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https://github.com/enricoros/big-AGI.git
synced 2026-05-10 21:50:14 -07:00
AIX: response API dispatcher
This commit is contained in:
Enrico Ros
@@ -0,0 +1,407 @@
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import { AixAPI_Model, AixAPIChatGenerate_Request, AixMessages_ChatMessage, AixMessages_SystemMessage, AixTools_ToolDefinition, AixTools_ToolsPolicy } from '../../../api/aix.wiretypes';
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import { OpenAIWire_API_Responses, OpenAIWire_Responses_InputTypes, OpenAIWire_Responses_Tools } from '../../wiretypes/openai.wiretypes';
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import { approxDocPart_To_String } from './anthropic.messageCreate';
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import { aixDocPart_to_OpenAITextContent, aixMetaRef_to_OpenAIText, aixTexts_to_OpenAIInstructionText } from '~/modules/aix/server/dispatch/chatGenerate/adapters/openai.chatCompletions';
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type TRequest = OpenAIWire_API_Responses.Request;
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type TRequestInput = OpenAIWire_Responses_InputTypes.InputItem;
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type TRequestTool = OpenAIWire_Responses_Tools.Tool;
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/**
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* OpenAI Responses request adapter
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*
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* Implementation notes:
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* - much side functionality is not implemented yet
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* - testing with o3-pro only for now
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*/
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export function aixToOpenAIResponses(model: AixAPI_Model, chatGenerate: AixAPIChatGenerate_Request, jsonOutput: boolean, streaming: boolean): TRequest {
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// [OpenAI] - o models
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const hotFixOpenAIOFamily = (
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model.id === 'o1' || model.id.startsWith('o1-') ||
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model.id === 'o3' || model.id.startsWith('o3-') ||
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model.id === 'o4' || model.id.startsWith('o4-') ||
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model.id === 'o5' || model.id.startsWith('o5-')
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);
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// Convert the chat messages to the OpenAI 4-Messages format
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const { requestInput, requestInstructions } = _toOpenAIResponsesRequestInput(chatGenerate.systemMessage, chatGenerate.chatSequence);
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// Construct the request payload
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const payload: TRequest = {
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model: model.id,
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input: requestInput,
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instructions: requestInstructions,
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max_output_tokens: model.maxTokens !== undefined ? model.maxTokens : undefined,
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...(model.temperature !== null ? { temperature: model.temperature !== undefined ? model.temperature : undefined } : {}),
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// top_p: undefined,
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tools: chatGenerate.tools && _toOpenAIResponsesTools(chatGenerate.tools),
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tool_choice: chatGenerate.toolsPolicy && _toOpenAIResponsesToolChoice(chatGenerate.toolsPolicy),
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// parallel_tool_calls: undefined,
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// text: ... below
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// reasoning: ... below
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stream: streaming,
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background: false,
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// truncation: 'auto', // TODO: enable this by default?
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// user: undefined,
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};
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// Top-P instead of temperature
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if (model.topP !== undefined) {
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delete payload.temperature;
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payload.top_p = model.topP;
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}
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// [OpenAI] Vendor-specific reasoning effort, for o1 models only as of 2024-12-24
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if (model.vndOaiReasoningEffort) {
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payload.reasoning = {
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effort: model.vndOaiReasoningEffort,
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summary: 'auto',
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};
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}
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// JSON output - with schema
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if (jsonOutput) {
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console.warn('[DEV] notImplemented: responses: jsonOutput');
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// payload.text = {
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// format: {
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// type: 'json_schema',
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// },
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// };
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}
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// [OpenAI] Vendor-specific restore markdown, for newer o1 models
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if (model.vndOaiRestoreMarkdown) {
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console.warn('notImplemented: responses: vndOaiRestoreMarkdown');
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}
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// [OpenAI] Vendor-specific web search context and/or geolocation
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if (model.vndOaiWebSearchContext || model.userGeolocation) {
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console.warn('notImplemented: responses: vndOaiWebSearchContext, userGeolocation');
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// payload.web_search_options = {};
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// if (model.vndOaiWebSearchContext)
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// payload.web_search_options.search_context_size = model.vndOaiWebSearchContext;
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// if (model.userGeolocation)
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// payload.web_search_options.user_location = {
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// type: 'approximate',
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// approximate: {
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// ...model.userGeolocation,
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// },
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// };
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}
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// Preemptive error detection with server-side payload validation before sending it upstream
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const validated = OpenAIWire_API_Responses.Request_schema.safeParse(payload);
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if (!validated.success) {
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console.warn('OpenAI: invalid Responses request payload. Error:', validated.error);
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throw new Error(`Invalid sequence for OpenAI models: ${validated.error.errors?.[0]?.message || validated.error.message || validated.error}.`);
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}
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return validated.data;
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}
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function _toOpenAIResponsesRequestInput(systemMessage: AixMessages_SystemMessage | null, chatSequence: AixMessages_ChatMessage[]): { requestInput: TRequestInput[], requestInstructions: TRequest['instructions'] } {
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/**
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* Instructions to the model
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*
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* Single-part texts stay as-is, while multi-part texts or docs are flattened to a string.
