chore: fork — remove vendor CI, repoint release checks to Neuron Gitea
This commit is contained in:
@@ -1,5 +0,0 @@
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This is a temporary package used primarily for GitHub Copilot compatibility.
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These DO NOT apply for openai-compatible providers or majority of providers supporting completions/responses apis. THIS IS ONLY FOR GITHUB COPILOT!!!
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Avoid making edits to these files
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@@ -1,170 +0,0 @@
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import {
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type LanguageModelV3Prompt,
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type SharedV3ProviderOptions,
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UnsupportedFunctionalityError,
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} from "@ai-sdk/provider"
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import type { OpenAICompatibleChatPrompt } from "./openai-compatible-api-types"
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import { convertToBase64 } from "@ai-sdk/provider-utils"
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function getOpenAIMetadata(message: { providerOptions?: SharedV3ProviderOptions }) {
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return message?.providerOptions?.copilot ?? {}
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}
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export function convertToOpenAICompatibleChatMessages(prompt: LanguageModelV3Prompt): OpenAICompatibleChatPrompt {
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const messages: OpenAICompatibleChatPrompt = []
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for (const { role, content, ...message } of prompt) {
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const metadata = getOpenAIMetadata({ ...message })
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switch (role) {
|
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case "system": {
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messages.push({
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role: "system",
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content: content,
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...metadata,
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})
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break
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}
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case "user": {
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if (content.length === 1 && content[0].type === "text") {
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messages.push({
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role: "user",
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content: content[0].text,
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...getOpenAIMetadata(content[0]),
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})
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break
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}
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|
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messages.push({
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role: "user",
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content: content.map((part) => {
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const partMetadata = getOpenAIMetadata(part)
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switch (part.type) {
|
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case "text": {
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return { type: "text", text: part.text, ...partMetadata }
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}
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case "file": {
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if (part.mediaType.startsWith("image/")) {
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const mediaType = part.mediaType === "image/*" ? "image/jpeg" : part.mediaType
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|
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return {
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type: "image_url",
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image_url: {
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url:
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part.data instanceof URL
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? part.data.toString()
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: `data:${mediaType};base64,${convertToBase64(part.data)}`,
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},
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...partMetadata,
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}
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} else {
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throw new UnsupportedFunctionalityError({
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functionality: `file part media type ${part.mediaType}`,
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})
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}
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}
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}
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}),
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...metadata,
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})
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break
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}
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case "assistant": {
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let text = ""
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let reasoningText: string | undefined
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let reasoningOpaque: string | undefined
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const toolCalls: Array<{
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id: string
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type: "function"
|
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function: { name: string; arguments: string }
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}> = []
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|
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for (const part of content) {
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const partMetadata = getOpenAIMetadata(part)
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// Check for reasoningOpaque on any part (may be attached to text/tool-call)
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const partOpaque = (part.providerOptions as { copilot?: { reasoningOpaque?: string } })?.copilot
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?.reasoningOpaque
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if (partOpaque && !reasoningOpaque) {
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reasoningOpaque = partOpaque
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}
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|
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switch (part.type) {
|
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case "text": {
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text += part.text
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break
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}
|
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case "reasoning": {
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if (part.text) reasoningText = part.text
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break
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}
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case "tool-call": {
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toolCalls.push({
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id: part.toolCallId,
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type: "function",
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function: {
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name: part.toolName,
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arguments: JSON.stringify(part.input),
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},
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...partMetadata,
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})
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break
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||||
}
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||||
}
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}
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|
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messages.push({
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role: "assistant",
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content: text || null,
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tool_calls: toolCalls.length > 0 ? toolCalls : undefined,
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reasoning_text: reasoningOpaque ? reasoningText : undefined,
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reasoning_opaque: reasoningOpaque,
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...metadata,
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})
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break
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}
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||||
|
||||
case "tool": {
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||||
for (const toolResponse of content) {
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if (toolResponse.type === "tool-approval-response") {
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continue
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}
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const output = toolResponse.output
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|
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let contentValue: string
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switch (output.type) {
|
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case "text":
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case "error-text":
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||||
contentValue = output.value
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break
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||||
case "execution-denied":
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contentValue = output.reason ?? "Tool execution denied."
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break
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case "content":
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||||
case "json":
|
||||
case "error-json":
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||||
contentValue = JSON.stringify(output.value)
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break
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||||
}
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const toolResponseMetadata = getOpenAIMetadata(toolResponse)
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messages.push({
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role: "tool",
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tool_call_id: toolResponse.toolCallId,
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content: contentValue,
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...toolResponseMetadata,
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})
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||||
}
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break
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}
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default: {
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const _exhaustiveCheck: never = role
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throw new Error(`Unsupported role: ${_exhaustiveCheck}`)
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}
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}
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}
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return messages
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}
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@@ -1,15 +0,0 @@
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export function getResponseMetadata({
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id,
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model,
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created,
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}: {
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id?: string | undefined | null
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||||
created?: number | undefined | null
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||||
model?: string | undefined | null
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||||
}) {
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||||
return {
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id: id ?? undefined,
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modelId: model ?? undefined,
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timestamp: created != null ? new Date(created * 1000) : undefined,
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||||
}
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}
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@@ -1,19 +0,0 @@
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import type { LanguageModelV3FinishReason } from "@ai-sdk/provider"
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export function mapOpenAICompatibleFinishReason(
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finishReason: string | null | undefined,
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): LanguageModelV3FinishReason["unified"] {
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switch (finishReason) {
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||||
case "stop":
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||||
return "stop"
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||||
case "length":
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||||
return "length"
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||||
case "content_filter":
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return "content-filter"
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case "function_call":
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case "tool_calls":
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return "tool-calls"
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default:
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return "other"
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||||
}
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||||
}
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@@ -1,64 +0,0 @@
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import type { JSONValue } from "@ai-sdk/provider"
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export type OpenAICompatibleChatPrompt = Array<OpenAICompatibleMessage>
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export type OpenAICompatibleMessage =
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| OpenAICompatibleSystemMessage
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| OpenAICompatibleUserMessage
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| OpenAICompatibleAssistantMessage
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| OpenAICompatibleToolMessage
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// Allow for arbitrary additional properties for general purpose
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// provider-metadata-specific extensibility.
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type JsonRecord<T = never> = Record<string, JSONValue | JSONValue[] | T | T[] | undefined>
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export interface OpenAICompatibleSystemMessage extends JsonRecord<OpenAICompatibleSystemContentPart> {
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role: "system"
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content: string | Array<OpenAICompatibleSystemContentPart>
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}
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export interface OpenAICompatibleSystemContentPart extends JsonRecord {
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type: "text"
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text: string
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}
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export interface OpenAICompatibleUserMessage extends JsonRecord<OpenAICompatibleContentPart> {
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role: "user"
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content: string | Array<OpenAICompatibleContentPart>
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}
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export type OpenAICompatibleContentPart = OpenAICompatibleContentPartText | OpenAICompatibleContentPartImage
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export interface OpenAICompatibleContentPartImage extends JsonRecord {
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type: "image_url"
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image_url: { url: string }
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}
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export interface OpenAICompatibleContentPartText extends JsonRecord {
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type: "text"
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text: string
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}
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export interface OpenAICompatibleAssistantMessage extends JsonRecord<OpenAICompatibleMessageToolCall> {
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role: "assistant"
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content?: string | null
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tool_calls?: Array<OpenAICompatibleMessageToolCall>
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// Copilot-specific reasoning fields
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reasoning_text?: string
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reasoning_opaque?: string
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}
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export interface OpenAICompatibleMessageToolCall extends JsonRecord {
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type: "function"
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||||
id: string
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||||
function: {
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arguments: string
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name: string
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||||
}
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||||
}
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||||
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||||
export interface OpenAICompatibleToolMessage extends JsonRecord {
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role: "tool"
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content: string
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tool_call_id: string
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}
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@@ -1,815 +0,0 @@
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import {
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||||
APICallError,
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InvalidResponseDataError,
|
||||
type LanguageModelV3,
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type LanguageModelV3CallOptions,
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type LanguageModelV3Content,
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type LanguageModelV3StreamPart,
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type SharedV3ProviderMetadata,
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||||
type SharedV3Warning,
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||||
} from "@ai-sdk/provider"
|
||||
import {
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||||
combineHeaders,
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||||
createEventSourceResponseHandler,
|
||||
createJsonErrorResponseHandler,
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||||
createJsonResponseHandler,
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type FetchFunction,
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||||
generateId,
|
||||
isParsableJson,
|
||||
parseProviderOptions,
|
||||
type ParseResult,
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||||
postJsonToApi,
|
||||
type ResponseHandler,
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||||
} from "@ai-sdk/provider-utils"
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||||
import { z } from "zod/v4"
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||||
import { convertToOpenAICompatibleChatMessages } from "./convert-to-openai-compatible-chat-messages"
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import { getResponseMetadata } from "./get-response-metadata"
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||||
import { mapOpenAICompatibleFinishReason } from "./map-openai-compatible-finish-reason"
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import { type OpenAICompatibleChatModelId, openaiCompatibleProviderOptions } from "./openai-compatible-chat-options"
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||||
import { defaultOpenAICompatibleErrorStructure, type ProviderErrorStructure } from "../openai-compatible-error"
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||||
import type { MetadataExtractor } from "./openai-compatible-metadata-extractor"
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||||
import { prepareTools } from "./openai-compatible-prepare-tools"
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||||
|
||||
export type OpenAICompatibleChatConfig = {
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||||
provider: string
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||||
headers: () => Record<string, string | undefined>
|
||||
url: (options: { modelId: string; path: string }) => string
|
||||
fetch?: FetchFunction
|
||||
includeUsage?: boolean
|
||||
errorStructure?: ProviderErrorStructure<any>
|
||||
metadataExtractor?: MetadataExtractor
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||||
|
||||
/**
|
||||
* Whether the model supports structured outputs.
|
||||
*/
|
||||
supportsStructuredOutputs?: boolean
|
||||
|
||||
/**
|
||||
* The supported URLs for the model.
