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https://github.com/getcompanion-ai/co-mono.git
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refactor(ai): share openai responses logic
This commit is contained in:
Markus Ylisiurunen
Mario Zechner
parent
085c378d34
commit
284ff81035
3 changed files with 445 additions and 888 deletions
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@ -1,51 +1,12 @@
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import type OpenAI from "openai";
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import { AzureOpenAI } from "openai";
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import type {
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Tool as OpenAITool,
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ResponseCreateParamsStreaming,
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ResponseFunctionToolCall,
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ResponseInput,
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ResponseInputContent,
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ResponseInputImage,
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ResponseInputText,
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ResponseOutputMessage,
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ResponseReasoningItem,
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} from "openai/resources/responses/responses.js";
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import { calculateCost } from "../models.js";
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import type { ResponseCreateParamsStreaming } from "openai/resources/responses/responses.js";
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import { getEnvApiKey } from "../stream.js";
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import type {
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Api,
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AssistantMessage,
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Context,
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Model,
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StopReason,
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StreamFunction,
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StreamOptions,
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TextContent,
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ThinkingContent,
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Tool,
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ToolCall,
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} from "../types.js";
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import type { Api, AssistantMessage, Context, Model, StreamFunction, StreamOptions } from "../types.js";
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import { AssistantMessageEventStream } from "../utils/event-stream.js";
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import { parseStreamingJson } from "../utils/json-parse.js";
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import { sanitizeSurrogates } from "../utils/sanitize-unicode.js";
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import { transformMessages } from "./transform-messages.js";
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/** Fast deterministic hash to shorten long strings */
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function shortHash(str: string): string {
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let h1 = 0xdeadbeef;
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let h2 = 0x41c6ce57;
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for (let i = 0; i < str.length; i++) {
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const ch = str.charCodeAt(i);
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h1 = Math.imul(h1 ^ ch, 2654435761);
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h2 = Math.imul(h2 ^ ch, 1597334677);
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}
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h1 = Math.imul(h1 ^ (h1 >>> 16), 2246822507) ^ Math.imul(h2 ^ (h2 >>> 13), 3266489909);
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h2 = Math.imul(h2 ^ (h2 >>> 16), 2246822507) ^ Math.imul(h1 ^ (h1 >>> 13), 3266489909);
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return (h2 >>> 0).toString(36) + (h1 >>> 0).toString(36);
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}
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import { convertResponsesMessages, convertResponsesTools, processResponsesStream } from "./openai-responses-shared.js";
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const DEFAULT_AZURE_API_VERSION = "v1";
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const AZURE_TOOL_CALL_PROVIDERS = new Set(["openai", "openai-codex", "opencode", "azure-openai-responses"]);
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function parseDeploymentNameMap(value: string | undefined): Map<string, string> {
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const map = new Map<string, string>();
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@ -122,213 +83,7 @@ export const streamAzureOpenAIResponses: StreamFunction<"azure-openai-responses"
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);
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stream.push({ type: "start", partial: output });
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let currentItem: ResponseReasoningItem | ResponseOutputMessage | ResponseFunctionToolCall | null = null;
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let currentBlock: ThinkingContent | TextContent | (ToolCall & { partialJson: string }) | null = null;
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const blocks = output.content;
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const blockIndex = () => blocks.length - 1;
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for await (const event of openaiStream) {
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// Handle output item start
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if (event.type === "response.output_item.added") {
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const item = event.item;
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if (item.type === "reasoning") {
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currentItem = item;
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currentBlock = { type: "thinking", thinking: "" };
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output.content.push(currentBlock);
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stream.push({ type: "thinking_start", contentIndex: blockIndex(), partial: output });
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} else if (item.type === "message") {
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currentItem = item;
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currentBlock = { type: "text", text: "" };
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output.content.push(currentBlock);
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stream.push({ type: "text_start", contentIndex: blockIndex(), partial: output });
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} else if (item.type === "function_call") {
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currentItem = item;
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currentBlock = {
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type: "toolCall",
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id: `${item.call_id}|${item.id}`,
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name: item.name,
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arguments: {},
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partialJson: item.arguments || "",
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};
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output.content.push(currentBlock);
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stream.push({ type: "toolcall_start", contentIndex: blockIndex(), partial: output });
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}
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}
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// Handle reasoning summary deltas
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else if (event.type === "response.reasoning_summary_part.added") {
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if (currentItem && currentItem.type === "reasoning") {
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currentItem.summary = currentItem.summary || [];
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currentItem.summary.push(event.part);
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}
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} else if (event.type === "response.reasoning_summary_text.delta") {
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if (
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currentItem &&
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currentItem.type === "reasoning" &&
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currentBlock &&
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currentBlock.type === "thinking"
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) {
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currentItem.summary = currentItem.summary || [];
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const lastPart = currentItem.summary[currentItem.summary.length - 1];
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if (lastPart) {
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currentBlock.thinking += event.delta;
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lastPart.text += event.delta;
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stream.push({
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type: "thinking_delta",
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contentIndex: blockIndex(),
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delta: event.delta,
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partial: output,
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});
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}
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}
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}
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// Add a new line between summary parts (hack...)
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else if (event.type === "response.reasoning_summary_part.done") {
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if (
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currentItem &&
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currentItem.type === "reasoning" &&
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currentBlock &&
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currentBlock.type === "thinking"
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) {
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currentItem.summary = currentItem.summary || [];
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const lastPart = currentItem.summary[currentItem.summary.length - 1];
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if (lastPart) {
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currentBlock.thinking += "\n\n";
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lastPart.text += "\n\n";
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stream.push({
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type: "thinking_delta",
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contentIndex: blockIndex(),
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delta: "\n\n",
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partial: output,
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});
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}
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}
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}
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// Handle text output deltas
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else if (event.type === "response.content_part.added") {
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if (currentItem && currentItem.type === "message") {
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currentItem.content = currentItem.content || [];
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// Filter out ReasoningText, only accept output_text and refusal
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if (event.part.type === "output_text" || event.part.type === "refusal") {
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currentItem.content.push(event.part);
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}
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}
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} else if (event.type === "response.output_text.delta") {
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if (currentItem && currentItem.type === "message" && currentBlock && currentBlock.type === "text") {
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if (!currentItem.content || currentItem.content.length === 0) {
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continue;
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}
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const lastPart = currentItem.content[currentItem.content.length - 1];
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if (lastPart && lastPart.type === "output_text") {
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currentBlock.text += event.delta;
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lastPart.text += event.delta;
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stream.push({
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type: "text_delta",
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contentIndex: blockIndex(),
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delta: event.delta,
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partial: output,
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});
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}
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}
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} else if (event.type === "response.refusal.delta") {
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if (currentItem && currentItem.type === "message" && currentBlock && currentBlock.type === "text") {
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if (!currentItem.content || currentItem.content.length === 0) {
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continue;
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}
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const lastPart = currentItem.content[currentItem.content.length - 1];
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if (lastPart && lastPart.type === "refusal") {
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currentBlock.text += event.delta;
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lastPart.refusal += event.delta;
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stream.push({
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type: "text_delta",
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contentIndex: blockIndex(),
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delta: event.delta,
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partial: output,
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});
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}
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}
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}
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// Handle function call argument deltas
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else if (event.type === "response.function_call_arguments.delta") {
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if (
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currentItem &&
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currentItem.type === "function_call" &&
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currentBlock &&
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currentBlock.type === "toolCall"
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) {
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currentBlock.partialJson += event.delta;
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currentBlock.arguments = parseStreamingJson(currentBlock.partialJson);
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stream.push({
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type: "toolcall_delta",
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contentIndex: blockIndex(),
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delta: event.delta,
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partial: output,
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});
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}
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}
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// Handle output item completion
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else if (event.type === "response.output_item.done") {
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const item = event.item;
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if (item.type === "reasoning" && currentBlock && currentBlock.type === "thinking") {
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currentBlock.thinking = item.summary?.map((s) => s.text).join("\n\n") || "";
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currentBlock.thinkingSignature = JSON.stringify(item);
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stream.push({
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type: "thinking_end",
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contentIndex: blockIndex(),
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content: currentBlock.thinking,
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partial: output,
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});
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currentBlock = null;
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} else if (item.type === "message" && currentBlock && currentBlock.type === "text") {
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currentBlock.text = item.content.map((c) => (c.type === "output_text" ? c.text : c.refusal)).join("");
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currentBlock.textSignature = item.id;
