mirror of
https://github.com/getcompanion-ai/co-mono.git
synced 2026-04-21 22:01:41 +00:00
Massive refactor of API
- Switch to function based API - Anthropic SDK style async generator - Fully typed with escape hatches for custom models
This commit is contained in:
Mario Zechner
parent
004de3c9d0
commit
66cefb236e
29 changed files with 5835 additions and 6225 deletions
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@ -10,58 +10,49 @@ import type {
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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 { QueuedGenerateStream } from "../generate.js";
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import { calculateCost } from "../models.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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LLM,
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LLMOptions,
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GenerateFunction,
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GenerateOptions,
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GenerateStream,
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Message,
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Model,
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StopReason,
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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 { transformMessages } from "./utils.js";
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export interface OpenAIResponsesLLMOptions extends LLMOptions {
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// OpenAI Responses-specific options
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export interface OpenAIResponsesOptions extends GenerateOptions {
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reasoningEffort?: "minimal" | "low" | "medium" | "high";
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reasoningSummary?: "auto" | "detailed" | "concise" | null;
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}
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export class OpenAIResponsesLLM implements LLM<OpenAIResponsesLLMOptions> {
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private client: OpenAI;
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private modelInfo: Model;
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/**
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* Generate function for OpenAI Responses API
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*/
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export const streamOpenAIResponses: GenerateFunction<"openai-responses"> = (
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model: Model<"openai-responses">,
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context: Context,
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options?: OpenAIResponsesOptions,
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): GenerateStream => {
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const stream = new QueuedGenerateStream();
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constructor(model: Model, apiKey?: string) {
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if (!apiKey) {
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if (!process.env.OPENAI_API_KEY) {
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throw new Error(
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"OpenAI API key is required. Set OPENAI_API_KEY environment variable or pass it as an argument.",
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);
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}
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apiKey = process.env.OPENAI_API_KEY;
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}
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this.client = new OpenAI({ apiKey, baseURL: model.baseUrl, dangerouslyAllowBrowser: true });
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this.modelInfo = model;
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}
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getModel(): Model {
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return this.modelInfo;
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}
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getApi(): string {
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return "openai-responses";
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}
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async generate(request: Context, options?: OpenAIResponsesLLMOptions): Promise<AssistantMessage> {
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// Start async processing
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(async () => {
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const output: AssistantMessage = {
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role: "assistant",
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content: [],
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api: this.getApi(),
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provider: this.modelInfo.provider,
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model: this.modelInfo.id,
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api: "openai-responses" as Api,
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provider: model.provider,
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model: model.id,
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usage: {
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input: 0,
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output: 0,
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@ -71,77 +62,31 @@ export class OpenAIResponsesLLM implements LLM<OpenAIResponsesLLMOptions> {
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},
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stopReason: "stop",
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};
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try {
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const input = this.convertToInput(request.messages, request.systemPrompt);
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// Create OpenAI client
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const client = createClient(model, options?.apiKey);
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const params = buildParams(model, context, options);
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const openaiStream = await client.responses.create(params, { signal: options?.signal });
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stream.push({ type: "start", partial: output });
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const params: ResponseCreateParamsStreaming = {
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model: this.modelInfo.id,
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input,
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stream: true,
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};
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if (options?.maxTokens) {
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params.max_output_tokens = options?.maxTokens;
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}
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if (options?.temperature !== undefined) {
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params.temperature = options?.temperature;
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}
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if (request.tools) {
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params.tools = this.convertTools(request.tools);
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}
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// Add reasoning options for models that support it
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if (this.modelInfo?.reasoning) {
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if (options?.reasoningEffort || options?.reasoningSummary) {
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params.reasoning = {
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effort: options?.reasoningEffort || "medium",
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summary: options?.reasoningSummary || "auto",
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};
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params.include = ["reasoning.encrypted_content"];
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} else {
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params.reasoning = {