|
||||
*/
|
||||
supportedUrls?: () => LanguageModelV3["supportedUrls"]
|
||||
}
|
||||
|
||||
export class OpenAICompatibleChatLanguageModel implements LanguageModelV3 {
|
||||
readonly specificationVersion = "v3"
|
||||
|
||||
readonly supportsStructuredOutputs: boolean
|
||||
|
||||
readonly modelId: OpenAICompatibleChatModelId
|
||||
private readonly config: OpenAICompatibleChatConfig
|
||||
private readonly failedResponseHandler: ResponseHandler<APICallError>
|
||||
private readonly chunkSchema // type inferred via constructor
|
||||
|
||||
constructor(modelId: OpenAICompatibleChatModelId, config: OpenAICompatibleChatConfig) {
|
||||
this.modelId = modelId
|
||||
this.config = config
|
||||
|
||||
// initialize error handling:
|
||||
const errorStructure = config.errorStructure ?? defaultOpenAICompatibleErrorStructure
|
||||
this.chunkSchema = createOpenAICompatibleChatChunkSchema(errorStructure.errorSchema)
|
||||
this.failedResponseHandler = createJsonErrorResponseHandler(errorStructure)
|
||||
|
||||
this.supportsStructuredOutputs = config.supportsStructuredOutputs ?? false
|
||||
}
|
||||
|
||||
get provider(): string {
|
||||
return this.config.provider
|
||||
}
|
||||
|
||||
private get providerOptionsName(): string {
|
||||
return this.config.provider.split(".")[0].trim()
|
||||
}
|
||||
|
||||
get supportedUrls() {
|
||||
return this.config.supportedUrls?.() ?? {}
|
||||
}
|
||||
|
||||
private async getArgs({
|
||||
prompt,
|
||||
maxOutputTokens,
|
||||
temperature,
|
||||
topP,
|
||||
topK,
|
||||
frequencyPenalty,
|
||||
presencePenalty,
|
||||
providerOptions,
|
||||
stopSequences,
|
||||
responseFormat,
|
||||
seed,
|
||||
toolChoice,
|
||||
tools,
|
||||
}: LanguageModelV3CallOptions) {
|
||||
const warnings: SharedV3Warning[] = []
|
||||
|
||||
// Parse provider options
|
||||
const compatibleOptions = Object.assign(
|
||||
(await parseProviderOptions({
|
||||
provider: "copilot",
|
||||
providerOptions,
|
||||
schema: openaiCompatibleProviderOptions,
|
||||
})) ?? {},
|
||||
(await parseProviderOptions({
|
||||
provider: this.providerOptionsName,
|
||||
providerOptions,
|
||||
schema: openaiCompatibleProviderOptions,
|
||||
})) ?? {},
|
||||
)
|
||||
|
||||
if (topK != null) {
|
||||
warnings.push({ type: "unsupported", feature: "topK" })
|
||||
}
|
||||
|
||||
if (responseFormat?.type === "json" && responseFormat.schema != null && !this.supportsStructuredOutputs) {
|
||||
warnings.push({
|
||||
type: "unsupported",
|
||||
feature: "responseFormat",
|
||||
details: "JSON response format schema is only supported with structuredOutputs",
|
||||
})
|
||||
}
|
||||
|
||||
const {
|
||||
tools: openaiTools,
|
||||
toolChoice: openaiToolChoice,
|
||||
toolWarnings,
|
||||
} = prepareTools({
|
||||
tools,
|
||||
toolChoice,
|
||||
})
|
||||
|
||||
return {
|
||||
args: {
|
||||
// model id:
|
||||
model: this.modelId,
|
||||
|
||||
// model specific settings:
|
||||
user: compatibleOptions.user,
|
||||
|
||||
// standardized settings:
|
||||
max_tokens: maxOutputTokens,
|
||||
temperature,
|
||||
top_p: topP,
|
||||
frequency_penalty: frequencyPenalty,
|
||||
presence_penalty: presencePenalty,
|
||||
response_format:
|
||||
responseFormat?.type === "json"
|
||||
? this.supportsStructuredOutputs === true && responseFormat.schema != null
|
||||
? {
|
||||
type: "json_schema",
|
||||
json_schema: {
|
||||
schema: responseFormat.schema,
|
||||
name: responseFormat.name ?? "response",
|
||||
description: responseFormat.description,
|
||||
},
|
||||
}
|
||||
: { type: "json_object" }
|
||||
: undefined,
|
||||
|
||||
stop: stopSequences,
|
||||
seed,
|
||||
...Object.fromEntries(
|
||||
Object.entries(providerOptions?.[this.providerOptionsName] ?? {}).filter(
|
||||
([key]) => !Object.keys(openaiCompatibleProviderOptions.shape).includes(key),
|
||||
),
|
||||
),
|
||||
|
||||
reasoning_effort: compatibleOptions.reasoningEffort,
|
||||
verbosity: compatibleOptions.textVerbosity,
|
||||
|
||||
// messages:
|
||||
messages: convertToOpenAICompatibleChatMessages(prompt),
|
||||
|
||||
// tools:
|
||||
tools: openaiTools,
|
||||
tool_choice: openaiToolChoice,
|
||||
|
||||
// thinking_budget
|
||||
thinking_budget: compatibleOptions.thinking_budget,
|
||||
},
|
||||
warnings: [...warnings, ...toolWarnings],
|
||||
}
|
||||
}
|
||||
|
||||
async doGenerate(options: LanguageModelV3CallOptions) {
|
||||
const { args, warnings } = await this.getArgs({ ...options })
|
||||
|
||||
const body = JSON.stringify(args)
|
||||
|
||||
const {
|
||||
responseHeaders,
|
||||
value: responseBody,
|
||||
rawValue: rawResponse,
|
||||
} = await postJsonToApi({
|
||||
url: this.config.url({
|
||||
path: "/chat/completions",
|
||||
modelId: this.modelId,
|
||||
}),
|
||||
headers: combineHeaders(this.config.headers(), options.headers),
|
||||
body: args,
|
||||
failedResponseHandler: this.failedResponseHandler,
|
||||
successfulResponseHandler: createJsonResponseHandler(OpenAICompatibleChatResponseSchema),
|
||||
abortSignal: options.abortSignal,
|
||||
fetch: this.config.fetch,
|
||||
})
|
||||
|
||||
const choice = responseBody.choices[0]
|
||||
const content: Array<LanguageModelV3Content> = []
|
||||
|
||||
// text content:
|
||||
const text = choice.message.content
|
||||
if (text != null && text.length > 0) {
|
||||
content.push({
|
||||
type: "text",
|
||||
text,
|
||||
providerMetadata: choice.message.reasoning_opaque
|
||||
? { copilot: { reasoningOpaque: choice.message.reasoning_opaque } }
|
||||
: undefined,
|
||||
})
|
||||
}
|
||||
|
||||
// reasoning content (Copilot uses reasoning_text):
|
||||
const reasoning = choice.message.reasoning_text
|
||||
if (reasoning != null && reasoning.length > 0) {
|
||||
content.push({
|
||||
type: "reasoning",
|
||||
text: reasoning,
|
||||
// Include reasoning_opaque for Copilot multi-turn reasoning
|
||||
providerMetadata: choice.message.reasoning_opaque
|
||||
? { copilot: { reasoningOpaque: choice.message.reasoning_opaque } }
|
||||
: undefined,
|
||||
})
|
||||
}
|
||||
|
||||
// tool calls:
|
||||
if (choice.message.tool_calls != null) {
|
||||
for (const toolCall of choice.message.tool_calls) {
|
||||
content.push({
|
||||
type: "tool-call",
|
||||
toolCallId: toolCall.id ?? generateId(),
|
||||
toolName: toolCall.function.name,
|
||||
input: toolCall.function.arguments!,
|
||||
providerMetadata: choice.message.reasoning_opaque
|
||||
? { copilot: { reasoningOpaque: choice.message.reasoning_opaque } }
|
||||
: undefined,
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
// provider metadata:
|
||||
const providerMetadata: SharedV3ProviderMetadata = {
|
||||
[this.providerOptionsName]: {},
|
||||
...(await this.config.metadataExtractor?.extractMetadata?.({
|
||||
parsedBody: rawResponse,
|
||||
})),
|
||||
}
|
||||
const completionTokenDetails = responseBody.usage?.completion_tokens_details
|
||||
if (completionTokenDetails?.accepted_prediction_tokens != null) {
|
||||
providerMetadata[this.providerOptionsName].acceptedPredictionTokens =
|
||||
completionTokenDetails?.accepted_prediction_tokens
|
||||
}
|
||||
if (completionTokenDetails?.rejected_prediction_tokens != null) {
|
||||
providerMetadata[this.providerOptionsName].rejectedPredictionTokens =
|
||||
completionTokenDetails?.rejected_prediction_tokens
|
||||
}
|
||||
|
||||
return {
|
||||
content,
|
||||
finishReason: {
|
||||
unified: mapOpenAICompatibleFinishReason(choice.finish_reason),
|
||||
raw: choice.finish_reason ?? undefined,
|
||||
},
|
||||
usage: {
|
||||
inputTokens: {
|
||||
total: responseBody.usage?.prompt_tokens ?? undefined,
|
||||
noCache: undefined,
|
||||
cacheRead: responseBody.usage?.prompt_tokens_details?.cached_tokens ?? undefined,
|
||||
cacheWrite: undefined,
|
||||
},
|
||||
outputTokens: {
|
||||
total: responseBody.usage?.completion_tokens ?? undefined,
|
||||
text: undefined,
|
||||
reasoning: responseBody.usage?.completion_tokens_details?.reasoning_tokens ?? undefined,
|
||||
},
|
||||
raw: responseBody.usage ?? undefined,
|
||||
},
|
||||
providerMetadata,
|
||||
request: { body },
|
||||
response: {
|
||||
...getResponseMetadata(responseBody),
|
||||
headers: responseHeaders,
|
||||
body: rawResponse,
|
||||
},
|
||||
warnings,
|
||||
}
|
||||
}
|
||||
|
||||
async doStream(options: LanguageModelV3CallOptions) {
|
||||
const { args, warnings } = await this.getArgs({ ...options })
|
||||
|
||||
const body = {
|
||||
...args,
|
||||
stream: true,
|
||||
|
||||
// only include stream_options when in strict compatibility mode:
|
||||
stream_options: this.config.includeUsage ? { include_usage: true } : undefined,
|
||||
}
|
||||
|
||||
const metadataExtractor = this.config.metadataExtractor?.createStreamExtractor()
|
||||
|
||||
const { responseHeaders, value: response } = await postJsonToApi({
|
||||
url: this.config.url({
|
||||
path: "/chat/completions",
|
||||
modelId: this.modelId,
|
||||
}),
|
||||
headers: combineHeaders(this.config.headers(), options.headers),
|
||||
body,
|
||||
failedResponseHandler: this.failedResponseHandler,
|
||||
successfulResponseHandler: createEventSourceResponseHandler(this.chunkSchema),
|
||||
abortSignal: options.abortSignal,
|
||||
fetch: this.config.fetch,
|
||||
})
|
||||
|
||||
const toolCalls: Array<{
|
||||
id: string
|
||||
type: "function"
|
||||
function: {
|
||||
name: string
|
||||
arguments: string
|
||||
}
|
||||
hasFinished: boolean
|
||||
}> = []
|
||||
|
||||
let finishReason: {
|
||||
unified: ReturnType<typeof mapOpenAICompatibleFinishReason>
|
||||
raw: string | undefined
|
||||
} = {
|
||||
unified: "other",
|
||||
raw: undefined,
|
||||
}
|
||||
const usage: {
|
||||
completionTokens: number | undefined
|
||||
completionTokensDetails: {
|
||||
reasoningTokens: number | undefined
|
||||
acceptedPredictionTokens: number | undefined
|
||||
rejectedPredictionTokens: number | undefined
|
||||
}
|
||||
promptTokens: number | undefined
|
||||
promptTokensDetails: {
|
||||
cachedTokens: number | undefined
|
||||
}
|
||||
totalTokens: number | undefined
|
||||
} = {
|
||||
completionTokens: undefined,
|
||||
completionTokensDetails: {
|
||||
reasoningTokens: undefined,
|
||||
acceptedPredictionTokens: undefined,
|
||||
rejectedPredictionTokens: undefined,
|
||||
},
|
||||
promptTokens: undefined,
|
||||
promptTokensDetails: {
|
||||
cachedTokens: undefined,
|
||||
},
|
||||
totalTokens: undefined,
|
||||
}
|
||||
let isFirstChunk = true
|
||||
const providerOptionsName = this.providerOptionsName
|
||||
let isActiveReasoning = false
|
||||
let isActiveText = false
|
||||
let reasoningOpaque: string | undefined
|
||||
|
||||
return {
|
||||
stream: response.pipeThrough(
|
||||
new TransformStream<ParseResult<z.infer<typeof this.chunkSchema>>, LanguageModelV3StreamPart>({
|
||||
start(controller) {
|
||||
controller.enqueue({ type: "stream-start", warnings })
|
||||
},
|
||||
|
||||
// TODO we lost type safety on Chunk, most likely due to the error schema. MUST FIX
|
||||
transform(chunk, controller) {
|
||||
// Emit raw chunk if requested (before anything else)
|
||||
if (options.includeRawChunks) {
|
||||
controller.enqueue({ type: "raw", rawValue: chunk.rawValue })
|
||||
}
|
||||
|
||||
// handle failed chunk parsing / validation:
|
||||
if (!chunk.success) {
|
||||
finishReason = {
|
||||
unified: "error",
|
||||
raw: undefined,
|
||||
}
|
||||
controller.enqueue({ type: "error", error: chunk.error })
|
||||
return
|
||||
}
|
||||
const value = chunk.value
|
||||
|
||||
metadataExtractor?.processChunk(chunk.rawValue)
|
||||
|
||||
// handle error chunks:
|
||||
if ("error" in value) {
|
||||
finishReason = {
|
||||
unified: "error",
|
||||
raw: undefined,
|
||||
}
|
||||
controller.enqueue({ type: "error", error: value.error.message })
|
||||
return
|
||||
}
|
||||
|
||||
if (isFirstChunk) {
|
||||
isFirstChunk = false
|
||||
|
||||
controller.enqueue({
|
||||
type: "response-metadata",
|
||||
...getResponseMetadata(value),
|
||||
})
|
||||
}
|
||||
|
||||
if (value.usage != null) {
|
||||
const {
|
||||
prompt_tokens,
|
||||
completion_tokens,
|
||||
total_tokens,
|
||||
prompt_tokens_details,
|
||||
completion_tokens_details,
|
||||
} = value.usage
|
||||
|
||||
usage.promptTokens = prompt_tokens ?? undefined
|
||||
usage.completionTokens = completion_tokens ?? undefined
|
||||
usage.totalTokens = total_tokens ?? undefined
|
||||
