All Implemented Interfaces:
```
public final class MessageBatchCreateParams.Request.Params

                    
```
Messages API creation parameters for the individual request.
See the /en/api/messages for full documentation on available parameters.

Nested Class Summary

Nested Classes
Modifier and Type	Class	Description
`public final class`	`MessageBatchCreateParams.Request.Params.Builder`	A builder for Params.
`public final class`	`MessageBatchCreateParams.Request.Params.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.

Field Summary

Fields
Modifier and Type	Field	Description

Constructor Summary

Constructors
Constructor	Description

Enum Constant Summary

Enum Constants
Enum Constant	Description

Method Summary

Modifier and Type	Method	Description
`final Long`	`maxTokens()`	The maximum number of tokens to generate before stopping.
`final List<MessageParam>`	`messages()`	Input messages.
`final Model`	`model()`	The model that will complete your prompt.
`final Optional<Metadata>`	`metadata()`	An object describing metadata about the request.
`final Optional<List<String>>`	`stopSequences()`	Custom text sequences that will cause the model to stop generating.
`final Optional<Boolean>`	`stream()`	Whether to incrementally stream the response using server-sent events.
`final Optional<MessageBatchCreateParams.Request.Params.System>`	`system()`	System prompt.
`final Optional<Double>`	`temperature()`	Amount of randomness injected into the response.
`final Optional<ToolChoice>`	`toolChoice()`	How the model should use the provided tools.
`final Optional<List<Tool>>`	`tools()`	Definitions of tools that the model may use.
`final Optional<Long>`	`topK()`	Only sample from the top K options for each subsequent token.
`final Optional<Double>`	`topP()`	Use nucleus sampling.
`final JsonField<Long>`	`_maxTokens()`	The maximum number of tokens to generate before stopping.
`final JsonField<List<MessageParam>>`	`_messages()`	Input messages.
`final JsonField<Model>`	`_model()`	The model that will complete your prompt.
`final JsonField<Metadata>`	`_metadata()`	An object describing metadata about the request.
`final JsonField<List<String>>`	`_stopSequences()`	Custom text sequences that will cause the model to stop generating.
`final JsonField<Boolean>`	`_stream()`	Whether to incrementally stream the response using server-sent events.
`final JsonField<MessageBatchCreateParams.Request.Params.System>`	`_system()`	System prompt.
`final JsonField<Double>`	`_temperature()`	Amount of randomness injected into the response.
`final JsonField<ToolChoice>`	`_toolChoice()`	How the model should use the provided tools.
`final JsonField<List<Tool>>`	`_tools()`	Definitions of tools that the model may use.
`final JsonField<Long>`	`_topK()`	Only sample from the top K options for each subsequent token.
`final JsonField<Double>`	`_topP()`	Use nucleus sampling.
`final Map<String, JsonValue>`	`_additionalProperties()`
`final MessageBatchCreateParams.Request.Params`	`validate()`
`final MessageBatchCreateParams.Request.Params.Builder`	`toBuilder()`
`Boolean`	`equals(Object other)`
`Integer`	`hashCode()`
`String`	`toString()`
`final static MessageBatchCreateParams.Request.Params.Builder`	`builder()`