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effort: this.modelInfo.name.startsWith("gpt-5") ? "minimal" : null,
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summary: null,
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};
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if (this.modelInfo.name.startsWith("gpt-5")) {
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// Jesus Christ, see https://community.openai.com/t/need-reasoning-false-option-for-gpt-5/1351588/7
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input.push({
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role: "developer",
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content: [
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{
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type: "input_text",
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text: "# Juice: 0 !important",
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},
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],
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});
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}
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}
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}
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const stream = await this.client.responses.create(params, {
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signal: options?.signal,
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});
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options?.onEvent?.({ type: "start", model: this.modelInfo.id, provider: this.modelInfo.provider });
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const outputItems: (ResponseReasoningItem | ResponseOutputMessage | ResponseFunctionToolCall)[] = [];
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let currentItem: ResponseReasoningItem | ResponseOutputMessage | ResponseFunctionToolCall | null = null;
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let currentBlock: ThinkingContent | TextContent | ToolCall | null = null;
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for await (const event of stream) {
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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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options?.onEvent?.({ type: "thinking_start" });
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outputItems.push(item);
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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", partial: output });
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} else if (item.type === "message") {
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options?.onEvent?.({ type: "text_start" });
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outputItems.push(item);
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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", partial: output });
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}
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}
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// Handle reasoning summary deltas
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@ -151,30 +96,42 @@ export class OpenAIResponsesLLM implements LLM<OpenAIResponsesLLMOptions> {
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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 (currentItem && currentItem.type === "reasoning") {
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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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options?.onEvent?.({
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stream.push({
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type: "thinking_delta",
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content: currentItem.summary.map((s) => s.text).join("\n\n"),
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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 (currentItem && currentItem.type === "reasoning") {
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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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options?.onEvent?.({
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stream.push({
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type: "thinking_delta",
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content: currentItem.summary.map((s) => s.text).join("\n\n"),
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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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@ -186,30 +143,28 @@ export class OpenAIResponsesLLM implements LLM<OpenAIResponsesLLMOptions> {
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currentItem.content.push(event.part);
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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") {
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if (currentItem && currentItem.type === "message" && currentBlock && currentBlock.type === "text") {
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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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options?.onEvent?.({
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stream.push({
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type: "text_delta",
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content: currentItem.content
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.map((c) => (c.type === "output_text" ? c.text : c.refusal))
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.join(""),
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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") {
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if (currentItem && currentItem.type === "message" && currentBlock && currentBlock.type === "text") {
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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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options?.onEvent?.({
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stream.push({
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type: "text_delta",
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content: currentItem.content
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.map((c) => (c.type === "output_text" ? c.text : c.refusal))
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.join(""),
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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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@ -218,14 +173,24 @@ export class OpenAIResponsesLLM implements LLM<OpenAIResponsesLLMOptions> {
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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") {
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outputItems[outputItems.length - 1] = item; // Update with final item
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const thinkingContent = item.summary?.map((s) => s.text).join("\n\n") || "";
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options?.onEvent?.({ type: "thinking_end", content: thinkingContent });
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} else if (item.type === "message") {
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outputItems[outputItems.length - 1] = item; // Update with final item
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const textContent = item.content.map((c) => (c.type === "output_text" ? c.text : c.refusal)).join("");
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options?.onEvent?.({ type: "text_end", content: textContent });
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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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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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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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@ -233,8 +198,8 @@ export class OpenAIResponsesLLM implements LLM<OpenAIResponsesLLMOptions> {
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name: item.name,
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arguments: JSON.parse(item.arguments),
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};
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options?.onEvent?.({ type: "toolCall", toolCall });
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outputItems.push(item);