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stream.push({
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type: "text_end",
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contentIndex: blockIndex(),
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content: currentBlock.text,
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partial: output,
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});
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currentBlock = null;
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} else if (item.type === "function_call") {
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const toolCall: ToolCall = {
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type: "toolCall",
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id: `${item.call_id}|${item.id}`,
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name: item.name,
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arguments: JSON.parse(item.arguments),
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};
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stream.push({ type: "toolcall_end", contentIndex: blockIndex(), toolCall, partial: output });
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}
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}
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// Handle completion
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else if (event.type === "response.completed") {
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const response = event.response;
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if (response?.usage) {
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const cachedTokens = response.usage.input_tokens_details?.cached_tokens || 0;
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output.usage = {
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// OpenAI includes cached tokens in input_tokens, so subtract to get non-cached input
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input: (response.usage.input_tokens || 0) - cachedTokens,
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output: response.usage.output_tokens || 0,
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cacheRead: cachedTokens,
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cacheWrite: 0,
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totalTokens: response.usage.total_tokens || 0,
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cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0, total: 0 },
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};
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}
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calculateCost(model, output.usage);
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// Map status to stop reason
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output.stopReason = mapStopReason(response?.status);
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if (output.content.some((b) => b.type === "toolCall") && output.stopReason === "stop") {
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output.stopReason = "toolUse";
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}
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}
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// Handle errors
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else if (event.type === "error") {
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throw new Error(`Error Code ${event.code}: ${event.message}` || "Unknown error");
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} else if (event.type === "response.failed") {
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throw new Error("Unknown error");
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}
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}
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await processResponsesStream(openaiStream, output, stream, model);
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if (options?.signal?.aborted) {
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throw new Error("Request was aborted");
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@ -341,7 +96,7 @@ export const streamAzureOpenAIResponses: StreamFunction<"azure-openai-responses"
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stream.push({ type: "done", reason: output.stopReason, message: output });
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stream.end();
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} catch (error) {
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for (const block of output.content) delete (block as any).index;
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for (const block of output.content) delete (block as { index?: number }).index;
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output.stopReason = options?.signal?.aborted ? "aborted" : "error";
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output.errorMessage = error instanceof Error ? error.message : JSON.stringify(error);
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stream.push({ type: "error", reason: output.stopReason, error: output });
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@ -424,7 +179,7 @@ function buildParams(
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options: AzureOpenAIResponsesOptions | undefined,
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deploymentName: string,
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) {
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const messages = convertMessages(model, context);
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const messages = convertResponsesMessages(model, context, AZURE_TOOL_CALL_PROVIDERS);
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const params: ResponseCreateParamsStreaming = {
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model: deploymentName,
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@ -442,7 +197,7 @@ function buildParams(
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}
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if (context.tools) {
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params.tools = convertTools(context.tools);
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params.tools = convertResponsesTools(context.tools);
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}
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if (model.reasoning) {
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@ -470,182 +225,3 @@ function buildParams(
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return params;
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}
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function convertMessages(model: Model<"azure-openai-responses">, context: Context): ResponseInput {
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const messages: ResponseInput = [];
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const normalizeToolCallId = (id: string): string => {
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const allowedProviders = new Set(["openai", "openai-codex", "opencode", "azure-openai-responses"]);
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if (!allowedProviders.has(model.provider)) return id;
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if (!id.includes("|")) return id;
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const [callId, itemId] = id.split("|");
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const sanitizedCallId = callId.replace(/[^a-zA-Z0-9_-]/g, "_");
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let sanitizedItemId = itemId.replace(/[^a-zA-Z0-9_-]/g, "_");
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// OpenAI Responses API requires item id to start with "fc"
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if (!sanitizedItemId.startsWith("fc")) {
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sanitizedItemId = `fc_${sanitizedItemId}`;
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}
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const normalizedCallId = sanitizedCallId.length > 64 ? sanitizedCallId.slice(0, 64) : sanitizedCallId;
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const normalizedItemId = sanitizedItemId.length > 64 ? sanitizedItemId.slice(0, 64) : sanitizedItemId;
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return `${normalizedCallId}|${normalizedItemId}`;
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};
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const transformedMessages = transformMessages(context.messages, model, normalizeToolCallId);
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if (context.systemPrompt) {
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const role = model.reasoning ? "developer" : "system";
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messages.push({
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role,
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content: sanitizeSurrogates(context.systemPrompt),
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});
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}
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let msgIndex = 0;
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for (const msg of transformedMessages) {
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if (msg.role === "user") {
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if (typeof msg.content === "string") {
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messages.push({
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role: "user",
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content: [{ type: "input_text", text: sanitizeSurrogates(msg.content) }],
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});
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} else {
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const content: ResponseInputContent[] = msg.content.map((item): ResponseInputContent => {
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if (item.type === "text") {
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return {
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type: "input_text",
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text: sanitizeSurrogates(item.text),
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} satisfies ResponseInputText;
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} else {
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return {
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type: "input_image",
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detail: "auto",
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image_url: `data:${item.mimeType};base64,${item.data}`,
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} satisfies ResponseInputImage;
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}
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});
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const filteredContent = !model.input.includes("image")
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? content.filter((c) => c.type !== "input_image")
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: content;
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if (filteredContent.length === 0) continue;
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messages.push({
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role: "user",
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content: filteredContent,
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});
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}
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} else if (msg.role === "assistant") {
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const output: ResponseInput = [];
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for (const block of msg.content) {
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if (block.type === "thinking") {
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if (block.thinkingSignature) {
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const reasoningItem = JSON.parse(block.thinkingSignature);
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output.push(reasoningItem);
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}
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} else if (block.type === "text") {
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const textBlock = block as TextContent;
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// OpenAI requires id to be max 64 characters
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let msgId = textBlock.textSignature;
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if (!msgId) {
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msgId = `msg_${msgIndex}`;
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} else if (msgId.length > 64) {
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msgId = `msg_${shortHash(msgId)}`;
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}
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output.push({
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type: "message",
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role: "assistant",
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content: [{ type: "output_text", text: sanitizeSurrogates(textBlock.text), annotations: [] }],
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status: "completed",
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id: msgId,
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} satisfies ResponseOutputMessage);
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} else if (block.type === "toolCall") {
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const toolCall = block as ToolCall;
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output.push({
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type: "function_call",
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id: toolCall.id.split("|")[1],
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call_id: toolCall.id.split("|")[0],
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name: toolCall.name,
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arguments: JSON.stringify(toolCall.arguments),
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});
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}
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}
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if (output.length === 0) continue;
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messages.push(...output);
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} else if (msg.role === "toolResult") {
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// Extract text and image content
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const textResult = msg.content
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.filter((c) => c.type === "text")
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.map((c) => (c as any).text)
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.join("\n");
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const hasImages = msg.content.some((c) => c.type === "image");
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// Always send function_call_output with text (or placeholder if only images)
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const hasText = textResult.length > 0;
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messages.push({
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type: "function_call_output",
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call_id: msg.toolCallId.split("|")[0],
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output: sanitizeSurrogates(hasText ? textResult : "(see attached image)"),
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});
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// If there are images and model supports them, send a follow-up user message with images
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if (hasImages && model.input.includes("image")) {
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const contentParts: ResponseInputContent[] = [];
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// Add text prefix
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contentParts.push({
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type: "input_text",
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text: "Attached image(s) from tool result:",
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} satisfies ResponseInputText);
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// Add images
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for (const block of msg.content) {
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if (block.type === "image") {
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contentParts.push({
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type: "input_image",
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detail: "auto",
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image_url: `data:${(block as any).mimeType};base64,${(block as any).data}`,
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} satisfies ResponseInputImage);
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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: contentParts,
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});
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}
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}
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msgIndex++;
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}
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return messages;
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}
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|
||||
function convertTools(tools: Tool[]): OpenAITool[] {
|
||||
return tools.map((tool) => ({
|
||||
type: "function",
|
||||
name: tool.name,
|
||||
description: tool.description,
|
||||
parameters: tool.parameters as any, // TypeBox already generates JSON Schema
|
||||
strict: false,
|
||||
}));
|
||||
}
|
||||
|
||||
function mapStopReason(status: OpenAI.Responses.ResponseStatus | undefined): StopReason {
|
||||
if (!status) return "stop";
|
||||
switch (status) {
|
||||
case "completed":
|
||||
return "stop";
|
||||
case "incomplete":
|
||||
return "length";
|
||||
case "failed":
|
||||
case "cancelled":
|
||||
return "error";
|
||||
// These two are wonky ...
