + constrain part of the model's response. + + Example with a single `user` message: + + ```json + [{ "role": "user", "content": "Hello, Claude" }] + ``` + + Example with multiple conversational turns: + + ```json + [ + { "role": "user", "content": "Hello there." }, + { "role": "assistant", "content": "Hi, I'm Claude. How can I help you?" }, + { "role": "user", "content": "Can you explain LLMs in plain English?" } + ] + ``` + + Example with a partially-filled response from Claude: + + ```json + [ + { + "role": "user", + "content": "What's the Greek name for Sun? (A) Sol (B) Helios (C) Sun" + }, + { "role": "assistant", "content": "The best answer is (" } + ] + ``` + + Each input message `content` may be either a single `string` or an array of + content blocks, where each block has a specific `type`. Using a `string` for + `content` is shorthand for an array of one content block of type `"text"`. The + following input messages are equivalent: + + ```json + { "role": "user", "content": "Hello, Claude" } + ``` + + ```json + { "role": "user", "content": [{ "type": "text", "text": "Hello, Claude" }] } + ``` + + Starting with Claude 3 models, you can also send image content blocks: + + ```json + { + "role": "user", + "content": [ + { + "type": "image", + "source": { + "type": "base64", + "media_type": "image/jpeg", + "data": "/9j/4AAQSkZJRg..." + } + }, + { "type": "text", "text": "What is in this image?" } + ] + } + ``` + + We currently support the `base64` source type for images, and the `image/jpeg`, + `image/png`, `image/gif`, and `image/webp` media types. + + See [examples](https://docs.anthropic.com/en/api/messages-examples#vision) for + more input examples. + + Note that if you want to include a + [system prompt](https://docs.anthropic.com/en/docs/system-prompts), you can use + the top-level `system` parameter — there is no `"system"` role for input + messages in the Messages API. + + model: The model that will complete your prompt.\n\nSee + [models](https://docs.anthropic.com/en/docs/models-overview) for additional + details and options. + + stream: Whether to incrementally stream the response using server-sent events. + + See [streaming](https://docs.anthropic.com/en/api/messages-streaming) for + details. + + metadata: An object describing metadata about the request. + + stop_sequences: Custom text sequences that will cause the model to stop generating. + + Our models will normally stop when they have naturally completed their turn, + which will result in a response `stop_reason` of `"end_turn"`. + + If you want the model to stop generating when it encounters custom strings of + text, you can use the `stop_sequences` parameter. If the model encounters one of + the custom sequences, the response `stop_reason` value will be `"stop_sequence"` + and the response `stop_sequence` value will contain the matched stop sequence. + + system: System prompt. + + A system prompt is a way of providing context and instructions to Claude, such + as specifying a particular goal or role. See our + [guide to system prompts](https://docs.anthropic.com/en/docs/system-prompts). + + temperature: Amount of randomness injected into the response. + + Defaults to `1.0`. Ranges from `0.0` to `1.0`. Use `temperature` closer to `0.0` + for analytical / multiple choice, and closer to `1.0` for creative and + generative tasks. + + Note that even with `temperature` of `0.0`, the results will not be fully + deterministic. + + thinking: Configuration for enabling Claude's extended thinking. + + When enabled, responses include `thinking` content blocks showing Claude's + thinking process before the final answer. Requires a minimum budget of 1,024 + tokens and counts towards your `max_tokens` limit. + + See + [extended thinking](https://docs.anthropic.com/en/docs/build-with-claude/extended-thinking) + for details. + + tool_choice: How the model should use the provided tools. The model can use a specific tool, + any available tool, decide by itself, or not use tools at all. + + tools: Definitions of tools that the model may use. + + If you include `tools` in your API request, the model may return `tool_use` + content blocks that represent the model's use of those tools. You can then run + those tools using the tool input generated by the model and then optionally + return results back to the model using `tool_result` content blocks. + + Each tool definition includes: + + - `name`: Name of the tool. + - `description`: Optional, but strongly-recommended description of the tool. + - `input_schema`: [JSON schema](https://json-schema.org/draft/2020-12) for the + tool `input` shape that the model will produce in `tool_use` output content + blocks. + + For example, if you defined `tools` as: + + ```json + [ + { + "name": "get_stock_price", + "description": "Get the current stock price for a given ticker symbol.", + "input_schema": { + "type": "object", + "properties": { + "ticker": { + "type": "string", + "description": "The stock ticker symbol, e.g. AAPL for Apple Inc." + } + }, + "required": ["ticker"] + } + } + ] + ``` + + And then asked the model "What's the S&P 500 at today?", the model might produce + `tool_use` content blocks in the response like this: + + ```json + [ + { + "type": "tool_use", + "id": "toolu_01D7FLrfh4GYq7yT1ULFeyMV", + "name": "get_stock_price", + "input": { "ticker": "^GSPC" } + } + ] + ``` + + You might then run your `get_stock_price` tool with `{"ticker": "^GSPC"}` as an + input, and return the following back to the model in a subsequent `user` + message: + + ```json + [ + { + "type": "tool_result", + "tool_use_id": "toolu_01D7FLrfh4GYq7yT1ULFeyMV", + "content": "259.75 USD" + } + ] + ``` + + Tools can be used for workflows that include running client-side tools and + functions, or more generally whenever you want the model to produce a particular + JSON structure of output. + + See our [guide](https://docs.anthropic.com/en/docs/tool-use) for more details. + + top_k: Only sample from the top K options for each subsequent token. + + Used to remove "long tail" low probability responses. + [Learn more technical details here](https://towardsdatascience.com/how-to-sample-from-language-models-682bceb97277). + + Recommended for advanced use cases only. You usually only need to use + `temperature`. + + top_p: Use nucleus sampling. + + In nucleus sampling, we compute the cumulative distribution over all the options + for each subsequent token in decreasing probability order and cut it off once it + reaches a particular probability specified by `top_p`. You should either alter + `temperature` or `top_p`, but not both. + + Recommended for advanced use cases only. You usually only need to use + `temperature`. + + 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 + """ + ... + + @required_args(["max_tokens", "messages", "model"], ["max_tokens", "messages", "model", "stream"]) + def create( + self, + *, + max_tokens: int, + messages: Iterable[MessageParam], + model: ModelParam, + metadata: MetadataParam | NotGiven = NOT_GIVEN, + stop_sequences: List[str] | NotGiven = NOT_GIVEN, + stream: Literal[False] | Literal[True] | NotGiven = NOT_GIVEN, + system: Union[str, Iterable[TextBlockParam]] | NotGiven = NOT_GIVEN, + temperature: float | NotGiven = NOT_GIVEN, + thinking: ThinkingConfigParam | NotGiven = NOT_GIVEN, + tool_choice: ToolChoiceParam | NotGiven = NOT_GIVEN, + tools: Iterable[ToolUnionParam] | NotGiven = NOT_GIVEN, + top_k: int | NotGiven = NOT_GIVEN, + top_p: float | 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, + ) -> Message | Stream[RawMessageStreamEvent]: + if not stream and not is_given(timeout) and self._client.timeout == DEFAULT_TIMEOUT: + timeout = self._client._calculate_nonstreaming_timeout(max_tokens) + + if model in DEPRECATED_MODELS: + warnings.warn( + f"The model '{model}' is deprecated and will reach end-of-life on {DEPRECATED_MODELS[model]}.