@n8n/n8n-nodes-langchain
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JavaScript
;
var __defProp = Object.defineProperty;
var __getOwnPropDesc = Object.getOwnPropertyDescriptor;
var __getOwnPropNames = Object.getOwnPropertyNames;
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))
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__defProp(to, key, { get: () => from[key], enumerable: !(desc = __getOwnPropDesc(from, key)) || desc.enumerable });
}
return to;
};
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_n8n_workflow = require("n8n-workflow");
var import_zod_to_json_schema = require("zod-to-json-schema");
var import_helpers = require("../../../../../utils/helpers");
var import_transport = require("../../transport");
var import_descriptions = require("../descriptions");
const properties = [
import_descriptions.modelRLC,
{
displayName: "Messages",
name: "messages",
type: "fixedCollection",
typeOptions: {
sortable: true,
multipleValues: true
},
placeholder: "Add Message",
default: { values: [{ content: "", role: "user" }] },
options: [
{
displayName: "Values",
name: "values",
values: [
{
displayName: "Content",
name: "content",
type: "string",
description: "The content of the message to be sent",
default: "",
placeholder: "e.g. Hello, how can you help me?",
typeOptions: {
rows: 2
}
},
{
displayName: "Role",
name: "role",
type: "options",
description: "The role of this message in the conversation",
options: [
{
name: "User",
value: "user",
description: "Message from the user"
},
{
name: "Assistant",
value: "assistant",
description: "Response from the assistant (for conversation history)"
}
],
default: "user"
}
]
}
]
},
{
displayName: "Simplify Output",
name: "simplify",
type: "boolean",
default: true,
description: "Whether to simplify the response or not"
},
{
displayName: "Options",
name: "options",
placeholder: "Add Option",
type: "collection",
default: {},
options: [
{
displayName: "System Message",
name: "system",
type: "string",
default: "",
placeholder: "e.g. You are a helpful assistant.",
description: "System message to set the context for the conversation",
typeOptions: {
rows: 2
}
},
{
displayName: "Temperature",
name: "temperature",
type: "number",
default: 0.8,
typeOptions: {
minValue: 0,
maxValue: 2,
numberPrecision: 2
},
description: "Controls randomness in responses. Lower values make output more focused."
},
{
displayName: "Output Randomness (Top P)",
name: "top_p",
default: 0.7,
description: "The maximum cumulative probability of tokens to consider when sampling",
type: "number",
typeOptions: {
minValue: 0,
maxValue: 1,
numberPrecision: 1
}
},
{
displayName: "Top K",
name: "top_k",
type: "number",
default: 40,
typeOptions: {
minValue: 1
},
description: "Controls diversity by limiting the number of top tokens to consider"
},
{
displayName: "Max Tokens",
name: "num_predict",
type: "number",
default: 1024,
typeOptions: {
minValue: 1,
numberPrecision: 0
},
description: "Maximum number of tokens to generate in the completion"
},
{
displayName: "Frequency Penalty",
name: "frequency_penalty",
type: "number",
default: 0,
typeOptions: {
minValue: 0,
numberPrecision: 2
},
description: "Adjusts the penalty for tokens that have already appeared in the generated text. Higher values discourage repetition."
},
{
displayName: "Presence Penalty",
name: "presence_penalty",
type: "number",
default: 0,
typeOptions: {
numberPrecision: 2
},
description: "Adjusts the penalty for tokens based on their presence in the generated text so far. Positive values penalize tokens that have already appeared, encouraging diversity."
},
{
displayName: "Repetition Penalty",
name: "repeat_penalty",
type: "number",
default: 1.1,
typeOptions: {
minValue: 0,
numberPrecision: 2
},
description: "Sets how strongly to penalize repetitions. A higher value (e.g., 1.5) will penalize repetitions more strongly, while a lower value (e.g., 0.9) will be more lenient."
},
{
displayName: "Context Length",
name: "num_ctx",
type: "number",
default: 4096,
typeOptions: {
minValue: 1,
numberPrecision: 0
},
description: "Sets the size of the context window used to generate the next token"
},
{
displayName: "Repeat Last N",
name: "repeat_last_n",
type: "number",
default: 64,
typeOptions: {
minValue: -1,
numberPrecision: 0
},
description: "Sets how far back for the model to look back to prevent repetition. (0 = disabled, -1 = num_ctx)."
},
{
displayName: "Min P",
name: "min_p",
type: "number",
default: 0,
typeOptions: {
minValue: 0,
maxValue: 1,
numberPrecision: 3
},
description: "Alternative to the top_p, and aims to ensure a balance of quality and variety. The parameter p represents the minimum probability for a token to be considered, relative to the probability of the most likely token."
