mirror of
https://github.com/Mintplex-Labs/anything-llm
synced 2026-04-25 17:15:37 +02:00
Patch AzureOpenAI tool calling from function to tool (#4922)
This commit is contained in:
Timothy Carambat
@@ -2,9 +2,13 @@ const { OpenAI } = require("openai");
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const { AzureOpenAiLLM } = require("../../../AiProviders/azureOpenAi");
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const Provider = require("./ai-provider.js");
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const { RetryError } = require("../error.js");
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const { v4 } = require("uuid");
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const { safeJsonParse } = require("../../../http");
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/**
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* The agent provider for the Azure OpenAI API.
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* Uses the tool calling format (not legacy function calling) for compatibility
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* with newer Azure OpenAI models.
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*/
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class AzureOpenAiProvider extends Provider {
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model;
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@@ -23,8 +27,215 @@ class AzureOpenAiProvider extends Provider {
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return true;
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}
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/**
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* Convert legacy function definitions to the tools format.
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* @param {Array} functions - Legacy function definitions
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* @returns {Array} Tools in the new format
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*/
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#formatFunctionsToTools(functions) {
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if (!Array.isArray(functions) || functions.length === 0) return [];
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return functions.map((func) => ({
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type: "function",
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function: {
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name: func.name,
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description: func.description,
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parameters: func.parameters,
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},
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}));
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}
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/**
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* Format messages to use tool calling format instead of legacy function format.
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* Converts role: "function" messages to role: "tool" messages.
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* @param {Array} messages - Messages array that may contain legacy function messages
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* @returns {Array} Messages formatted for tool calling
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*/
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#formatMessagesForTools(messages) {
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const formattedMessages = [];
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for (const message of messages) {
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if (message.role === "function") {
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// Convert legacy function result to tool result format
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// We need the tool_call_id from the originalFunctionCall
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if (message.originalFunctionCall?.id) {
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// First, add the assistant message with the tool_call if not already present
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// Check if previous message already has this tool call
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const prevMsg = formattedMessages[formattedMessages.length - 1];
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if (!prevMsg || prevMsg.role !== "assistant" || !prevMsg.tool_calls) {
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formattedMessages.push({
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role: "assistant",
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content: null,
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tool_calls: [
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{
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id: message.originalFunctionCall.id,
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type: "function",
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function: {
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name: message.originalFunctionCall.name,
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arguments:
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typeof message.originalFunctionCall.arguments === "string"
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? message.originalFunctionCall.arguments
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: JSON.stringify(
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message.originalFunctionCall.arguments
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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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// Add the tool result
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formattedMessages.push({
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role: "tool",
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tool_call_id: message.originalFunctionCall.id,
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content:
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typeof message.content === "string"
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? message.content
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: JSON.stringify(message.content),
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});
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} else {
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// Fallback: generate a tool_call_id if not present
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const toolCallId = `call_${v4()}`;
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formattedMessages.push({
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role: "assistant",
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content: null,
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tool_calls: [
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{
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id: toolCallId,
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type: "function",
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function: {
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name: message.name,
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arguments: "{}",
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},
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},
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],
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});
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formattedMessages.push({
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role: "tool",
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tool_call_id: toolCallId,
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content:
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typeof message.content === "string"
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? message.content
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: JSON.stringify(message.content),
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});
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}
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} else {
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formattedMessages.push(message);
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}
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}
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return formattedMessages;
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}
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/**
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* Stream a chat completion from the LLM with tool calling.
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* Uses the tool calling format instead of legacy function calling.
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*
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* @param {any[]} messages - The messages to send to the LLM.
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* @param {any[]} functions - The functions to use in the LLM.
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* @param {function} eventHandler - The event handler to use to report stream events.
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* @returns {Promise<{ functionCall: any, textResponse: string }>} - The result of the chat completion.