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*/
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const instructionsParts: string[] = [];
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systemMessage?.parts.forEach((part) => {
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switch (part.pt) {
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case 'text':
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instructionsParts.push(part.text);
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break;
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case 'doc':
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instructionsParts.push(aixDocPart_to_OpenAITextContent(part).text);
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break;
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case 'meta_cache_control':
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// ignore this breakpoint hint - Anthropic only
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break;
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default:
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const _exhaustiveCheck: never = part;
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throw new Error(`Unsupported part type in System message: ${(part as any).pt}`);
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}
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});
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const requestInstructions: TRequest['instructions'] = instructionsParts.length ? aixTexts_to_OpenAIInstructionText(instructionsParts) : undefined;
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// We decide to adopt these schemas for the conversion (API gives us a few choices)
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const chatMessages: (UserMessage | ModelMessage | FunctionCallMessage | FunctionCallOutputMessage)[] = [];
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type UserMessage = Omit<OpenAIWire_Responses_InputTypes.UserItemMessage, 'role'> & { role: 'user' };
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type ModelMessage = Omit<OpenAIWire_Responses_InputTypes.InputMessage_Compat, 'role'> & { role: 'assistant' };
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type FunctionCallMessage = OpenAIWire_Responses_InputTypes.FunctionToolCall;
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type FunctionCallOutputMessage = OpenAIWire_Responses_InputTypes.FunctionToolCallOutput;
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function userMessage() {
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// Ensure the last message is a user message, or create a new one
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let lastMessage = chatMessages.length ? chatMessages[chatMessages.length - 1] : undefined;
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if (lastMessage && lastMessage.type === 'message' && lastMessage.role === 'user')
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return lastMessage;
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const newMessage: UserMessage = {
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type: 'message',
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role: 'user',
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content: [],
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};
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chatMessages.push(newMessage);
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return newMessage;
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}
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function modelMessage() {
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// Ensure the last message is a model message, or create a new one
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let lastMessage = chatMessages.length ? chatMessages[chatMessages.length - 1] : undefined;
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if (lastMessage && lastMessage.type === 'message' && lastMessage.role === 'assistant')
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return lastMessage;
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const newMessage: ModelMessage = {
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type: 'message',
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role: 'assistant',
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content: [],
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};
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chatMessages.push(newMessage);
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return newMessage;
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}
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function newFunctionCallMessage(callId: string, functionName: string, functionArguments: string) {
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const newMessage: FunctionCallMessage = {
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type: 'function_call',
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call_id: callId,
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name: functionName,
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arguments: functionArguments,
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};
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chatMessages.push(newMessage);
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return newMessage;
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}
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function newFunctionCallOutputMessage(callId: string, functionOutputJson: string) {
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const newMessage: FunctionCallOutputMessage = {
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type: 'function_call_output',
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call_id: callId,
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output: functionOutputJson,
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};
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chatMessages.push(newMessage);
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return newMessage;
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}
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/**
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* Input Messages
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*
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* Conversion from the AIX input format to the OpenAI Responses Input format is straightforward.
