feat: create llms package and mv files
This commit is contained in:
Simon
214
packages/llms/src/utils.ts
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214
packages/llms/src/utils.ts
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/**
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* Utility functions for LLM integration
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*/
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import chalk from 'chalk'
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import { z } from 'zod'
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import type { MacroToolInput } from '../PageAgent'
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import { InvokeError, InvokeErrorType } from './errors'
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import type { Tool } from './types'
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/**
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* Convert Zod schema to OpenAI tool format
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* Uses Zod 4 native z.toJSONSchema()
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*/
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export function zodToOpenAITool(name: string, tool: Tool) {
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return {
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type: 'function' as const,
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function: {
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name,
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description: tool.description,
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parameters: z.toJSONSchema(tool.inputSchema, { target: 'openapi-3.0' }),
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},
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}
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}
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/**
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* Although some models cannot guarantee correct response. Common issues are fixable:
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* - Instead of returning a proper tool call. Return the tool call parameters in the message content.
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* - Returned tool calls or messages don't follow the nested MacroToolInput format.
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*/
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export function lenientParseMacroToolCall(
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responseData: any,
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inputSchema: z.ZodObject<MacroToolInput & Record<string, any>>
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): MacroToolInput {
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// check
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const choice = responseData.choices?.[0]
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if (!choice) {
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throw new InvokeError(InvokeErrorType.UNKNOWN, 'No choices in response', responseData)
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}
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// check
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switch (choice.finish_reason) {
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case 'tool_calls':
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case 'function_call': // gemini
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case 'stop': // will try a robust parse
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// ✅ Normal
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break
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case 'length':
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// ⚠️ Token limit reached
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throw new InvokeError(
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InvokeErrorType.CONTEXT_LENGTH,
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'Response truncated: max tokens reached'
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)
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case 'content_filter':
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// ❌ Content filtered
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throw new InvokeError(InvokeErrorType.CONTENT_FILTER, 'Content filtered by safety system')
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default:
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throw new InvokeError(
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InvokeErrorType.UNKNOWN,
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`Unexpected finish_reason: ${choice.finish_reason}`
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)
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}
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// Extract action schema from MacroToolInput schema
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const actionSchema = inputSchema.shape.action
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if (!actionSchema) {
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throw new Error('inputSchema must have an "action" field')
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}
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// patch stopReason mis-format
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let arg: string | null = null
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// try to use tool call
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const toolCall = choice.message?.tool_calls?.[0]?.function
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arg = toolCall?.arguments ?? null
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if (arg && toolCall.name !== 'AgentOutput') {
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// TODO: check if toolCall.name is a valid action name
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// case: instead of AgentOutput, the model returned a action name as tool call
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console.log(chalk.yellow('lenientParseMacroToolCall: #1 fixing incorrect tool call'))
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let tmpArg
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try {
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tmpArg = JSON.parse(arg)
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} catch (error) {
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throw new InvokeError(
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InvokeErrorType.INVALID_TOOL_ARGS,
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'Failed to parse tool arguments as JSON',
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error
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)
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}
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arg = JSON.stringify({ action: { [toolCall.name]: tmpArg } })
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}
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if (!arg) {
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// try to use message content as JSON
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arg = choice.message?.content.trim() || null
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}
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if (!arg) {
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throw new InvokeError(
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InvokeErrorType.NO_TOOL_CALL,
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'No tool call or content found in response',
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responseData
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)
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}
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// make sure is valid JSON
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let parsedArgs: any
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try {
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parsedArgs = JSON.parse(arg)
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} catch (error) {
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throw new InvokeError(
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InvokeErrorType.INVALID_TOOL_ARGS,
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'Failed to parse tool arguments as JSON',
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error
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)
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}
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// patch incomplete formats
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if (parsedArgs.action || parsedArgs.evaluation_previous_goal || parsedArgs.next_goal) {
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// case: nested MacroToolInput format (correct format)
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// some models may give a empty action (they may think reasoning and action should be separate)
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if (!parsedArgs.action) {
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console.log(chalk.yellow('lenientParseMacroToolCall: #2 fixing incorrect tool call'))
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parsedArgs.action = {
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wait: { seconds: 1 },
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}
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}
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} else if (parsedArgs.type && parsedArgs.function) {
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// case: upper level function call format provided. only keep its arguments
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// TODO: check if function name is a valid action name
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if (parsedArgs.function.name !== 'AgentOutput')
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throw new InvokeError(
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InvokeErrorType.INVALID_TOOL_ARGS,
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`Expected function name "AgentOutput", got "${parsedArgs.function.name}"`,
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null
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)
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console.log(chalk.yellow('lenientParseMacroToolCall: #3 fixing incorrect tool call'))
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parsedArgs = parsedArgs.function.arguments
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} else if (parsedArgs.name && parsedArgs.arguments) {
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// case: upper level function call format provided. only keep its arguments
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// TODO: check if function name is a valid action name
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if (parsedArgs.name !== 'AgentOutput')
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throw new InvokeError(
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InvokeErrorType.INVALID_TOOL_ARGS,
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`Expected function name "AgentOutput", got "${parsedArgs.name}"`,
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null
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)
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console.log(chalk.yellow('lenientParseMacroToolCall: #4 fixing incorrect tool call'))
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parsedArgs = parsedArgs.arguments
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} else {
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// case: only action parameters provided, wrap into MacroToolInput
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// TODO: check if action name is valid
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console.log(chalk.yellow('lenientParseMacroToolCall: #5 fixing incorrect tool call'))
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parsedArgs = { action: parsedArgs } as MacroToolInput
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}
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// make sure it's not wrapped as string
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if (typeof parsedArgs === 'string') {
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console.log(chalk.yellow('lenientParseMacroToolCall: #6 fixing incorrect tool call'))
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try {
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parsedArgs = JSON.parse(parsedArgs)
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} catch (error) {
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throw new InvokeError(
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InvokeErrorType.INVALID_TOOL_ARGS,
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'Failed to parse nested tool arguments as JSON',
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error
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)
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}
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}
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const validation = inputSchema.safeParse(parsedArgs)
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if (validation.success) {
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return validation.data as unknown as MacroToolInput
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} else {
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const action = parsedArgs.action ?? {}
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const actionName = Object.keys(action)[0] || 'unknown'
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const actionArgs = JSON.stringify(action[actionName] || 'unknown')
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// TODO: check if action name is valid. give a readable error message
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throw new InvokeError(
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InvokeErrorType.INVALID_TOOL_ARGS,
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`Tool arguments validation failed: action "${actionName}" with args ${actionArgs}`,
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validation.error
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)
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}
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}
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export function modelPatch(body: Record<string, any>) {
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const model: string = body.model || ''
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if (model.toLowerCase().startsWith('claude')) {
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body.tool_choice = { type: 'tool', name: 'AgentOutput' }
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body.thinking = { type: 'disabled' }
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// body.reasoning = { enabled: 'disabled' }
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}
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if (model.toLowerCase().includes('grok')) {
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console.log('Applying Grok patch: removing tool_choice')
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delete body.tool_choice
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console.log('Applying Grok patch: disable reasoning and thinking')
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body.thinking = { type: 'disabled', effort: 'minimal' }
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body.reasoning = { enabled: false, effort: 'low' }
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}
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return body
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}
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