bug: non-openai mode - fix for gemini content: null, fix 429 to throw before stream (#563)
Gemini's API is finicky, it 400's without an error when you pass content: null Also fixed the rate limiting issues by throwing outside of the iterator. I think there's a separate issue with the second isRateLimit check in agent-loop - turnInput is cleared by that time, so it retries without the last message.
This commit is contained in:
Daniel Nakov
GitHub
@@ -3,6 +3,7 @@ import type {
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ResponseCreateParams,
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Response,
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} from "openai/resources/responses/responses";
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// Define interfaces based on OpenAI API documentation
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type ResponseCreateInput = ResponseCreateParams;
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type ResponseOutput = Response;
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@@ -260,31 +261,7 @@ function convertTools(
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}));
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}
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// Main function with overloading
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async function responsesCreateViaChatCompletions(
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openai: OpenAI,
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input: ResponseCreateInput & { stream: true },
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): Promise<AsyncGenerator<ResponseEvent>>;
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async function responsesCreateViaChatCompletions(
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openai: OpenAI,
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input: ResponseCreateInput & { stream?: false },
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): Promise<ResponseOutput>;
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async function responsesCreateViaChatCompletions(
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openai: OpenAI,
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input: ResponseCreateInput,
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): Promise<ResponseOutput | AsyncGenerator<ResponseEvent>> {
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if (input.stream) {
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return streamResponses(openai, input);
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} else {
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return nonStreamResponses(openai, input);
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}
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}
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// Non-streaming implementation
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async function nonStreamResponses(
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openai: OpenAI,
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input: ResponseCreateInput,
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): Promise<ResponseOutput> {
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const createCompletion = (openai: OpenAI, input: ResponseCreateInput) => {
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const fullMessages = getFullMessages(input);
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const chatTools = convertTools(input.tools);
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const webSearchOptions = input.tools?.some(
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@@ -298,17 +275,55 @@ async function nonStreamResponses(
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messages: fullMessages,
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tools: chatTools,
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web_search_options: webSearchOptions,
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temperature: input.temperature,
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top_p: input.top_p,
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temperature: input.temperature ?? 1.0,
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top_p: input.top_p ?? 1.0,
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tool_choice: (input.tool_choice === "auto"
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? "auto"
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: input.tool_choice) as OpenAI.Chat.Completions.ChatCompletionCreateParams["tool_choice"],
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stream: input.stream || false,
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user: input.user,
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metadata: input.metadata,
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};
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return openai.chat.completions.create(chatInput);
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};
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// Main function with overloading
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async function responsesCreateViaChatCompletions(
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openai: OpenAI,
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input: ResponseCreateInput & { stream: true },
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): Promise<AsyncGenerator<ResponseEvent>>;
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async function responsesCreateViaChatCompletions(
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openai: OpenAI,
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input: ResponseCreateInput & { stream?: false },
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): Promise<ResponseOutput>;
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async function responsesCreateViaChatCompletions(
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openai: OpenAI,
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input: ResponseCreateInput,
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): Promise<ResponseOutput | AsyncGenerator<ResponseEvent>> {
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const completion = await createCompletion(openai, input);
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if (input.stream) {
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return streamResponses(