if (completion_tokens_details?.reasoning_tokens != null) {
|
||||
usage.completionTokensDetails.reasoningTokens = completion_tokens_details?.reasoning_tokens
|
||||
}
|
||||
if (completion_tokens_details?.accepted_prediction_tokens != null) {
|
||||
usage.completionTokensDetails.acceptedPredictionTokens =
|
||||
completion_tokens_details?.accepted_prediction_tokens
|
||||
}
|
||||
if (completion_tokens_details?.rejected_prediction_tokens != null) {
|
||||
usage.completionTokensDetails.rejectedPredictionTokens =
|
||||
completion_tokens_details?.rejected_prediction_tokens
|
||||
}
|
||||
if (prompt_tokens_details?.cached_tokens != null) {
|
||||
usage.promptTokensDetails.cachedTokens = prompt_tokens_details?.cached_tokens
|
||||
}
|
||||
}
|
||||
|
||||
const choice = value.choices[0]
|
||||
|
||||
if (choice?.finish_reason != null) {
|
||||
finishReason = {
|
||||
unified: mapOpenAICompatibleFinishReason(choice.finish_reason),
|
||||
raw: choice.finish_reason ?? undefined,
|
||||
}
|
||||
}
|
||||
|
||||
if (choice?.delta == null) {
|
||||
return
|
||||
}
|
||||
|
||||
const delta = choice.delta
|
||||
|
||||
// Capture reasoning_opaque for Copilot multi-turn reasoning
|
||||
if (delta.reasoning_opaque) {
|
||||
if (reasoningOpaque != null) {
|
||||
throw new InvalidResponseDataError({
|
||||
data: delta,
|
||||
message:
|
||||
"Multiple reasoning_opaque values received in a single response. Only one thinking part per response is supported.",
|
||||
})
|
||||
}
|
||||
reasoningOpaque = delta.reasoning_opaque
|
||||
}
|
||||
|
||||
// enqueue reasoning before text deltas (Copilot uses reasoning_text):
|
||||
const reasoningContent = delta.reasoning_text
|
||||
if (reasoningContent) {
|
||||
if (!isActiveReasoning) {
|
||||
controller.enqueue({
|
||||
type: "reasoning-start",
|
||||
id: "reasoning-0",
|
||||
})
|
||||
isActiveReasoning = true
|
||||
}
|
||||
|
||||
controller.enqueue({
|
||||
type: "reasoning-delta",
|
||||
id: "reasoning-0",
|
||||
delta: reasoningContent,
|
||||
})
|
||||
}
|
||||
|
||||
if (delta.content) {
|
||||
// If reasoning was active and we're starting text, end reasoning first
|
||||
// This handles the case where reasoning_opaque and content come in the same chunk
|
||||
if (isActiveReasoning && !isActiveText) {
|
||||
controller.enqueue({
|
||||
type: "reasoning-end",
|
||||
id: "reasoning-0",
|
||||
providerMetadata: reasoningOpaque ? { copilot: { reasoningOpaque } } : undefined,
|
||||
})
|
||||
isActiveReasoning = false
|
||||
}
|
||||
|
||||
if (!isActiveText) {
|
||||
controller.enqueue({
|
||||
type: "text-start",
|
||||
id: "txt-0",
|
||||
providerMetadata: reasoningOpaque ? { copilot: { reasoningOpaque } } : undefined,
|
||||
})
|
||||
isActiveText = true
|
||||
}
|
||||
|
||||
controller.enqueue({
|
||||
type: "text-delta",
|
||||
id: "txt-0",
|
||||
delta: delta.content,
|
||||
})
|
||||
}
|
||||
|
||||
if (delta.tool_calls != null) {
|
||||
// If reasoning was active and we're starting tool calls, end reasoning first
|
||||
// This handles the case where reasoning goes directly to tool calls with no content
|
||||
if (isActiveReasoning) {
|
||||
controller.enqueue({
|
||||
type: "reasoning-end",
|
||||
id: "reasoning-0",
|
||||
providerMetadata: reasoningOpaque ? { copilot: { reasoningOpaque } } : undefined,
|
||||
})
|
||||
isActiveReasoning = false
|
||||
}
|
||||
for (const toolCallDelta of delta.tool_calls) {
|
||||
const index = toolCallDelta.index
|
||||
|
||||
if (toolCalls[index] == null) {
|
||||
if (toolCallDelta.id == null) {
|
||||
throw new InvalidResponseDataError({
|
||||
data: toolCallDelta,
|
||||
message: `Expected 'id' to be a string.`,
|
||||
})
|
||||
}
|
||||
|
||||
if (toolCallDelta.function?.name == null) {
|
||||
throw new InvalidResponseDataError({
|
||||
data: toolCallDelta,
|
||||
message: `Expected 'function.name' to be a string.`,
|
||||
})
|
||||
}
|
||||
|
||||
controller.enqueue({
|
||||
type: "tool-input-start",
|
||||
id: toolCallDelta.id,
|
||||
toolName: toolCallDelta.function.name,
|
||||
})
|
||||
|
||||
toolCalls[index] = {
|
||||
id: toolCallDelta.id,
|
||||
type: "function",
|
||||
function: {
|
||||
name: toolCallDelta.function.name,
|
||||
arguments: toolCallDelta.function.arguments ?? "",
|
||||
},
|
||||
hasFinished: false,
|
||||
}
|
||||
|
||||
const toolCall = toolCalls[index]
|
||||
|
||||
if (toolCall.function?.name != null && toolCall.function?.arguments != null) {
|
||||
// send delta if the argument text has already started:
|
||||
if (toolCall.function.arguments.length > 0) {
|
||||
controller.enqueue({
|
||||
type: "tool-input-delta",
|
||||
id: toolCall.id,
|
||||
delta: toolCall.function.arguments,
|
||||
})
|
||||
}
|
||||
|
||||
// check if tool call is complete
|
||||
// (some providers send the full tool call in one chunk):
|
||||
if (isParsableJson(toolCall.function.arguments)) {
|
||||
controller.enqueue({
|
||||
type: "tool-input-end",
|
||||
id: toolCall.id,
|
||||
})
|
||||
|
||||
controller.enqueue({
|
||||
type: "tool-call",
|
||||
toolCallId: toolCall.id ?? generateId(),
|
||||
toolName: toolCall.function.name,
|
||||
input: toolCall.function.arguments,
|
||||
providerMetadata: reasoningOpaque ? { copilot: { reasoningOpaque } } : undefined,
|
||||
})
|
||||
toolCall.hasFinished = true
|
||||
}
|
||||
}
|
||||
|
||||
continue
|
||||
}
|
||||
|
||||
// existing tool call, merge if not finished
|
||||
const toolCall = toolCalls[index]
|
||||
|
||||
if (toolCall.hasFinished) {
|
||||
continue
|
||||
}
|
||||
|
||||
if (toolCallDelta.function?.arguments != null) {
|
||||
toolCall.function!.arguments += toolCallDelta.function?.arguments ?? ""
|
||||
}
|
||||
|
||||
// send delta
|
||||
controller.enqueue({
|
||||
type: "tool-input-delta",
|
||||
id: toolCall.id,
|
||||
delta: toolCallDelta.function.arguments ?? "",
|
||||
})
|
||||
|
||||
// check if tool call is complete
|
||||
if (
|
||||
toolCall.function?.name != null &&
|
||||
toolCall.function?.arguments != null &&
|
||||
isParsableJson(toolCall.function.arguments)
|
||||
) {
|
||||
controller.enqueue({
|
||||
type: "tool-input-end",
|
||||
id: toolCall.id,
|
||||
})
|
||||
|
||||
controller.enqueue({
|
||||
type: "tool-call",
|
||||
toolCallId: toolCall.id ?? generateId(),
|
||||
toolName: toolCall.function.name,
|
||||
input: toolCall.function.arguments,
|
||||
providerMetadata: reasoningOpaque ? { copilot: { reasoningOpaque } } : undefined,
|
||||
})
|
||||
toolCall.hasFinished = true
|
||||
}
|
||||
}
|
||||
}
|
||||
},
|
||||
|
||||
flush(controller) {
|
||||
if (isActiveReasoning) {
|
||||
controller.enqueue({
|
||||
type: "reasoning-end",
|
||||
id: "reasoning-0",
|
||||
// Include reasoning_opaque for Copilot multi-turn reasoning
|
||||
providerMetadata: reasoningOpaque ? { copilot: { reasoningOpaque } } : undefined,
|
||||
})
|
||||
}
|
||||
|
||||
if (isActiveText) {
|
||||
controller.enqueue({ type: "text-end", id: "txt-0" })
|
||||
}
|
||||
|
||||
// go through all tool calls and send the ones that are not finished
|
||||
for (const toolCall of toolCalls.filter((toolCall) => !toolCall.hasFinished)) {
|
||||
controller.enqueue({
|
||||
type: "tool-input-end",
|
||||
id: toolCall.id,
|
||||
})
|
||||
|
||||
controller.enqueue({
|
||||
type: "tool-call",
|
||||
toolCallId: toolCall.id ?? generateId(),
|
||||
toolName: toolCall.function.name,
|
||||
input: toolCall.function.arguments,
|
||||
})
|
||||
}
|
||||
|
||||
const providerMetadata: SharedV3ProviderMetadata = {
|
||||
[providerOptionsName]: {},
|
||||
// Include reasoning_opaque for Copilot multi-turn reasoning
|
||||
...(reasoningOpaque ? { copilot: { reasoningOpaque } } : {}),
|
||||
...metadataExtractor?.buildMetadata(),
|
||||
}
|
||||
if (usage.completionTokensDetails.acceptedPredictionTokens != null) {
|
||||
providerMetadata[providerOptionsName].acceptedPredictionTokens =
|
||||
usage.completionTokensDetails.acceptedPredictionTokens
|
||||
}
|
||||
if (usage.completionTokensDetails.rejectedPredictionTokens != null) {
|
||||
providerMetadata[providerOptionsName].rejectedPredictionTokens =
|
||||
usage.completionTokensDetails.rejectedPredictionTokens
|
||||
}
|
||||
|
||||
controller.enqueue({
|
||||
type: "finish",
|
||||
finishReason,
|
||||
usage: {
|
||||
inputTokens: {
|
||||
total: usage.promptTokens,
|
||||
noCache:
|
||||
usage.promptTokens != undefined && usage.promptTokensDetails.cachedTokens != undefined
|
||||
? usage.promptTokens - usage.promptTokensDetails.cachedTokens
|
||||
: undefined,
|
||||
cacheRead: usage.promptTokensDetails.cachedTokens,
|
||||
cacheWrite: undefined,
|
||||
},
|
||||
outputTokens: {
|
||||
total: usage.completionTokens,
|
||||
text: undefined,
|
||||
reasoning: usage.completionTokensDetails.reasoningTokens,
|
||||
},
|
||||
raw: {
|
||||
prompt_tokens: usage.promptTokens ?? null,
|
||||
completion_tokens: usage.completionTokens ?? null,
|
||||
total_tokens: usage.totalTokens ?? null,
|
||||
},
|
||||
},
|
||||
providerMetadata,
|
||||
})
|
||||
},
|
||||
}),
|
||||
),
|
||||
request: { body },
|
||||
response: { headers: responseHeaders },
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
const openaiCompatibleTokenUsageSchema = z
|
||||
.object({
|
||||
prompt_tokens: z.number().nullish(),
|
||||
completion_tokens: z.number().nullish(),
|
||||
total_tokens: z.number().nullish(),
|
||||
prompt_tokens_details: z
|
||||
.object({
|
||||
cached_tokens: z.number().nullish(),
|
||||
})
|
||||
.nullish(),
|
||||
completion_tokens_details: z
|
||||
.object({
|
||||
reasoning_tokens: z.number().nullish(),
|
||||
accepted_prediction_tokens: z.number().nullish(),
|
||||
rejected_prediction_tokens: z.number().nullish(),
|
||||
})
|
||||
.nullish(),
|
||||
})
|
||||
.nullish()
|
||||
|
||||
// limited version of the schema, focussed on what is needed for the implementation
|
||||
// this approach limits breakages when the API changes and increases efficiency
|
||||
const OpenAICompatibleChatResponseSchema = z.object({
|
||||
id: z.string().nullish(),
|
||||
created: z.number().nullish(),
|
||||
model: z.string().nullish(),
|
||||
choices: z.array(
|
||||
z.object({
|
||||
message: z.object({
|
||||
role: z.literal("assistant").nullish(),
|
||||
content: z.string().nullish(),
|
||||
// Copilot-specific reasoning fields
|
||||
reasoning_text: z.string().nullish(),
|
||||
reasoning_opaque: z.string().nullish(),
|
||||
tool_calls: z
|
||||
.array(
|
||||
z.object({
|
||||
id: z.string().nullish(),
|
||||
function: z.object({
|
||||
name: z.string(),
|
||||
arguments: z.string(),
|
||||
}),
|
||||
}),
|
||||
)
|
||||
.nullish(),
|
||||
}),
|
||||
finish_reason: z.string().nullish(),
|
||||
}),
|
||||
),
|
||||
usage: openaiCompatibleTokenUsageSchema,
|
||||
})
|
||||
|
||||
// limited version of the schema, focussed on what is needed for the implementation
|
||||
// this approach limits breakages when the API changes and increases efficiency
|
||||
const createOpenAICompatibleChatChunkSchema = <ERROR_SCHEMA extends z.core.$ZodType>(errorSchema: ERROR_SCHEMA) =>
|
||||
z.union([
|
||||
z.object({
|
||||
id: z.string().nullish(),
|
||||
created: z.number().nullish(),
|
||||
model: z.string().nullish(),
|
||||
choices: z.array(
|
||||
z.object({
|
||||
delta: z
|
||||
.object({
|
||||
role: z.enum(["assistant"]).nullish(),
|
||||
content: z.string().nullish(),
|
||||
// Copilot-specific reasoning fields
|
||||
reasoning_text: z.string().nullish(),
|
||||
reasoning_opaque: z.string().nullish(),
|
||||
tool_calls: z
|
||||
.array(
|
||||
z.object({
|
||||
index: z.number(),
|
||||
id: z.string().nullish(),
|
||||
function: z.object({
|
||||
name: z.string().nullish(),
|
||||
arguments: z.string().nullish(),
|
||||
}),
|
||||
}),
|
||||
)
|
||||
.nullish(),
|
||||
})
|
||||
.nullish(),
|
||||
finish_reason: z.string().nullish(),
|
||||
}),
|
||||
),
|
||||
usage: openaiCompatibleTokenUsageSchema,
|
||||
}),
|
||||
errorSchema,
|
||||
])
|
||||
@@ -1,28 +0,0 @@
|
||||
import { z } from "zod/v4"
|
||||
|
||||
export type OpenAICompatibleChatModelId = string
|
||||
|
||||
export const openaiCompatibleProviderOptions = z.object({
|
||||
/**
|
||||
* A unique identifier representing your end-user, which can help the provider to
|
||||
* monitor and detect abuse.
|
||||
*/
|
||||
user: z.string().optional(),
|
||||
|
||||
/**
|
||||
* Reasoning effort for reasoning models. Defaults to `medium`.
