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# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
from __future__ import annotations
import warnings
from typing import Union, Iterable, Optional
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, Omit, Query, Headers, NotGiven, SequenceNotStr, omit, not_given
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, MODEL_NONSTREAMING_TOKENS
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-3-opus-20240229": "January 5th, 2026",
"claude-2.1": "July 21st, 2025",
"claude-2.0": "July 21st, 2025",
"claude-3-7-sonnet-latest": "February 19th, 2026",
"claude-3-7-sonnet-20250219": "February 19th, 2026",
}
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 | Omit = omit,
service_tier: Literal["auto", "standard_only"] | Omit = omit,
stop_sequences: SequenceNotStr[str] | Omit = omit,
stream: Literal[False] | Omit = omit,
system: Union[str, Iterable[TextBlockParam]] | Omit = omit,
temperature: float | Omit = omit,
thinking: ThinkingConfigParam | Omit = omit,
tool_choice: ToolChoiceParam | Omit = omit,
tools: Iterable[ToolUnionParam] | Omit = omit,
top_k: int | Omit = omit,
top_p: float | Omit = omit,
# 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](https://docs.claude.com/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.claude.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" }] }
```
See [input examples](https://docs.claude.com/en/api/messages-examples).
Note that if you want to include a
[system prompt](https://docs.claude.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.
There is a limit of 100,000 messages in a single request.
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.
service_tier: Determines whether to use priority capacity (if available) or standard capacity
for this request.
Anthropic offers different levels of service for your API requests. See
[service-tiers](https://docs.claude.com/en/api/service-tiers) for details.
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.claude.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.claude.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.claude.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.
There are two types of tools: **client tools** and **server tools**. The
behavior described below applies to client tools. For
[server tools](https://docs.claude.com/en/docs/agents-and-tools/tool-use/overview#server-tools),
see their individual documentation as each has its own behavior (e.g., the
[web search tool](https://docs.claude.com/en/docs/agents-and-tools/tool-use/web-search-tool)).
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.claude.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 | Omit = omit,
service_tier: Literal["auto", "standard_only"] | Omit = omit,
stop_sequences: SequenceNotStr[str] | Omit = omit,
system: Union[str, Iterable[TextBlockParam]] | Omit = omit,
temperature: float | Omit = omit,
thinking: ThinkingConfigParam | Omit = omit,
tool_choice: ToolChoiceParam | Omit = omit,
tools: Iterable[ToolUnionParam] | Omit = omit,
top_k: int | Omit = omit,
top_p: float | Omit = omit,
# 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](https://docs.claude.com/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.claude.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" }] }
```
See [input examples](https://docs.claude.com/en/api/messages-examples).
Note that if you want to include a
[system prompt](https://docs.claude.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.
There is a limit of 100,000 messages in a single request.
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.claude.com/en/api/messages-streaming) for details.
metadata: An object describing metadata about the request.
service_tier: Determines whether to use priority capacity (if available) or standard capacity
for this request.
Anthropic offers different levels of service for your API requests. See
[service-tiers](https://docs.claude.com/en/api/service-tiers) for details.
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.claude.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.claude.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.
There are two types of tools: **client tools** and **server tools**. The
behavior described below applies to client tools. For
[server tools](https://docs.claude.com/en/docs/agents-and-tools/tool-use/overview#server-tools),
see their individual documentation as each has its own behavior (e.g., the
[web search tool](https://docs.claude.com/en/docs/agents-and-tools/tool-use/web-search-tool)).
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.claude.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 | Omit = omit,
service_tier: Literal["auto", "standard_only"] | Omit = omit,
stop_sequences: SequenceNotStr[str] | Omit = omit,
system: Union[str, Iterable[TextBlockParam]] | Omit = omit,
temperature: float | Omit = omit,
thinking: ThinkingConfigParam | Omit = omit,
tool_choice: ToolChoiceParam | Omit = omit,
tools: Iterable[ToolUnionParam] | Omit = omit,
top_k: int | Omit = omit,
top_p: float | Omit = omit,
# 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](https://docs.claude.com/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.claude.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" }] }
```
See [input examples](https://docs.claude.com/en/api/messages-examples).
Note that if you want to include a
[system prompt](https://docs.claude.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.
There is a limit of 100,000 messages in a single request.
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.claude.com/en/api/messages-streaming) for details.
metadata: An object describing metadata about the request.
service_tier: Determines whether to use priority capacity (if available) or standard capacity
for this request.
Anthropic offers different levels of service for your API requests. See
[service-tiers](https://docs.claude.com/en/api/service-tiers) for details.
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.claude.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.claude.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.