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Constructor Detail
- Method Detail
  - maxTokens
```
 final Long maxTokens()
```
    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 for details.
  - messages
```
 final List<MessageParam> 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:
    [{ "role": "user", "content": "Hello, Claude" }]
    Example with multiple conversational turns:
    [ { "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:
    [ { "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:
    { "role": "user", "content": "Hello, Claude" }
    { "role": "user", "content": [{ "type": "text", "text": "Hello, Claude" }] }
    Starting with Claude 3 models, you can also send image content blocks:
    { "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 for more input examples.
    Note that if you want to include a system prompt, you can use the top-level system parameter — there is no "system" role for input messages in the Messages API.
  - model
```
 final Model model()
```
    The model that will complete your prompt.\n\nSee models for additional details and options.
  - metadata
```
 final Optional<Metadata> metadata()
```
    An object describing metadata about the request.
  - stopSequences
```
 final Optional<List<String>> stopSequences()
```
    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
```
 final Optional<Boolean> stream()
```
    Whether to incrementally stream the response using server-sent events.
    See streaming for details.
  - system
```
 final Optional<MessageBatchCreateParams.Request.Params.System> 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.
  - temperature
```
 final Optional<Double> 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.
  - toolChoice
```
 final Optional<ToolChoice> toolChoice()
```
    How the model should use the provided tools. The model can use a specific tool, any available tool, or decide by itself.
  - tools
```
 final Optional<List<Tool>> 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 for the tool input shape that the model will produce in tool_use output content blocks.
    For example, if you defined tools as:
    [ { "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:
    [ { "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:
    [ { "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 for more details.
  - topK
```
 final Optional<Long> topK()
```
    Only sample from the top K options for each subsequent token.
    Used to remove "long tail" low probability responses. Learn more technical details here.
    Recommended for advanced use cases only. You usually only need to use temperature.
  - topP
```
 final Optional<Double> topP()
```
    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.
  - _maxTokens
```
 final JsonField<Long> _maxTokens()
```
    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 for details.
  - _messages
```
 final JsonField<List<MessageParam>> _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:
    [{ "role": "user", "content": "Hello, Claude" }]
    Example with multiple conversational turns:
    [ { "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:
    [ { "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:
    { "role": "user", "content": "Hello, Claude" }
    { "role": "user", "content": [{ "type": "text", "text": "Hello, Claude" }] }
    Starting with Claude 3 models, you can also send image content blocks:
    { "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 for more input examples.
    Note that if you want to include a system prompt, you can use the top-level system parameter — there is no "system" role for input messages in the Messages API.
  - _model
```
 final JsonField<Model> _model()
```
    The model that will complete your prompt.\n\nSee models for additional details and options.
  - _metadata
```
 final JsonField<Metadata> _metadata()
```
    An object describing metadata about the request.
  - _stopSequences
```
 final JsonField<List<String>> _stopSequences()
```
    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
```
 final JsonField<Boolean> _stream()
```
    Whether to incrementally stream the response using server-sent events.
    See streaming for details.
  - _system
```
 final JsonField<MessageBatchCreateParams.Request.Params.System> _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.
  - _temperature
```
 final JsonField<Double> _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.
  - _toolChoice
```
 final JsonField<ToolChoice> _toolChoice()
```
    How the model should use the provided tools. The model can use a specific tool, any available tool, or decide by itself.
  - _tools
```
 final JsonField<List<Tool>> _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 for the tool input shape that the model will produce in tool_use output content blocks.
    For example, if you defined tools as:
    [ { "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:
    [ { "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:
    [ { "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 for more details.
  - _topK
```
 final JsonField<Long> _topK()
```
    Only sample from the top K options for each subsequent token.
    Used to remove "long tail" low probability responses. Learn more technical details here.
    Recommended for advanced use cases only. You usually only need to use temperature.
  - _topP
```
 final JsonField<Double> _topP()
```
    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.
  - _additionalProperties
```
 final Map<String, JsonValue> _additionalProperties()
```
  - validate
```
 final MessageBatchCreateParams.Request.Params validate()
```
  - toBuilder
```
 final MessageBatchCreateParams.Request.Params.Builder toBuilder()
```
  - equals
```
 Boolean equals(Object other)
```
  - hashCode
```
 Integer hashCode()
```
  - toString
```
 String toString()
```
  - builder
```
 final static MessageBatchCreateParams.Request.Params.Builder builder()
```

Class MessageBatchCreateParams.Request.Params

Nested Class Summary

Field Summary

Constructor Summary

Enum Constant Summary

Method Summary

Methods inherited from class java.lang.Object

Constructor Detail

Method Detail

maxTokens

messages

model

metadata

stopSequences

stream

system

temperature

toolChoice

tools

topK

topP

_maxTokens

_messages

_model

_metadata

_stopSequences

_stream

_system

_temperature

_toolChoice

_tools

_topK

_topP

_additionalProperties

validate

toBuilder

equals

hashCode

toString

builder