AI SDK CoregenerateText
generateText
Generates text and calls tools for a given prompt using a language model.
It is ideal for non-interactive use cases such as automation tasks where you need to write text (e.g. drafting email or summarizing web pages) and for agents that use tools.
Example
import { openai } from '@ai-sdk/openai';import { generateText } from 'ai';
const result = await generateText({ model: openai('gpt-4-turbo'), prompt: 'Invent a new holiday and describe its traditions.',});
Parameters
model:
The language model to use. Example: openai('gpt-4-turbo')
system:
The system prompt to use that specifies the behavior of the model.
prompt:
The input prompt to generate the text from.
messages:
A list of messages that represent a conversation.
CoreUserMessage
role:
The role for the user message.
content:
The content of the message.
TextPart
type:
The type of the message part.
text:
The text content of the message part.
ImagePart
type:
The type of the message part.
image:
The image content of the message part.
CoreAssistantMessage
role:
The role for the assistant message.
content:
The content of the message.
TextPart
type:
The type of the message part.
text:
The text content of the message part.
ToolCallPart
type:
The type of the message part.
toolCallId:
The id of the tool call.
toolName:
The name of the tool, which typically would be the name of the function.
args:
Parameters generated by the model to be used by the tool.
CoreToolMessage
role:
The role for the assistant message.
content:
The content of the message.
ToolResultPart
type:
The type of the message part.
toolCallId:
The id of the tool call the result corresponds to.
toolName:
The name of the tool the result corresponds to.
result:
The result returned by the tool after execution.
isError?:
Whether the result is an error or an error message.
tools:
Tools that are accessible to and can be called by the model. The model needs to support calling tools.
CoreTool
description?:
Information about the purpose of the tool including details on how and when it can be used by the model.
parameters:
The schema of the input that the tool expects. The language model will use this to generate the input. It is also used to validate the output of the language model. Use descriptions to make the input understandable for the language model.
execute?:
An async function that is called with the arguments from the tool call and produces a result. If not provided, the tool will not be executed automatically.
maxTokens?:
Maximum number of tokens to generate.
temperature?:
Temperature setting. The value is passed through to the provider. The range depends on the provider and model. It is recommended to set either `temperature` or `topP`, but not both.
topP?:
Nucleus sampling. The value is passed through to the provider. The range depends on the provider and model. It is recommended to set either `temperature` or `topP`, but not both.
presencePenalty?:
Presence penalty setting. It affects the likelihood of the model to repeat information that is already in the prompt. The value is passed through to the provider. The range depends on the provider and model.
frequencyPenalty?:
Frequency penalty setting. It affects the likelihood of the model to repeatedly use the same words or phrases. The value is passed through to the provider. The range depends on the provider and model.
seed?:
The seed (integer) to use for random sampling. If set and supported by the model, calls will generate deterministic results.
maxRetries?:
Maximum number of retries. Set to 0 to disable retries. Default: 2.
abortSignal?:
An optional abort signal that can be used to cancel the call.
Result Object
text:
The generated text by the model.
toolCalls:
A list of tool calls made by the model.
toolResults:
A list of tool results returned as responses to earlier tool calls.
finishReason:
The reason the model finished generating the text.
usage:
The token usage of the generated text.
TokenUsage
promptTokens:
The total number of tokens in the prompt.
completionTokens:
The total number of tokens in the completion.
totalTokens:
The total number of tokens generated.
rawResponse:
Optional raw response data.
RawResponse
header:
Response headers.
warnings:
Warnings from the model provider (e.g. unsupported settings).
More Examples
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