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*/
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async stream(messages, functions = [], eventHandler = null) {
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this.providerLog("Provider.stream - will process this chat completion.");
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const msgUUID = v4();
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try {
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const formattedMessages = this.#formatMessagesForTools(messages);
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const tools = this.#formatFunctionsToTools(functions);
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const stream = await this.client.chat.completions.create({
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model: this.model,
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stream: true,
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messages: formattedMessages,
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...(tools.length > 0 ? { tools } : {}),
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});
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const result = {
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functionCall: null,
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textResponse: "",
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};
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// For accumulating tool calls during streaming
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let currentToolCall = null;
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for await (const chunk of stream) {
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if (!chunk?.choices?.[0]) continue;
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const choice = chunk.choices[0];
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if (choice.delta?.content) {
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result.textResponse += choice.delta.content;
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eventHandler?.("reportStreamEvent", {
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type: "textResponseChunk",
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uuid: msgUUID,
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content: choice.delta.content,
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});
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}
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// Handle tool calls (new format)
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if (choice.delta?.tool_calls) {
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for (const toolCall of choice.delta.tool_calls) {
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if (toolCall.id) {
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// New tool call starting
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currentToolCall = {
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id: toolCall.id,
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name: toolCall.function?.name || "",
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arguments: toolCall.function?.arguments || "",
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};
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} else if (currentToolCall) {
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// Continuation of existing tool call
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if (toolCall.function?.name) {
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currentToolCall.name += toolCall.function.name;
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}
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if (toolCall.function?.arguments) {
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currentToolCall.arguments += toolCall.function.arguments;
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}
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}
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if (currentToolCall) {
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eventHandler?.("reportStreamEvent", {
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uuid: `${msgUUID}:tool_call_invocation`,
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type: "toolCallInvocation",
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content: `Assembling Tool Call: ${currentToolCall.name}(${currentToolCall.arguments})`,
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});
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}
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}
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}
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}
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// Set the function call result if we have a tool call
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if (currentToolCall) {
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result.functionCall = {
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id: currentToolCall.id,
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name: currentToolCall.name,
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arguments: safeJsonParse(currentToolCall.arguments, {}),
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};
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}
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return {
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textResponse: result.textResponse,
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functionCall: result.functionCall,
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};
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} catch (error) {
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console.error(error.message, error);
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// If invalid Auth error we need to abort because no amount of waiting
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// will make auth better.
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if (error instanceof OpenAI.AuthenticationError) throw error;
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if (
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error instanceof OpenAI.RateLimitError ||
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error instanceof OpenAI.InternalServerError ||
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error instanceof OpenAI.APIError
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) {
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throw new RetryError(error.message);
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}
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throw error;
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}
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}
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/**
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* Create a completion based on the received messages.
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* Uses the tool calling format instead of legacy function calling.
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*
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* @param messages A list of messages to send to the OpenAI API.
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* @param functions
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@@ -32,45 +243,53 @@ class AzureOpenAiProvider extends Provider {
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*/
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async complete(messages, functions = []) {
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try {
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const formattedMessages = this.#formatMessagesForTools(messages);
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const tools = this.#formatFunctionsToTools(functions);
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const response = await this.client.chat.completions.create({
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model: this.model,
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stream: false,
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messages,
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...(Array.isArray(functions) && functions?.length > 0
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? { functions }
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: {}),
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messages: formattedMessages,
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...(tools.length > 0 ? { tools } : {}),
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});
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// Right now, we only support one completion,
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// so we just take the first one in the list
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const completion = response.choices[0].message;
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const cost = this.getCost(response.usage);
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// treat function calls
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if (completion.function_call) {
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// Handle tool calls (new format)
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if (completion.tool_calls && completion.tool_calls.length > 0) {
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const toolCall = completion.tool_calls[0];
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let functionArgs = {};
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try {
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functionArgs = JSON.parse(completion.function_call.arguments);
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functionArgs = JSON.parse(toolCall.function.arguments);
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} catch (error) {
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// call the complete function again in case it gets a json error
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// Call the complete function again in case of JSON error
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const toolCallId = toolCall.id;
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return this.complete(
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[
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...messages,
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{
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role: "function",
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name: completion.function_call.name,
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function_call: completion.function_call,
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name: toolCall.function.name,
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content: error?.message,
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originalFunctionCall: {
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id: toolCallId,
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name: toolCall.function.name,
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arguments: toolCall.function.arguments,
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},
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},
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],
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functions
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);
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}
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// console.log(completion, { functionArgs })
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return {
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textResponse: null,
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functionCall: {
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name: completion.function_call.name,
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id: toolCall.id,
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name: toolCall.function.name,
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arguments: functionArgs,
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},
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cost,
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