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import type { APICallError, ModelMessage } from "ai"
import { unique } from "remeda"
import type { JSONSchema } from "zod/v4/core"
import type { Provider } from "./provider"
export namespace ProviderTransform {
function normalizeMessages(msgs: ModelMessage[], model: Provider.Model): ModelMessage[] {
if (model.api.id.includes("claude")) {
return msgs.map((msg) => {
if ((msg.role === "assistant" || msg.role === "tool") && Array.isArray(msg.content)) {
msg.content = msg.content.map((part) => {
if ((part.type === "tool-call" || part.type === "tool-result") && "toolCallId" in part) {
return {
...part,
toolCallId: part.toolCallId.replace(/[^a-zA-Z0-9_-]/g, "_"),
}
}
return part
})
}
return msg
})
}
if (model.providerID === "mistral" || model.api.id.toLowerCase().includes("mistral")) {
const result: ModelMessage[] = []
for (let i = 0; i < msgs.length; i++) {
const msg = msgs[i]
const nextMsg = msgs[i + 1]
if ((msg.role === "assistant" || msg.role === "tool") && Array.isArray(msg.content)) {
msg.content = msg.content.map((part) => {
if ((part.type === "tool-call" || part.type === "tool-result") && "toolCallId" in part) {
// Mistral requires alphanumeric tool call IDs with exactly 9 characters
const normalizedId = part.toolCallId
.replace(/[^a-zA-Z0-9]/g, "") // Remove non-alphanumeric characters
.substring(0, 9) // Take first 9 characters
.padEnd(9, "0") // Pad with zeros if less than 9 characters
return {
...part,
toolCallId: normalizedId,
}
}
return part
})
}
result.push(msg)
// Fix message sequence: tool messages cannot be followed by user messages
if (msg.role === "tool" && nextMsg?.role === "user") {
result.push({
role: "assistant",
content: [
{
type: "text",
text: "Done.",
},
],
})
}
}
return result
}
// DeepSeek: Handle reasoning_content for tool call continuations
// - With tool calls: Include reasoning_content in providerOptions so model can continue reasoning
// - Without tool calls: Strip reasoning (new turn doesn't need previous reasoning)
// See: https://api-docs.deepseek.com/guides/thinking_mode
if (model.providerID === "deepseek" || model.api.id.toLowerCase().includes("deepseek")) {
return msgs.map((msg) => {
if (msg.role === "assistant" && Array.isArray(msg.content)) {
const reasoningParts = msg.content.filter((part: any) => part.type === "reasoning")
const hasToolCalls = msg.content.some((part: any) => part.type === "tool-call")
const reasoningText = reasoningParts.map((part: any) => part.text).join("")
// Filter out reasoning parts from content
const filteredContent = msg.content.filter((part: any) => part.type !== "reasoning")
// If this message has tool calls and reasoning, include reasoning_content
// so DeepSeek can continue reasoning after tool execution
if (hasToolCalls && reasoningText) {
return {
...msg,
content: filteredContent,
providerOptions: {
...msg.providerOptions,
openaiCompatible: {
...(msg.providerOptions as any)?.openaiCompatible,
reasoning_content: reasoningText,
},
},
}
}
// For final answers (no tool calls), just strip reasoning
return {
...msg,
content: filteredContent,
}
}
return msg
})
}
return msgs
}
function applyCaching(msgs: ModelMessage[], providerID: string): ModelMessage[] {
const system = msgs.filter((msg) => msg.role === "system").slice(0, 2)
const final = msgs.filter((msg) => msg.role !== "system").slice(-2)
const providerOptions = {
anthropic: {
cacheControl: { type: "ephemeral" },
},
openrouter: {
cache_control: { type: "ephemeral" },
},
bedrock: {
cachePoint: { type: "ephemeral" },
},
openaiCompatible: {
cache_control: { type: "ephemeral" },
},
}
for (const msg of unique([...system, ...final])) {
const shouldUseContentOptions = providerID !== "anthropic" && Array.isArray(msg.content) && msg.content.length > 0
if (shouldUseContentOptions) {
const lastContent = msg.content[msg.content.length - 1]
if (lastContent && typeof lastContent === "object") {
lastContent.providerOptions = {
...lastContent.providerOptions,
...providerOptions,
}
continue
}
}
msg.providerOptions = {
...msg.providerOptions,
...providerOptions,
}
}
return msgs
}
export function message(msgs: ModelMessage[], model: Provider.Model) {
msgs = normalizeMessages(msgs, model)
if (model.providerID === "anthropic" || model.api.id.includes("anthropic") || model.api.id.includes("claude")) {
msgs = applyCaching(msgs, model.providerID)
}
return msgs
}
export function temperature(model: Provider.Model) {
if (model.api.id.toLowerCase().includes("qwen")) return 0.55
if (model.api.id.toLowerCase().includes("claude")) return undefined
if (model.api.id.toLowerCase().includes("gemini-3-pro")) return 1.0
return 0
}
export function topP(model: Provider.Model) {
if (model.api.id.toLowerCase().includes("qwen")) return 1