There are two types of tools: **client tools** and **server tools**. The
behavior described below applies to client tools. For
[server tools](https://docs.claude.com/en/docs/agents-and-tools/tool-use/overview#server-tools),
see their individual documentation as each has its own behavior (e.g., the
[web search tool](https://docs.claude.com/en/docs/agents-and-tools/tool-use/web-search-tool)).
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.claude.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 | Omit = omit,
service_tier: Literal["auto", "standard_only"] | Omit = omit,
stop_sequences: SequenceNotStr[str] | Omit = omit,
stream: Literal[False] | Literal[True] | Omit = omit,
system: Union[str, Iterable[TextBlockParam]] | Omit = omit,
temperature: float | Omit = omit,
thinking: ThinkingConfigParam | Omit = omit,
tool_choice: ToolChoiceParam | Omit = omit,
tools: Iterable[ToolUnionParam] | Omit = omit,
top_k: int | Omit = omit,
top_p: float | Omit = omit,
# 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, MODEL_NONSTREAMING_TOKENS.get(model, None)
)
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,
"service_tier": service_tier,
"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.MessageCreateParamsStreaming
if stream
else message_create_params.MessageCreateParamsNonStreaming,
),
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 | Omit = omit,
container: Optional[str] | Omit = omit,
service_tier: Literal["auto", "standard_only"] | Omit = omit,
stop_sequences: SequenceNotStr[str] | Omit = omit,
system: Union[str, Iterable[TextBlockParam]] | Omit = omit,
temperature: float | Omit = omit,
top_k: int | Omit = omit,
top_p: float | Omit = omit,
thinking: ThinkingConfigParam | Omit = omit,
tool_choice: ToolChoiceParam | Omit = omit,
tools: Iterable[ToolUnionParam] | Omit = omit,
# 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-Helper-Method": "stream",
"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,
"container": container,
"service_tier": service_tier,
"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]] | Omit = omit,
thinking: ThinkingConfigParam | Omit = omit,
tool_choice: ToolChoiceParam | Omit = omit,
tools: Iterable[MessageCountTokensToolParam] | Omit = omit,
# 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](https://docs.claude.com/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" }] }
```
See [input examples](https://docs.claude.com/en/api/messages-examples).
Note that if you want to include a
[system prompt](https://docs.claude.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.
There is a limit of 100,000 messages in a single request.
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.claude.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.claude.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.
There are two types of tools: **client tools** and **server tools**. The
behavior described below applies to client tools. For
[server tools](https://docs.claude.com/en/docs/agents-and-tools/tool-use/overview#server-tools),
see their individual documentation as each has its own behavior (e.g., the
[web search tool](https://docs.claude.com/en/docs/agents-and-tools/tool-use/web-search-tool)).
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.claude.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 | Omit = omit,
service_tier: Literal["auto", "standard_only"] | Omit = omit,
stop_sequences: SequenceNotStr[str] | Omit = omit,
stream: Literal[False] | Omit = omit,
system: Union[str, Iterable[TextBlockParam]] | Omit = omit,
temperature: float | Omit = omit,
thinking: ThinkingConfigParam | Omit = omit,
tool_choice: ToolChoiceParam | Omit = omit,
tools: Iterable[ToolUnionParam] | Omit = omit,
top_k: int | Omit = omit,
top_p: float | Omit = omit,
# 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](https://docs.claude.com/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.claude.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" }] }
```
See [input examples](https://docs.claude.com/en/api/messages-examples).
Note that if you want to include a
[system prompt](https://docs.claude.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.
There is a limit of 100,000 messages in a single request.
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.
service_tier: Determines whether to use priority capacity (if available) or standard capacity
for this request.
Anthropic offers different levels of service for your API requests. See
[service-tiers](https://docs.claude.com/en/api/service-tiers) for details.
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.claude.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.claude.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.claude.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.
There are two types of tools: **client tools** and **server tools**. The
behavior described below applies to client tools. For
[server tools](https://docs.claude.com/en/docs/agents-and-tools/tool-use/overview#server-tools),
see their individual documentation as each has its own behavior (e.g., the
[web search tool](https://docs.claude.com/en/docs/agents-and-tools/tool-use/web-search-tool)).
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.claude.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 | Omit = omit,
service_tier: Literal["auto", "standard_only"] | Omit = omit,
stop_sequences: SequenceNotStr[str] | Omit = omit,
system: Union[str, Iterable[TextBlockParam]] | Omit = omit,
temperature: float | Omit = omit,
thinking: ThinkingConfigParam | Omit = omit,
tool_choice: ToolChoiceParam | Omit = omit,
tools: Iterable[ToolUnionParam] | Omit = omit,
top_k: int | Omit = omit,
top_p: float | Omit = omit,
# 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](https://docs.claude.com/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.claude.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" }] }
```
See [input examples](https://docs.claude.com/en/api/messages-examples).
Note that if you want to include a
[system prompt](https://docs.claude.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.
There is a limit of 100,000 messages in a single request.
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.claude.com/en/api/messages-streaming) for details.
metadata: An object describing metadata about the request.
service_tier: Determines whether to use priority capacity (if available) or standard capacity
for this request.
Anthropic offers different levels of service for your API requests. See
[service-tiers](https://docs.claude.com/en/api/service-tiers) for details.
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.claude.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.claude.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.
There are two types of tools: **client tools** and **server tools**. The
behavior described below applies to client tools. For
[server tools](https://docs.claude.com/en/docs/agents-and-tools/tool-use/overview#server-tools),
see their individual documentation as each has its own behavior (e.g., the
[web search tool](https://docs.claude.com/en/docs/agents-and-tools/tool-use/web-search-tool)).
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.claude.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 | Omit = omit,
service_tier: Literal["auto", "standard_only"] | Omit = omit,
stop_sequences: SequenceNotStr[str] | Omit = omit,
system: Union[str, Iterable[TextBlockParam]] | Omit = omit,
temperature: float | Omit = omit,
thinking: ThinkingConfigParam | Omit = omit,
tool_choice: ToolChoiceParam | Omit = omit,
tools: Iterable[ToolUnionParam] | Omit = omit,
top_k: int | Omit = omit,
top_p: float | Omit = omit,
# 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](https://docs.claude.com/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.claude.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" }] }
```
See [input examples](https://docs.claude.com/en/api/messages-examples).
Note that if you want to include a
[system prompt](https://docs.claude.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.
There is a limit of 100,000 messages in a single request.
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.claude.com/en/api/messages-streaming) for details.
metadata: An object describing metadata about the request.
service_tier: Determines whether to use priority capacity (if available) or standard capacity
for this request.
Anthropic offers different levels of service for your API requests. See
[service-tiers](https://docs.claude.com/en/api/service-tiers) for details.
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.claude.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.claude.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.
There are two types of tools: **client tools** and **server tools**. The
behavior described below applies to client tools. For
[server tools](https://docs.claude.com/en/docs/agents-and-tools/tool-use/overview#server-tools),
see their individual documentation as each has its own behavior (e.g., the
[web search tool](https://docs.claude.com/en/docs/agents-and-tools/tool-use/web-search-tool)).
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.claude.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 | Omit = omit,
service_tier: Literal["auto", "standard_only"] | Omit = omit,
stop_sequences: SequenceNotStr[str] | Omit = omit,
stream: Literal[False] | Literal[True] | Omit = omit,
system: Union[str, Iterable[TextBlockParam]] | Omit = omit,
temperature: float | Omit = omit,
thinking: ThinkingConfigParam | Omit = omit,
tool_choice: ToolChoiceParam | Omit = omit,
tools: Iterable[ToolUnionParam] | Omit = omit,
top_k: int | Omit = omit,
top_p: float | Omit = omit,
# 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, MODEL_NONSTREAMING_TOKENS.get(model, None)
)
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,
"service_tier": service_tier,
"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.MessageCreateParamsStreaming
if stream
else message_create_params.MessageCreateParamsNonStreaming,
),
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 | Omit = omit,
container: Optional[str] | Omit = omit,
service_tier: Literal["auto", "standard_only"] | Omit = omit,
stop_sequences: SequenceNotStr[str] | Omit = omit,
system: Union[str, Iterable[TextBlockParam]] | Omit = omit,
temperature: float | Omit = omit,
top_k: int | Omit = omit,
top_p: float | Omit = omit,
thinking: ThinkingConfigParam | Omit = omit,
tool_choice: ToolChoiceParam | Omit = omit,
tools: Iterable[ToolUnionParam] | Omit = omit,
# 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-Helper-Method": "stream",
"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,
"container": container,
"service_tier": service_tier,
"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]] | Omit = omit,
thinking: ThinkingConfigParam | Omit = omit,
tool_choice: ToolChoiceParam | Omit = omit,
tools: Iterable[MessageCountTokensToolParam] | Omit = omit,
# 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](https://docs.claude.com/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" }] }
```
See [input examples](https://docs.claude.com/en/api/messages-examples).
Note that if you want to include a
[system prompt](https://docs.claude.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.
There is a limit of 100,000 messages in a single request.
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.claude.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.claude.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.
There are two types of tools: **client tools** and **server tools**. The
behavior described below applies to client tools. For
[server tools](https://docs.claude.com/en/docs/agents-and-tools/tool-use/overview#server-tools),
see their individual documentation as each has its own behavior (e.g., the
[web search tool](https://docs.claude.com/en/docs/agents-and-tools/tool-use/web-search-tool)).
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.claude.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)
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