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input,
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completion as AsyncIterable<OpenAI.ChatCompletionChunk>,
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);
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} else {
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return nonStreamResponses(
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input,
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completion as unknown as OpenAI.Chat.Completions.ChatCompletion,
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);
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}
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}
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// Non-streaming implementation
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async function nonStreamResponses(
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input: ResponseCreateInput,
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completion: OpenAI.Chat.Completions.ChatCompletion,
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): Promise<ResponseOutput> {
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const fullMessages = getFullMessages(input);
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try {
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const chatResponse = await openai.chat.completions.create(chatInput);
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const chatResponse = completion;
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if (!("choices" in chatResponse) || chatResponse.choices.length === 0) {
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throw new Error("No choices in chat completion response");
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}
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@@ -429,56 +444,211 @@ async function nonStreamResponses(
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// Streaming implementation
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async function* streamResponses(
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openai: OpenAI,
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input: ResponseCreateInput,
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completion: AsyncIterable<OpenAI.ChatCompletionChunk>,
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): AsyncGenerator<ResponseEvent> {
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const fullMessages = getFullMessages(input);
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const chatTools = convertTools(input.tools);
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const webSearchOptions = input.tools?.some(
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(tool) => tool.type === "function" && tool.name === "web_search",
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)
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? {}
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: undefined;
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const chatInput: OpenAI.Chat.Completions.ChatCompletionCreateParams = {
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const responseId = generateId("resp");
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const outputItemId = generateId("msg");
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let textContentAdded = false;
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let textContent = "";
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const toolCalls = new Map<number, ToolCallData>();
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let usage: UsageData | null = null;
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const finalOutputItem: Array<ResponseContentOutput> = [];
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// Initial response
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const initialResponse: Partial<ResponseOutput> = {
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id: responseId,
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object: "response" as const,
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created_at: Math.floor(Date.now() / 1000),
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status: "in_progress" as const,
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model: input.model,
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messages: fullMessages,
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tools: chatTools,
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web_search_options: webSearchOptions,
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output: [],
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error: null,
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incomplete_details: null,
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instructions: null,
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max_output_tokens: null,
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parallel_tool_calls: true,
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previous_response_id: input.previous_response_id ?? null,
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reasoning: null,
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temperature: input.temperature ?? 1.0,
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text: { format: { type: "text" } },
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tool_choice: input.tool_choice ?? "auto",
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tools: input.tools ?? [],
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top_p: input.top_p ?? 1.0,
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tool_choice: (input.tool_choice === "auto"
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? "auto"
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: input.tool_choice) as OpenAI.Chat.Completions.ChatCompletionCreateParams["tool_choice"],
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stream: true,
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user: input.user,
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metadata: input.metadata,
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truncation: input.truncation ?? "disabled",
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usage: undefined,
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user: input.user ?? undefined,
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metadata: input.metadata ?? {},
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output_text: "",
