@juspay/neurolink/dist/telemetry/chatMessages.js

The pipe layer of an AI nervous system: one interface connecting provider neurons to your application, across three inference types — generate, stream and decide. `decide` returns typed, calibrated judgements from a non-generative model (~400ms, ~$0.00002

import { stringify } from "./spanSerialization.js";
function chatToolCall(call) {
    const args = call.args ?? call.input ?? call.arguments ?? call.function?.arguments;
    return {
        id: call.id ?? call.toolCallId,
        type: "function",
        function: {
            name: call.name ?? call.toolName ?? call.function?.name,
            arguments: typeof args === "string" ? args : stringify(args ?? {}),
        },
    };
}
function isChatMessages(output) {
    return (output.length > 0 &&
        output.every((entry) => "role" in entry && typeof entry.role === "string"));
}
function isAssistantReply(output) {
    return (typeof output === "object" &&
        !Array.isArray(output) &&
        ("text" in output || "toolCalls" in output));
}
/**
 * @description Reads the role of a chat message, treating anything without a string role as a user message.
 * @param message {T} - Message of any provider shape.
 * @returns {string} The message role, or `user` when it declares none.
 */
export function messageRole(message) {
    return typeof message === "object" &&
        message !== null &&
        "role" in message &&
        typeof message.role === "string"
        ? message.role
        : "user";
}
/**
 * @description Shapes a model response as an assistant message array that Langfuse renders as a conversation.
 * @param output {AssistantOutput | undefined} - A text string, a reply with `text`, `reasoning` and `toolCalls`, a message array, or provider content parts.
 * @returns {object[] | undefined} `[{ role: "assistant", content, tool_calls? }]`, the input untouched when it is already a message array, or `undefined` when there is no output.
 */
export function assistantMessages(output) {
    if (output === undefined) {
        return undefined;
    }
    if (Array.isArray(output) && isChatMessages(output)) {
        return output;
    }
    if (!isAssistantReply(output)) {
        return [{ role: "assistant", content: output }];
    }
    const toolCalls = (output.toolCalls ?? []).map(chatToolCall);
    return [
        {
            role: "assistant",
            content: output.text ?? "",
            ...(output.reasoning ? { reasoning: output.reasoning } : {}),
            ...(toolCalls.length > 0 ? { tool_calls: toolCalls } : {}),
        },
    ];
}