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 # File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
from __future__ import annotations
from typing import List, Union, Iterable, overload
from functools import partial
from typing_extensions import Literal
import httpx
from .. import _legacy_response
from ..types import message_create_params
from .._types import NOT_GIVEN, Body, Query, Headers, NotGiven
from .._utils import (
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 .._streaming import Stream, AsyncStream
from .._base_client import make_request_options
from ..lib.streaming import MessageStreamManager, AsyncMessageStreamManager
from ..types.message import Message
from ..types.tool_param import ToolParam
from ..types.message_param import MessageParam
from ..types.text_block_param import TextBlockParam
from ..types.raw_message_stream_event import RawMessageStreamEvent
__all__ = ["Messages", "AsyncMessages"]
class Messages(SyncAPIResource):
@cached_property
def with_raw_response(self) -> MessagesWithRawResponse:
return MessagesWithRawResponse(self)
@cached_property
def with_streaming_response(self) -> MessagesWithStreamingResponse:
return MessagesWithStreamingResponse(self)
@overload
def create(
self,
*,
max_tokens: int,
messages: Iterable[MessageParam],
model: Union[
str,
Literal[
"claude-3-5-sonnet-20240620",
"claude-3-opus-20240229",
"claude-3-sonnet-20240229",
"claude-3-haiku-20240307",
"claude-2.1",
"claude-2.0",
"claude-instant-1.2",
],
],
metadata: message_create_params.Metadata | 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,
tool_choice: message_create_params.ToolChoice | NotGiven = NOT_GIVEN,
tools: Iterable[ToolParam] | 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 = 600,
) -> Message:
"""
Create a 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.
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.
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. The first message must always use the `user` role.
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) 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.
See [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.
tool_choice: How the model should use the provided tools. The model can use a specific tool,
any available tool, or decide by itself.
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/) 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: Union[
str,
Literal[
"claude-3-5-sonnet-20240620",
"claude-3-opus-20240229",
"claude-3-sonnet-20240229",
"claude-3-haiku-20240307",
"claude-2.1",
"claude-2.0",
"claude-instant-1.2",
],
],
stream: Literal[True],
metadata: message_create_params.Metadata | NotGiven = NOT_GIVEN,
stop_sequences: List[str] | NotGiven = NOT_GIVEN,
system: Union[str, Iterable[TextBlockParam]] | NotGiven = NOT_GIVEN,
temperature: float | NotGiven = NOT_GIVEN,
tool_choice: message_create_params.ToolChoice | NotGiven = NOT_GIVEN,
tools: Iterable[ToolParam] | 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 = 600,
) -> Stream[RawMessageStreamEvent]:
"""
Create a 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.
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.
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. The first message must always use the `user` role.
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) 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.
See [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.
tool_choice: How the model should use the provided tools. The model can use a specific tool,
any available tool, or decide by itself.
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/) 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: Union[
str,
Literal[
"claude-3-5-sonnet-20240620",
"claude-3-opus-20240229",
"claude-3-sonnet-20240229",
"claude-3-haiku-20240307",
"claude-2.1",
"claude-2.0",
"claude-instant-1.2",
],
],
stream: bool,
metadata: message_create_params.Metadata | NotGiven = NOT_GIVEN,
stop_sequences: List[str] | NotGiven = NOT_GIVEN,
system: Union[str, Iterable[TextBlockParam]] | NotGiven = NOT_GIVEN,
temperature: float | NotGiven = NOT_GIVEN,
tool_choice: message_create_params.ToolChoice | NotGiven = NOT_GIVEN,
tools: Iterable[ToolParam] | 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 = 600,
) -> Message | Stream[RawMessageStreamEvent]:
"""
Create a 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.
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.
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. The first message must always use the `user` role.
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) 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.
See [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.
tool_choice: How the model should use the provided tools. The model can use a specific tool,
any available tool, or decide by itself.
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/) 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: Union[
str,
Literal[
"claude-3-5-sonnet-20240620",
"claude-3-opus-20240229",
"claude-3-sonnet-20240229",
"claude-3-haiku-20240307",
"claude-2.1",
"claude-2.0",
"claude-instant-1.2",
],
],
metadata: message_create_params.Metadata | 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,
tool_choice: message_create_params.ToolChoice | NotGiven = NOT_GIVEN,
tools: Iterable[ToolParam] | 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 = 600,
) -> Message | Stream[RawMessageStreamEvent]:
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,
"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: Union[
str,
Literal[
"claude-3-5-sonnet-20240620",
"claude-3-opus-20240229",
"claude-3-sonnet-20240229",
"claude-3-haiku-20240307",
"claude-2.1",
"claude-2.0",
"claude-instant-1.2",
],
],
metadata: message_create_params.Metadata | 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,
tool_choice: message_create_params.ToolChoice | NotGiven = NOT_GIVEN,
tools: Iterable[ToolParam] | 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"""
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,
"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)
class AsyncMessages(AsyncAPIResource):
@cached_property
def with_raw_response(self) -> AsyncMessagesWithRawResponse:
return AsyncMessagesWithRawResponse(self)
@cached_property
def with_streaming_response(self) -> AsyncMessagesWithStreamingResponse:
return AsyncMessagesWithStreamingResponse(self)
@overload
async def create(
self,
*,
max_tokens: int,
messages: Iterable[MessageParam],
model: Union[
str,
Literal[
"claude-3-5-sonnet-20240620",
"claude-3-opus-20240229",
"claude-3-sonnet-20240229",
"claude-3-haiku-20240307",
"claude-2.1",
"claude-2.0",
"claude-instant-1.2",
],
],
metadata: message_create_params.Metadata | 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,
tool_choice: message_create_params.ToolChoice | NotGiven = NOT_GIVEN,
tools: Iterable[ToolParam] | 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 = 600,
) -> Message:
"""
Create a 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.