|
||||
case "in_progress":
|
||||
case "queued":
|
||||
return "stop";
|
||||
default: {
|
||||
const _exhaustive: never = status;
|
||||
throw new Error(`Unhandled stop reason: ${_exhaustive}`);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
|
|||
426
packages/ai/src/providers/openai-responses-shared.ts
Normal file
426
packages/ai/src/providers/openai-responses-shared.ts
Normal file
|
|
@ -0,0 +1,426 @@
|
|||
import type OpenAI from "openai";
|
||||
import type {
|
||||
Tool as OpenAITool,
|
||||
ResponseCreateParamsStreaming,
|
||||
ResponseFunctionToolCall,
|
||||
ResponseInput,
|
||||
ResponseInputContent,
|
||||
ResponseInputImage,
|
||||
ResponseInputText,
|
||||
ResponseOutputMessage,
|
||||
ResponseReasoningItem,
|
||||
ResponseStreamEvent,
|
||||
} from "openai/resources/responses/responses.js";
|
||||
import { calculateCost } from "../models.js";
|
||||
import type {
|
||||
Api,
|
||||
AssistantMessage,
|
||||
Context,
|
||||
ImageContent,
|
||||
Model,
|
||||
StopReason,
|
||||
TextContent,
|
||||
ThinkingContent,
|
||||
Tool,
|
||||
ToolCall,
|
||||
Usage,
|
||||
} from "../types.js";
|
||||
import type { AssistantMessageEventStream } from "../utils/event-stream.js";
|
||||
import { parseStreamingJson } from "../utils/json-parse.js";
|
||||
import { sanitizeSurrogates } from "../utils/sanitize-unicode.js";
|
||||
import { transformMessages } from "./transform-messages.js";
|
||||
|
||||
/** Fast deterministic hash to shorten long strings */
|
||||
function shortHash(str: string): string {
|
||||
let h1 = 0xdeadbeef;
|
||||
let h2 = 0x41c6ce57;
|
||||
for (let i = 0; i < str.length; i++) {
|
||||
const ch = str.charCodeAt(i);
|
||||
h1 = Math.imul(h1 ^ ch, 2654435761);
|
||||
h2 = Math.imul(h2 ^ ch, 1597334677);
|
||||
}
|
||||
h1 = Math.imul(h1 ^ (h1 >>> 16), 2246822507) ^ Math.imul(h2 ^ (h2 >>> 13), 3266489909);
|
||||
h2 = Math.imul(h2 ^ (h2 >>> 16), 2246822507) ^ Math.imul(h1 ^ (h1 >>> 13), 3266489909);
|
||||
return (h2 >>> 0).toString(36) + (h1 >>> 0).toString(36);
|
||||
}
|
||||
|
||||
export interface OpenAIResponsesStreamOptions {
|
||||
serviceTier?: ResponseCreateParamsStreaming["service_tier"];
|
||||
applyServiceTierPricing?: (
|
||||
usage: Usage,
|
||||
serviceTier: ResponseCreateParamsStreaming["service_tier"] | undefined,
|
||||
) => void;
|
||||
}
|
||||
|
||||
export function convertResponsesMessages<TApi extends Api>(
|
||||
model: Model<TApi>,
|
||||
context: Context,
|
||||
allowedToolCallProviders: ReadonlySet<string>,
|
||||
): ResponseInput {
|
||||
const messages: ResponseInput = [];
|
||||
|
||||
const normalizeToolCallId = (id: string): string => {
|
||||
if (!allowedToolCallProviders.has(model.provider)) return id;
|
||||
if (!id.includes("|")) return id;
|
||||
const [callId, itemId] = id.split("|");
|
||||
const sanitizedCallId = callId.replace(/[^a-zA-Z0-9_-]/g, "_");
|
||||
let sanitizedItemId = itemId.replace(/[^a-zA-Z0-9_-]/g, "_");
|
||||
// OpenAI Responses API requires item id to start with "fc"
|
||||
if (!sanitizedItemId.startsWith("fc")) {
|
||||
sanitizedItemId = `fc_${sanitizedItemId}`;
|
||||
}
|
||||
const normalizedCallId = sanitizedCallId.length > 64 ? sanitizedCallId.slice(0, 64) : sanitizedCallId;
|
||||
const normalizedItemId = sanitizedItemId.length > 64 ? sanitizedItemId.slice(0, 64) : sanitizedItemId;
|
||||
return `${normalizedCallId}|${normalizedItemId}`;
|
||||
};
|
||||
|
||||
const transformedMessages = transformMessages(context.messages, model, normalizeToolCallId);
|
||||
|
||||
if (context.systemPrompt) {
|
||||
const role = model.reasoning ? "developer" : "system";
|
||||
messages.push({
|
||||
role,
|
||||
content: sanitizeSurrogates(context.systemPrompt),
|
||||
});
|
||||
}
|
||||
|
||||
let msgIndex = 0;
|
||||
for (const msg of transformedMessages) {
|
||||
if (msg.role === "user") {
|
||||
if (typeof msg.content === "string") {
|
||||
messages.push({
|
||||
role: "user",
|
||||
content: [{ type: "input_text", text: sanitizeSurrogates(msg.content) }],
|
||||
});
|
||||
} else {
|
||||
const content: ResponseInputContent[] = msg.content.map((item): ResponseInputContent => {
|
||||
if (item.type === "text") {
|
||||
return {
|
||||
type: "input_text",
|
||||
text: sanitizeSurrogates(item.text),
|
||||
} satisfies ResponseInputText;
|
||||
}
|
||||
return {
|
||||
type: "input_image",
|
||||
detail: "auto",
|
||||
image_url: `data:${item.mimeType};base64,${item.data}`,
|
||||
} satisfies ResponseInputImage;
|
||||
});
|
||||
const filteredContent = !model.input.includes("image")
|
||||
? content.filter((c) => c.type !== "input_image")
|
||||
: content;
|
||||
if (filteredContent.length === 0) continue;
|
||||
messages.push({
|
||||
role: "user",
|
||||
content: filteredContent,
|
||||
});
|
||||
}
|
||||
} else if (msg.role === "assistant") {
|
||||
const output: ResponseInput = [];
|
||||
|
||||
for (const block of msg.content) {
|
||||
if (block.type === "thinking") {
|
||||
if (block.thinkingSignature) {
|
||||
const reasoningItem = JSON.parse(block.thinkingSignature) as ResponseReasoningItem;
|
||||
output.push(reasoningItem);
|
||||
}
|
||||
} else if (block.type === "text") {
|
||||
const textBlock = block as TextContent;
|
||||
// OpenAI requires id to be max 64 characters
|
||||
let msgId = textBlock.textSignature;
|
||||
if (!msgId) {
|
||||
msgId = `msg_${msgIndex}`;
|
||||
} else if (msgId.length > 64) {