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*
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* - user messages will be converted to the new Input Item format (which doesn't need IDs)
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* - assistant messages to the old Input Message format (which doesn't need IDs)
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*
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*/
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for (const { role: messageRole, parts: messageParts } of chatSequence) {
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switch (messageRole) {
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case 'user':
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for (const userPart of messageParts) {
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const uPt = userPart.pt;
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switch (uPt) {
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case 'text':
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userMessage().content.push({
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type: 'input_text',
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text: userPart.text,
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});
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break;
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case 'doc':
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const docText = userPart.data.text.startsWith('```') ? userPart.data.text : approxDocPart_To_String(userPart);
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userMessage().content.push({
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type: 'input_text',
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text: docText,
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});
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break;
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case 'inline_image':
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// create a new OpenAIWire_ImageContentPart
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const { mimeType, base64 } = userPart;
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const base64DataUrl = `data:${mimeType};base64,${base64}`;
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userMessage().content.push({
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type: 'input_image',
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detail: 'high', // TODO: check if user images shall always be 'high' detail
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image_url: base64DataUrl,
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});
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break;
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case 'meta_in_reference_to':
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userMessage().content.push({
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type: 'input_text',
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text: aixMetaRef_to_OpenAIText(userPart),
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});
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break;
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case 'meta_cache_control':
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// ignored - Anthropic only
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break;
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default:
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const _exhaustiveCheck: never = uPt;
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throw new Error(`Unsupported part type in User message: ${uPt}`);
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}
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}
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break;
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case 'model':
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for (const modelPart of messageParts) {
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const mPt = modelPart.pt;
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switch (mPt) {
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case 'text':
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modelMessage().content.push({
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type: 'input_text',
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text: modelPart.text,
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});
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break;
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case 'inline_audio':
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// Workaround for OpenAI Responses API: - TODO: verify if this is still needed
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// - audio (file) parts are not supported on the assistant side, but on the user side, so we upload as user
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// create a new OpenAI_AudioContentPart of type User
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// const audioFormat = _toOpenAIAudioFormat(modelPart);
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const audioBase64DataUrl = `data:${modelPart.mimeType};base64,${modelPart.base64}`;
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userMessage().content.push({
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type: 'input_file',
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file_data: audioBase64DataUrl,
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});
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break;
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case 'inline_image':
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// Workaround: as User part - TODO: verify if this is still needed
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const { mimeType, base64 } = modelPart;
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const base64DataUrl = `data:${mimeType};base64,${base64}`;
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userMessage().content.push({
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type: 'input_image',
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detail: 'high', // TODO: check if model images shall always be 'high' detail
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image_url: base64DataUrl,
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});
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break;
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case 'tool_invocation':
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const invocation = modelPart.invocation;
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const invocationType = invocation.type;
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switch (invocationType) {
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case 'function_call':
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newFunctionCallMessage(modelPart.id, invocation.name, invocation.args || '');
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break;
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case 'code_execution':
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console.warn('[DEV] notImplemented: OpenAI Responses: code execution tool calls');
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newFunctionCallMessage(modelPart.id, 'execute_code', invocation.code || '');
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break;
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default:
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const _exhaustiveCheck: never = invocation;
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throw new Error(`Unsupported tool call type in Model message: ${mPt}`);
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}
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break;
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case 'ma':
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// TODO: support this in the future - may contain the encrypted reasoning data, although we don't parse this yet
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break;
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case 'meta_cache_control':
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// ignored - Anthropic only
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break;
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default:
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const _exhaustiveCheck: never = mPt;
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throw new Error(`Unsupported part type in Model message: ${mPt}`);
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}
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}
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break;
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case 'tool':
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for (const toolPart of messageParts) {
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const tPt = toolPart.pt;
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switch (tPt) {
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case 'tool_response':
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const toolResponseType = toolPart.response.type;
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switch (toolResponseType) {
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case 'function_call':
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const { result: functionCallOutput } = toolPart.response;
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newFunctionCallOutputMessage(toolPart.id, functionCallOutput);
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break;
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case 'code_execution':
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const { result: codeExecutionOutput } = toolPart.response;
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newFunctionCallOutputMessage(toolPart.id, codeExecutionOutput);
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break;
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default:
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const _exhaustiveCheck: never = toolResponseType;
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throw new Error(`Unsupported tool response type in Tool message: ${tPt}/${toolResponseType}`);
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}
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break;
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case 'meta_cache_control':
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// ignored - Anthropic only
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break;
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default:
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const _exhaustiveCheck: never = tPt;
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throw new Error(`Unsupported part type in Tool message: ${tPt}`);
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}
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}
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break;
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default:
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const _exhaustiveCheck: never = messageRole;
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break;
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}
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}
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// return the instruction and input sequence
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return {
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requestInstructions,
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requestInput: chatMessages,
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};
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}
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function _toOpenAIResponsesTools(itds: AixTools_ToolDefinition[]): NonNullable<TRequestTool[]> {
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return itds.map(itd => {
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switch (itd.type) {
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case 'function_call':
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const { name, description, input_schema } = itd.function_call;
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return {
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type: 'function',
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name: name,
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description: description,
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parameters: {
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type: 'object',
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properties: input_schema?.properties ?? {},
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required: input_schema?.required,
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},
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};
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case 'code_execution':
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throw new Error('Gemini code interpreter is not supported');
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}
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});
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}
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function _toOpenAIResponsesToolChoice(itp: AixTools_ToolsPolicy): NonNullable<TRequest['tool_choice']> {
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// NOTE: we don't support forcing hosted tools yet
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const itpType = itp.type;
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switch (itpType) {
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case 'auto':
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return 'auto';
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case 'any':
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return 'required';
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case 'function_call':
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return { type: 'function' as const, name: itp.function_call.name };
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default:
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const _exhaustiveCheck: never = itpType;
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throw new Error(`Unsupported tools policy type: ${itpType}`);
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}
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}
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