\nPlease migrate to a newer model. Visit https://docs.anthropic.com/en/docs/resources/model-deprecations for more information.", + DeprecationWarning, + stacklevel=3, + ) + + return self._post( + "/v1/messages", + body=maybe_transform( + { + "max_tokens": max_tokens, + "messages": messages, + "model": model, + "metadata": metadata, + "stop_sequences": stop_sequences, + "stream": stream, + "system": system, + "temperature": temperature, + "thinking": thinking, + "tool_choice": tool_choice, + "tools": tools, + "top_k": top_k, + "top_p": top_p, + }, + message_create_params.MessageCreateParams, + ), + options=make_request_options( + extra_headers=extra_headers, extra_query=extra_query, extra_body=extra_body, timeout=timeout + ), + cast_to=Message, + stream=stream or False, + stream_cls=Stream[RawMessageStreamEvent], + ) + + def stream( + self, + *, + max_tokens: int, + messages: Iterable[MessageParam], + model: ModelParam, + metadata: MetadataParam | NotGiven = NOT_GIVEN, + stop_sequences: List[str] | NotGiven = NOT_GIVEN, + system: Union[str, Iterable[TextBlockParam]] | NotGiven = NOT_GIVEN, + temperature: float | NotGiven = NOT_GIVEN, + top_k: int | NotGiven = NOT_GIVEN, + top_p: float | NotGiven = NOT_GIVEN, + thinking: ThinkingConfigParam | NotGiven = NOT_GIVEN, + tool_choice: ToolChoiceParam | NotGiven = NOT_GIVEN, + tools: Iterable[ToolUnionParam] | 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, + ) -> MessageStreamManager: + """Create a Message stream""" + if model in DEPRECATED_MODELS: + warnings.warn( + f"The model '{model}' is deprecated and will reach end-of-life on {DEPRECATED_MODELS[model]}.\nPlease migrate to a newer model. Visit https://docs.anthropic.com/en/docs/resources/model-deprecations for more information.", + DeprecationWarning, + stacklevel=3, + ) + + extra_headers = { + "X-Stainless-Stream-Helper": "messages", + **(extra_headers or {}), + } + make_request = partial( + self._post, + "/v1/messages", + body=maybe_transform( + { + "max_tokens": max_tokens, + "messages": messages, + "model": model, + "metadata": metadata, + "stop_sequences": stop_sequences, + "system": system, + "temperature": temperature, + "top_k": top_k, + "top_p": top_p, + "tools": tools, + "thinking": thinking, + "tool_choice": tool_choice, + "stream": True, + }, + message_create_params.MessageCreateParams, + ), + options=make_request_options( + extra_headers=extra_headers, extra_query=extra_query, extra_body=extra_body, timeout=timeout + ), + cast_to=Message, + stream=True, + stream_cls=Stream[RawMessageStreamEvent], + ) + return MessageStreamManager(make_request) + + def count_tokens( + self, + *, + messages: Iterable[MessageParam], + model: ModelParam, + system: Union[str, Iterable[TextBlockParam]] | NotGiven = NOT_GIVEN, + thinking: ThinkingConfigParam | NotGiven = NOT_GIVEN, + tool_choice: ToolChoiceParam | NotGiven = NOT_GIVEN, + tools: Iterable[MessageCountTokensToolParam] | 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, + ) -> MessageTokensCount: + """ + Count the number of tokens in a Message. + + The Token Count API can be used to count the number of tokens in a Message, + including tools, images, and documents, without creating it. + + Learn more about token counting in our + [user guide](/en/docs/build-with-claude/token-counting) + + Args: + messages: Input messages. + + Our models are trained to operate on alternating `user` and `assistant` + conversational turns. When creating a new `Message`, you specify the prior + conversational turns with the `messages` parameter, and the model then generates + the next `Message` in the conversation. Consecutive `user` or `assistant` turns + in your request will be combined into a single turn. + + Each input message must be an object with a `role` and `content`. You can + specify a single `user`-role message, or you can include multiple `user` and + `assistant` messages. + + If the final message uses the `assistant` role, the response content will + continue immediately from the content in that message. This can be used to + constrain part of the model's response. + + Example with a single `user` message: + + ```json + [{ "role": "user", "content": "Hello, Claude" }] + ``` + + Example with multiple conversational turns: + + ```json + [ + { "role": "user", "content": "Hello there." }, + { "role": "assistant", "content": "Hi, I'm Claude. How can I help you?" }, + { "role": "user", "content": "Can you explain LLMs in plain English?" } + ] + ``` + + Example with a partially-filled response from Claude: + + ```json + [ + { + "role": "user", + "content": "What's the Greek name for Sun? (A) Sol (B) Helios (C) Sun" + }, + { "role": "assistant", "content": "The best answer is (" } + ] + ``` + + Each input message `content` may be either a single `string` or an array of + content blocks, where each block has a specific `type`. Using a `string` for + `content` is shorthand for an array of one content block of type `"text"`. The + following input messages are equivalent: + + ```json + { "role": "user", "content": "Hello, Claude" } + ``` + + ```json + { "role": "user", "content": [{ "type": "text", "text": "Hello, Claude" }] } + ``` + + Starting with Claude 3 models, you can also send image content blocks: + + ```json + { + "role": "user", + "content": [ + { + "type": "image", + "source": { + "type": "base64", + "media_type": "image/jpeg", + "data": "/9j/4AAQSkZJRg..." + } + }, + { "type": "text", "text": "What is in this image?" } + ] + } + ``` + + We currently support the `base64` source type for images, and the `image/jpeg`, + `image/png`, `image/gif`, and `image/webp` media types. + + See [examples](https://docs.anthropic.com/en/api/messages-examples#vision) for + more input examples. + + Note that if you want to include a + [system prompt](https://docs.anthropic.com/en/docs/system-prompts), you can use + the top-level `system` parameter — there is no `"system"` role for input + messages in the Messages API. + + model: The model that will complete your prompt.