|
||||
msgId = `msg_${shortHash(msgId)}`;
|
||||
}
|
||||
output.push({
|
||||
type: "message",
|
||||
role: "assistant",
|
||||
content: [{ type: "output_text", text: sanitizeSurrogates(textBlock.text), annotations: [] }],
|
||||
status: "completed",
|
||||
id: msgId,
|
||||
} satisfies ResponseOutputMessage);
|
||||
} else if (block.type === "toolCall") {
|
||||
const toolCall = block as ToolCall;
|
||||
const [callId, itemId] = toolCall.id.split("|");
|
||||
output.push({
|
||||
type: "function_call",
|
||||
id: itemId,
|
||||
call_id: callId,
|
||||
name: toolCall.name,
|
||||
arguments: JSON.stringify(toolCall.arguments),
|
||||
});
|
||||
}
|
||||
}
|
||||
if (output.length === 0) continue;
|
||||
messages.push(...output);
|
||||
} else if (msg.role === "toolResult") {
|
||||
// Extract text and image content
|
||||
const textResult = msg.content
|
||||
.filter((c): c is TextContent => c.type === "text")
|
||||
.map((c) => c.text)
|
||||
.join("\n");
|
||||
const hasImages = msg.content.some((c): c is ImageContent => c.type === "image");
|
||||
|
||||
// Always send function_call_output with text (or placeholder if only images)
|
||||
const hasText = textResult.length > 0;
|
||||
const [callId] = msg.toolCallId.split("|");
|
||||
messages.push({
|
||||
type: "function_call_output",
|
||||
call_id: callId,
|
||||
output: sanitizeSurrogates(hasText ? textResult : "(see attached image)"),
|
||||
});
|
||||
|
||||
// If there are images and model supports them, send a follow-up user message with images
|
||||
if (hasImages && model.input.includes("image")) {
|
||||
const contentParts: ResponseInputContent[] = [];
|
||||
|
||||
// Add text prefix
|
||||
contentParts.push({
|
||||
type: "input_text",
|
||||
text: "Attached image(s) from tool result:",
|
||||
} satisfies ResponseInputText);
|
||||
|
||||
// Add images
|
||||
for (const block of msg.content) {
|
||||
if (block.type === "image") {
|
||||
contentParts.push({
|
||||
type: "input_image",
|
||||
detail: "auto",
|
||||
image_url: `data:${block.mimeType};base64,${block.data}`,
|
||||
} satisfies ResponseInputImage);
|
||||
}
|
||||
}
|
||||
|
||||
messages.push({
|
||||
role: "user",
|
||||
content: contentParts,
|
||||
});
|
||||
}
|
||||
}
|
||||
msgIndex++;
|
||||
}
|
||||
|
||||
return messages;
|
||||
}
|
||||
|
||||
export function convertResponsesTools(tools: Tool[]): OpenAITool[] {
|
||||
return tools.map((tool) => ({
|
||||
type: "function",
|
||||
name: tool.name,
|
||||
description: tool.description,
|
||||
parameters: tool.parameters as any, // TypeBox already generates JSON Schema
|
||||
strict: false,
|
||||
}));
|
||||
}
|
||||
|
||||
export async function processResponsesStream<TApi extends Api>(
|
||||
openaiStream: AsyncIterable<ResponseStreamEvent>,
|
||||
output: AssistantMessage,
|
||||
stream: AssistantMessageEventStream,
|
||||
model: Model<TApi>,
|
||||
options?: OpenAIResponsesStreamOptions,
|
||||
): Promise<void> {
|
||||
let currentItem: ResponseReasoningItem | ResponseOutputMessage | ResponseFunctionToolCall | null = null;
|
||||
let currentBlock: ThinkingContent | TextContent | (ToolCall & { partialJson: string }) | null = null;
|
||||
const blocks = output.content;
|
||||
const blockIndex = () => blocks.length - 1;
|
||||
|
||||
for await (const event of openaiStream) {
|
||||
if (event.type === "response.output_item.added") {
|
||||
const item = event.item;
|
||||
if (item.type === "reasoning") {
|
||||
currentItem = item;
|
||||
currentBlock = { type: "thinking", thinking: "" };
|
||||
output.content.push(currentBlock);
|
||||
stream.push({ type: "thinking_start", contentIndex: blockIndex(), partial: output });
|
||||
} else if (item.type === "message") {
|
||||
currentItem = item;
|
||||
currentBlock = { type: "text", text: "" };
|
||||
output.content.push(currentBlock);
|
||||
stream.push({ type: "text_start", contentIndex: blockIndex(), partial: output });
|
||||
} else if (item.type === "function_call") {
|
||||
currentItem = item;
|
||||
currentBlock = {
|
||||
type: "toolCall",
|
||||
id: `${item.call_id}|${item.id}`,
|
||||
name: item.name,
|
||||
arguments: {},
|
||||
partialJson: item.arguments || "",
|
||||
};
|
||||
output.content.push(currentBlock);
|
||||
stream.push({ type: "toolcall_start", contentIndex: blockIndex(), partial: output });
|
||||
}
|
||||
} else if (event.type === "response.reasoning_summary_part.added") {
|
||||
if (currentItem && currentItem.type === "reasoning") {
|
||||
currentItem.summary = currentItem.summary || [];
|
||||
currentItem.summary.push(event.part);
|
||||
}
|
||||
} else if (event.type === "response.reasoning_summary_text.delta") {
|
||||
if (currentItem?.type === "reasoning" && currentBlock?.type === "thinking") {
|
||||
currentItem.summary = currentItem.summary || [];
|
||||
const lastPart = currentItem.summary[currentItem.summary.length - 1];
|
||||
if (lastPart) {
|
||||
currentBlock.thinking += event.delta;
|
||||
lastPart.text += event.delta;
|
||||
stream.push({
|
||||
type: "thinking_delta",
|
||||
contentIndex: blockIndex(),
|
||||
delta: event.delta,
|
||||
partial: output,
|
||||
});
|
||||
}
|
||||
}
|
||||
} else if (event.type === "response.reasoning_summary_part.done") {
|
||||
if (currentItem?.type === "reasoning" && currentBlock?.type === "thinking") {
|
||||
currentItem.summary = currentItem.summary || [];
|
||||