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};
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yield { type: "response.created", response: initialResponse };
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yield { type: "response.in_progress", response: initialResponse };
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let isToolCall = false;
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for await (const chunk of completion as AsyncIterable<OpenAI.ChatCompletionChunk>) {
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// console.error('\nCHUNK: ', JSON.stringify(chunk));
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const choice = chunk.choices[0];
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if (!choice) {
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continue;
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}
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if (
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!isToolCall &&
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(("tool_calls" in choice.delta && choice.delta.tool_calls) ||
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choice.finish_reason === "tool_calls")
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) {
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isToolCall = true;
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}
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try {
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// console.error("chatInput", JSON.stringify(chatInput));
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const stream = await openai.chat.completions.create(chatInput);
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if (chunk.usage) {
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usage = {
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prompt_tokens: chunk.usage.prompt_tokens,
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completion_tokens: chunk.usage.completion_tokens,
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total_tokens: chunk.usage.total_tokens,
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input_tokens: chunk.usage.prompt_tokens,
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input_tokens_details: { cached_tokens: 0 },
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output_tokens: chunk.usage.completion_tokens,
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output_tokens_details: { reasoning_tokens: 0 },
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};
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}
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if (isToolCall) {
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for (const tcDelta of choice.delta.tool_calls || []) {
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const tcIndex = tcDelta.index;
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const content_index = textContentAdded ? tcIndex + 1 : tcIndex;
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// Initialize state
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const responseId = generateId("resp");
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const outputItemId = generateId("msg");
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let textContentAdded = false;
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let textContent = "";
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const toolCalls = new Map<number, ToolCallData>();
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let usage: UsageData | null = null;
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const finalOutputItem: Array<ResponseContentOutput> = [];
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// Initial response
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const initialResponse: Partial<ResponseOutput> = {
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if (!toolCalls.has(tcIndex)) {
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// New tool call
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const toolCallId = tcDelta.id || generateId("call");
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const functionName = tcDelta.function?.name || "";
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yield {
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type: "response.output_item.added",
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item: {
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type: "function_call",
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id: outputItemId,
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status: "in_progress",
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call_id: toolCallId,
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name: functionName,
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arguments: "",
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},
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output_index: 0,
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};
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toolCalls.set(tcIndex, {
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id: toolCallId,
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name: functionName,
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arguments: "",
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});
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}
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if (tcDelta.function?.arguments) {
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const current = toolCalls.get(tcIndex);
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if (current) {
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current.arguments += tcDelta.function.arguments;
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yield {
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type: "response.function_call_arguments.delta",
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item_id: outputItemId,
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output_index: 0,
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content_index,
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delta: tcDelta.function.arguments,
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};
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}
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}
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}