\n\nSee + [models](https://docs.anthropic.com/en/docs/models-overview) for additional + details and options. + + system: System prompt. + + A system prompt is a way of providing context and instructions to Claude, such + as specifying a particular goal or role. See our + [guide to system prompts](https://docs.anthropic.com/en/docs/system-prompts). + + thinking: Configuration for enabling Claude's extended thinking. + + When enabled, responses include `thinking` content blocks showing Claude's + thinking process before the final answer. Requires a minimum budget of 1,024 + tokens and counts towards your `max_tokens` limit. + + See + [extended thinking](https://docs.anthropic.com/en/docs/build-with-claude/extended-thinking) + for details. + + tool_choice: How the model should use the provided tools. The model can use a specific tool, + any available tool, decide by itself, or not use tools at all. + + tools: Definitions of tools that the model may use. + + If you include `tools` in your API request, the model may return `tool_use` + content blocks that represent the model's use of those tools. You can then run + those tools using the tool input generated by the model and then optionally + return results back to the model using `tool_result` content blocks. + + Each tool definition includes: + + - `name`: Name of the tool. + - `description`: Optional, but strongly-recommended description of the tool. + - `input_schema`: [JSON schema](https://json-schema.org/draft/2020-12) for the + tool `input` shape that the model will produce in `tool_use` output content + blocks. + + For example, if you defined `tools` as: + + ```json + [ + { + "name": "get_stock_price", + "description": "Get the current stock price for a given ticker symbol.", + "input_schema": { + "type": "object", + "properties": { + "ticker": { + "type": "string", + "description": "The stock ticker symbol, e.g. AAPL for Apple Inc." + } + }, + "required": ["ticker"] + } + } + ] + ``` + + And then asked the model "What's the S&P 500 at today?", the model might produce + `tool_use` content blocks in the response like this: + + ```json + [ + { + "type": "tool_use", + "id": "toolu_01D7FLrfh4GYq7yT1ULFeyMV", + "name": "get_stock_price", + "input": { "ticker": "^GSPC" } + } + ] + ``` + + You might then run your `get_stock_price` tool with `{"ticker": "^GSPC"}` as an + input, and return the following back to the model in a subsequent `user` + message: + + ```json + [ + { + "type": "tool_result", + "tool_use_id": "toolu_01D7FLrfh4GYq7yT1ULFeyMV", + "content": "259.75 USD" + } + ] + ``` + + Tools can be used for workflows that include running client-side tools and + functions, or more generally whenever you want the model to produce a particular + JSON structure of output. + + See our [guide](https://docs.anthropic.com/en/docs/tool-use) for more details. + + 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( + "/v1/messages/count_tokens", + body=maybe_transform( + { + "messages": messages, + "model": model, + "system": system, + "thinking": thinking, + "tool_choice": tool_choice, + "tools": tools, + }, + message_count_tokens_params.MessageCountTokensParams, + ), + options=make_request_options( + extra_headers=extra_headers, extra_query=extra_query, extra_body=extra_body, timeout=timeout + ), + cast_to=MessageTokensCount, + ) + + +class AsyncMessages(AsyncAPIResource): + @cached_property + def batches(self) -> AsyncBatches: + return AsyncBatches(self._client) + + @cached_property + def with_raw_response(self) -> AsyncMessagesWithRawResponse: + """ + This property can be used as a prefix for any HTTP method call to return + the raw response object instead of the parsed content. + + For more information, see https://www.github.com/anthropics/anthropic-sdk-python#accessing-raw-response-data-eg-headers + """ + return AsyncMessagesWithRawResponse(self) + + @cached_property + def with_streaming_response(self) -> AsyncMessagesWithStreamingResponse: + """ + An alternative to `.with_raw_response` that doesn't eagerly read the response body. + + For more information, see https://www.github.com/anthropics/anthropic-sdk-python#with_streaming_response + """ + return AsyncMessagesWithStreamingResponse(self) + + @overload + async def create( + self, + *, + max_tokens: int, + messages: Iterable[MessageParam], + model: ModelParam, + metadata: MetadataParam | NotGiven = NOT_GIVEN, + stop_sequences: List[str] | NotGiven = NOT_GIVEN, + stream: Literal[False] | NotGiven = NOT_GIVEN, + system: Union[str, Iterable[TextBlockParam]] | NotGiven = NOT_GIVEN, + temperature: float | NotGiven = NOT_GIVEN, + thinking: ThinkingConfigParam | NotGiven = NOT_GIVEN, + tool_choice: ToolChoiceParam | NotGiven = NOT_GIVEN, + tools: Iterable[ToolUnionParam] | NotGiven = NOT_GIVEN, + top_k: int | NotGiven = NOT_GIVEN, + top_p: float | 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, + ) -> Message: + """ + Send a structured list of input messages with text and/or image content, and the + model will generate the next message in the conversation. + + The Messages API can be used for either single queries or stateless multi-turn + conversations. + + Learn more about the Messages API in our [user guide](/en/docs/initial-setup) + + Args: + max_tokens: The maximum number of tokens to generate before stopping. + + Note that our models may stop _before_ reaching this maximum. This parameter + only specifies the absolute maximum number of tokens to generate. + + Different models have different maximum values for this parameter. See + [models](https://docs.anthropic.com/en/docs/models-overview) for details. + + messages: Input messages. + + Our models are trained to operate on alternating `user` and `assistant` + conversational turns. When creating a new `Message`, you specify the prior + conversational turns with the `messages` parameter, and the model then generates + the next `Message` in the conversation. Consecutive `user` or `assistant` turns + in your request will be combined into a single turn. + + Each input message must be an object with a `role` and `content`. You can + specify a single `user`-role message, or you can include multiple `user` and + `assistant` messages. + + If the final message uses the `assistant` role, the response content will + continue immediately from the content in that message. This can be used to + constrain part of the model's response. + + Example with a single `user` message: + + ```json + [{ "role": "user", "content": "Hello, Claude" }] + ``` + + Example with multiple conversational turns: + + ```json + [ + { "role": "user", "content": "Hello there." }, + { "role": "assistant", "content": "Hi, I'm Claude. How can I help you?" }, + { "role": "user", "content": "Can you explain LLMs in plain English?" } + ] + ``` + + Example with a partially-filled response from Claude: + + ```json + [ + { + "role": "user", + "content": "What's the Greek name for Sun? (A) Sol (B) Helios (C) Sun" + }, + { "role": "assistant", "content": "The best answer is (" } + ] + ``` + + Each input message `content` may be either a single `string` or an array of + content blocks, where each block has a specific `type`. Using a `string` for + `content` is shorthand for an array of one content block of type `"text"`. The + following input messages are equivalent: + + ```json + { "role": "user", "content": "Hello, Claude" } + ``` + + ```json + { "role": "user", "content": [{ "type": "text", "text": "Hello, Claude" }] } + ``` + + Starting with Claude 3 models, you can also send image content blocks: + + ```json + { + "role": "user", + "content": [ + { + "type": "image", + "source": { + "type": "base64", + "media_type": "image/jpeg", + "data": "/9j/4AAQSkZJRg..." + } + }, + { "type": "text", "text": "What is in this image?" } + ] + } + ``` + + We currently support the `base64` source type for images, and the `image/jpeg`, + `image/png`, `image/gif`, and `image/webp` media types. + + See [examples](https://docs.anthropic.com/en/api/messages-examples#vision) for + more input examples. + + Note that if you want to include a + [system prompt](https://docs.anthropic.com/en/docs/system-prompts), you can use + the top-level `system` parameter — there is no `"system"` role for input + messages in the Messages API. + + model: The model that will complete your prompt.