const lastPart = currentItem.summary[currentItem.summary.length - 1];
|
||||
if (lastPart) {
|
||||
currentBlock.thinking += "\n\n";
|
||||
lastPart.text += "\n\n";
|
||||
stream.push({
|
||||
type: "thinking_delta",
|
||||
contentIndex: blockIndex(),
|
||||
delta: "\n\n",
|
||||
partial: output,
|
||||
});
|
||||
}
|
||||
}
|
||||
} else if (event.type === "response.content_part.added") {
|
||||
if (currentItem?.type === "message") {
|
||||
currentItem.content = currentItem.content || [];
|
||||
// Filter out ReasoningText, only accept output_text and refusal
|
||||
if (event.part.type === "output_text" || event.part.type === "refusal") {
|
||||
currentItem.content.push(event.part);
|
||||
}
|
||||
}
|
||||
} else if (event.type === "response.output_text.delta") {
|
||||
if (currentItem?.type === "message" && currentBlock?.type === "text") {
|
||||
if (!currentItem.content || currentItem.content.length === 0) {
|
||||
continue;
|
||||
}
|
||||
const lastPart = currentItem.content[currentItem.content.length - 1];
|
||||
if (lastPart?.type === "output_text") {
|
||||
currentBlock.text += event.delta;
|
||||
lastPart.text += event.delta;
|
||||
stream.push({
|
||||
type: "text_delta",
|
||||
contentIndex: blockIndex(),
|
||||
delta: event.delta,
|
||||
partial: output,
|
||||
});
|
||||
}
|
||||
}
|
||||
} else if (event.type === "response.refusal.delta") {
|
||||
if (currentItem?.type === "message" && currentBlock?.type === "text") {
|
||||
if (!currentItem.content || currentItem.content.length === 0) {
|
||||
continue;
|
||||
}
|
||||
const lastPart = currentItem.content[currentItem.content.length - 1];
|
||||
if (lastPart?.type === "refusal") {
|
||||
currentBlock.text += event.delta;
|
||||
lastPart.refusal += event.delta;
|
||||
stream.push({
|
||||
type: "text_delta",
|
||||
contentIndex: blockIndex(),
|
||||
delta: event.delta,
|
||||
partial: output,
|
||||
});
|
||||
}
|
||||
}
|
||||
} else if (event.type === "response.function_call_arguments.delta") {
|
||||
if (currentItem?.type === "function_call" && currentBlock?.type === "toolCall") {
|
||||
currentBlock.partialJson += event.delta;
|
||||
currentBlock.arguments = parseStreamingJson(currentBlock.partialJson);
|
||||
stream.push({
|
||||
type: "toolcall_delta",
|
||||
contentIndex: blockIndex(),
|
||||
delta: event.delta,
|
||||
partial: output,
|
||||
});
|
||||
}
|
||||
} else if (event.type === "response.output_item.done") {
|
||||
const item = event.item;
|
||||
|
||||
if (item.type === "reasoning" && currentBlock?.type === "thinking") {
|
||||
currentBlock.thinking = item.summary?.map((s) => s.text).join("\n\n") || "";
|
||||
currentBlock.thinkingSignature = JSON.stringify(item);
|
||||
stream.push({
|
||||
type: "thinking_end",
|
||||
contentIndex: blockIndex(),
|
||||
content: currentBlock.thinking,
|
||||
partial: output,
|
||||
});
|
||||
currentBlock = null;
|
||||
} else if (item.type === "message" && currentBlock?.type === "text") {
|
||||
currentBlock.text = item.content.map((c) => (c.type === "output_text" ? c.text : c.refusal)).join("");
|
||||
currentBlock.textSignature = item.id;
|
||||
stream.push({
|
||||
type: "text_end",
|
||||
contentIndex: blockIndex(),
|
||||
content: currentBlock.text,
|
||||
partial: output,
|
||||
});
|
||||
currentBlock = null;
|
||||
} else if (item.type === "function_call") {
|
||||
const toolCall: ToolCall = {
|
||||
type: "toolCall",
|
||||
id: `${item.call_id}|${item.id}`,
|
||||
name: item.name,
|
||||
arguments: JSON.parse(item.arguments),
|
||||
};
|
||||
|
||||
stream.push({ type: "toolcall_end", contentIndex: blockIndex(), toolCall, partial: output });
|
||||
}
|
||||
} else if (event.type === "response.completed") {
|
||||
const response = event.response;
|
||||
if (response?.usage) {
|
||||
const cachedTokens = response.usage.input_tokens_details?.cached_tokens || 0;
|
||||
output.usage = {
|
||||
// OpenAI includes cached tokens in input_tokens, so subtract to get non-cached input
|
||||
input: (response.usage.input_tokens || 0) - cachedTokens,
|
||||
output: response.usage.output_tokens || 0,
|
||||
cacheRead: cachedTokens,
|
||||
cacheWrite: 0,
|
||||
totalTokens: response.usage.total_tokens || 0,
|
||||
cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0, total: 0 },
|
||||
};
|
||||
}
|
||||
calculateCost(model, output.usage);
|
||||
if (options?.applyServiceTierPricing) {
|
||||
const serviceTier = response?.service_tier ?? options.serviceTier;
|
||||
options.applyServiceTierPricing(output.usage, serviceTier);
|
||||
}
|
||||
// Map status to stop reason
|
||||
output.stopReason = mapStopReason(response?.status);
|
||||
if (output.content.some((b) => b.type === "toolCall") && output.stopReason === "stop") {
|
||||
output.stopReason = "toolUse";
|
||||
}
|
||||
} else if (event.type === "error") {
|
||||
throw new Error(`Error Code ${event.code}: ${event.message}` || "Unknown error");
|
||||
} else if (event.type === "response.failed") {
|
||||
throw new Error("Unknown error");
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
function mapStopReason(status: OpenAI.Responses.ResponseStatus | undefined): StopReason {
|
||||
if (!status) return "stop";
|
||||
switch (status) {
|
||||
case "completed":
|
||||
return "stop";
|
||||
case "incomplete":
|
||||
return "length";
|
||||
case "failed":
|
||||
case "cancelled":
|
||||
return "error";
|
||||
// These two are wonky ...