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output.content.push(toolCall);
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stream.push({ type: "toolCall", toolCall, partial: output });
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}
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}
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// Handle completion
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@ -249,10 +214,10 @@ export class OpenAIResponsesLLM implements LLM<OpenAIResponsesLLMOptions> {
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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(this.modelInfo, output.usage);
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calculateCost(model, output.usage);
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// Map status to stop reason
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output.stopReason = this.mapStopReason(response?.status);
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if (outputItems.some((b) => b.type === "function_call") && output.stopReason === "stop") {
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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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@ -260,173 +225,215 @@ export class OpenAIResponsesLLM implements LLM<OpenAIResponsesLLMOptions> {
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else if (event.type === "error") {
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output.stopReason = "error";
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output.error = `Code ${event.code}: ${event.message}` || "Unknown error";
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options?.onEvent?.({ type: "error", error: output.error });
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stream.push({ type: "error", error: output.error, partial: output });
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stream.end();
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return output;
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} else if (event.type === "response.failed") {
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output.stopReason = "error";
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output.error = "Unknown error";
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options?.onEvent?.({ type: "error", error: output.error });
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stream.push({ type: "error", error: output.error, partial: output });
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stream.end();
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return output;
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}
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}
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// Convert output items to blocks
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for (const item of outputItems) {
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if (item.type === "reasoning") {
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output.content.push({
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type: "thinking",
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thinking: item.summary?.map((s: any) => s.text).join("\n\n") || "",
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thinkingSignature: JSON.stringify(item), // Full item for resubmission
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});
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} else if (item.type === "message") {
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output.content.push({
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type: "text",
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text: item.content.map((c) => (c.type === "output_text" ? c.text : c.refusal)).join(""),
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textSignature: item.id, // ID for resubmission
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});
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} else if (item.type === "function_call") {
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output.content.push({
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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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}
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}
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if (options?.signal?.aborted) {
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throw new Error("Request was aborted");
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}
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options?.onEvent?.({ type: "done", reason: output.stopReason, message: output });
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return output;
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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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output.stopReason = "error";
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output.error = error instanceof Error ? error.message : JSON.stringify(error);
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options?.onEvent?.({ type: "error", error: output.error });
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return output;
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stream.push({ type: "error", error: output.error, partial: output });
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stream.end();
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}
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})();
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return stream;
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};
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function createClient(model: Model<"openai-responses">, apiKey?: string) {
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if (!apiKey) {
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if (!process.env.OPENAI_API_KEY) {
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throw new Error(
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"OpenAI API key is required. Set OPENAI_API_KEY environment variable or pass it as an argument.",
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);
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}
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apiKey = process.env.OPENAI_API_KEY;
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}
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return new OpenAI({ apiKey, baseURL: model.baseUrl, dangerouslyAllowBrowser: true });
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}
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function buildParams(model: Model<"openai-responses">, context: Context, options?: OpenAIResponsesOptions) {
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const messages = convertMessages(model, context);
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const params: ResponseCreateParamsStreaming = {
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model: model.id,
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input: messages,
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stream: true,
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};
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if (options?.maxTokens) {
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params.max_output_tokens = options?.maxTokens;
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}
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private convertToInput(messages: Message[], systemPrompt?: string): ResponseInput {
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const input: ResponseInput = [];
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if (options?.temperature !== undefined) {
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params.temperature = options?.temperature;
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}
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// Transform messages for cross-provider compatibility
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const transformedMessages = transformMessages(messages, this.modelInfo, this.getApi());
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if (context.tools) {
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params.tools = convertTools(context.tools);
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}
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// Add system prompt if provided
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if (systemPrompt) {