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if (choice.finish_reason === "tool_calls") {
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for (const [tcIndex, tc] of toolCalls) {
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const item = {
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type: "function_call",
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id: outputItemId,
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status: "completed",
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call_id: tc.id,
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name: tc.name,
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arguments: tc.arguments,
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};
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yield {
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type: "response.function_call_arguments.done",
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item_id: outputItemId,
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output_index: tcIndex,
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content_index: textContentAdded ? tcIndex + 1 : tcIndex,
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arguments: tc.arguments,
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};
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yield {
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type: "response.output_item.done",
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output_index: tcIndex,
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item,
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};
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finalOutputItem.push(item as unknown as ResponseContentOutput);
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}
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} else {
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continue;
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}
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} else {
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if (!textContentAdded) {
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yield {
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type: "response.content_part.added",
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item_id: outputItemId,
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output_index: 0,
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content_index: 0,
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part: { type: "output_text", text: "", annotations: [] },
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};
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textContentAdded = true;
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}
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if (choice.delta.content?.length) {
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yield {
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type: "response.output_text.delta",
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item_id: outputItemId,
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output_index: 0,
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content_index: 0,
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delta: choice.delta.content,
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};
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textContent += choice.delta.content;
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}
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if (choice.finish_reason) {
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yield {
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type: "response.output_text.done",
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item_id: outputItemId,
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output_index: 0,
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content_index: 0,
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text: textContent,
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};
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yield {
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type: "response.content_part.done",
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item_id: outputItemId,
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output_index: 0,
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content_index: 0,
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part: { type: "output_text", text: textContent, annotations: [] },
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};
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const item = {
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type: "message",
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id: outputItemId,
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status: "completed",
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role: "assistant",
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content: [
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{ type: "output_text", text: textContent, annotations: [] },
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],
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};
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yield {
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type: "response.output_item.done",
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output_index: 0,
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item,
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};
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finalOutputItem.push(item as unknown as ResponseContentOutput);
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} else {
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continue;
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}
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}
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// Construct final response
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const finalResponse: ResponseOutput = {
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id: responseId,
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object: "response" as const,
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created_at: Math.floor(Date.now() / 1000),
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status: "in_progress" as const,
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model: input.model,