\n\nSee + [models](https://docs.anthropic.com/en/docs/models-overview) for additional + details and options. + + metadata: An object describing metadata about the request. + + stop_sequences: Custom text sequences that will cause the model to stop generating. + + Our models will normally stop when they have naturally completed their turn, + which will result in a response `stop_reason` of `"end_turn"`. + + If you want the model to stop generating when it encounters custom strings of + text, you can use the `stop_sequences` parameter. If the model encounters one of + the custom sequences, the response `stop_reason` value will be `"stop_sequence"` + and the response `stop_sequence` value will contain the matched stop sequence. + + stream: Whether to incrementally stream the response using server-sent events. + + See [streaming](https://docs.anthropic.com/en/api/messages-streaming) for + details. + + system: System prompt. + + A system prompt is a way of providing context and instructions to Claude, such + as specifying a particular goal or role. See our + [guide to system prompts](https://docs.anthropic.com/en/docs/system-prompts). + + temperature: Amount of randomness injected into the response. + + Defaults to `1.0`. Ranges from `0.0` to `1.0`. Use `temperature` closer to `0.0` + for analytical / multiple choice, and closer to `1.0` for creative and + generative tasks. + + Note that even with `temperature` of `0.0`, the results will not be fully + deterministic. + + thinking: Configuration for enabling Claude's extended thinking. + + When enabled, responses include `thinking` content blocks showing Claude's + thinking process before the final answer. Requires a minimum budget of 1,024 + tokens and counts towards your `max_tokens` limit. + + See + [extended thinking](https://docs.anthropic.com/en/docs/build-with-claude/extended-thinking) + for details. + + tool_choice: How the model should use the provided tools. The model can use a specific tool, + any available tool, decide by itself, or not use tools at all. + + tools: Definitions of tools that the model may use. + + If you include `tools` in your API request, the model may return `tool_use` + content blocks that represent the model's use of those tools. You can then run + those tools using the tool input generated by the model and then optionally + return results back to the model using `tool_result` content blocks. + + Each tool definition includes: + + - `name`: Name of the tool. + - `description`: Optional, but strongly-recommended description of the tool. + - `input_schema`: [JSON schema](https://json-schema.org/draft/2020-12) for the + tool `input` shape that the model will produce in `tool_use` output content + blocks. + + For example, if you defined `tools` as: + + ```json + [ + { + "name": "get_stock_price", + "description": "Get the current stock price for a given ticker symbol.", + "input_schema": { + "type": "object", + "properties": { + "ticker": { + "type": "string", + "description": "The stock ticker symbol, e.g. AAPL for Apple Inc." + } + }, + "required": ["ticker"] + } + } + ] + ``` + + And then asked the model "What's the S&P 500 at today?", the model might produce + `tool_use` content blocks in the response like this: + + ```json + [ + { + "type": "tool_use", + "id": "toolu_01D7FLrfh4GYq7yT1ULFeyMV", + "name": "get_stock_price", + "input": { "ticker": "^GSPC" } + } + ] + ``` + + You might then run your `get_stock_price` tool with `{"ticker": "^GSPC"}` as an + input, and return the following back to the model in a subsequent `user` + message: + + ```json + [ + { + "type": "tool_result", + "tool_use_id": "toolu_01D7FLrfh4GYq7yT1ULFeyMV", + "content": "259.75 USD" + } + ] + ``` + + Tools can be used for workflows that include running client-side tools and + functions, or more generally whenever you want the model to produce a particular + JSON structure of output. + + See our [guide](https://docs.anthropic.com/en/docs/tool-use) for more details. + + top_k: Only sample from the top K options for each subsequent token. + + Used to remove "long tail" low probability responses. + [Learn more technical details here](https://towardsdatascience.com/how-to-sample-from-language-models-682bceb97277). + + Recommended for advanced use cases only. You usually only need to use + `temperature`. + + top_p: Use nucleus sampling. + + In nucleus sampling, we compute the cumulative distribution over all the options + for each subsequent token in decreasing probability order and cut it off once it + reaches a particular probability specified by `top_p`. You should either alter + `temperature` or `top_p`, but not both. + + Recommended for advanced use cases only. You usually only need to use + `temperature`. + + 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 + """ + ... + + @overload + async def create( + self, + *, + max_tokens: int, + messages: Iterable[MessageParam], + model: ModelParam, + stream: Literal[True], + metadata: MetadataParam | NotGiven = NOT_GIVEN, + stop_sequences: List[str] | NotGiven = NOT_GIVEN, + system: Union[str, Iterable[TextBlockParam]] | NotGiven = NOT_GIVEN, + temperature: float | NotGiven = NOT_GIVEN, + thinking: ThinkingConfigParam | NotGiven = NOT_GIVEN, + tool_choice: ToolChoiceParam | NotGiven = NOT_GIVEN, + tools: Iterable[ToolUnionParam] | NotGiven = NOT_GIVEN, + top_k: int | NotGiven = NOT_GIVEN, + top_p: float | 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, + ) -> AsyncStream[RawMessageStreamEvent]: + """ + Send a structured list of input messages with text and/or image content, and the + model will generate the next message in the conversation. + + The Messages API can be used for either single queries or stateless multi-turn + conversations. + + Learn more about the Messages API in our [user guide](/en/docs/initial-setup) + + Args: + max_tokens: The maximum number of tokens to generate before stopping. + + Note that our models may stop _before_ reaching this maximum. This parameter + only specifies the absolute maximum number of tokens to generate. + + Different models have different maximum values for this parameter. See + [models](https://docs.anthropic.com/en/docs/models-overview) for details. + + messages: Input messages. + + Our models are trained to operate on alternating `user` and `assistant` + conversational turns. When creating a new `Message`, you specify the prior + conversational turns with the `messages` parameter, and the model then generates + the next `Message` in the conversation. Consecutive `user` or `assistant` turns + in your request will be combined into a single turn. + + Each input message must be an object with a `role` and `content`. You can + specify a single `user`-role message, or you can include multiple `user` and + `assistant` messages. + + If the final message uses the `assistant` role, the response content will + continue immediately from the content in that message. This can be used to + constrain part of the model's response. + + Example with a single `user` message: + + ```json + [{ "role": "user", "content": "Hello, Claude" }] + ``` + + Example with multiple conversational turns: + + ```json + [ + { "role": "user", "content": "Hello there." }, + { "role": "assistant", "content": "Hi, I'm Claude. How can I help you?" }, + { "role": "user", "content": "Can you explain LLMs in plain English?" } + ] + ``` + + Example with a partially-filled response from Claude: + + ```json + [ + { + "role": "user", + "content": "What's the Greek name for Sun? (A) Sol (B) Helios (C) Sun" + }, + { "role": "assistant", "content": "The best answer is (" } + ] + ``` + + Each input message `content` may be either a single `string` or an array of + content blocks, where each block has a specific `type`. Using a `string` for + `content` is shorthand for an array of one content block of type `"text"`. The + following input messages are equivalent: + + ```json + { "role": "user", "content": "Hello, Claude" } + ``` + + ```json + { "role": "user", "content": [{ "type": "text", "text": "Hello, Claude" }] } + ``` + + Starting with Claude 3 models, you can also send image content blocks: + + ```json + { + "role": "user", + "content": [ + { + "type": "image", + "source": { + "type": "base64", + "media_type": "image/jpeg", + "data": "/9j/4AAQSkZJRg..." + } + }, + { "type": "text", "text": "What is in this image?" } + ] + } + ``` + + We currently support the `base64` source type for images, and the `image/jpeg`, + `image/png`, `image/gif`, and `image/webp` media types. + + See [examples](https://docs.anthropic.com/en/api/messages-examples#vision) for + more input examples. + + Note that if you want to include a + [system prompt](https://docs.anthropic.com/en/docs/system-prompts), you can use + the top-level `system` parameter — there is no `"system"` role for input + messages in the Messages API. + + model: The model that will complete your prompt.\n\nSee + [models](https://docs.anthropic.com/en/docs/models-overview) for additional + details and options. + + stream: Whether to incrementally stream the response using server-sent events. + + See [streaming](https://docs.anthropic.com/en/api/messages-streaming) for + details. + + metadata: An object describing metadata about the request. + + stop_sequences: Custom text sequences that will cause the model to stop generating. + + Our models will normally stop when they have naturally completed their turn, + which will result in a response `stop_reason` of `"end_turn"`. + + If you want the model to stop generating when it encounters custom strings of + text, you can use the `stop_sequences` parameter. If the model encounters one of + the custom sequences, the response `stop_reason` value will be `"stop_sequence"` + and the response `stop_sequence` value will contain the matched stop sequence. + + system: System prompt. + + A system prompt is a way of providing context and instructions to Claude, such + as specifying a particular goal or role. See our + [guide to system prompts](https://docs.anthropic.com/en/docs/system-prompts). + + temperature: Amount of randomness injected into the response. + + Defaults to `1.0`. Ranges from `0.0` to `1.0`. Use `temperature` closer to `0.0` + for analytical / multiple choice, and closer to `1.0` for creative and + generative tasks. + + Note that even with `temperature` of `0.0`, the results will not be fully + deterministic. + + thinking: Configuration for enabling Claude's extended thinking. + + When enabled, responses include `thinking` content blocks showing Claude's + thinking process before the final answer. Requires a minimum budget of 1,024 + tokens and counts towards your `max_tokens` limit. + + See + [extended thinking](https://docs.anthropic.com/en/docs/build-with-claude/extended-thinking) + for details. + + tool_choice: How the model should use the provided tools. The model can use a specific tool, + any available tool, decide by itself, or not use tools at all. + + tools: Definitions of tools that the model may use. + + If you include `tools` in your API request, the model may return `tool_use` + content blocks that represent the model's use of those tools. You can then run + those tools using the tool input generated by the model and then optionally + return results back to the model using `tool_result` content blocks. + + Each tool definition includes: + + - `name`: Name of the tool. + - `description`: Optional, but strongly-recommended description of the tool. + - `input_schema`: [JSON schema](https://json-schema.org/draft/2020-12) for the + tool `input` shape that the model will produce in `tool_use` output content + blocks. + + For example, if you defined `tools` as: + + ```json + [ + { + "name": "get_stock_price", + "description": "Get the current stock price for a given ticker symbol.", + "input_schema": { + "type": "object", + "properties": { + "ticker": { + "type": "string", + "description": "The stock ticker symbol, e.g. AAPL for Apple Inc." + } + }, + "required": ["ticker"] + } + } + ] + ``` + + And then asked the model "What's the S&P 500 at today?", the model might produce + `tool_use` content blocks in the response like this: + + ```json + [ + { + "type": "tool_use", + "id": "toolu_01D7FLrfh4GYq7yT1ULFeyMV", + "name": "get_stock_price", + "input": { "ticker": "^GSPC" } + } + ] + ``` + + You might then run your `get_stock_price` tool with `{"ticker": "^GSPC"}` as an + input, and return the following back to the model in a subsequent `user` + message: + + ```json + [ + { + "type": "tool_result", + "tool_use_id": "toolu_01D7FLrfh4GYq7yT1ULFeyMV", + "content": "259.75 USD" + } + ] + ``` + + Tools can be used for workflows that include running client-side tools and + functions, or more generally whenever you want the model to produce a particular + JSON structure of output. + + See our [guide](https://docs.anthropic.com/en/docs/tool-use) for more details. + + top_k: Only sample from the top K options for each subsequent token. + + Used to remove "long tail" low probability responses. + [Learn more technical details here](https://towardsdatascience.com/how-to-sample-from-language-models-682bceb97277). + + Recommended for advanced use cases only. You usually only need to use + `temperature`. + + top_p: Use nucleus sampling. + + In nucleus sampling, we compute the cumulative distribution over all the options + for each subsequent token in decreasing probability order and cut it off once it + reaches a