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const role = this.modelInfo?.reasoning ? "developer" : "system";
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input.push({
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role,
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content: systemPrompt,
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});
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}
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if (model.reasoning) {
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if (options?.reasoningEffort || options?.reasoningSummary) {
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params.reasoning = {
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effort: options?.reasoningEffort || "medium",
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summary: options?.reasoningSummary || "auto",
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};
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params.include = ["reasoning.encrypted_content"];
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} else {
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params.reasoning = {
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effort: model.name.startsWith("gpt-5") ? "minimal" : null,
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summary: null,
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};
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// Convert messages
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for (const msg of transformedMessages) {
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if (msg.role === "user") {
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// Handle both string and array content
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if (typeof msg.content === "string") {
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input.push({
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role: "user",
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content: [{ type: "input_text", text: msg.content }],
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});
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} else {
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// Convert array content to OpenAI Responses format
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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: item.text,
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} satisfies ResponseInputText;
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||||
} else {
|
||||
// Image content - OpenAI Responses uses data URLs
|
||||
return {
|
||||
type: "input_image",
|
||||
detail: "auto",
|
||||
image_url: `data:${item.mimeType};base64,${item.data}`,
|
||||
} satisfies ResponseInputImage;
|
||||
}
|
||||
});
|
||||
const filteredContent = !this.modelInfo?.input.includes("image")
|
||||
? content.filter((c) => c.type !== "input_image")
|
||||
: content;
|
||||
input.push({
|
||||
role: "user",
|
||||
content: filteredContent,
|
||||
});
|
||||
}
|
||||
} else if (msg.role === "assistant") {
|
||||
// Process content blocks in order
|
||||
const output: ResponseInput = [];
|
||||
|
||||
for (const block of msg.content) {
|
||||
// Do not submit thinking blocks if the completion had an error (i.e. abort)
|
||||
if (block.type === "thinking" && msg.stopReason !== "error") {
|
||||
// Push the full reasoning item(s) from signature
|
||||
if (block.thinkingSignature) {
|
||||
const reasoningItem = JSON.parse(block.thinkingSignature);
|
||||
output.push(reasoningItem);
|
||||
}
|
||||
} else if (block.type === "text") {
|
||||
const textBlock = block as TextContent;
|
||||
output.push({
|
||||
type: "message",
|
||||
role: "assistant",
|
||||
content: [{ type: "output_text", text: textBlock.text, annotations: [] }],
|
||||
status: "completed",
|
||||
id: textBlock.textSignature || "msg_" + Math.random().toString(36).substring(2, 15),
|
||||
} satisfies ResponseOutputMessage);
|
||||
// Do not submit thinking blocks if the completion had an error (i.e. abort)
|
||||
} else if (block.type === "toolCall" && msg.stopReason !== "error") {
|
||||
const toolCall = block as ToolCall;
|
||||
output.push({
|
||||
type: "function_call",
|
||||
id: toolCall.id.split("|")[1], // Extract original ID
|
||||
call_id: toolCall.id.split("|")[0], // Extract call session ID
|
||||
name: toolCall.name,
|
||||
arguments: JSON.stringify(toolCall.arguments),
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
// Add all output items to input
|
||||
input.push(...output);
|
||||
} else if (msg.role === "toolResult") {
|
||||
// Tool results are sent as function_call_output
|
||||
input.push({
|
||||
type: "function_call_output",
|
||||
call_id: msg.toolCallId.split("|")[0], // Extract call session ID
|
||||
output: msg.content,
|
||||
if (model.name.startsWith("gpt-5")) {
|
||||
// Jesus Christ, see https://community.openai.com/t/need-reasoning-false-option-for-gpt-5/1351588/7
|
||||
messages.push({
|
||||
role: "developer",
|
||||
content: [
|
||||
{
|
||||
type: "input_text",
|
||||
text: "# Juice: 0 !important",
|
||||
},
|
||||
],
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
return input;
|
||||
}
|
||||
|
||||
private convertTools(tools: Tool[]): OpenAITool[] {
|
||||
return tools.map((tool) => ({
|
||||
type: "function",
|
||||
name: tool.name,
|
||||
description: tool.description,
|
||||
parameters: tool.parameters,
|
||||
strict: null,
|
||||
}));
|
||||
return params;
|
||||
}
|
||||
|
||||
function convertMessages(model: Model<"openai-responses">, context: Context): ResponseInput {
|
||||
const messages: ResponseInput = [];
|
||||
|
||||
const transformedMessages = transformMessages(context.messages, model);
|
||||
|
||||
if (context.systemPrompt) {
|
||||
const role = model.reasoning ? "developer" : "system";
|
||||
messages.push({
|
||||
role,
|
||||
content: context.systemPrompt,
|
||||
});
|
||||
}
|
||||
|
||||
private mapStopReason(status: string | undefined): StopReason {
|
||||
switch (status) {
|
||||
case "completed":
|
||||
return "stop";
|
||||
case "incomplete":
|
||||
return "length";
|
||||
case "failed":
|
||||
case "cancelled":
|
||||
return "error";
|
||||
default:
|
||||
return "stop";
|
||||
for (const msg of transformedMessages) {
|
||||
if (msg.role === "user") {
|
||||
if (typeof msg.content === "string") {
|
||||
messages.push({
|
||||
role: "user",
|
||||
content: [{ type: "input_text", text: msg.content }],
|
||||
});
|
||||
} else {
|
||||
const content: ResponseInputContent[] = msg.content.map((item): ResponseInputContent => {
|
||||
if (item.type === "text") {
|
||||
return {
|
||||
type: "input_text",
|
||||
text: 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 = [];
|
||||
|
||||
for (const block of msg.content) {
|
||||
// Do not submit thinking blocks if the completion had an error (i.e. abort)
|
||||
if (block.type === "thinking" && msg.stopReason !== "error") {
|
||||
if (block.thinkingSignature) {
|
||||
const reasoningItem = JSON.parse(block.thinkingSignature);
|
||||
output.push(reasoningItem);
|
||||
}
|
||||
} else if (block.type === "text") {
|
||||
const textBlock = block as TextContent;
|
||||
output.push({
|
||||
type: "message",
|
||||
role: "assistant",
|
||||
content: [{ type: "output_text", text: textBlock.text, annotations: [] }],
|
||||
status: "completed",
|
||||
id: textBlock.textSignature || "msg_" + Math.random().toString(36).substring(2, 15),
|
||||
} satisfies ResponseOutputMessage);
|
||||
// Do not submit toolcall blocks if the completion had an error (i.e. abort)
|
||||
} else if (block.type === "toolCall" && msg.stopReason !== "error") {
|
||||
const toolCall = block as ToolCall;
|
||||
output.push({
|
||||
type: "function_call",
|
||||
id: toolCall.id.split("|")[1],
|
||||
call_id: toolCall.id.split("|")[0],
|
||||
name: toolCall.name,
|
||||
arguments: JSON.stringify(toolCall.arguments),
|
||||
});
|
||||
}
|
||||
}
|
||||
if (output.length === 0) continue;
|
||||
messages.push(...output);
|
||||
} else if (msg.role === "toolResult") {
|
||||
messages.push({
|
||||
type: "function_call_output",
|
||||
call_id: msg.toolCallId.split("|")[0],
|
||||
output: msg.content,
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
return messages;
|
||||
}
|
||||
|
||||
function convertTools(tools: Tool[]): OpenAITool[] {
|
||||
return tools.map((tool) => ({
|
||||
type: "function",
|
||||
name: tool.name,
|
||||
description: tool.description,
|
||||
parameters: tool.parameters,
|
||||
strict: null,
|
||||
}));
|
||||
}
|
||||
|
||||
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