# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.

from __future__ import annotations

from typing import Union, Iterable, Optional
from typing_extensions import Required, TypedDict

from .model_param import ModelParam
from .message_param import MessageParam
from .text_block_param import TextBlockParam
from .tool_choice_param import ToolChoiceParam
from .output_config_param import OutputConfigParam
from .thinking_config_param import ThinkingConfigParam
from .cache_control_ephemeral_param import CacheControlEphemeralParam
from .message_count_tokens_tool_param import MessageCountTokensToolParam

__all__ = ["MessageCountTokensParams"]


class MessageCountTokensParams(TypedDict, total=False):
    messages: Required[Iterable[MessageParam]]
    """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: Required[ModelParam]
    """The model that will complete your prompt.

    See [models](https://docs.anthropic.com/en/docs/models-overview) for additional
    details and options.
    """

    cache_control: Optional[CacheControlEphemeralParam]
    """
    Top-level cache control automatically applies a cache_control marker to the last
    cacheable block in the request.
    """

    output_config: OutputConfigParam
    """Configuration options for the model's output, such as the output format."""

    system: Union[str, Iterable[TextBlockParam]]
    """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: ThinkingConfigParam
    """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: ToolChoiceParam
    """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: Iterable[MessageCountTokensToolParam]
    """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.
    """