|
||||
case "in_progress":
|
||||
case "queued":
|
||||
return "stop";
|
||||
default: {
|
||||
const _exhaustive: never = status;
|
||||
throw new Error(`Unhandled stop reason: ${_exhaustive}`);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -1,49 +1,11 @@
|
|||
import OpenAI from "openai";
|
||||
import type {
|
||||
Tool as OpenAITool,
|
||||
ResponseCreateParamsStreaming,
|
||||
ResponseFunctionToolCall,
|
||||
ResponseInput,
|
||||
ResponseInputContent,
|
||||
ResponseInputImage,
|
||||
ResponseInputText,
|
||||
ResponseOutputMessage,
|
||||
ResponseReasoningItem,
|
||||
} from "openai/resources/responses/responses.js";
|
||||
import { calculateCost } from "../models.js";
|
||||
import type { ResponseCreateParamsStreaming } from "openai/resources/responses/responses.js";
|
||||
import { getEnvApiKey } from "../stream.js";
|
||||
import type {
|
||||
Api,
|
||||
AssistantMessage,
|
||||
Context,
|
||||
Model,
|
||||
StopReason,
|
||||
StreamFunction,
|
||||
StreamOptions,
|
||||
TextContent,
|
||||
ThinkingContent,
|
||||
Tool,
|
||||
ToolCall,
|
||||
Usage,
|
||||
} from "../types.js";
|
||||
import type { Api, AssistantMessage, Context, Model, StreamFunction, StreamOptions, Usage } from "../types.js";
|
||||
import { AssistantMessageEventStream } from "../utils/event-stream.js";
|
||||
import { parseStreamingJson } from "../utils/json-parse.js";
|
||||
import { sanitizeSurrogates } from "../utils/sanitize-unicode.js";
|
||||
import { transformMessages } from "./transform-messages.js";
|
||||
import { convertResponsesMessages, convertResponsesTools, processResponsesStream } from "./openai-responses-shared.js";
|
||||
|
||||
/** Fast deterministic hash to shorten long strings */
|
||||
function shortHash(str: string): string {
|
||||
let h1 = 0xdeadbeef;
|
||||
let h2 = 0x41c6ce57;
|
||||
for (let i = 0; i < str.length; i++) {
|
||||
const ch = str.charCodeAt(i);
|
||||
h1 = Math.imul(h1 ^ ch, 2654435761);
|
||||
h2 = Math.imul(h2 ^ ch, 1597334677);
|
||||
}
|
||||
h1 = Math.imul(h1 ^ (h1 >>> 16), 2246822507) ^ Math.imul(h2 ^ (h2 >>> 13), 3266489909);
|
||||
h2 = Math.imul(h2 ^ (h2 >>> 16), 2246822507) ^ Math.imul(h1 ^ (h1 >>> 13), 3266489909);
|
||||
return (h2 >>> 0).toString(36) + (h1 >>> 0).toString(36);
|
||||
}
|
||||
const OPENAI_TOOL_CALL_PROVIDERS = new Set(["openai", "openai-codex", "opencode"]);
|
||||
|
||||
// OpenAI Responses-specific options
|
||||
export interface OpenAIResponsesOptions extends StreamOptions {
|
||||
|
|
@ -94,219 +56,10 @@ export const streamOpenAIResponses: StreamFunction<"openai-responses"> = (
|
|||
);
|
||||
stream.push({ type: "start", partial: output });
|
||||
|
||||
let currentItem: ResponseReasoningItem | ResponseOutputMessage | ResponseFunctionToolCall | null = null;
|
||||
let currentBlock: ThinkingContent | TextContent | (ToolCall & { partialJson: string }) | null = null;
|
||||
const blocks = output.content;
|
||||
const blockIndex = () => blocks.length - 1;
|
||||
|
||||
for await (const event of openaiStream) {
|
||||
// Handle output item start
|
||||
if (event.type === "response.output_item.added") {
|
||||
const item = event.item;
|
||||
if (item.type === "reasoning") {
|
||||
currentItem = item;
|
||||
currentBlock = { type: "thinking", thinking: "" };
|
||||
output.content.push(currentBlock);
|
||||
stream.push({ type: "thinking_start", contentIndex: blockIndex(), partial: output });
|
||||
} else if (item.type === "message") {
|
||||
currentItem = item;
|
||||
currentBlock = { type: "text", text: "" };
|
||||
output.content.push(currentBlock);
|
||||
stream.push({ type: "text_start", contentIndex: blockIndex(), partial: output });
|
||||
} else if (item.type === "function_call") {
|
||||
currentItem = item;
|
||||
currentBlock = {
|
||||
type: "toolCall",
|
||||
id: `${item.call_id}|${item.id}`,
|
||||
name: item.name,
|
||||
arguments: {},
|
||||
partialJson: item.arguments || "",
|
||||
};
|
||||
output.content.push(currentBlock);
|
||||
stream.push({ type: "toolcall_start", contentIndex: blockIndex(), partial: output });
|
||||
}
|
||||
}
|
||||
// Handle reasoning summary deltas
|
||||
else if (event.type === "response.reasoning_summary_part.added") {
|
||||
if (currentItem && currentItem.type === "reasoning") {
|
||||
currentItem.summary = currentItem.summary || [];
|
||||
currentItem.summary.push(event.part);
|
||||
}
|
||||
} else if (event.type === "response.reasoning_summary_text.delta") {
|
||||
if (
|
||||
currentItem &&
|
||||
currentItem.type === "reasoning" &&
|
||||
currentBlock &&
|
||||
currentBlock.type === "thinking"
|
||||
) {
|
||||
currentItem.summary = currentItem.summary || [];
|
||||
const lastPart = currentItem.summary[currentItem.summary.length - 1];
|
||||
if (lastPart) {
|
||||
currentBlock.thinking += event.delta;
|
||||
lastPart.text += event.delta;
|
||||
stream.push({
|
||||
type: "thinking_delta",
|
||||
contentIndex: blockIndex(),
|
||||
delta: event.delta,
|
||||
partial: output,
|
||||
});
|
||||
}
|
||||
}
|
||||
}
|
||||
// Add a new line between summary parts (hack...)
|
||||
else if (event.type === "response.reasoning_summary_part.done") {
|
||||
if (
|
||||
currentItem &&
|
||||
currentItem.type === "reasoning" &&
|
||||
currentBlock &&
|
||||
currentBlock.type === "thinking"
|
||||
) {
|
||||
currentItem.summary = currentItem.summary || [];
|
||||
const lastPart = currentItem.summary[currentItem.summary.length - 1];
|
||||
if (lastPart) {
|
||||
currentBlock.thinking += "\n\n";
|
||||
lastPart.text += "\n\n";
|
||||
stream.push({
|
||||
type: "thinking_delta",
|
||||
contentIndex: blockIndex(),
|
||||
delta: "\n\n",
|
||||
partial: output,
|
||||
});
|
||||
}
|
||||
}
|
||||
}
|
||||
// Handle text output deltas
|
||||
else if (event.type === "response.content_part.added") {
|
||||
if (currentItem && currentItem.type === "message") {
|
||||
currentItem.content = currentItem.content || [];
|
||||
// Filter out ReasoningText, only accept output_text and refusal
|
||||
if (event.part.type === "output_text" || event.part.type === "refusal") {
|
||||
currentItem.content.push(event.part);
|
||||
}
|
||||
}
|
||||
} else if (event.type === "response.output_text.delta") {
|
||||
if (currentItem && currentItem.type === "message" && currentBlock && currentBlock.type === "text") {
|
||||
const lastPart = currentItem.content[currentItem.content.length - 1];
|
||||
if (lastPart && lastPart.type === "output_text") {
|
||||
currentBlock.text += event.delta;
|
||||
lastPart.text += event.delta;
|
||||
stream.push({
|
||||
type: "text_delta",
|
||||
contentIndex: blockIndex(),
|
||||
delta: event.delta,
|
||||
partial: output,
|
||||
});
|
||||
}
|
||||
}
|
||||
} else if (event.type === "response.refusal.delta") {
|
||||
if (currentItem && currentItem.type === "message" && currentBlock && currentBlock.type === "text") {
|
||||
const lastPart = currentItem.content[currentItem.content.length - 1];
|
||||
if (lastPart && lastPart.type === "refusal") {
|
||||
currentBlock.text += event.delta;
|
||||
lastPart.refusal += event.delta;
|
||||
stream.push({
|
||||
type: "text_delta",
|
||||
contentIndex: blockIndex(),
|
||||
delta: event.delta,
|
||||
partial: output,
|
||||
});
|
||||
}
|
||||
}
|
||||
}
|
||||
// Handle function call argument deltas
|
||||
else if (event.type === "response.function_call_arguments.delta") {
|
||||
if (
|
||||
currentItem &&
|
||||
currentItem.type === "function_call" &&
|
||||
currentBlock &&
|
||||
currentBlock.type === "toolCall"
|
||||
) {
|
||||
currentBlock.partialJson += event.delta;
|
||||
currentBlock.arguments = parseStreamingJson(currentBlock.partialJson);
|
||||
stream.push({
|
||||
type: "toolcall_delta",
|
||||
contentIndex: blockIndex(),
|
||||
delta: event.delta,
|