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output: [],
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created_at: initialResponse.created_at || Math.floor(Date.now() / 1000),
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status: "completed" as const,
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error: null,
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incomplete_details: null,
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instructions: null,
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max_output_tokens: null,
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model: chunk.model || input.model,
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output: finalOutputItem as unknown as ResponseOutput["output"],
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parallel_tool_calls: true,
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previous_response_id: input.previous_response_id ?? null,
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reasoning: null,
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@@ -488,243 +658,54 @@ async function* streamResponses(
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tools: input.tools ?? [],
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top_p: input.top_p ?? 1.0,
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truncation: input.truncation ?? "disabled",
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usage: undefined,
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usage: usage as ResponseOutput["usage"],
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user: input.user ?? undefined,
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metadata: input.metadata ?? {},
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output_text: "",
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};
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yield { type: "response.created", response: initialResponse };
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yield { type: "response.in_progress", response: initialResponse };
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let isToolCall = false;
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for await (const chunk of stream as AsyncIterable<OpenAI.ChatCompletionChunk>) {
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// console.error('\nCHUNK: ', JSON.stringify(chunk));
|
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const choice = chunk.choices[0];
|
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if (!choice) {
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continue;
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}
|
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if (
|
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!isToolCall &&
|
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(("tool_calls" in choice.delta && choice.delta.tool_calls) ||
|
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choice.finish_reason === "tool_calls")
|
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) {
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isToolCall = true;
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}
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} as ResponseOutput;
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|
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if (chunk.usage) {
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usage = {
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prompt_tokens: chunk.usage.prompt_tokens,
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completion_tokens: chunk.usage.completion_tokens,
|
||||
total_tokens: chunk.usage.total_tokens,
|
||||
input_tokens: chunk.usage.prompt_tokens,
|
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input_tokens_details: { cached_tokens: 0 },
|
||||
output_tokens: chunk.usage.completion_tokens,
|
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output_tokens_details: { reasoning_tokens: 0 },
|
||||
};
|
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}
|
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if (isToolCall) {
|
||||
for (const tcDelta of choice.delta.tool_calls || []) {
|
||||
const tcIndex = tcDelta.index;
|
||||
const content_index = textContentAdded ? tcIndex + 1 : tcIndex;
|
||||
|
||||
if (!toolCalls.has(tcIndex)) {
|
||||
// New tool call
|
||||
const toolCallId = tcDelta.id || generateId("call");
|
||||
const functionName = tcDelta.function?.name || "";
|
||||
|
||||
yield {
|
||||
type: "response.output_item.added",
|
||||
item: {
|
||||
type: "function_call",
|
||||
id: outputItemId,
|
||||
status: "in_progress",
|
||||
call_id: toolCallId,
|
||||
name: functionName,
|
||||
arguments: "",
|
||||
},
|
||||
output_index: 0,
|
||||
};
|
||||
toolCalls.set(tcIndex, {
|
||||
id: toolCallId,
|
||||
name: functionName,
|
||||
arguments: "",
|
||||
});
|
||||
}
|
||||
|
||||
if (tcDelta.function?.arguments) {
|
||||
const current = toolCalls.get(tcIndex);
|
||||
if (current) {
|
||||
current.arguments += tcDelta.function.arguments;
|
||||
yield {
|
||||
type: "response.function_call_arguments.delta",
|
||||
item_id: outputItemId,
|
||||
output_index: 0,
|
||||
content_index,
|
||||
delta: tcDelta.function.arguments,
|
||||
};
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if (choice.finish_reason === "tool_calls") {
|
||||
for (const [tcIndex, tc] of toolCalls) {
|
||||
const item = {
|
||||
type: "function_call",
|
||||
id: outputItemId,
|
||||
status: "completed",
|
||||
call_id: tc.id,
|
||||
name: tc.name,
|
||||
arguments: tc.arguments,
|
||||
};
|
||||
yield {
|
||||
type: "response.function_call_arguments.done",
|
||||
item_id: outputItemId,
|
||||
output_index: tcIndex,
|
||||
content_index: textContentAdded ? tcIndex + 1 : tcIndex,
|
||||
arguments: tc.arguments,
|
||||
};
|
||||
yield {
|
||||
type: "response.output_item.done",
|
||||
output_index: tcIndex,
|
||||
item,
|
||||
};
|
||||
finalOutputItem.push(item as unknown as ResponseContentOutput);
|
||||
}
|
||||
} else {
|
||||
continue;
|
||||
}
|
||||
} else {
|
||||
if (!textContentAdded) {
|
||||
yield {
|
||||
type: "response.content_part.added",
|
||||
item_id: outputItemId,
|
||||
output_index: 0,
|
||||
content_index: 0,
|
||||
part: { type: "output_text", text: "", annotations: [] },
|
||||
};
|
||||
textContentAdded = true;
|
||||
}
|
||||
if (choice.delta.content?.length) {
|
||||
yield {
|
||||
type: "response.output_text.delta",