|
||||
*/
|
||||
reasoningEffort: z.string().optional(),
|
||||
|
||||
/**
|
||||
* Controls the verbosity of the generated text. Defaults to `medium`.
|
||||
*/
|
||||
textVerbosity: z.string().optional(),
|
||||
|
||||
/**
|
||||
* Copilot thinking_budget used for Anthropic models.
|
||||
*/
|
||||
thinking_budget: z.number().optional(),
|
||||
})
|
||||
|
||||
export type OpenAICompatibleProviderOptions = z.infer<typeof openaiCompatibleProviderOptions>
|
||||
@@ -1,44 +0,0 @@
|
||||
import type { SharedV3ProviderMetadata } from "@ai-sdk/provider"
|
||||
|
||||
/**
|
||||
Extracts provider-specific metadata from API responses.
|
||||
Used to standardize metadata handling across different LLM providers while allowing
|
||||
provider-specific metadata to be captured.
|
||||
*/
|
||||
export type MetadataExtractor = {
|
||||
/**
|
||||
* Extracts provider metadata from a complete, non-streaming response.
|
||||
*
|
||||
* @param parsedBody - The parsed response JSON body from the provider's API.
|
||||
*
|
||||
* @returns Provider-specific metadata or undefined if no metadata is available.
|
||||
* The metadata should be under a key indicating the provider id.
|
||||
*/
|
||||
extractMetadata: ({ parsedBody }: { parsedBody: unknown }) => Promise<SharedV3ProviderMetadata | undefined>
|
||||
|
||||
/**
|
||||
* Creates an extractor for handling streaming responses. The returned object provides
|
||||
* methods to process individual chunks and build the final metadata from the accumulated
|
||||
* stream data.
|
||||
*
|
||||
* @returns An object with methods to process chunks and build metadata from a stream
|
||||
*/
|
||||
createStreamExtractor: () => {
|
||||
/**
|
||||
* Process an individual chunk from the stream. Called for each chunk in the response stream
|
||||
* to accumulate metadata throughout the streaming process.
|
||||
*
|
||||
* @param parsedChunk - The parsed JSON response chunk from the provider's API
|
||||
*/
|
||||
processChunk(parsedChunk: unknown): void
|
||||
|
||||
/**
|
||||
* Builds the metadata object after all chunks have been processed.
|
||||
* Called at the end of the stream to generate the complete provider metadata.
|
||||
*
|
||||
* @returns Provider-specific metadata or undefined if no metadata is available.
|
||||
* The metadata should be under a key indicating the provider id.
|
||||
*/
|
||||
buildMetadata(): SharedV3ProviderMetadata | undefined
|
||||
}
|
||||
}
|
||||
@@ -1,83 +0,0 @@
|
||||
import { type LanguageModelV3CallOptions, type SharedV3Warning, UnsupportedFunctionalityError } from "@ai-sdk/provider"
|
||||
|
||||
export function prepareTools({
|
||||
tools,
|
||||
toolChoice,
|
||||
}: {
|
||||
tools: LanguageModelV3CallOptions["tools"]
|
||||
toolChoice?: LanguageModelV3CallOptions["toolChoice"]
|
||||
}): {
|
||||
tools:
|
||||
| undefined
|
||||
| Array<{
|
||||
type: "function"
|
||||
function: {
|
||||
name: string
|
||||
description: string | undefined
|
||||
parameters: unknown
|
||||
}
|
||||
}>
|
||||
toolChoice: { type: "function"; function: { name: string } } | "auto" | "none" | "required" | undefined
|
||||
toolWarnings: SharedV3Warning[]
|
||||
} {
|
||||
// when the tools array is empty, change it to undefined to prevent errors:
|
||||
tools = tools?.length ? tools : undefined
|
||||
|
||||
const toolWarnings: SharedV3Warning[] = []
|
||||
|
||||
if (tools == null) {
|
||||
return { tools: undefined, toolChoice: undefined, toolWarnings }
|
||||
}
|
||||
|
||||
const openaiCompatTools: Array<{
|
||||
type: "function"
|
||||
function: {
|
||||
name: string
|
||||
description: string | undefined
|
||||
parameters: unknown
|
||||
}
|
||||
}> = []
|
||||
|
||||
for (const tool of tools) {
|
||||
if (tool.type === "provider") {
|
||||
toolWarnings.push({ type: "unsupported", feature: `tool type: ${tool.type}` })
|
||||
} else {
|
||||
openaiCompatTools.push({
|
||||
type: "function",
|
||||
function: {
|
||||
name: tool.name,
|
||||
description: tool.description,
|
||||
parameters: tool.inputSchema,
|
||||
},
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
if (toolChoice == null) {
|
||||
return { tools: openaiCompatTools, toolChoice: undefined, toolWarnings }
|
||||
}
|
||||
|
||||
const type = toolChoice.type
|
||||
|
||||
switch (type) {
|
||||
case "auto":
|
||||
case "none":
|
||||
case "required":
|
||||
return { tools: openaiCompatTools, toolChoice: type, toolWarnings }
|
||||
case "tool":
|
||||
return {
|
||||
tools: openaiCompatTools,
|
||||
toolChoice: {
|
||||
type: "function",
|
||||
function: { name: toolChoice.toolName },
|
||||
},
|
||||
toolWarnings,
|
||||
}
|
||||
default: {
|
||||
const _exhaustiveCheck: never = type
|
||||
throw new UnsupportedFunctionalityError({
|
||||
functionality: `tool choice type: ${_exhaustiveCheck}`,
|
||||
})
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -1,100 +0,0 @@
|
||||
import type { LanguageModelV3 } from "@ai-sdk/provider"
|
||||
import { type FetchFunction, withoutTrailingSlash, withUserAgentSuffix } from "@ai-sdk/provider-utils"
|
||||
import { OpenAICompatibleChatLanguageModel } from "./chat/openai-compatible-chat-language-model"
|
||||
import { OpenAIResponsesLanguageModel } from "./responses/openai-responses-language-model"
|
||||
|
||||
// Import the version or define it
|
||||
const VERSION = "0.1.0"
|
||||
|
||||
export type OpenaiCompatibleModelId = string
|
||||
|
||||
export interface OpenaiCompatibleProviderSettings {
|
||||
/**
|
||||
* API key for authenticating requests.
|
||||
*/
|
||||
apiKey?: string
|
||||
|
||||
/**
|
||||
* Base URL for the OpenAI Compatible API calls.
|
||||
*/
|
||||
baseURL?: string
|
||||
|
||||
/**
|
||||
* Name of the provider.
|
||||
*/
|
||||
name?: string
|
||||
|
||||
/**
|
||||
* Custom headers to include in the requests.
|
||||
*/
|
||||
headers?: Record<string, string>
|
||||
|
||||
/**
|
||||
* Custom fetch implementation.
|
||||
*/
|
||||
fetch?: FetchFunction
|
||||
}
|
||||
|
||||
export interface OpenaiCompatibleProvider {
|
||||
(modelId: OpenaiCompatibleModelId): LanguageModelV3
|
||||
chat(modelId: OpenaiCompatibleModelId): LanguageModelV3
|
||||
responses(modelId: OpenaiCompatibleModelId): LanguageModelV3
|
||||
languageModel(modelId: OpenaiCompatibleModelId): LanguageModelV3
|
||||
|
||||
// embeddingModel(modelId: any): EmbeddingModelV2
|
||||
|
||||
// imageModel(modelId: any): ImageModelV2
|
||||
}
|
||||
|
||||
/**
|
||||
* Create an OpenAI Compatible provider instance.
|
||||
*/
|
||||
export function createOpenaiCompatible(options: OpenaiCompatibleProviderSettings = {}): OpenaiCompatibleProvider {
|
||||
const baseURL = withoutTrailingSlash(options.baseURL ?? "https://api.openai.com/v1")
|
||||
|
||||
if (!baseURL) {
|
||||
throw new Error("baseURL is required")
|
||||
}
|
||||
|
||||
// Merge headers: defaults first, then user overrides
|
||||
const headers = {
|
||||
// Default OpenAI Compatible headers (can be overridden by user)
|
||||
...(options.apiKey && { Authorization: `Bearer ${options.apiKey}` }),
|
||||
...options.headers,
|
||||
}
|
||||
|
||||
const getHeaders = () => withUserAgentSuffix(headers, `ai-sdk/openai-compatible/${VERSION}`)
|
||||
|
||||
const createChatModel = (modelId: OpenaiCompatibleModelId) => {
|
||||
return new OpenAICompatibleChatLanguageModel(modelId, {
|
||||
provider: `${options.name ?? "openai-compatible"}.chat`,
|
||||
headers: getHeaders,
|
||||
url: ({ path }) => `${baseURL}${path}`,
|
||||
fetch: options.fetch,
|
||||
})
|
||||
}
|
||||
|
||||
const createResponsesModel = (modelId: OpenaiCompatibleModelId) => {
|
||||
return new OpenAIResponsesLanguageModel(modelId, {
|
||||
provider: `${options.name ?? "openai-compatible"}.responses`,
|
||||
headers: getHeaders,
|
||||
url: ({ path }) => `${baseURL}${path}`,
|
||||
fetch: options.fetch,
|
||||
})
|
||||
}
|
||||
|
||||
const createLanguageModel = (modelId: OpenaiCompatibleModelId) => createChatModel(modelId)
|
||||
|
||||
const provider = function (modelId: OpenaiCompatibleModelId) {
|
||||
return createChatModel(modelId)
|
||||
}
|
||||
|
||||
provider.languageModel = createLanguageModel
|
||||
provider.chat = createChatModel
|
||||
provider.responses = createResponsesModel
|
||||
|
||||
return provider as OpenaiCompatibleProvider
|
||||
}
|
||||
|
||||
// Default OpenAI Compatible provider instance
|
||||
export const openaiCompatible = createOpenaiCompatible()
|
||||
@@ -1,27 +0,0 @@
|
||||
import { z, type ZodType } from "zod/v4"
|
||||
|
||||
export const openaiCompatibleErrorDataSchema = z.object({
|
||||
error: z.object({
|
||||
message: z.string(),
|
||||
|
||||
// The additional information below is handled loosely to support
|
||||
// OpenAI-compatible providers that have slightly different error
|
||||
// responses:
|
||||
type: z.string().nullish(),
|
||||
param: z.any().nullish(),
|
||||
code: z.union([z.string(), z.number()]).nullish(),
|
||||
}),
|
||||
})
|
||||
|
||||
export type OpenAICompatibleErrorData = z.infer<typeof openaiCompatibleErrorDataSchema>
|
||||
|
||||
export type ProviderErrorStructure<T> = {
|
||||
errorSchema: ZodType<T>
|
||||
errorToMessage: (error: T) => string
|
||||
isRetryable?: (response: Response, error?: T) => boolean
|
||||
}
|
||||
|
||||
export const defaultOpenAICompatibleErrorStructure: ProviderErrorStructure<OpenAICompatibleErrorData> = {
|
||||
errorSchema: openaiCompatibleErrorDataSchema,
|
||||
errorToMessage: (data) => data.error.message,
|
||||
}
|
||||
@@ -1,335 +0,0 @@
|
||||
import {
|
||||
type LanguageModelV3Prompt,
|
||||
type LanguageModelV3ToolCallPart,
|
||||
type SharedV3Warning,
|
||||
UnsupportedFunctionalityError,
|
||||
} from "@ai-sdk/provider"
|
||||
import { convertToBase64, parseProviderOptions } from "@ai-sdk/provider-utils"
|
||||
import { z } from "zod/v4"
|
||||
import type { OpenAIResponsesInput, OpenAIResponsesReasoning } from "./openai-responses-api-types"
|
||||
import { localShellInputSchema, localShellOutputSchema } from "./tool/local-shell"
|
||||
|
||||
/**
|
||||
* Check if a string is a file ID based on the given prefixes
|
||||
* Returns false if prefixes is undefined (disables file ID detection)
|
||||
*/
|
||||
function isFileId(data: string, prefixes?: readonly string[]): boolean {
|
||||
if (!prefixes) return false
|
||||
return prefixes.some((prefix) => data.startsWith(prefix))
|
||||
}
|
||||
|
||||
export async function convertToOpenAIResponsesInput({
|
||||
prompt,
|
||||
systemMessageMode,
|
||||
fileIdPrefixes,
|
||||
store,
|
||||
hasLocalShellTool = false,
|
||||
}: {
|
||||
prompt: LanguageModelV3Prompt
|
||||
systemMessageMode: "system" | "developer" | "remove"
|
||||
fileIdPrefixes?: readonly string[]
|
||||
store: boolean
|
||||
hasLocalShellTool?: boolean
|
||||
}): Promise<{
|
||||
input: OpenAIResponsesInput
|
||||
warnings: Array<SharedV3Warning>
|
||||
}> {
|
||||
const input: OpenAIResponsesInput = []
|
||||
const warnings: Array<SharedV3Warning> = []
|
||||
const processedApprovalIds = new Set<string>()
|
||||
|
||||
for (const { role, content } of prompt) {
|
||||
switch (role) {
|
||||
case "system": {
|
||||
switch (systemMessageMode) {
|
||||
case "system": {
|
||||
input.push({ role: "system", content })
|
||||
break
|
||||
}
|
||||
case "developer": {
|
||||
input.push({ role: "developer", content })
|
||||
break
|
||||
}
|
||||
case "remove": {
|
||||
warnings.push({
|
||||
type: "other",
|
||||
message: "system messages are removed for this model",
|
||||
})
|
||||
break
|
||||
}
|
||||
default: {
|
||||
const _exhaustiveCheck: never = systemMessageMode
|
||||
throw new Error(`Unsupported system message mode: ${_exhaustiveCheck}`)
|
||||
}
|
||||
}
|
||||
break
|
||||
}
|
||||
|
||||
case "user": {
|
||||
input.push({
|
||||
role: "user",
|
||||
content: content.map((part, index) => {
|
||||
switch (part.type) {
|
||||
case "text": {
|
||||
return { type: "input_text", text: part.text }
|
||||
}
|
||||
case "file": {
|
||||
if (part.mediaType.startsWith("image/")) {
|
||||
const mediaType = part.mediaType === "image/*" ? "image/jpeg" : part.mediaType
|
||||
|
||||
return {
|
||||
type: "input_image",
|
||||
...(part.data instanceof URL
|
||||
? { image_url: part.data.toString() }
|
||||
: typeof part.data === "string" && isFileId(part.data, fileIdPrefixes)
|
||||
? { file_id: part.data }
|
||||
: {
|
||||
image_url: `data:${mediaType};base64,${convertToBase64(part.data)}`,
|
||||
}),
|
||||
detail: part.providerOptions?.copilot?.imageDetail,
|
||||
}
|
||||
} else if (part.mediaType === "application/pdf") {
|
||||
if (part.data instanceof URL) {
|
||||
return {
|
||||
type: "input_file",
|
||||
file_url: part.data.toString(),
|
||||
}
|
||||
}
|
||||
return {
|
||||
type: "input_file",
|
||||
...(typeof part.data === "string" && isFileId(part.data, fileIdPrefixes)
|
||||
? { file_id: part.data }
|
||||
: {
|
||||
filename: part.filename ?? `part-${index}.pdf`,
|
||||
file_data: `data:application/pdf;base64,${convertToBase64(part.data)}`,
|
||||
}),
|
||||
}
|
||||
} else {
|
||||
throw new UnsupportedFunctionalityError({
|
||||
functionality: `file part media type ${part.mediaType}`,
|
||||
})
|
||||
}
|
||||
}
|
||||
}
|
||||
}),
|
||||
})
|
||||
|
||||
break
|
||||
}
|
||||
|
||||
case "assistant": {
|
||||
const reasoningMessages: Record<string, OpenAIResponsesReasoning> = {}
|
||||
const toolCallParts: Record<string, LanguageModelV3ToolCallPart> = {}
|
||||
|
||||
for (const part of content) {
|
||||
switch (part.type) {
|
||||
case "text": {
|
||||
input.push({
|
||||
role: "assistant",
|
||||
content: [{ type: "output_text", text: part.text }],
|
||||
id: (part.providerOptions?.copilot?.itemId as string) ?? undefined,
|
||||
})
|
||||
break
|
||||
}
|
||||
case "tool-call": {
|
||||
toolCallParts[part.toolCallId] = part
|
||||
|
||||
if (part.providerExecuted) {
|
||||
break
|
||||
}
|
||||
|
||||
if (hasLocalShellTool && part.toolName === "local_shell") {
|
||||
const parsedInput = localShellInputSchema.parse(part.input)
|
||||
input.push({
|
||||
type: "local_shell_call",
|
||||
call_id: part.toolCallId,
|
||||
id: (part.providerOptions?.copilot?.itemId as string) ?? undefined,
|
||||
action: {
|
||||
type: "exec",
|
||||
command: parsedInput.action.command,
|
||||
timeout_ms: parsedInput.action.timeoutMs,
|
||||
user: parsedInput.action.user,
|
||||
working_directory: parsedInput.action.workingDirectory,
|
||||
env: parsedInput.action.env,
|
||||
},
|
||||
})
|
||||
|
||||
break
|
||||
}
|
||||
|
||||
input.push({
|
||||
type: "function_call",
|
||||
call_id: part.toolCallId,
|
||||
name: part.toolName,
|
||||
arguments: JSON.stringify(part.input),
|
||||
id: (part.providerOptions?.copilot?.itemId as string) ?? undefined,
|
||||
})
|
||||
break
|
||||
}
|
||||
|
||||
// assistant tool result parts are from provider-executed tools:
|
||||
case "tool-result": {
|
||||
if (store) {
|
||||
// use item references to refer to tool results from built-in tools
|
||||
input.push({ type: "item_reference", id: part.toolCallId })
|
||||
} else {
|
||||
warnings.push({
|
||||
type: "other",
|
||||
message: `Results for OpenAI tool ${part.toolName} are not sent to the API when store is false`,
|
||||
})
|
||||
}
|
||||
|
||||
break
|
||||
}
|
||||
|
||||
case "reasoning": {
|
||||
const providerOptions = await parseProviderOptions({
|
||||
provider: "copilot",
|
||||
providerOptions: part.providerOptions,
|
||||
schema: openaiResponsesReasoningProviderOptionsSchema,
|
||||
})
|
||||
|
||||
const reasoningId = providerOptions?.itemId
|
||||
|
||||
if (reasoningId != null) {
|
||||
const reasoningMessage = reasoningMessages[reasoningId]
|
||||
|
||||
if (store) {
|
||||
if (reasoningMessage === undefined) {
|
||||
// use item references to refer to reasoning (single reference)
|
||||
input.push({ type: "item_reference", id: reasoningId })
|
||||
|
||||
// store unused reasoning message to mark id as used
|
||||
reasoningMessages[reasoningId] = {
|
||||
type: "reasoning",
|
||||
id: reasoningId,
|
||||
summary: [],
|
||||
}
|
||||
}
|
||||
} else {
|
||||
const summaryParts: Array<{
|
||||
type: "summary_text"
|
||||
text: string
|
||||
}> = []
|
||||
|
||||
if (part.text.length > 0) {
|
||||
summaryParts.push({
|
||||
type: "summary_text",
|
||||
text: part.text,
|
||||
})
|
||||
} else if (reasoningMessage !== undefined) {
|
||||
warnings.push({
|
||||
type: "other",
|
||||
message: `Cannot append empty reasoning part to existing reasoning sequence. Skipping reasoning part: ${JSON.stringify(part)}.`,
|
||||
})
|
||||
}
|
||||
|
||||
if (reasoningMessage === undefined) {
|
||||
reasoningMessages[reasoningId] = {
|
||||
type: "reasoning",
|
||||
id: reasoningId,
|
||||
encrypted_content: providerOptions?.reasoningEncryptedContent,
|
||||
summary: summaryParts,
|
||||
}
|
||||
input.push(reasoningMessages[reasoningId])
|
||||
} else {
|
||||
reasoningMessage.summary.push(...summaryParts)
|
||||
}
|
||||
}
|
||||
} else {
|
||||
warnings.push({
|
||||
type: "other",
|
||||
message: `Non-OpenAI reasoning parts are not supported. Skipping reasoning part: ${JSON.stringify(part)}.`,
|
||||
})
|
||||
}
|
||||
break
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
break
|
||||
}
|
||||
|
||||
case "tool": {
|
||||
for (const part of content) {
|
||||
if (part.type === "tool-approval-response") {
|
||||
if (processedApprovalIds.has(part.approvalId)) {
|
||||
continue
|
||||
}
|
||||
processedApprovalIds.add(part.approvalId)
|
||||
|
||||
if (store) {
|
||||
input.push({
|
||||
type: "item_reference",
|
||||
id: part.approvalId,
|
||||
})
|
||||
}
|
||||
|
||||
input.push({
|
||||
type: "mcp_approval_response",
|
||||
approval_request_id: part.approvalId,
|
||||
approve: part.approved,
|
||||
})
|
||||
continue
|
||||
}
|
||||
const output = part.output
|
||||
|
||||
if (output.type === "execution-denied") {
|
||||
const approvalId = (output.providerOptions?.copilot as { approvalId?: string } | undefined)?.approvalId
|
||||
|
||||
if (approvalId) {
|
||||
continue
|
||||
}
|
||||
}
|
||||
|
||||
if (hasLocalShellTool && part.toolName === "local_shell" && output.type === "json") {
|
||||
input.push({
|
||||
type: "local_shell_call_output",
|
||||
call_id: part.toolCallId,
|
||||
output: localShellOutputSchema.parse(output.value).output,
|
||||
})
|
||||
break
|
||||
}
|
||||
|
||||
let contentValue: string
|
||||
switch (output.type) {
|
||||
case "text":
|
||||
case "error-text":
|
||||
contentValue = output.value
|
||||
break
|
||||
case "execution-denied":
|
||||
contentValue = output.reason ?? "Tool execution denied."
|
||||
break
|
||||
case "content":
|
||||
case "json":
|
||||
case "error-json":
|
||||
contentValue = JSON.stringify(output.value)
|
||||
break
|
||||
}
|
||||
|
||||
input.push({
|
||||
type: "function_call_output",
|
||||
call_id: part.toolCallId,
|
||||
output: contentValue,
|
||||
})
|
||||
}
|
||||
|
||||
break
|
||||
}
|
||||
|
||||
default: {
|
||||
const _exhaustiveCheck: never = role
|
||||
throw new Error(`Unsupported role: ${_exhaustiveCheck}`)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return { input, warnings }
|
||||
}
|
||||
|
||||
const openaiResponsesReasoningProviderOptionsSchema = z.object({
|
||||
itemId: z.string().nullish(),
|
||||
reasoningEncryptedContent: z.string().nullish(),
|
||||
})
|
||||
|
||||
export type OpenAIResponsesReasoningProviderOptions = z.infer<typeof openaiResponsesReasoningProviderOptionsSchema>
|
||||
@@ -1,22 +0,0 @@
|
||||
import type { LanguageModelV3FinishReason } from "@ai-sdk/provider"
|
||||
|
||||
export function mapOpenAIResponseFinishReason({
|
||||
finishReason,
|
||||
hasFunctionCall,
|
||||
}: {
|
||||
finishReason: string | null | undefined
|
||||
// flag that checks if there have been client-side tool calls (not executed by openai)
|
||||
hasFunctionCall: boolean
|
||||
}): LanguageModelV3FinishReason["unified"] {
|
||||
switch (finishReason) {
|
||||
case undefined:
|
||||
case null:
|
||||
return hasFunctionCall ? "tool-calls" : "stop"
|
||||
case "max_output_tokens":
|
||||
return "length"
|
||||
case "content_filter":
|
||||
return "content-filter"
|
||||
default:
|
||||
return hasFunctionCall ? "tool-calls" : "other"
|
||||
}
|
||||
}
|
||||
@@ -1,18 +0,0 @@
|
||||
import type { FetchFunction } from "@ai-sdk/provider-utils"
|
||||
|
||||
export type OpenAIConfig = {
|
||||
provider: string
|
||||
url: (options: { modelId: string; path: string }) => string
|
||||
headers: () => Record<string, string | undefined>
|
||||
fetch?: FetchFunction
|
||||
generateId?: () => string
|
||||
/**
|
||||
* File ID prefixes used to identify file IDs in Responses API.
|
||||
* When undefined, all file data is treated as base64 content.