||||
partial: output,
|
||||
});
|
||||
}
|
||||
}
|
||||
// Handle function call arguments done (some providers send this instead of deltas)
|
||||
else if (event.type === "response.function_call_arguments.done") {
|
||||
if (currentItem?.type === "function_call" && currentBlock?.type === "toolCall") {
|
||||
currentBlock.partialJson = event.arguments;
|
||||
currentBlock.arguments = parseStreamingJson(currentBlock.partialJson);
|
||||
}
|
||||
}
|
||||
// Handle output item completion
|
||||
else if (event.type === "response.output_item.done") {
|
||||
const item = event.item;
|
||||
|
||||
if (item.type === "reasoning" && currentBlock && currentBlock.type === "thinking") {
|
||||
currentBlock.thinking = item.summary?.map((s) => s.text).join("\n\n") || "";
|
||||
currentBlock.thinkingSignature = JSON.stringify(item);
|
||||
stream.push({
|
||||
type: "thinking_end",
|
||||
contentIndex: blockIndex(),
|
||||
content: currentBlock.thinking,
|
||||
partial: output,
|
||||
});
|
||||
currentBlock = null;
|
||||
} else if (item.type === "message" && currentBlock && currentBlock.type === "text") {
|
||||
currentBlock.text = item.content.map((c) => (c.type === "output_text" ? c.text : c.refusal)).join("");
|
||||
currentBlock.textSignature = item.id;
|
||||
stream.push({
|
||||
type: "text_end",
|
||||
contentIndex: blockIndex(),
|
||||
content: currentBlock.text,
|
||||
partial: output,
|
||||
});
|
||||
currentBlock = null;
|
||||
} else if (item.type === "function_call") {
|
||||
const args =
|
||||
currentBlock?.type === "toolCall" && currentBlock.partialJson
|
||||
? JSON.parse(currentBlock.partialJson)
|
||||
: JSON.parse(item.arguments);
|
||||
const toolCall: ToolCall = {
|
||||
type: "toolCall",
|
||||
id: `${item.call_id}|${item.id}`,
|
||||
name: item.name,
|
||||
arguments: args,
|
||||
};
|
||||
currentBlock = null;
|
||||
stream.push({ type: "toolcall_end", contentIndex: blockIndex(), toolCall, partial: output });
|
||||
}
|
||||
}
|
||||
// Handle completion
|
||||
else if (event.type === "response.completed") {
|
||||
const response = event.response;
|
||||
if (response?.usage) {
|
||||
const cachedTokens = response.usage.input_tokens_details?.cached_tokens || 0;
|
||||
output.usage = {
|
||||
// OpenAI includes cached tokens in input_tokens, so subtract to get non-cached input
|
||||
input: (response.usage.input_tokens || 0) - cachedTokens,
|
||||
output: response.usage.output_tokens || 0,
|
||||
cacheRead: cachedTokens,
|
||||
cacheWrite: 0,
|
||||
totalTokens: response.usage.total_tokens || 0,
|
||||
cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0, total: 0 },
|
||||
};
|
||||
}
|
||||
calculateCost(model, output.usage);
|
||||
applyServiceTierPricing(output.usage, response?.service_tier ?? options?.serviceTier);
|
||||
// Map status to stop reason
|
||||
output.stopReason = mapStopReason(response?.status);
|
||||
if (output.content.some((b) => b.type === "toolCall") && output.stopReason === "stop") {
|
||||
output.stopReason = "toolUse";
|
||||
}
|
||||
}
|
||||
// Handle errors
|
||||
else if (event.type === "error") {
|
||||
throw new Error(`Error Code ${event.code}: ${event.message}` || "Unknown error");
|
||||
} else if (event.type === "response.failed") {
|
||||
throw new Error("Unknown error");
|
||||
}
|
||||
}
|
||||
await processResponsesStream(openaiStream, output, stream, model, {
|
||||
serviceTier: options?.serviceTier,
|
||||
applyServiceTierPricing,
|
||||
});
|
||||
|
||||
if (options?.signal?.aborted) {
|
||||
throw new Error("Request was aborted");
|
||||
|
|
@ -319,7 +72,7 @@ export const streamOpenAIResponses: StreamFunction<"openai-responses"> = (
|
|||
stream.push({ type: "done", reason: output.stopReason, message: output });
|
||||
stream.end();
|
||||
} catch (error) {
|
||||
for (const block of output.content) delete (block as any).index;
|
||||
for (const block of output.content) delete (block as { index?: number }).index;
|
||||
output.stopReason = options?.signal?.aborted ? "aborted" : "error";
|
||||
output.errorMessage = error instanceof Error ? error.message : JSON.stringify(error);
|
||||
stream.push({ type: "error", reason: output.stopReason, error: output });
|
||||
|
|
@ -385,7 +138,7 @@ function createClient(
|
|||
}
|
||||
|
||||
function buildParams(model: Model<"openai-responses">, context: Context, options?: OpenAIResponsesOptions) {
|
||||
const messages = convertMessages(model, context);
|
||||
const messages = convertResponsesMessages(model, context, OPENAI_TOOL_CALL_PROVIDERS);
|
||||
|
||||
const params: ResponseCreateParamsStreaming = {
|
||||
model: model.id,
|
||||
|
|
@ -407,7 +160,7 @@ function buildParams(model: Model<"openai-responses">, context: Context, options
|
|||
}
|
||||
|
||||
if (context.tools) {
|
||||
params.tools = convertTools(context.tools);
|
||||
params.tools = convertResponsesTools(context.tools);
|
||||
}
|
||||
|
||||
if (model.reasoning) {
|
||||
|
|
@ -436,183 +189,6 @@ function buildParams(model: Model<"openai-responses">, context: Context, options
|
|||
return params;
|
||||
}
|
||||
|
||||
function convertMessages(model: Model<"openai-responses">, context: Context): ResponseInput {
|
||||
const messages: ResponseInput = [];
|
||||
|
||||
const normalizeToolCallId = (id: string): string => {
|
||||
const allowedProviders = new Set(["openai", "openai-codex", "opencode"]);
|
||||
if (!allowedProviders.has(model.provider)) return id;
|
||||
if (!id.includes("|")) return id;
|
||||
const [callId, itemId] = id.split("|");
|
||||
const sanitizedCallId = callId.replace(/[^a-zA-Z0-9_-]/g, "_");
|
||||
let sanitizedItemId = itemId.replace(/[^a-zA-Z0-9_-]/g, "_");
|
||||
// OpenAI Responses API requires item id to start with "fc"
|
||||
if (!sanitizedItemId.startsWith("fc")) {
|
||||
sanitizedItemId = `fc_${sanitizedItemId}`;
|
||||
}
|
||||
const normalizedCallId = sanitizedCallId.length > 64 ? sanitizedCallId.slice(0, 64) : sanitizedCallId;
|
||||
const normalizedItemId = sanitizedItemId.length > 64 ? sanitizedItemId.slice(0, 64) : sanitizedItemId;
|
||||
return `${normalizedCallId}|${normalizedItemId}`;
|
||||
};
|
||||
|
||||
const transformedMessages = transformMessages(context.messages, model, normalizeToolCallId);
|
||||
|
||||
if (context.systemPrompt) {
|
||||
const role = model.reasoning ? "developer" : "system";
|
||||
messages.push({
|
||||
role,
|
||||
content: sanitizeSurrogates(context.systemPrompt),
|
||||
});
|
||||
}
|
||||
|
||||
let msgIndex = 0;
|
||||
for (const msg of transformedMessages) {
|
||||
if (msg.role === "user") {
|
||||
if (typeof msg.content === "string") {
|
||||
messages.push({
|
||||
role: "user",
|
||||
content: [{ type: "input_text", text: sanitizeSurrogates(msg.content) }],
|
||||
});
|
||||
} else {
|
||||
const content: ResponseInputContent[] = msg.content.map((item): ResponseInputContent => {
|
||||
if (item.type === "text") {
|
||||
return {
|
||||
type: "input_text",
|
||||
text: sanitizeSurrogates(item.text),
|
||||
} satisfies ResponseInputText;
|
||||
} else {
|
||||
return {
|
||||
type: "input_image",
|
||||
detail: "auto",
|
||||
image_url: `data:${item.mimeType};base64,${item.data}`,
|
||||
} satisfies ResponseInputImage;
|
||||
}
|
||||
});
|
||||
const filteredContent = !model.input.includes("image")
|
||||
? content.filter((c) => c.type !== "input_image")
|
||||
: content;
|
||||
if (filteredContent.length === 0) continue;
|
||||
messages.push({
|
||||
role: "user",
|
||||
content: filteredContent,
|
||||
});
|
||||
}
|
||||
} else if (msg.role === "assistant") {
|
||||
const output: ResponseInput = [];
|
||||
const assistantMsg = msg as AssistantMessage;
|
||||
|
||||
// Check if this message is from a different model (same provider, different model ID).