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.
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. The first message must always use the `user` role.
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) 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.
See [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.
tool_choice: How the model should use the provided tools. The model can use a specific tool,
any available tool, or decide by itself.
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/) 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: Union[
str,
Literal[
"claude-3-5-sonnet-20240620",
"claude-3-opus-20240229",
"claude-3-sonnet-20240229",
"claude-3-haiku-20240307",
"claude-2.1",
"claude-2.0",
"claude-instant-1.2",
],
],
stream: Literal[True],
metadata: message_create_params.Metadata | NotGiven = NOT_GIVEN,
stop_sequences: List[str] | NotGiven = NOT_GIVEN,
system: Union[str, Iterable[TextBlockParam]] | NotGiven = NOT_GIVEN,
temperature: float | NotGiven = NOT_GIVEN,
tool_choice: message_create_params.ToolChoice | NotGiven = NOT_GIVEN,
tools: Iterable[ToolParam] | 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 = 600,
) -> AsyncStream[RawMessageStreamEvent]:
"""
Create a 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.
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.
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. The first message must always use the `user` role.
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) 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.
See [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.
tool_choice: How the model should use the provided tools. The model can use a specific tool,
any available tool, or decide by itself.
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/) 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: Union[
str,
Literal[
"claude-3-5-sonnet-20240620",
"claude-3-opus-20240229",
"claude-3-sonnet-20240229",
"claude-3-haiku-20240307",
"claude-2.1",
"claude-2.0",
"claude-instant-1.2",
],
],
stream: bool,
metadata: message_create_params.Metadata | NotGiven = NOT_GIVEN,
stop_sequences: List[str] | NotGiven = NOT_GIVEN,
system: Union[str, Iterable[TextBlockParam]] | NotGiven = NOT_GIVEN,
temperature: float | NotGiven = NOT_GIVEN,
tool_choice: message_create_params.ToolChoice | NotGiven = NOT_GIVEN,
tools: Iterable[ToolParam] | 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 = 600,
) -> Message | AsyncStream[RawMessageStreamEvent]:
"""
Create a 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.
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.
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. The first message must always use the `user` role.
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) 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.
See [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.
tool_choice: How the model should use the provided tools. The model can use a specific tool,
any available tool, or decide by itself.
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/) 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: Union[
str,
Literal[
"claude-3-5-sonnet-20240620",
"claude-3-opus-20240229",
"claude-3-sonnet-20240229",
"claude-3-haiku-20240307",
"claude-2.1",
"claude-2.0",
"claude-instant-1.2",
],
],
metadata: message_create_params.Metadata | 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,
tool_choice: message_create_params.ToolChoice | NotGiven = NOT_GIVEN,
tools: Iterable[ToolParam] | 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 = 600,
) -> Message | AsyncStream[RawMessageStreamEvent]:
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,
"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: Union[
str,
Literal[
"claude-3-5-sonnet-20240620",
"claude-3-opus-20240229",
"claude-3-sonnet-20240229",
"claude-3-haiku-20240307",
"claude-2.1",
"claude-2.0",
"claude-instant-1.2",
],
],
metadata: message_create_params.Metadata | 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,
tool_choice: message_create_params.ToolChoice | NotGiven = NOT_GIVEN,
tools: Iterable[ToolParam] | 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"""
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,
"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)
class MessagesWithRawResponse:
def __init__(self, messages: Messages) -> None:
self._messages = messages
self.create = _legacy_response.to_raw_response_wrapper(
messages.create,
)
class AsyncMessagesWithRawResponse:
def __init__(self, messages: AsyncMessages) -> None:
self._messages = messages
self.create = _legacy_response.async_to_raw_response_wrapper(
messages.create,
)
class MessagesWithStreamingResponse:
def __init__(self, messages: Messages) -> None:
self._messages = messages
self.create = to_streamed_response_wrapper(
messages.create,
)
class AsyncMessagesWithStreamingResponse:
def __init__(self, messages: AsyncMessages) -> None:
self._messages = messages
self.create = async_to_streamed_response_wrapper(
messages.create,
)