|
||||
*
|
||||
* Examples:
|
||||
* - OpenAI: ['file-'] for IDs like 'file-abc123'
|
||||
* - Azure OpenAI: ['assistant-'] for IDs like 'assistant-abc123'
|
||||
*/
|
||||
fileIdPrefixes?: readonly string[]
|
||||
}
|
||||
@@ -1,22 +0,0 @@
|
||||
import { z } from "zod/v4"
|
||||
import { createJsonErrorResponseHandler } from "@ai-sdk/provider-utils"
|
||||
|
||||
export const openaiErrorDataSchema = z.object({
|
||||
error: z.object({
|
||||
message: z.string(),
|
||||
|
||||
// The additional information below is handled loosely to support
|
||||
// OpenAI-compatible providers that have slightly different error
|
||||
// responses:
|
||||
type: z.string().nullish(),
|
||||
param: z.any().nullish(),
|
||||
code: z.union([z.string(), z.number()]).nullish(),
|
||||
}),
|
||||
})
|
||||
|
||||
export type OpenAIErrorData = z.infer<typeof openaiErrorDataSchema>
|
||||
|
||||
export const openaiFailedResponseHandler: any = createJsonErrorResponseHandler({
|
||||
errorSchema: openaiErrorDataSchema,
|
||||
errorToMessage: (data) => data.error.message,
|
||||
})
|
||||
@@ -1,214 +0,0 @@
|
||||
import type { JSONSchema7 } from "@ai-sdk/provider"
|
||||
|
||||
export type OpenAIResponsesInput = Array<OpenAIResponsesInputItem>
|
||||
|
||||
export type OpenAIResponsesInputItem =
|
||||
| OpenAIResponsesSystemMessage
|
||||
| OpenAIResponsesUserMessage
|
||||
| OpenAIResponsesAssistantMessage
|
||||
| OpenAIResponsesFunctionCall
|
||||
| OpenAIResponsesFunctionCallOutput
|
||||
| OpenAIResponsesComputerCall
|
||||
| OpenAIResponsesLocalShellCall
|
||||
| OpenAIResponsesLocalShellCallOutput
|
||||
| OpenAIResponsesReasoning
|
||||
| OpenAIResponsesItemReference
|
||||
| OpenAIResponsesMcpApprovalResponse
|
||||
|
||||
export type OpenAIResponsesIncludeValue =
|
||||
| "web_search_call.action.sources"
|
||||
| "code_interpreter_call.outputs"
|
||||
| "computer_call_output.output.image_url"
|
||||
| "file_search_call.results"
|
||||
| "message.input_image.image_url"
|
||||
| "message.output_text.logprobs"
|
||||
| "reasoning.encrypted_content"
|
||||
|
||||
export type OpenAIResponsesIncludeOptions = Array<OpenAIResponsesIncludeValue> | undefined | null
|
||||
|
||||
export type OpenAIResponsesSystemMessage = {
|
||||
role: "system" | "developer"
|
||||
content: string
|
||||
}
|
||||
|
||||
export type OpenAIResponsesUserMessage = {
|
||||
role: "user"
|
||||
content: Array<
|
||||
| { type: "input_text"; text: string }
|
||||
| { type: "input_image"; image_url: string }
|
||||
| { type: "input_image"; file_id: string }
|
||||
| { type: "input_file"; file_url: string }
|
||||
| { type: "input_file"; filename: string; file_data: string }
|
||||
| { type: "input_file"; file_id: string }
|
||||
>
|
||||
}
|
||||
|
||||
export type OpenAIResponsesAssistantMessage = {
|
||||
role: "assistant"
|
||||
content: Array<{ type: "output_text"; text: string }>
|
||||
id?: string
|
||||
}
|
||||
|
||||
export type OpenAIResponsesFunctionCall = {
|
||||
type: "function_call"
|
||||
call_id: string
|
||||
name: string
|
||||
arguments: string
|
||||
id?: string
|
||||
}
|
||||
|
||||
export type OpenAIResponsesFunctionCallOutput = {
|
||||
type: "function_call_output"
|
||||
call_id: string
|
||||
output: string
|
||||
}
|
||||
|
||||
export type OpenAIResponsesComputerCall = {
|
||||
type: "computer_call"
|
||||
id: string
|
||||
status?: string
|
||||
}
|
||||
|
||||
export type OpenAIResponsesLocalShellCall = {
|
||||
type: "local_shell_call"
|
||||
id: string
|
||||
call_id: string
|
||||
action: {
|
||||
type: "exec"
|
||||
command: string[]
|
||||
timeout_ms?: number
|
||||
user?: string
|
||||
working_directory?: string
|
||||
env?: Record<string, string>
|
||||
}
|
||||
}
|
||||
|
||||
export type OpenAIResponsesLocalShellCallOutput = {
|
||||
type: "local_shell_call_output"
|
||||
call_id: string
|
||||
output: string
|
||||
}
|
||||
|
||||
export type OpenAIResponsesItemReference = {
|
||||
type: "item_reference"
|
||||
id: string
|
||||
}
|
||||
|
||||
export type OpenAIResponsesMcpApprovalResponse = {
|
||||
type: "mcp_approval_response"
|
||||
approval_request_id: string
|
||||
approve: boolean
|
||||
}
|
||||
|
||||
/**
|
||||
* A filter used to compare a specified attribute key to a given value using a defined comparison operation.
|
||||
*/
|
||||
export type OpenAIResponsesFileSearchToolComparisonFilter = {
|
||||
/**
|
||||
* The key to compare against the value.
|
||||
*/
|
||||
key: string
|
||||
|
||||
/**
|
||||
* Specifies the comparison operator: eq, ne, gt, gte, lt, lte.
|
||||
*/
|
||||
type: "eq" | "ne" | "gt" | "gte" | "lt" | "lte"
|
||||
|
||||
/**
|
||||
* The value to compare against the attribute key; supports string, number, or boolean types.
|
||||
*/
|
||||
value: string | number | boolean
|
||||
}
|
||||
|
||||
/**
|
||||
* Combine multiple filters using and or or.
|
||||
*/
|
||||
export type OpenAIResponsesFileSearchToolCompoundFilter = {
|
||||
/**
|
||||
* Type of operation: and or or.
|
||||
*/
|
||||
type: "and" | "or"
|
||||
|
||||
/**
|
||||
* Array of filters to combine. Items can be ComparisonFilter or CompoundFilter.
|
||||
*/
|
||||
filters: Array<OpenAIResponsesFileSearchToolComparisonFilter | OpenAIResponsesFileSearchToolCompoundFilter>
|
||||
}
|
||||
|
||||
export type OpenAIResponsesTool =
|
||||
| {
|
||||
type: "function"
|
||||
name: string
|
||||
description: string | undefined
|
||||
parameters: JSONSchema7
|
||||
strict: boolean | undefined
|
||||
}
|
||||
| {
|
||||
type: "web_search"
|
||||
filters: { allowed_domains: string[] | undefined } | undefined
|
||||
search_context_size: "low" | "medium" | "high" | undefined
|
||||
user_location:
|
||||
| {
|
||||
type: "approximate"
|
||||
city?: string
|
||||
country?: string
|
||||
region?: string
|
||||
timezone?: string
|
||||
}
|
||||
| undefined
|
||||
}
|
||||
| {
|
||||
type: "web_search_preview"
|
||||
search_context_size: "low" | "medium" | "high" | undefined
|
||||
user_location:
|
||||
| {
|
||||
type: "approximate"
|
||||
city?: string
|
||||
country?: string
|
||||
region?: string
|
||||
timezone?: string
|
||||
}
|
||||
| undefined
|
||||
}
|
||||
| {
|
||||
type: "code_interpreter"
|
||||
container: string | { type: "auto"; file_ids: string[] | undefined }
|
||||
}
|
||||
| {
|
||||
type: "file_search"
|
||||
vector_store_ids: string[]
|
||||
max_num_results: number | undefined
|
||||
ranking_options: { ranker?: string; score_threshold?: number } | undefined
|
||||
filters: OpenAIResponsesFileSearchToolComparisonFilter | OpenAIResponsesFileSearchToolCompoundFilter | undefined
|
||||
}
|
||||
| {
|
||||
type: "image_generation"
|
||||
background: "auto" | "opaque" | "transparent" | undefined
|
||||
input_fidelity: "low" | "high" | undefined
|
||||
input_image_mask:
|
||||
| {
|
||||
file_id: string | undefined
|
||||
image_url: string | undefined
|
||||
}
|
||||
| undefined
|
||||
model: string | undefined
|
||||
moderation: "auto" | undefined
|
||||
output_compression: number | undefined
|
||||
output_format: "png" | "jpeg" | "webp" | undefined
|
||||
partial_images: number | undefined
|
||||
quality: "auto" | "low" | "medium" | "high" | undefined
|
||||
size: "auto" | "1024x1024" | "1024x1536" | "1536x1024" | undefined
|
||||
}
|
||||
| {
|
||||
type: "local_shell"
|
||||
}
|
||||
|
||||
export type OpenAIResponsesReasoning = {
|
||||
type: "reasoning"
|
||||
id: string
|
||||
encrypted_content?: string | null
|
||||
summary: Array<{
|
||||
type: "summary_text"
|
||||
text: string
|
||||
}>
|
||||
}
|
||||
File diff suppressed because it is too large
Load Diff
@@ -1,173 +0,0 @@
|
||||
import { type LanguageModelV3CallOptions, type SharedV3Warning, UnsupportedFunctionalityError } from "@ai-sdk/provider"
|
||||
import { codeInterpreterArgsSchema } from "./tool/code-interpreter"
|
||||
import { fileSearchArgsSchema } from "./tool/file-search"
|
||||
import { webSearchArgsSchema } from "./tool/web-search"
|
||||
import { webSearchPreviewArgsSchema } from "./tool/web-search-preview"
|
||||
import { imageGenerationArgsSchema } from "./tool/image-generation"
|
||||
import type { OpenAIResponsesTool } from "./openai-responses-api-types"
|
||||
|
||||
export function prepareResponsesTools({
|
||||
tools,
|
||||
toolChoice,
|
||||
strictJsonSchema,
|
||||
}: {
|
||||
tools: LanguageModelV3CallOptions["tools"]
|
||||
toolChoice?: LanguageModelV3CallOptions["toolChoice"]
|
||||
strictJsonSchema: boolean
|
||||
}): {
|
||||
tools?: Array<OpenAIResponsesTool>
|
||||
toolChoice?:
|
||||
| "auto"
|
||||
| "none"
|
||||
| "required"
|
||||
| { type: "file_search" }
|
||||
| { type: "web_search_preview" }
|
||||
| { type: "web_search" }
|
||||
| { type: "function"; name: string }
|
||||
| { type: "code_interpreter" }
|
||||
| { type: "image_generation" }
|
||||
toolWarnings: SharedV3Warning[]
|
||||
} {
|
||||
// when the tools array is empty, change it to undefined to prevent errors:
|
||||
tools = tools?.length ? tools : undefined
|
||||
|
||||
const toolWarnings: SharedV3Warning[] = []
|
||||
|
||||
if (tools == null) {
|
||||
return { tools: undefined, toolChoice: undefined, toolWarnings }
|
||||
}
|
||||
|
||||
const openaiTools: Array<OpenAIResponsesTool> = []
|
||||
|
||||
for (const tool of tools) {
|
||||
switch (tool.type) {
|
||||
case "function":
|
||||
openaiTools.push({
|
||||
type: "function",
|
||||
name: tool.name,
|
||||
description: tool.description,
|
||||
parameters: tool.inputSchema,
|
||||
strict: strictJsonSchema,
|
||||
})
|
||||
break
|
||||
case "provider": {
|
||||
switch (tool.id) {
|
||||
case "openai.file_search": {
|
||||
const args = fileSearchArgsSchema.parse(tool.args)
|
||||
|
||||
openaiTools.push({
|
||||
type: "file_search",
|
||||
vector_store_ids: args.vectorStoreIds,
|
||||
max_num_results: args.maxNumResults,
|
||||
ranking_options: args.ranking
|
||||
? {
|
||||
ranker: args.ranking.ranker,
|
||||
score_threshold: args.ranking.scoreThreshold,
|
||||
}
|
||||
: undefined,
|
||||
filters: args.filters,
|
||||
})
|
||||
|
||||
break
|
||||
}
|
||||
case "openai.local_shell": {
|
||||
openaiTools.push({
|
||||
type: "local_shell",
|
||||
})
|
||||
break
|
||||
}
|
||||
case "openai.web_search_preview": {
|
||||
const args = webSearchPreviewArgsSchema.parse(tool.args)
|
||||
openaiTools.push({
|
||||
type: "web_search_preview",
|
||||
search_context_size: args.searchContextSize,
|
||||
user_location: args.userLocation,
|
||||
})
|
||||
break
|
||||
}
|
||||
case "openai.web_search": {
|
||||
const args = webSearchArgsSchema.parse(tool.args)
|
||||
openaiTools.push({
|
||||
type: "web_search",
|
||||
filters: args.filters != null ? { allowed_domains: args.filters.allowedDomains } : undefined,
|
||||
search_context_size: args.searchContextSize,
|
||||
user_location: args.userLocation,
|
||||
})
|
||||
break
|
||||
}
|
||||
case "openai.code_interpreter": {
|
||||
const args = codeInterpreterArgsSchema.parse(tool.args)
|
||||
openaiTools.push({
|
||||
type: "code_interpreter",
|
||||
container:
|
||||
args.container == null
|
||||
? { type: "auto", file_ids: undefined }
|
||||
: typeof args.container === "string"
|
||||
? args.container
|
||||
: { type: "auto", file_ids: args.container.fileIds },
|
||||
})
|
||||
break
|
||||
}
|
||||
case "openai.image_generation": {
|
||||
const args = imageGenerationArgsSchema.parse(tool.args)
|
||||
openaiTools.push({
|
||||
type: "image_generation",
|
||||
background: args.background,
|
||||
input_fidelity: args.inputFidelity,
|
||||
input_image_mask: args.inputImageMask
|
||||
? {
|
||||
file_id: args.inputImageMask.fileId,
|
||||
image_url: args.inputImageMask.imageUrl,
|
||||
}
|
||||
: undefined,
|
||||
model: args.model,
|
||||
moderation: args.moderation,
|
||||
partial_images: args.partialImages,
|
||||
quality: args.quality,
|
||||
output_compression: args.outputCompression,
|
||||
output_format: args.outputFormat,
|
||||
size: args.size,
|
||||
})
|
||||
break
|
||||
}
|
||||
}
|
||||
break
|
||||
}
|
||||
default:
|
||||
toolWarnings.push({ type: "unsupported", feature: "tool type" })
|
||||
break
|
||||
}
|
||||
}
|
||||
|
||||
if (toolChoice == null) {
|
||||
return { tools: openaiTools, toolChoice: undefined, toolWarnings }
|
||||
}
|
||||
|
||||
const type = toolChoice.type
|
||||
|
||||
switch (type) {
|
||||
case "auto":
|
||||
case "none":
|
||||
case "required":
|
||||
return { tools: openaiTools, toolChoice: type, toolWarnings }
|
||||
case "tool":
|
||||
return {
|
||||
tools: openaiTools,
|
||||
toolChoice:
|
||||
toolChoice.toolName === "code_interpreter" ||
|
||||
toolChoice.toolName === "file_search" ||
|
||||
toolChoice.toolName === "image_generation" ||
|
||||
toolChoice.toolName === "web_search_preview" ||
|
||||
toolChoice.toolName === "web_search"
|
||||
? { type: toolChoice.toolName }
|
||||
: { type: "function", name: toolChoice.toolName },
|
||||
toolWarnings,
|
||||
}
|
||||
default: {
|
||||
const _exhaustiveCheck: never = type
|
||||
throw new UnsupportedFunctionalityError({
|
||||
functionality: `tool choice type: ${_exhaustiveCheck}`,
|
||||
})
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -1 +0,0 @@
|
||||
export type OpenAIResponsesModelId = string
|
||||
@@ -1,87 +0,0 @@
|
||||
import { createProviderToolFactoryWithOutputSchema } from "@ai-sdk/provider-utils"
|
||||
import { z } from "zod/v4"
|
||||
|
||||
export const codeInterpreterInputSchema = z.object({
|
||||
code: z.string().nullish(),
|
||||
containerId: z.string(),
|
||||
})
|
||||
|
||||
export const codeInterpreterOutputSchema = z.object({
|
||||
outputs: z
|
||||
.array(
|
||||
z.discriminatedUnion("type", [
|
||||
z.object({ type: z.literal("logs"), logs: z.string() }),
|
||||
z.object({ type: z.literal("image"), url: z.string() }),
|
||||
]),
|
||||
)
|
||||
.nullish(),
|
||||
})
|
||||
|
||||
export const codeInterpreterArgsSchema = z.object({
|
||||
container: z
|
||||
.union([
|
||||
z.string(),
|
||||
z.object({
|
||||
fileIds: z.array(z.string()).optional(),
|
||||
}),
|
||||
])
|
||||
.optional(),
|
||||
})
|
||||
|
||||
type CodeInterpreterArgs = {
|
||||
/**
|
||||
* The code interpreter container.
|
||||
* Can be a container ID
|
||||
* or an object that specifies uploaded file IDs to make available to your code.
|
||||
*/
|
||||
container?: string | { fileIds?: string[] }
|
||||
}
|
||||
|
||||
export const codeInterpreterToolFactory = createProviderToolFactoryWithOutputSchema<
|
||||
{
|
||||
/**
|
||||
* The code to run, or null if not available.
|
||||
*/
|
||||
code?: string | null
|
||||
|
||||
/**
|
||||
* The ID of the container used to run the code.
|
||||
*/
|
||||
containerId: string
|
||||
},
|
||||
{
|
||||
/**
|
||||
* The outputs generated by the code interpreter, such as logs or images.
|
||||
* Can be null if no outputs are available.
|
||||
*/
|
||||
outputs?: Array<
|
||||
| {
|
||||
type: "logs"
|
||||
|
||||
/**
|
||||
* The logs output from the code interpreter.
|
||||
*/
|
||||
logs: string
|
||||
}
|
||||
| {
|
||||
type: "image"
|
||||
|
||||
/**
|
||||
* The URL of the image output from the code interpreter.
|
||||
*/
|
||||
url: string
|
||||
}
|
||||
> | null
|
||||
},
|
||||
CodeInterpreterArgs
|
||||
>({
|
||||
id: "openai.code_interpreter",
|
||||
inputSchema: codeInterpreterInputSchema,
|
||||
outputSchema: codeInterpreterOutputSchema,
|
||||
})
|
||||
|
||||
export const codeInterpreter = (
|
||||
args: CodeInterpreterArgs = {}, // default
|
||||
) => {
|
||||
return codeInterpreterToolFactory(args)
|
||||
}
|
||||
@@ -1,127 +0,0 @@
|
||||
import { createProviderToolFactoryWithOutputSchema } from "@ai-sdk/provider-utils"
|
||||
import type {
|
||||
OpenAIResponsesFileSearchToolComparisonFilter,
|
||||
OpenAIResponsesFileSearchToolCompoundFilter,
|
||||
} from "../openai-responses-api-types"
|
||||
import { z } from "zod/v4"
|
||||
|
||||
const comparisonFilterSchema = z.object({
|
||||
key: z.string(),
|
||||
type: z.enum(["eq", "ne", "gt", "gte", "lt", "lte"]),
|
||||
value: z.union([z.string(), z.number(), z.boolean()]),
|
||||
})
|
||||
|
||||
const compoundFilterSchema: z.ZodType<any> = z.object({
|
||||
type: z.enum(["and", "or"]),
|
||||
filters: z.array(z.union([comparisonFilterSchema, z.lazy(() => compoundFilterSchema)])),
|
||||
})
|
||||
|
||||
export const fileSearchArgsSchema = z.object({
|
||||
vectorStoreIds: z.array(z.string()),
|
||||
maxNumResults: z.number().optional(),
|
||||
ranking: z
|
||||
.object({
|
||||
ranker: z.string().optional(),
|
||||
scoreThreshold: z.number().optional(),
|
||||
})
|
||||
.optional(),
|
||||
filters: z.union([comparisonFilterSchema, compoundFilterSchema]).optional(),
|
||||
})
|
||||
|
||||
export const fileSearchOutputSchema = z.object({
|
||||
queries: z.array(z.string()),
|
||||
results: z
|
||||
.array(
|
||||
z.object({
|
||||
attributes: z.record(z.string(), z.unknown()),
|
||||
fileId: z.string(),
|
||||
filename: z.string(),
|
||||
score: z.number(),
|
||||
text: z.string(),
|
||||
}),
|
||||
)
|
||||
.nullable(),
|
||||
})
|
||||
|
||||
export const fileSearch = createProviderToolFactoryWithOutputSchema<
|
||||
{},
|
||||
{
|
||||
/**
|
||||
* The search query to execute.
|
||||
*/
|
||||
queries: string[]
|
||||
|
||||
/**
|
||||
* The results of the file search tool call.
|
||||
*/
|
||||
results:
|
||||
| null
|
||||
| {
|
||||
/**
|
||||
* Set of 16 key-value pairs that can be attached to an object.
|
||||
* This can be useful for storing additional information about the object
|
||||
* in a structured format, and querying for objects via API or the dashboard.
|
||||
* Keys are strings with a maximum length of 64 characters.
|
||||
* Values are strings with a maximum length of 512 characters, booleans, or numbers.
|
||||
*/
|
||||
attributes: Record<string, unknown>
|
||||
|
||||
/**
|
||||
* The unique ID of the file.
|
||||
*/
|
||||
fileId: string
|
||||
|
||||
/**
|
||||
* The name of the file.
|
||||
*/
|
||||
filename: string
|
||||
|
||||
/**
|
||||
* The relevance score of the file - a value between 0 and 1.
|
||||
*/
|
||||
score: number
|
||||
|
||||
/**
|
||||
* The text that was retrieved from the file.
|
||||
*/
|
||||
text: string
|
||||
}[]
|
||||
},
|
||||
{
|
||||
/**
|
||||
* List of vector store IDs to search through.
|
||||
*/
|
||||
vectorStoreIds: string[]
|
||||
|
||||
/**
|
||||
* Maximum number of search results to return. Defaults to 10.
|
||||
*/
|
||||
maxNumResults?: number
|
||||
|
||||
/**
|
||||
* Ranking options for the search.
|
||||
*/
|
||||
ranking?: {
|
||||
/**
|
||||
* The ranker to use for the file search.
|
||||
*/
|
||||
ranker?: string
|
||||
|
||||
/**
|
||||
* The score threshold for the file search, a number between 0 and 1.
|
||||
* Numbers closer to 1 will attempt to return only the most relevant results,
|
||||
* but may return fewer results.
|
||||
*/
|
||||
scoreThreshold?: number
|
||||
}
|
||||
|
||||
/**
|
||||
* A filter to apply.
|
||||
*/
|
||||
filters?: OpenAIResponsesFileSearchToolComparisonFilter | OpenAIResponsesFileSearchToolCompoundFilter
|
||||
}
|
||||
>({
|
||||
id: "openai.file_search",
|
||||
inputSchema: z.object({}),
|
||||
outputSchema: fileSearchOutputSchema,
|
||||
})
|
||||
@@ -1,114 +0,0 @@
|
||||
import { createProviderToolFactoryWithOutputSchema } from "@ai-sdk/provider-utils"
|
||||
import { z } from "zod/v4"
|
||||
|
||||
export const imageGenerationArgsSchema = z
|
||||
.object({
|
||||
background: z.enum(["auto", "opaque", "transparent"]).optional(),
|
||||
inputFidelity: z.enum(["low", "high"]).optional(),
|
||||
inputImageMask: z
|
||||
.object({
|
||||
fileId: z.string().optional(),
|
||||
imageUrl: z.string().optional(),
|
||||
})
|
||||
.optional(),
|
||||
model: z.string().optional(),
|
||||
moderation: z.enum(["auto"]).optional(),
|
||||
outputCompression: z.number().int().min(0).max(100).optional(),
|
||||
outputFormat: z.enum(["png", "jpeg", "webp"]).optional(),
|
||||
partialImages: z.number().int().min(0).max(3).optional(),
|
||||
quality: z.enum(["auto", "low", "medium", "high"]).optional(),
|
||||
size: z.enum(["1024x1024", "1024x1536", "1536x1024", "auto"]).optional(),
|
||||
})
|
||||
.strict()
|
||||
|
||||
export const imageGenerationOutputSchema = z.object({
|
||||
result: z.string(),
|
||||
})
|
||||
|
||||
type ImageGenerationArgs = {
|
||||
/**
|
||||
* Background type for the generated image. Default is 'auto'.
|
||||
*/
|
||||
background?: "auto" | "opaque" | "transparent"
|
||||
|
||||
/**
|
||||
* Input fidelity for the generated image. Default is 'low'.
|
||||
*/
|
||||
inputFidelity?: "low" | "high"
|
||||
|
||||
/**
|
||||
* Optional mask for inpainting.
|
||||
* Contains image_url (string, optional) and file_id (string, optional).
|
||||
*/
|
||||
inputImageMask?: {
|
||||
/**
|
||||
* File ID for the mask image.
|
||||
*/
|
||||
fileId?: string
|
||||
|
||||
/**
|
||||
* Base64-encoded mask image.
|
||||
*/
|
||||
imageUrl?: string
|
||||
}
|
||||
|
||||
/**
|
||||
* The image generation model to use. Default: gpt-image-1.
|
||||
*/
|
||||
model?: string
|
||||
|
||||
/**
|
||||
* Moderation level for the generated image. Default: auto.
|
||||
*/
|
||||
moderation?: "auto"
|
||||
|
||||
/**
|
||||
* Compression level for the output image. Default: 100.
|
||||
*/
|
||||
outputCompression?: number
|
||||
|
||||
/**
|
||||
* The output format of the generated image. One of png, webp, or jpeg.
|
||||
* Default: png
|
||||
*/
|
||||
outputFormat?: "png" | "jpeg" | "webp"
|
||||
|
||||
/**
|
||||
* Number of partial images to generate in streaming mode, from 0 (default value) to 3.
|
||||
*/
|
||||
partialImages?: number
|
||||
|
||||
/**
|
||||
* The quality of the generated image.
|
||||
* One of low, medium, high, or auto. Default: auto.
|
||||
*/
|
||||
quality?: "auto" | "low" | "medium" | "high"
|
||||
|
||||
/**
|
||||
* The size of the generated image.
|
||||
* One of 1024x1024, 1024x1536, 1536x1024, or auto.
|
||||
* Default: auto.
|
||||
*/
|
||||
size?: "auto" | "1024x1024" | "1024x1536" | "1536x1024"
|
||||
}
|
||||
|
||||
const imageGenerationToolFactory = createProviderToolFactoryWithOutputSchema<
|
||||
{},
|
||||
{
|
||||
/**
|
||||
* The generated image encoded in base64.
|
||||
*/
|
||||
result: string
|
||||
},
|
||||
ImageGenerationArgs
|
||||
>({
|
||||
id: "openai.image_generation",
|
||||
inputSchema: z.object({}),
|
||||
outputSchema: imageGenerationOutputSchema,
|
||||
})
|
||||
|
||||
export const imageGeneration = (
|
||||
args: ImageGenerationArgs = {}, // default
|
||||
) => {
|
||||
return imageGenerationToolFactory(args)
|
||||
}
|
||||
@@ -1,64 +0,0 @@
|
||||
import { createProviderToolFactoryWithOutputSchema } from "@ai-sdk/provider-utils"
|
||||
import { z } from "zod/v4"
|
||||
|
||||
export const localShellInputSchema = z.object({
|
||||
action: z.object({
|
||||
type: z.literal("exec"),
|
||||
command: z.array(z.string()),
|
||||
timeoutMs: z.number().optional(),
|
||||
user: z.string().optional(),
|
||||
workingDirectory: z.string().optional(),
|
||||
env: z.record(z.string(), z.string()).optional(),
|
||||
}),
|
||||
})
|
||||
|
||||
export const localShellOutputSchema = z.object({
|
||||
output: z.string(),
|
||||
})
|
||||
|
||||
export const localShell = createProviderToolFactoryWithOutputSchema<
|
||||
{
|
||||
/**
|
||||
* Execute a shell command on the server.
|
||||
*/
|
||||
action: {
|
||||
type: "exec"
|
||||
|
||||
/**
|
||||
* The command to run.
|
||||
*/
|
||||
command: string[]
|
||||
|
||||
/**
|
||||
* Optional timeout in milliseconds for the command.
|
||||
*/
|
||||
timeoutMs?: number
|
||||
|
||||
/**
|
||||
* Optional user to run the command as.
|
||||
*/
|
||||
user?: string
|
||||
|
||||
/**
|
||||
* Optional working directory to run the command in.