|
||||
// For such messages, tool call IDs with fc_ prefix need to be stripped to avoid
|
||||
// OpenAI's reasoning/function_call pairing validation errors.
|
||||
const isDifferentModel =
|
||||
assistantMsg.model !== model.id &&
|
||||
assistantMsg.provider === model.provider &&
|
||||
assistantMsg.api === model.api;
|
||||
|
||||
for (const block of msg.content) {
|
||||
if (block.type === "thinking") {
|
||||
if (block.thinkingSignature) {
|
||||
const reasoningItem = JSON.parse(block.thinkingSignature);
|
||||
output.push(reasoningItem);
|
||||
}
|
||||
} else if (block.type === "text") {
|
||||
const textBlock = block as TextContent;
|
||||
// OpenAI requires id to be max 64 characters
|
||||
let msgId = textBlock.textSignature;
|
||||
if (!msgId) {
|
||||
msgId = `msg_${msgIndex}`;
|
||||
} else if (msgId.length > 64) {
|
||||
msgId = `msg_${shortHash(msgId)}`;
|
||||
}
|
||||
output.push({
|
||||
type: "message",
|
||||
role: "assistant",
|
||||
content: [{ type: "output_text", text: sanitizeSurrogates(textBlock.text), annotations: [] }],
|
||||
status: "completed",
|
||||
id: msgId,
|
||||
} satisfies ResponseOutputMessage);
|
||||
} else if (block.type === "toolCall") {
|
||||
const toolCall = block as ToolCall;
|
||||
const callId = toolCall.id.split("|")[0];
|
||||
let itemId: string | undefined = toolCall.id.split("|")[1];
|
||||
|
||||
// For different-model messages, set id to undefined to avoid pairing validation.
|
||||
// OpenAI tracks which fc_xxx IDs were paired with rs_xxx reasoning items.
|
||||
// By omitting the id, we avoid triggering that validation (like cross-provider does).
|
||||
if (isDifferentModel && itemId?.startsWith("fc_")) {
|
||||
itemId = undefined;
|
||||
}
|
||||
|
||||
output.push({
|
||||
type: "function_call",
|
||||
id: itemId,
|
||||
call_id: callId,
|
||||
name: toolCall.name,
|
||||
arguments: JSON.stringify(toolCall.arguments),
|
||||
});
|
||||
}
|
||||
}
|
||||
if (output.length === 0) continue;
|
||||
messages.push(...output);
|
||||
} else if (msg.role === "toolResult") {
|
||||
// Extract text and image content
|
||||
const textResult = msg.content
|
||||
.filter((c) => c.type === "text")
|
||||
.map((c) => (c as any).text)
|
||||
.join("\n");
|
||||
const hasImages = msg.content.some((c) => c.type === "image");
|
||||
|
||||
// Always send function_call_output with text (or placeholder if only images)
|
||||
const hasText = textResult.length > 0;
|
||||
messages.push({
|
||||
type: "function_call_output",
|
||||
call_id: msg.toolCallId.split("|")[0],
|
||||
output: sanitizeSurrogates(hasText ? textResult : "(see attached image)"),
|
||||
});
|
||||
|
||||
// If there are images and model supports them, send a follow-up user message with images
|
||||
if (hasImages && model.input.includes("image")) {
|
||||
const contentParts: ResponseInputContent[] = [];
|
||||
|
||||
// Add text prefix
|
||||
contentParts.push({
|
||||
type: "input_text",
|
||||
text: "Attached image(s) from tool result:",
|
||||
} satisfies ResponseInputText);
|
||||
|
||||
// Add images
|
||||
for (const block of msg.content) {
|
||||
if (block.type === "image") {
|
||||
contentParts.push({
|
||||
type: "input_image",
|
||||
detail: "auto",
|
||||
image_url: `data:${(block as any).mimeType};base64,${(block as any).data}`,
|
||||
} satisfies ResponseInputImage);
|
||||
}
|
||||
}
|
||||
|
||||
messages.push({
|
||||
role: "user",
|
||||
content: contentParts,
|
||||
});
|
||||
}
|
||||
}
|
||||
msgIndex++;
|
||||
}
|
||||
|
||||
return messages;
|
||||
}
|
||||
|
||||
function convertTools(tools: Tool[]): OpenAITool[] {
|
||||
return tools.map((tool) => ({
|
||||
type: "function",
|
||||
name: tool.name,
|
||||
description: tool.description,
|
||||
parameters: tool.parameters as any, // TypeBox already generates JSON Schema
|
||||
strict: false,
|
||||
}));
|
||||
}
|
||||
|
||||
function getServiceTierCostMultiplier(serviceTier: ResponseCreateParamsStreaming["service_tier"] | undefined): number {
|
||||
switch (serviceTier) {
|
||||
case "flex":
|
||||
|
|
@ -634,24 +210,3 @@ function applyServiceTierPricing(usage: Usage, serviceTier: ResponseCreateParams
|
|||
usage.cost.cacheWrite *= multiplier;
|
||||
usage.cost.total = usage.cost.input + usage.cost.output + usage.cost.cacheRead + usage.cost.cacheWrite;
|
||||
}
|
||||
|
||||
function mapStopReason(status: OpenAI.Responses.ResponseStatus | undefined): StopReason {
|
||||
if (!status) return "stop";
|
||||
switch (status) {
|
||||
case "completed":
|
||||
return "stop";
|
||||
case "incomplete":
|
||||
return "length";
|
||||
case "failed":
|
||||
case "cancelled":
|
||||
return "error";
|
||||
// These two are wonky ...
|
||||
case "in_progress":
|
||||
case "queued":
|
||||
return "stop";
|
||||
default: {
|
||||
const _exhaustive: never = status;
|
||||
throw new Error(`Unhandled stop reason: ${_exhaustive}`);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue