@n8n/n8n-nodes-langchain
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JavaScript
;
var __create = Object.create;
var __defProp = Object.defineProperty;
var __getOwnPropDesc = Object.getOwnPropertyDescriptor;
var __getOwnPropNames = Object.getOwnPropertyNames;
var __getProtoOf = Object.getPrototypeOf;
var __hasOwnProp = Object.prototype.hasOwnProperty;
var __export = (target, all) => {
for (var name in all)
__defProp(target, name, { get: all[name], enumerable: true });
};
var __copyProps = (to, from, except, desc) => {
if (from && typeof from === "object" || typeof from === "function") {
for (let key of __getOwnPropNames(from))
if (!__hasOwnProp.call(to, key) && key !== except)
__defProp(to, key, { get: () => from[key], enumerable: !(desc = __getOwnPropDesc(from, key)) || desc.enumerable });
}
return to;
};
var __toESM = (mod, isNodeMode, target) => (target = mod != null ? __create(__getProtoOf(mod)) : {}, __copyProps(
// If the importer is in node compatibility mode or this is not an ESM
// file that has been converted to a CommonJS file using a Babel-
// compatible transform (i.e. "__esModule" has not been set), then set
// "default" to the CommonJS "module.exports" for node compatibility.
isNodeMode || !mod || !mod.__esModule ? __defProp(target, "default", { value: mod, enumerable: true }) : target,
mod
));
var __toCommonJS = (mod) => __copyProps(__defProp({}, "__esModule", { value: true }), mod);
var message_operation_exports = {};
__export(message_operation_exports, {
description: () => description,
execute: () => execute
});
module.exports = __toCommonJS(message_operation_exports);
var import_omit = __toESM(require("lodash/omit"));
var import_n8n_workflow = require("n8n-workflow");
var import_helpers = require("../../../../../../utils/helpers");
var import_constants = require("../../../helpers/constants");
var import_utils = require("../../../helpers/utils");
var import_transport = require("../../../transport");
var import_descriptions = require("../descriptions");
const properties = [
(0, import_descriptions.modelRLC)("modelSearch"),
{
displayName: "Messages",
name: "messages",
type: "fixedCollection",
typeOptions: {
sortable: true,
multipleValues: true
},
placeholder: "Add Message",
default: { values: [{ content: "" }] },
options: [
{
displayName: "Values",
name: "values",
values: [
{
displayName: "Prompt",
name: "content",
type: "string",
description: "The content of the message to be send",
default: "",
placeholder: "e.g. Hello, how can you help me?",
typeOptions: {
rows: 2
}
},
{
displayName: "Role",
name: "role",
type: "options",
description: "Role in shaping the model's response, it tells the model how it should behave and interact with the user",
options: [
{
name: "User",
value: "user",
description: "Send a message as a user and get a response from the model"
},
{
name: "Assistant",
value: "assistant",
description: "Tell the model to adopt a specific tone or personality"
},
{
name: "System",
value: "system",
description: "Usually used to set the model's behavior or context for the next user message"
}
],
default: "user"
}
]
}
]
},
{
displayName: "Simplify Output",
name: "simplify",
type: "boolean",
default: true,
description: "Whether to return a simplified version of the response instead of the raw data"
},
{
displayName: "Output Content as JSON",
name: "jsonOutput",
type: "boolean",
description: "Whether to attempt to return the response in JSON format. Compatible with GPT-4 Turbo and all GPT-3.5 Turbo models newer than gpt-3.5-turbo-1106.",
default: false
},
{
displayName: "Hide Tools",
name: "hideTools",
type: "hidden",
default: "hide",
displayOptions: {
show: {
modelId: import_constants.MODELS_NOT_SUPPORT_FUNCTION_CALLS,
"@version": [{ _cnd: { gte: 1.2 } }]
}
}
},
{
displayName: "Connect your own custom n8n tools to this node on the canvas",
name: "noticeTools",
type: "notice",
default: "",
displayOptions: {
hide: {
hideTools: ["hide"]
}
}
},
{
displayName: "Options",
name: "options",
placeholder: "Add Option",
type: "collection",
default: {},
options: [
{
displayName: "Frequency Penalty",
name: "frequency_penalty",
default: 0,
typeOptions: { maxValue: 2, minValue: -2, numberPrecision: 1 },
description: "Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim",
type: "number"
},
{
displayName: "Maximum Number of Tokens",
name: "maxTokens",
default: 16,
description: "The maximum number of tokens to generate in the completion. Most models have a context length of 2048 tokens (except for the newest models, which support 32,768).",
type: "number",
typeOptions: {
maxValue: 32768
}
},
{
displayName: "Number of Completions",
name: "n",
default: 1,
description: "How many completions to generate for each prompt. Note: Because this parameter generates many completions, it can quickly consume your token quota. Use carefully and ensure that you have reasonable settings for max_tokens and stop.",
type: "number"
},
{
displayName: "Presence Penalty",
name: "presence_penalty",
default: 0,
typeOptions: { maxValue: 2, minValue: -2, numberPrecision: 1 },
description: "Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics",
type: "number"
},
{
displayName: "Output Randomness (Temperature)",
name: "temperature",
default: 1,
typeOptions: { maxValue: 1, minValue: 0, numberPrecision: 1 },
description: "Controls randomness: Lowering results in less random completions. As the temperature approaches zero, the model will become deterministic and repetitive. We generally recommend altering this or temperature but not both.",
type: "number"
},
{
displayName: "Output Randomness (Top P)",
name: "topP",
default: 1,
typeOptions: { maxValue: 1, minValue: 0, numberPrecision: 1 },
description: "An alternative to sampling with temperature, controls diversity via nucleus sampling: 0.5 means half of all likelihood-weighted options are considered. We generally recommend altering this or temperature but not both.",
type: "number"
},
{
displayName: "Reasoning Effort",
name: "reasoning_effort",
default: "medium",
description: 'Controls the amount of reasoning tokens to use. A value of "low" will favor speed and economical token usage, "high" will favor more complete reasoning at the cost of more tokens generated and slower responses.',
type: "options",
options: [
{
name: "Low",
value: "low",
description: "Favors speed and economical token usage"
},
{
name: "Medium",
value: "medium",
description: "Balance between speed and reasoning accuracy"
},
{
name: "High",
value: "high",
description: "Favors more complete reasoning at the cost of more tokens generated and slower responses"
}
],
displayOptions: {
show: {
// reasoning_effort is only available on o1, o1-versioned, or on o3-mini and beyond, and gpt-5 models. Not on o1-mini or other GPT-models.
"/modelId": [{ _cnd: { regex: "(^o1([-\\d]+)?$)|(^o[3-9].*)|(^gpt-5.*)" } }]
}
}
},
{
displayName: "Max Tool Calls Iterations",
name: "maxToolsIterations",
type: "number",
default: 15,
description: "The maximum number of tool iteration cycles the LLM will run before stopping. A single iteration can contain multiple tool calls. Set to 0 for no limit.",
displayOptions: {
show: {
"@version": [{ _cnd: { gte: 1.5 } }]
}
}
}
]
}
];
const displayOptions = {
show: {
operation: ["message"],
resource: ["text"]
}
};
const description = (0, import_n8n_workflow.updateDisplayOptions)(displayOptions, properties);
async function execute(i) {
const nodeVersion = this.getNode().typeVersion;
const model = this.getNodeParameter("modelId", i, "", { extractValue: true });
let messages = this.getNodeParameter("messages.values", i, []);
const options = this.getNodeParameter("options", i, {});
const jsonOutput = this.getNodeParameter("jsonOutput", i, false);
const maxToolsIterations = nodeVersion >= 1.5 ? this.getNodeParameter("options.maxToolsIterations", i, 15) : 0;
const abortSignal = this.getExecutionCancelSignal();
if (options.maxTokens !== void 0) {
options.max_completion_tokens = options.maxTokens;
delete options.maxTokens;
}
if (options.topP !== void 0) {
options.top_p = options.topP;
delete options.topP;
}
let response_format;
if (jsonOutput) {
response_format = { type: "json_object" };
messages = [
{
role: "system",
content: "You are a helpful assistant designed to output JSON."
},
...messages
];
}
const hideTools = this.getNodeParameter("hideTools", i, "");
let tools;
let externalTools = [];
if (hideTools !== "hide") {
const enforceUniqueNames = nodeVersion > 1;
externalTools = await (0, import_helpers.getConnectedTools)(this, enforceUniqueNames, false);
}
if (externalTools.length) {
tools = externalTools.length ? externalTools?.map(import_utils.formatToOpenAIAssistantTool) : void 0;
}
const body = {
model,
messages,
tools,
response_format,
...(0, import_omit.default)(options, ["maxToolsIterations"])
};
let response = await import_transport.apiRequest.call(this, "POST", "/chat/completions", {
body
});
if (!response) return [];
let currentIteration = 1;
let toolCalls = response?.choices[0]?.message?.tool_calls;
while (toolCalls?.length) {
if (abortSignal?.aborted || maxToolsIterations > 0 && currentIteration >= maxToolsIterations) {
break;
}
messages.push(response.choices[0].message);
for (const toolCall of toolCalls) {
const functionName = toolCall.function.name;
const functionArgs = toolCall.function.arguments;
let functionResponse;
for (const tool of externalTools ?? []) {
if (tool.name === functionName) {
const parsedArgs = (0, import_n8n_workflow.jsonParse)(functionArgs);
const functionInput = parsedArgs.input ?? parsedArgs ?? functionArgs;
functionResponse = await tool.invoke(functionInput);
}
}
if (typeof functionResponse === "object") {
functionResponse = JSON.stringify(functionResponse);
}
messages.push({
tool_call_id: toolCall.id,
role: "tool",
content: functionResponse
});
}
response = await import_transport.apiRequest.call(this, "POST", "/chat/completions", {
body
});
toolCalls = response.choices[0].message.tool_calls;
currentIteration += 1;
}
if (response_format) {
response.choices = response.choices.map((choice) => {
try {
choice.message.content = JSON.parse(choice.message.content);
} catch (error) {
}
return choice;
});
}
const simplify = this.getNodeParameter("simplify", i);
const returnData = [];
if (simplify) {
for (const entry of response.choices) {
returnData.push({
json: entry,
pairedItem: { item: i }
});
}
} else {
returnData.push({ json: response, pairedItem: { item: i } });
}
return returnData;
}
// Annotate the CommonJS export names for ESM import in node:
0 && (module.exports = {
description,
execute
});
//# sourceMappingURL=message.operation.js.map