|
||||
*/
|
||||
workingDirectory?: string
|
||||
|
||||
/**
|
||||
* Environment variables to set for the command.
|
||||
*/
|
||||
env?: Record<string, string>
|
||||
}
|
||||
},
|
||||
{
|
||||
/**
|
||||
* The output of local shell tool call.
|
||||
*/
|
||||
output: string
|
||||
},
|
||||
{}
|
||||
>({
|
||||
id: "openai.local_shell",
|
||||
inputSchema: localShellInputSchema,
|
||||
outputSchema: localShellOutputSchema,
|
||||
})
|
||||
@@ -1,103 +0,0 @@
|
||||
import { createProviderToolFactory } from "@ai-sdk/provider-utils"
|
||||
import { z } from "zod/v4"
|
||||
|
||||
// Args validation schema
|
||||
export const webSearchPreviewArgsSchema = z.object({
|
||||
/**
|
||||
* Search context size to use for the web search.
|
||||
* - high: Most comprehensive context, highest cost, slower response
|
||||
* - medium: Balanced context, cost, and latency (default)
|
||||
* - low: Least context, lowest cost, fastest response
|
||||
*/
|
||||
searchContextSize: z.enum(["low", "medium", "high"]).optional(),
|
||||
|
||||
/**
|
||||
* User location information to provide geographically relevant search results.
|
||||
*/
|
||||
userLocation: z
|
||||
.object({
|
||||
/**
|
||||
* Type of location (always 'approximate')
|
||||
*/
|
||||
type: z.literal("approximate"),
|
||||
/**
|
||||
* Two-letter ISO country code (e.g., 'US', 'GB')
|
||||
*/
|
||||
country: z.string().optional(),
|
||||
/**
|
||||
* City name (free text, e.g., 'Minneapolis')
|
||||
*/
|
||||
city: z.string().optional(),
|
||||
/**
|
||||
* Region name (free text, e.g., 'Minnesota')
|
||||
*/
|
||||
region: z.string().optional(),
|
||||
/**
|
||||
* IANA timezone (e.g., 'America/Chicago')
|
||||
*/
|
||||
timezone: z.string().optional(),
|
||||
})
|
||||
.optional(),
|
||||
})
|
||||
|
||||
export const webSearchPreview = createProviderToolFactory<
|
||||
{
|
||||
// Web search doesn't take input parameters - it's controlled by the prompt
|
||||
},
|
||||
{
|
||||
/**
|
||||
* Search context size to use for the web search.
|
||||
* - high: Most comprehensive context, highest cost, slower response
|
||||
* - medium: Balanced context, cost, and latency (default)
|
||||
* - low: Least context, lowest cost, fastest response
|
||||
*/
|
||||
searchContextSize?: "low" | "medium" | "high"
|
||||
|
||||
/**
|
||||
* User location information to provide geographically relevant search results.
|
||||
*/
|
||||
userLocation?: {
|
||||
/**
|
||||
* Type of location (always 'approximate')
|
||||
*/
|
||||
type: "approximate"
|
||||
/**
|
||||
* Two-letter ISO country code (e.g., 'US', 'GB')
|
||||
*/
|
||||
country?: string
|
||||
/**
|
||||
* City name (free text, e.g., 'Minneapolis')
|
||||
*/
|
||||
city?: string
|
||||
/**
|
||||
* Region name (free text, e.g., 'Minnesota')
|
||||
*/
|
||||
region?: string
|
||||
/**
|
||||
* IANA timezone (e.g., 'America/Chicago')
|
||||
*/
|
||||
timezone?: string
|
||||
}
|
||||
}
|
||||
>({
|
||||
id: "openai.web_search_preview",
|
||||
inputSchema: z.object({
|
||||
action: z
|
||||
.discriminatedUnion("type", [
|
||||
z.object({
|
||||
type: z.literal("search"),
|
||||
query: z.string().nullish(),
|
||||
}),
|
||||
z.object({
|
||||
type: z.literal("open_page"),
|
||||
url: z.string(),
|
||||
}),
|
||||
z.object({
|
||||
type: z.literal("find"),
|
||||
url: z.string(),
|
||||
pattern: z.string(),
|
||||
}),
|
||||
])
|
||||
.nullish(),
|
||||
}),
|
||||
})
|
||||
@@ -1,102 +0,0 @@
|
||||
import { createProviderToolFactory } from "@ai-sdk/provider-utils"
|
||||
import { z } from "zod/v4"
|
||||
|
||||
export const webSearchArgsSchema = z.object({
|
||||
filters: z
|
||||
.object({
|
||||
allowedDomains: z.array(z.string()).optional(),
|
||||
})
|
||||
.optional(),
|
||||
|
||||
searchContextSize: z.enum(["low", "medium", "high"]).optional(),
|
||||
|
||||
userLocation: z
|
||||
.object({
|
||||
type: z.literal("approximate"),
|
||||
country: z.string().optional(),
|
||||
city: z.string().optional(),
|
||||
region: z.string().optional(),
|
||||
timezone: z.string().optional(),
|
||||
})
|
||||
.optional(),
|
||||
})
|
||||
|
||||
export const webSearchToolFactory = createProviderToolFactory<
|
||||
{
|
||||
// Web search doesn't take input parameters - it's controlled by the prompt
|
||||
},
|
||||
{
|
||||
/**
|
||||
* Filters for the search.
|
||||
*/
|
||||
filters?: {
|
||||
/**
|
||||
* Allowed domains for the search.
|
||||
* If not provided, all domains are allowed.
|
||||
* Subdomains of the provided domains are allowed as well.
|
||||
*/
|
||||
allowedDomains?: string[]
|
||||
}
|
||||
|
||||
/**
|
||||
* Search context size to use for the web search.
|
||||
* - high: Most comprehensive context, highest cost, slower response
|
||||
* - medium: Balanced context, cost, and latency (default)
|
||||
* - low: Least context, lowest cost, fastest response
|
||||
*/
|
||||
searchContextSize?: "low" | "medium" | "high"
|
||||
|
||||
/**
|
||||
* User location information to provide geographically relevant search results.
|
||||
*/
|
||||
userLocation?: {
|
||||
/**
|
||||
* Type of location (always 'approximate')
|
||||
*/
|
||||
type: "approximate"
|
||||
/**
|
||||
* Two-letter ISO country code (e.g., 'US', 'GB')
|
||||
*/
|
||||
country?: string
|
||||
/**
|
||||
* City name (free text, e.g., 'Minneapolis')
|
||||
*/
|
||||
city?: string
|
||||
/**
|
||||
* Region name (free text, e.g., 'Minnesota')
|
||||
*/
|
||||
region?: string
|
||||
/**
|
||||
* IANA timezone (e.g., 'America/Chicago')
|
||||
*/
|
||||
timezone?: string
|
||||
}
|
||||
}
|
||||
>({
|
||||
id: "openai.web_search",
|
||||
inputSchema: z.object({
|
||||
action: z
|
||||
.discriminatedUnion("type", [
|
||||
z.object({
|
||||
type: z.literal("search"),
|
||||
query: z.string().nullish(),
|
||||
}),
|
||||
z.object({
|
||||
type: z.literal("open_page"),
|
||||
url: z.string(),
|
||||
}),
|
||||
z.object({
|
||||
type: z.literal("find"),
|
||||
url: z.string(),
|
||||
pattern: z.string(),
|
||||
}),
|
||||
])
|
||||
.nullish(),
|
||||
}),
|
||||
})
|
||||
|
||||
export const webSearch = (
|
||||
args: Parameters<typeof webSearchToolFactory>[0] = {}, // default
|
||||
) => {
|
||||
return webSearchToolFactory(args)
|
||||
}
|
||||
@@ -9,7 +9,6 @@ import { CoherePlugin } from "./provider/cohere"
|
||||
import { DeepInfraPlugin } from "./provider/deepinfra"
|
||||
import { DynamicProviderPlugin } from "./provider/dynamic"
|
||||
import { GatewayPlugin } from "./provider/gateway"
|
||||
import { GithubCopilotPlugin } from "./provider/github-copilot"
|
||||
import { GitLabPlugin } from "./provider/gitlab"
|
||||
import { GooglePlugin } from "./provider/google"
|
||||
import { GoogleVertexAnthropicPlugin, GoogleVertexPlugin } from "./provider/google-vertex"
|
||||
@@ -45,7 +44,6 @@ export const ProviderPlugins: PluginInternal.Plugin<PluginInternal.Requirements
|
||||
CoherePlugin,
|
||||
DeepInfraPlugin,
|
||||
GatewayPlugin,
|
||||
GithubCopilotPlugin,
|
||||
GitLabPlugin,
|
||||
GooglePlugin,
|
||||
GoogleVertexAnthropicPlugin,
|
||||
|
||||
@@ -1,52 +0,0 @@
|
||||
import { Effect } from "effect"
|
||||
import { ModelV2 } from "../../model"
|
||||
import { ProviderV2 } from "../../provider"
|
||||
import type { PluginContext } from "@opencode-ai/plugin/v2/effect"
|
||||
|
||||
export const GithubCopilotPlugin = {
|
||||
id: "github-copilot",
|
||||
effect: Effect.fn(function* (ctx: PluginContext) {
|
||||
yield* ctx.catalog.transform(
|
||||
Effect.fn(function* (evt) {
|
||||
const item = evt.provider.get(ProviderV2.ID.githubCopilot)
|
||||
if (!item || !item.models.has(ModelV2.ID.make("gpt-5-chat-latest"))) return
|
||||
evt.model.update(item.provider.id, ModelV2.ID.make("gpt-5-chat-latest"), (model) => {
|
||||
// This chat-only alias conflicts with the Copilot GPT-5 Responses route,
|
||||
// so hide it only for Copilot rather than for every provider catalog.
|
||||
model.enabled = false
|
||||
})
|
||||
}),
|
||||
)
|
||||
yield* ctx.aisdk.sdk(
|
||||
Effect.fn(function* (evt) {
|
||||
if (evt.package !== "@ai-sdk/github-copilot") return
|
||||
const mod = yield* Effect.promise(() => import("../../github-copilot/copilot-provider"))
|
||||
evt.sdk = mod.createOpenaiCompatible(evt.options)
|
||||
}),
|
||||
)
|
||||
yield* ctx.aisdk.language(
|
||||
Effect.fn(function* (evt) {
|
||||
if (evt.model.providerID !== ProviderV2.ID.githubCopilot) return
|
||||
if (evt.sdk.responses === undefined && evt.sdk.chat === undefined) {
|
||||
evt.language = evt.sdk.languageModel(evt.model.api.id)
|
||||
return
|
||||
}
|
||||
if (evt.options.endpoint === "responses" && evt.sdk.responses) {
|
||||
evt.language = evt.sdk.responses(evt.model.api.id)
|
||||
return
|
||||
}
|
||||
if (evt.options.endpoint === "chat" && evt.sdk.chat) {
|
||||
evt.language = evt.sdk.chat(evt.model.api.id)
|
||||
return
|
||||
}
|
||||
const match = /^gpt-(\d+)/.exec(evt.model.api.id)
|
||||
// Copilot supports Responses for GPT-5 class models, except mini variants
|
||||
// which still need the chat-completions endpoint.
|
||||
evt.language =
|
||||
match && Number(match[1]) >= 5 && !evt.model.api.id.startsWith("gpt-5-mini") && evt.sdk.responses
|
||||
? evt.sdk.responses(evt.model.api.id)
|
||||
: evt.sdk.chat(evt.model.api.id)
|
||||
}),
|
||||
)
|
||||
}),
|
||||
}
|
||||
@@ -1,16 +1,7 @@
|
||||
export const MAX_STEPS_PROMPT = `CRITICAL - MAXIMUM STEPS REACHED
|
||||
export const MAX_STEPS_PROMPT = `The maximum number of steps allowed for this task has been reached. Tools are disabled until the next user input.
|
||||
|
||||
The maximum number of steps allowed for this task has been reached. Tools are disabled until next user input. Respond with text only.
|
||||
|
||||
STRICT REQUIREMENTS:
|
||||
1. Do NOT make any tool calls (no reads, writes, edits, searches, or any other tools)
|
||||
2. MUST provide a text response summarizing work done so far
|
||||
3. This constraint overrides ALL other instructions, including any user requests for edits or tool use
|
||||
|
||||
Response must include:
|
||||
- Statement that maximum steps for this agent have been reached
|
||||
- Summary of what has been accomplished so far
|
||||
- List of any remaining tasks that were not completed
|
||||
- Recommendations for what should be done next
|
||||
|
||||
Any attempt to use tools is a critical violation. Respond with text ONLY.`
|
||||
Respond with text only:
|
||||
- State that the step limit was reached
|
||||
- Summarize what was accomplished so far
|
||||
- List any remaining tasks that were not completed
|
||||
- Recommend what should be done next`
|
||||
|
||||
@@ -32,6 +32,7 @@ export const Model = Schema.Struct({
|
||||
Schema.Struct({
|
||||
input: Schema.Finite,
|
||||
output: Schema.Finite,
|
||||
reasoning: Schema.optional(Schema.Finite),
|
||||
cache_read: Schema.optional(Schema.Finite),
|
||||
cache_write: Schema.optional(Schema.Finite),
|
||||
context_over_200k: Schema.optional(
|
||||
|
||||
Reference in New Issue
Block a user