particular probability specified by `top_p`. You should either alter + `temperature` or `top_p`, but not both. + + Recommended for advanced use cases only. You usually only need to use + `temperature`. + + 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 + """ + ... + + @overload + async def create( + self, + *, + max_tokens: int, + messages: Iterable[MessageParam], + model: ModelParam, + stream: bool, + metadata: MetadataParam | NotGiven = NOT_GIVEN, + stop_sequences: List[str] | NotGiven = NOT_GIVEN, + system: Union[str, Iterable[TextBlockParam]] | NotGiven = NOT_GIVEN, + temperature: float | NotGiven = NOT_GIVEN, + thinking: ThinkingConfigParam | NotGiven = NOT_GIVEN, + tool_choice: ToolChoiceParam | NotGiven = NOT_GIVEN, + tools: Iterable[ToolUnionParam] | NotGiven = NOT_GIVEN, + top_k: int | NotGiven = NOT_GIVEN, + top_p: float | 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, + ) -> Message | AsyncStream[RawMessageStreamEvent]: + """ + Send a structured list of input messages with text and/or image content, and the + model will generate the next message in the conversation. + + The Messages API can be used for either single queries or stateless multi-turn + conversations. + + Learn more about the Messages API in our [user guide](/en/docs/initial-setup) + + Args: + max_tokens: The maximum number of tokens to generate before stopping. + + Note that our models may stop _before_ reaching this maximum. This parameter + only specifies the absolute maximum number of tokens to generate. + + Different models have different maximum values for this parameter. See + [models](https://docs.anthropic.com/en/docs/models-overview) for details. + + messages: Input messages. + + Our models are trained to operate on alternating `user` and `assistant` + conversational turns. When creating a new `Message`, you specify the prior + conversational turns with the `messages` parameter, and the model then generates + the next `Message` in the conversation. Consecutive `user` or `assistant` turns + in your request will be combined into a single turn. + + Each input message must be an object with a `role` and `content`. You can + specify a single `user`-role message, or you can include multiple `user` and + `assistant` messages. + + If the final message uses the `assistant` role, the response content will + continue immediately from the content in that message. This can be used to + constrain part of the model's response. + + Example with a single `user` message: + + ```json + [{ "role": "user", "content": "Hello, Claude" }] + ``` + + Example with multiple conversational turns: + + ```json + [ + { "role": "user", "content": "Hello there." }, + { "role": "assistant", "content": "Hi, I'm Claude. How can I help you?" }, + { "role": "user", "content": "Can you explain LLMs in plain English?" } + ] + ``` + + Example with a partially-filled response from Claude: + + ```json + [ + { + "role": "user", + "content": "What's the Greek name for Sun? (A) Sol (B) Helios (C) Sun" + }, + { "role": "assistant", "content": "The best answer is (" } + ] + ``` + + Each input message `content` may be either a single `string` or an array of + content blocks, where each block has a specific `type`. Using a `string` for + `content` is shorthand for an array of one content block of type `"text"`. The + following input messages are equivalent: + + ```json + { "role": "user", "content": "Hello, Claude" } + ``` + + ```json + { "role": "user", "content": [{ "type": "text", "text": "Hello, Claude" }] } + ``` + + Starting with Claude 3 models, you can also send image content blocks: + + ```json + { + "role": "user", + "content": [ + { + "type": "image", + "source": { + "type": "base64", + "media_type": "image/jpeg", + "data": "/9j/4AAQSkZJRg..." + } + }, + { "type": "text", "text": "What is in this image?" } + ] + } + ``` + + We currently support the `base64` source type for images, and the `image/jpeg`, + `image/png`, `image/gif`, and `image/webp` media types. + + See [examples](https://docs.anthropic.com/en/api/messages-examples#vision) for + more input examples. + + Note that if you want to include a + [system prompt](https://docs.anthropic.com/en/docs/system-prompts), you can use + the top-level `system` parameter — there is no `"system"` role for input + messages in the Messages API. + + model: The model that will complete your prompt.\n\nSee + [models](https://docs.anthropic.com/en/docs/models-overview) for additional + details and options. + + stream: Whether to incrementally stream the response using server-sent events. + + See [streaming](https://docs.anthropic.com/en/api/messages-streaming) for + details. + + metadata: An object describing metadata about the request. + + stop_sequences: Custom text sequences that will cause the model to stop generating. + + Our models will normally stop when they have naturally completed their turn, + which will result in a response `stop_reason` of `"end_turn"`. + + If you want the model to stop generating when it encounters custom strings of + text, you can use the `stop_sequences` parameter. If the model encounters one of + the custom sequences, the response `stop_reason` value will be `"stop_sequence"` + and the response `stop_sequence` value will contain the matched stop sequence. + + system: System prompt. + + A system prompt is a way of providing context and instructions to Claude, such + as specifying a particular goal or role. See our + [guide to system prompts](https://docs.anthropic.com/en/docs/system-prompts). + + temperature: Amount of randomness injected into the response. + + Defaults to `1.0`. Ranges from `0.0` to `1.0`. Use `temperature` closer to `0.0` + for analytical / multiple choice, and closer to `1.0` for creative and + generative tasks. + + Note that even with `temperature` of `0.0`, the results will not be fully + deterministic. + + thinking: Configuration for enabling Claude's extended thinking. + + When enabled, responses include `thinking` content blocks showing Claude's + thinking process before the final answer. Requires a minimum budget of 1,024 + tokens and counts towards your `max_tokens` limit. + + See + [extended thinking](https://docs.anthropic.com/en/docs/build-with-claude/extended-thinking) + for details. + + tool_choice: How the model should use the provided tools. The model can use a specific tool, + any available tool, decide by itself, or not use tools at all. + + tools: Definitions of tools that the model may use. + + If you include `tools` in your API request, the model may return `tool_use` + content blocks that represent the model's use of those tools. You can then run + those tools using the tool input generated by the model and then optionally + return results back to the model using `tool_result` content blocks. + + Each tool definition includes: + + - `name`: Name of the tool. + - `description`: Optional, but strongly-recommended description of the tool. + - `input_schema`: [JSON schema](https://json-schema.org/draft/2020-12) for the + tool `input` shape that the model will produce in `tool_use` output content + blocks. + + For example, if you defined `tools` as: + + ```json + [ + { + "name": "get_stock_price", + "description": "Get the current stock price for a given ticker symbol.", + "input_schema": { + "type": "object", + "properties": { + "ticker": { + "type": "string", + "description": "The stock ticker symbol, e.g. AAPL for Apple Inc." + } + }, + "required": ["ticker"] + } + } + ] + ``` + + And then asked the model "What's the S&P 500 at today?", the model might produce + `tool_use` content blocks in the response like this: + + ```json + [ + { + "type": "tool_use", + "id": "toolu_01D7FLrfh4GYq7yT1ULFeyMV", + "name": "get_stock_price", + "input": { "ticker": "^GSPC" } + } + ] + ``` + + You might then run your `get_stock_price` tool with `{"ticker": "^GSPC"}` as an + input, and return the following back to the model in a subsequent `user` + message: + + ```json + [ + { + "type": "tool_result", + "tool_use_id": "toolu_01D7FLrfh4GYq7yT1ULFeyMV", + "content": "259.75 USD" + } + ] + ``` + + Tools can be used for workflows that include running client-side tools and + functions, or more generally whenever you want the model to produce a particular + JSON structure of output. + + See our [guide](https://docs.anthropic.com/en/docs/tool-use) for more details. + + top_k: Only sample from the top K options for each subsequent token. + + Used to remove "long tail" low probability responses. + [Learn more technical details here](https://towardsdatascience.com/how-to-sample-from-language-models-682bceb97277). + + Recommended for advanced use cases only. You usually only need to use + `temperature`. + + top_p: Use nucleus sampling. + + In nucleus sampling, we compute the cumulative distribution over all the options + for each subsequent token in decreasing probability order and cut it off once it + reaches a particular probability specified by `top_p`. You should either alter + `temperature` or `top_p`, but not both. + + Recommended for advanced use cases only. You usually only need to use + `temperature`. + + 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 + """ + ... + + @required_args(["max_tokens", "messages", "model"], ["max_tokens", "messages", "model", "stream"]) + async def create( + self, + *, + max_tokens: int, + messages: Iterable[MessageParam], + model: ModelParam, + metadata: MetadataParam | NotGiven = NOT_GIVEN, + stop_sequences: List[str] | NotGiven = NOT_GIVEN, + stream: Literal[False] | Literal[True] | NotGiven = NOT_GIVEN, + system: Union[str, Iterable[TextBlockParam]] | NotGiven = NOT_GIVEN, + temperature: float | NotGiven = NOT_GIVEN, + thinking: ThinkingConfigParam | NotGiven = NOT_GIVEN, + tool_choice: ToolChoiceParam | NotGiven = NOT_GIVEN, + tools: Iterable[ToolUnionParam] | NotGiven = NOT_GIVEN, + top_k: int | NotGiven = NOT_GIVEN, + top_p: float | 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, + ) -> Message | AsyncStream[RawMessageStreamEvent]: + if not stream and not is_given(timeout) and self._client.timeout == DEFAULT_TIMEOUT: + timeout = self._client._calculate_nonstreaming_timeout(max_tokens) + + if model in DEPRECATED_MODELS: + warnings.warn( + f"The model '{model}' is deprecated and will reach end-of-life on {DEPRECATED_MODELS[model]}.\nPlease migrate to a newer model. Visit https://docs.anthropic.com/en/docs/resources/model-deprecations for more information.", + DeprecationWarning, + stacklevel=3, + ) + + return await self._post( + "/v1/messages", + body=await async_maybe_transform( + { + "max_tokens": max_tokens, + "messages": messages, + "model": model, + "metadata": metadata, + "stop_sequences": stop_sequences, + "stream": stream, + "system": system, + "temperature": temperature, + "thinking": thinking, + "tool_choice": tool_choice, + "tools": tools, + "top_k": top_k, + "top_p": top_p, + }, + message_create_params.MessageCreateParams, + ), + options=make_request_options( + extra_headers=extra_headers, extra_query=extra_query, extra_body=extra_body, timeout=timeout + ), + cast_to=Message, + stream=stream or False, + stream_cls=AsyncStream[RawMessageStreamEvent], + ) + + def stream( + self, + *, + max_tokens: int, + messages: Iterable[MessageParam], + model: ModelParam, + metadata: MetadataParam | NotGiven = NOT_GIVEN, + stop_sequences: List[str] | NotGiven = NOT_GIVEN, + system: Union[str, Iterable[TextBlockParam]] | NotGiven = NOT_GIVEN, + temperature: float | NotGiven = NOT_GIVEN, + top_k: int | NotGiven = NOT_GIVEN, + top_p: float | NotGiven = NOT_GIVEN, + thinking: ThinkingConfigParam | NotGiven = NOT_GIVEN, + tool_choice: ToolChoiceParam | NotGiven = NOT_GIVEN, + tools: Iterable[ToolUnionParam] | 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, + ) -> AsyncMessageStreamManager: + """Create a Message stream""" + if model in DEPRECATED_MODELS: + warnings.warn( + f"The model '{model}' is deprecated and will reach end-of-life on {DEPRECATED_MODELS[model]}.\nPlease migrate to a newer model. Visit https://docs.anthropic.com/en/docs/resources/model-deprecations for more information.", + DeprecationWarning, + stacklevel=3, + ) + + extra_headers = { + "X-Stainless-Stream-Helper": "messages", + **(extra_headers or {}), + } + request = self._post( + "/v1/messages", + body=maybe_transform( + { + "max_tokens": max_tokens, + "messages": messages, + "model": model, + "metadata": metadata, + "stop_sequences": stop_sequences, + "system": system, + "temperature": temperature, + "top_k": top_k, + "top_p": top_p, + "tools": tools, + "thinking": thinking, + "tool_choice": tool_choice, + "stream": True, + }, + message_create_params.MessageCreateParams, + ), + options=make_request_options( + extra_headers=extra_headers, extra_query=extra_query, extra_body=extra_body, timeout=timeout + ), + cast_to=Message, + stream=True, + stream_cls=AsyncStream[RawMessageStreamEvent], + ) + return AsyncMessageStreamManager(request) + + async def count_tokens( + self, + *, + messages: Iterable[MessageParam], + model: ModelParam, + system: Union[str, Iterable[TextBlockParam]] | NotGiven = NOT_GIVEN, + thinking: ThinkingConfigParam | NotGiven = NOT_GIVEN, + tool_choice: ToolChoiceParam | NotGiven = NOT_GIVEN, + tools: Iterable[MessageCountTokensToolParam] | 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, + ) -> MessageTokensCount: + """ + Count the number of tokens in a Message. + + The Token Count API can be used to count the number of tokens in a Message, + including tools, images, and documents, without creating it. + + Learn more about token counting in our + [user guide](/en/docs/build-with-claude/token-counting) + + Args: + messages: Input messages. + + Our models are trained to operate on alternating `user` and `assistant` + conversational turns. When creating a new `Message`, you specify the prior + conversational turns with the `messages` parameter, and the model then generates + the next `Message` in the conversation. Consecutive `user` or `assistant` turns + in your request will be combined into a single turn. + + Each input message must be an object with a `role` and `content`. You can + specify a single `user`-role message, or you can include multiple `user` and + `assistant` messages. + + If the final message uses the `assistant` role, the response content will + continue immediately from the content in that message. This can be used to + constrain part of the model's response. + + Example with a single `user` message: + + ```json + [{ "role": "user", "content": "Hello, Claude" }] + ``` + + Example with multiple conversational turns: + + ```json + [ + { "role": "user", "content": "Hello there." }, + { "role": "assistant", "content": "Hi, I'm Claude. How can I help you?" }, + { "role": "user", "content": "Can you explain LLMs in plain English?" } + ] + ``` + + Example with a partially-filled response from Claude: + + ```json + [ + { + "role": "user", + "content": "What's the Greek name for Sun? (A) Sol (B) Helios (C) Sun" + }, + { "role": "assistant", "content": "The best answer is (" } + ] + ``` + + Each input message `content` may be either a single `string` or an array of + content blocks, where each block has a specific `type`. Using a `string` for + `content` is shorthand for an array of one content block of type `"text"`. The + following input messages are equivalent: + + ```json + { "role": "user", "content": "Hello, Claude" } + ``` + + ```json + { "role": "user", "content": [{ "type": "text", "text": "Hello, Claude" }] } + ``` + + Starting with Claude 3 models, you can also send image content blocks: + + ```json + { + "role": "user", + "content": [ + { + "type": "image", + "source": { + "type": "base64", + "media_type": "image/jpeg", + "data": "/9j/4AAQSkZJRg..." + } + }, + { "type": "text", "text": "What is in this image?" } + ] + } + ``` + + We currently support the `base64` source type for images, and the `image/jpeg`, + `image/png`, `image/gif`, and `image/webp` media types. + + See [examples](https://docs.anthropic.com/en/api/messages-examples#vision) for + more input examples. + + Note that if you want to include a + [system prompt](https://docs.anthropic.com/en/docs/system-prompts), you can use + the top-level `system` parameter — there is no `"system"` role for input + messages in the Messages API. + + model: The model that will complete your prompt.\n\nSee + [models](https://docs.anthropic.com/en/docs/models-overview) for additional + details and options. + + system: System prompt. + + A system prompt is a way of providing context and instructions to Claude, such + as specifying a particular goal or role. See our + [guide to system prompts](https://docs.anthropic.com/en/docs/system-prompts). + + thinking: Configuration for enabling Claude's extended thinking. + + When enabled, responses include `thinking` content blocks showing Claude's + thinking process before the final answer. Requires a minimum budget of 1,024 + tokens and counts towards your `max_tokens` limit. + + See + [extended thinking](https://docs.anthropic.com/en/docs/build-with-claude/extended-thinking) + for details. + + tool_choice: How the model should use the provided tools. The model can use a specific tool, + any available tool, decide by itself, or not use tools at all. + + tools: Definitions of tools that the model may use. + + If you include `tools` in your API request, the model may return `tool_use` + content blocks that represent the model's use of those tools. You can then run + those tools using the tool input generated by the model and then optionally + return results back to the model using `tool_result` content blocks. + + Each tool definition includes: + + - `name`: Name of the tool. + - `description`: Optional, but strongly-recommended description of the tool. + - `input_schema`: [JSON schema](https://json-schema.org/draft/2020-12) for the + tool `input` shape that the model will produce in `tool_use` output content + blocks. + + For example, if you defined `tools` as: + + ```json + [ + { + "name": "get_stock_price", + "description": "Get the current stock price for a given ticker symbol.", + "input_schema": { + "type": "object", + "properties": { + "ticker": { + "type": "string", + "description": "The stock ticker symbol, e.g. AAPL for Apple Inc." + } + }, + "required": ["ticker"] + } + } + ] + ``` + + And then asked the model "What's the S&P 500 at today?", the model might produce + `tool_use` content blocks in the response like this: + + ```json + [ + { + "type": "tool_use", + "id": "toolu_01D7FLrfh4GYq7yT1ULFeyMV", + "name": "get_stock_price", + "input": { "ticker": "^GSPC" } + } + ] + ``` + + You might then run your `get_stock_price` tool with `{"ticker": "^GSPC"}` as an + input, and return the following back to the model in a subsequent `user` + message: + + ```json + [ + { + "type": "tool_result", + "tool_use_id": "toolu_01D7FLrfh4GYq7yT1ULFeyMV", + "content": "259.75 USD" + } + ] + ``` + + Tools can be used for workflows that include running client-side tools and + functions, or more generally whenever you want the model to produce a particular + JSON structure of output. + + See our [guide](https://docs.anthropic.com/en/docs/tool-use) for more details. + + 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( + "/v1/messages/count_tokens", + body=await async_maybe_transform( + { + "messages": messages, + "model": model, + "system": system, + "thinking": thinking, + "tool_choice": tool_choice, + "tools": tools, + }, + message_count_tokens_params.MessageCountTokensParams, + ), + options=make_request_options( + extra_headers=extra_headers, extra_query=extra_query, extra_body=extra_body, timeout=timeout + ), + cast_to=MessageTokensCount, + ) + + +class MessagesWithRawResponse: + def __init__(self, messages: Messages) -> None: + self._messages = messages + + self.create = _legacy_response.to_raw_response_wrapper( + messages.create, + ) + self.count_tokens = _legacy_response.to_raw_response_wrapper( + messages.count_tokens, + ) + + @cached_property + def batches(self) -> BatchesWithRawResponse: + return BatchesWithRawResponse(self._messages.batches) + + +class AsyncMessagesWithRawResponse: + def __init__(self, messages: AsyncMessages) -> None: + self._messages = messages + + self.create = _legacy_response.async_to_raw_response_wrapper( + messages.create, + ) + self.count_tokens = _legacy_response.async_to_raw_response_wrapper( + messages.count_tokens, + ) + + @cached_property + def batches(self) -> AsyncBatchesWithRawResponse: + return AsyncBatchesWithRawResponse(self._messages.batches) + + +class MessagesWithStreamingResponse: + def __init__(self, messages: Messages) -> None: + self._messages = messages + + self.create = to_streamed_response_wrapper( + messages.create, + ) + self.count_tokens = to_streamed_response_wrapper( + messages.count_tokens, + ) + + @cached_property + def batches(self) -> BatchesWithStreamingResponse: + return BatchesWithStreamingResponse(self._messages.batches) + + +class AsyncMessagesWithStreamingResponse: + def __init__(self, messages: AsyncMessages) -> None: + self._messages = messages + + self.create = async_to_streamed_response_wrapper( + messages.create, + ) + self.count_tokens = async_to_streamed_response_wrapper( + messages.count_tokens, + ) + + @cached_property + def batches(self) -> AsyncBatchesWithStreamingResponse: + return AsyncBatchesWithStreamingResponse(self._messages.batches) |