return undefined
}
export function options(
model: Provider.Model,
sessionID: string,
providerOptions?: Record<string, any>,
): Record<string, any> {
const result: Record<string, any> = {}
// switch to providerID later, for now use this
if (model.api.npm === "@openrouter/ai-sdk-provider") {
result["usage"] = {
include: true,
}
}
if (model.providerID === "openai" || providerOptions?.setCacheKey) {
result["promptCacheKey"] = sessionID
}
if (
model.providerID === "google" ||
(model.providerID.startsWith("opencode") && model.api.id.includes("gemini-3"))
) {
result["thinkingConfig"] = {
includeThoughts: true,
}
}
if (model.api.id.includes("gpt-5") && !model.api.id.includes("gpt-5-chat")) {
if (model.providerID.includes("codex")) {
result["store"] = false
}
if (!model.api.id.includes("codex") && !model.api.id.includes("gpt-5-pro")) {
result["reasoningEffort"] = "medium"
}
if (model.api.id.endsWith("gpt-5.1") && model.providerID !== "azure") {
result["textVerbosity"] = "low"
}
if (model.providerID.startsWith("opencode")) {
result["promptCacheKey"] = sessionID
result["include"] = ["reasoning.encrypted_content"]
result["reasoningSummary"] = "auto"
}
}
return result
}
export function smallOptions(model: Provider.Model) {
const options: Record<string, any> = {}
if (model.providerID === "openai" || model.api.id.includes("gpt-5")) {
if (model.api.id.includes("5.1")) {
options["reasoningEffort"] = "low"
} else {
options["reasoningEffort"] = "minimal"
}
}
if (model.providerID === "google") {
options["thinkingConfig"] = {
thinkingBudget: 0,
}
}
return options
}
export function providerOptions(npm: string | undefined, providerID: string, options: { [x: string]: any }) {
switch (npm) {
case "@ai-sdk/openai":
case "@ai-sdk/azure":
return {
["openai" as string]: options,
}
case "@ai-sdk/amazon-bedrock":
return {
["bedrock" as string]: options,
}
case "@ai-sdk/anthropic":
return {
["anthropic" as string]: options,
}
case "@ai-sdk/google":
return {
["google" as string]: options,
}
case "@ai-sdk/gateway":
return {
["gateway" as string]: options,
}
case "@openrouter/ai-sdk-provider":
return {
["openrouter" as string]: options,
}
default:
return {
[providerID]: options,
}
}
}
export function maxOutputTokens(
npm: string,
options: Record<string, any>,
modelLimit: number,
globalLimit: number,
): number {
const modelCap = modelLimit || globalLimit
const standardLimit = Math.min(modelCap, globalLimit)
if (npm === "@ai-sdk/anthropic") {
const thinking = options?.["thinking"]
const budgetTokens = typeof thinking?.["budgetTokens"] === "number" ? thinking["budgetTokens"] : 0
const enabled = thinking?.["type"] === "enabled"
if (enabled && budgetTokens > 0) {
// Return text tokens so that text + thinking <= model cap, preferring 32k text when possible.
if (budgetTokens + standardLimit <= modelCap) {
return standardLimit
}
return modelCap - budgetTokens
}
}
return standardLimit
}
export function schema(model: Provider.Model, schema: JSONSchema.BaseSchema) {
/*
if (["openai", "azure"].includes(providerID)) {
if (schema.type === "object" && schema.properties) {
for (const [key, value] of Object.entries(schema.properties)) {
if (schema.required?.includes(key)) continue
schema.properties[key] = {
anyOf: [
value as JSONSchema.JSONSchema,
{
type: "null",
},
],
}
}
}
}
*/
// Convert integer enums to string enums for Google/Gemini
if (model.providerID === "google" || model.api.id.includes("gemini")) {
const sanitizeGemini = (obj: any): any => {
if (obj === null || typeof obj !== "object") {
return obj
}
if (Array.isArray(obj)) {
return obj.map(sanitizeGemini)
}
const result: any = {}
for (const [key, value] of Object.entries(obj)) {
if (key === "enum" && Array.isArray(value)) {
// Convert all enum values to strings
result[key] = value.map((v) => String(v))
// If we have integer type with enum, change type to string
if (result.type === "integer" || result.type === "number") {
result.type = "string"
}
} else if (typeof value === "object" && value !== null) {
result[key] = sanitizeGemini(value)
} else {
result[key] = value
}
}
// Filter required array to only include fields that exist in properties
if (result.type === "object" && result.properties && Array.isArray(result.required)) {
result.required = result.required.filter((field: any) => field in result.properties)
}
return result
}
schema = sanitizeGemini(schema)
}
return schema
}
export function error(providerID: string, error: APICallError) {
let message = error.message
if (providerID === "github-copilot" && message.includes("The requested model is not supported")) {
return (
message +
"\n\nMake sure the model is enabled in your copilot settings: https://github.com/settings/copilot/features"
)
}
return message
}
}