|
||||
item_id: outputItemId,
|
||||
output_index: 0,
|
||||
content_index: 0,
|
||||
delta: choice.delta.content,
|
||||
};
|
||||
textContent += choice.delta.content;
|
||||
}
|
||||
if (choice.finish_reason) {
|
||||
yield {
|
||||
type: "response.output_text.done",
|
||||
item_id: outputItemId,
|
||||
output_index: 0,
|
||||
content_index: 0,
|
||||
text: textContent,
|
||||
};
|
||||
yield {
|
||||
type: "response.content_part.done",
|
||||
item_id: outputItemId,
|
||||
output_index: 0,
|
||||
content_index: 0,
|
||||
part: { type: "output_text", text: textContent, annotations: [] },
|
||||
};
|
||||
const item = {
|
||||
type: "message",
|
||||
id: outputItemId,
|
||||
status: "completed",
|
||||
role: "assistant",
|
||||
content: [
|
||||
{ type: "output_text", text: textContent, annotations: [] },
|
||||
],
|
||||
};
|
||||
yield {
|
||||
type: "response.output_item.done",
|
||||
output_index: 0,
|
||||
item,
|
||||
};
|
||||
finalOutputItem.push(item as unknown as ResponseContentOutput);
|
||||
} else {
|
||||
continue;
|
||||
}
|
||||
}
|
||||
|
||||
// Construct final response
|
||||
const finalResponse: ResponseOutput = {
|
||||
id: responseId,
|
||||
object: "response" as const,
|
||||
created_at: initialResponse.created_at || Math.floor(Date.now() / 1000),
|
||||
status: "completed" as const,
|
||||
error: null,
|
||||
incomplete_details: null,
|
||||
instructions: null,
|
||||
max_output_tokens: null,
|
||||
model: chunk.model || input.model,
|
||||
output: finalOutputItem as unknown as ResponseOutput["output"],
|
||||
parallel_tool_calls: true,
|
||||
previous_response_id: input.previous_response_id ?? null,
|
||||
reasoning: null,
|
||||
temperature: input.temperature ?? 1.0,
|
||||
text: { format: { type: "text" } },
|
||||
tool_choice: input.tool_choice ?? "auto",
|
||||
tools: input.tools ?? [],
|
||||
top_p: input.top_p ?? 1.0,
|
||||
truncation: input.truncation ?? "disabled",
|
||||
usage: usage as ResponseOutput["usage"],
|
||||
user: input.user ?? undefined,
|
||||
metadata: input.metadata ?? {},
|
||||
output_text: "",
|
||||
} as ResponseOutput;
|
||||
|
||||
// Store history
|
||||
const assistantMessage = {
|
||||
// Store history
|
||||
const assistantMessage: OpenAI.Chat.Completions.ChatCompletionMessageParam =
|
||||
{
|
||||
role: "assistant" as const,
|
||||
content: textContent || null,
|
||||
};
|
||||
|
||||
// Add tool_calls property if needed
|
||||
if (toolCalls.size > 0) {
|
||||
const toolCallsArray = Array.from(toolCalls.values()).map((tc) => ({
|
||||
id: tc.id,
|
||||
type: "function" as const,
|
||||
function: { name: tc.name, arguments: tc.arguments },
|
||||
}));
|
||||
|
||||
// Define a more specific type for the assistant message with tool calls
|
||||
type AssistantMessageWithToolCalls =
|
||||
OpenAI.Chat.Completions.ChatCompletionMessageParam & {
|
||||
tool_calls: Array<{
|
||||
id: string;
|
||||
type: "function";
|
||||
function: {
|
||||
name: string;
|
||||
arguments: string;
|
||||
};
|
||||
}>;
|
||||
};
|
||||
|
||||
// Use type assertion with the defined type
|
||||
(assistantMessage as AssistantMessageWithToolCalls).tool_calls =
|
||||
toolCallsArray;
|
||||
}
|
||||
const newHistory = [...fullMessages, assistantMessage];
|
||||
conversationHistories.set(responseId, {
|
||||
previous_response_id: input.previous_response_id ?? null,
|
||||
messages: newHistory,
|
||||
});
|
||||
|
||||
yield { type: "response.completed", response: finalResponse };
|
||||
if (textContent) {
|
||||
assistantMessage.content = textContent;
|
||||
}
|
||||
} catch (error) {
|
||||
// console.error('\nERROR: ', JSON.stringify(error));
|
||||
yield {
|
||||
type: "error",
|
||||
code:
|
||||
error instanceof Error && "code" in error
|
||||
? (error as { code: string }).code
|
||||
: "unknown",
|
||||
message: error instanceof Error ? error.message : String(error),
|
||||
param: null,
|
||||
};
|
||||
|
||||
// Add tool_calls property if needed
|
||||
if (toolCalls.size > 0) {
|
||||
const toolCallsArray = Array.from(toolCalls.values()).map((tc) => ({
|
||||
id: tc.id,
|
||||
type: "function" as const,
|
||||
function: { name: tc.name, arguments: tc.arguments },
|
||||
}));
|
||||
|
||||
// Define a more specific type for the assistant message with tool calls
|
||||
type AssistantMessageWithToolCalls =
|
||||
OpenAI.Chat.Completions.ChatCompletionMessageParam & {
|
||||
tool_calls: Array<{
|
||||
id: string;
|
||||
type: "function";
|
||||
function: {
|
||||
name: string;
|
||||
arguments: string;
|
||||
};
|
||||
}>;
|
||||
};
|
||||
|
||||
// Use type assertion with the defined type
|
||||
(assistantMessage as AssistantMessageWithToolCalls).tool_calls =
|
||||
toolCallsArray;
|
||||
}
|
||||
const newHistory = [...fullMessages, assistantMessage];
|
||||
conversationHistories.set(responseId, {
|
||||
previous_response_id: input.previous_response_id ?? null,
|
||||
messages: newHistory,
|
||||
});
|
||||
|
||||
yield { type: "response.completed", response: finalResponse };
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
@@ -294,7 +294,7 @@ describe("responsesCreateViaChatCompletions", () => {
|
||||
expect(callArgs.messages).toEqual([
|
||||
{ role: "user", content: "Hello world" },
|
||||
]);
|
||||
expect(callArgs.stream).toBeUndefined();
|
||||
expect(callArgs.stream).toBe(false);
|
||||
}
|
||||
|
||||
// Verify result format
|
||||
@@ -736,33 +736,6 @@ describe("responsesCreateViaChatCompletions", () => {
|
||||
}
|
||||
});
|
||||
|
||||
it("should handle errors gracefully", async () => {
|
||||
// Setup mock to throw an error
|
||||
openAiState.createSpy = vi
|
||||
.fn()
|
||||
.mockRejectedValue(new Error("API connection error"));
|
||||
|
||||
const openaiClient = new (await import("openai")).default({
|
||||
apiKey: "test-key",
|
||||
}) as unknown as OpenAI;
|
||||
|
||||
const inputMessage = createTestInput({
|
||||
model: "gpt-4o",
|
||||
userMessage: "Test message",
|
||||
stream: false,
|
||||
});
|
||||
|
||||
// Expect the function to throw an error
|
||||
await expect(
|
||||
responsesModule.responsesCreateViaChatCompletions(
|
||||
openaiClient,
|
||||
inputMessage as unknown as ResponseCreateParamsNonStreaming & {
|
||||
stream?: false | undefined;
|
||||
},
|
||||
),
|
||||
).rejects.toThrow("Failed to process chat completion");
|
||||
});
|
||||
|
||||
it("handles streaming with tool calls", async () => {
|
||||
// Mock a streaming response with tool calls
|
||||
const mockStream = createToolCallsStream();
|
||||
|
||||
Reference in New Issue
Block a user