},
{
displayName: "Seed",
name: "seed",
type: "number",
default: 0,
typeOptions: {
minValue: 0,
numberPrecision: 0
},
description: "Sets the random number seed to use for generation. Setting this to a specific number will make the model generate the same text for the same prompt."
},
{
displayName: "Stop Sequences",
name: "stop",
type: "string",
default: "",
description: "Sets the stop sequences to use. When this pattern is encountered the LLM will stop generating text and return. Separate multiple patterns with commas"
},
{
displayName: "Keep Alive",
name: "keep_alive",
type: "string",
default: "5m",
description: "Specifies the duration to keep the loaded model in memory after use. Format: 1h30m (1 hour 30 minutes)."
},
{
displayName: "Low VRAM Mode",
name: "low_vram",
type: "boolean",
default: false,
description: "Whether to activate low VRAM mode, which reduces memory usage at the cost of slower generation speed. Useful for GPUs with limited memory."
},
{
displayName: "Main GPU ID",
name: "main_gpu",
type: "number",
default: 0,
typeOptions: {
minValue: 0,
numberPrecision: 0
},
description: "Specifies the ID of the GPU to use for the main computation. Only change this if you have multiple GPUs."
},
{
displayName: "Context Batch Size",
name: "num_batch",
type: "number",
default: 512,
typeOptions: {
minValue: 1,
numberPrecision: 0
},
description: "Sets the batch size for prompt processing. Larger batch sizes may improve generation speed but increase memory usage."
},
{
displayName: "Number of GPUs",
name: "num_gpu",
type: "number",
default: -1,
typeOptions: {
minValue: -1,
numberPrecision: 0
},
description: "Specifies the number of GPUs to use for parallel processing. Set to -1 for auto-detection."
},
{
displayName: "Number of CPU Threads",
name: "num_thread",
type: "number",
default: 0,
typeOptions: {
minValue: 0,
numberPrecision: 0
},
description: "Specifies the number of CPU threads to use for processing. Set to 0 for auto-detection."
},
{
displayName: "Penalize Newlines",
name: "penalize_newline",
type: "boolean",
default: true,
description: "Whether the model will be less likely to generate newline characters, encouraging longer continuous sequences of text"
},
{
displayName: "Use Memory Locking",
name: "use_mlock",
type: "boolean",
default: false,
description: "Whether to lock the model in memory to prevent swapping. This can improve performance but requires sufficient available memory."
},
{
displayName: "Use Memory Mapping",
name: "use_mmap",
type: "boolean",
default: true,
description: "Whether to use memory mapping for loading the model. This can reduce memory usage but may impact performance."
},
{
displayName: "Load Vocabulary Only",
name: "vocab_only",
type: "boolean",
default: false,
description: "Whether to only load the model vocabulary without the weights. Useful for quickly testing tokenization."
},
{
displayName: "Output Format",
name: "format",
type: "options",
options: [
{ name: "Default", value: "" },
{ name: "JSON", value: "json" }
],
default: "",
description: "Specifies the format of the API response"
}
]
}
];
const displayOptions = {
show: {
operation: ["message"],
resource: ["text"]
}
};
const description = (0, import_n8n_workflow.updateDisplayOptions)(displayOptions, properties);
async function execute(i) {
const model = this.getNodeParameter("modelId", i, "", { extractValue: true });
const messages = this.getNodeParameter("messages.values", i, []);
const simplify = this.getNodeParameter("simplify", i, true);
const options = this.getNodeParameter("options", i, {});
const { tools, connectedTools } = await getTools.call(this);
if (options.system) {
messages.unshift({
role: "system",
content: options.system
});
}
delete options.system;
const processedOptions = { ...options };
if (processedOptions.stop && typeof processedOptions.stop === "string") {
processedOptions.stop = processedOptions.stop.split(",").map((s) => s.trim()).filter(Boolean);
}
const body = {
model,
messages,
stream: false,
tools,
options: processedOptions
};
let response = await import_transport.apiRequest.call(this, "POST", "/api/chat", {
body
});
if (tools.length > 0 && response.message.tool_calls && response.message.tool_calls.length > 0) {
const toolCalls = response.message.tool_calls;
messages.push(response.message);
for (const toolCall of toolCalls) {
let toolResponse = "";
let toolFound = false;
for (const tool of connectedTools) {
if (tool.name === toolCall.function.name) {
toolFound = true;
try {
const result = await tool.invoke(toolCall.function.arguments);
toolResponse = typeof result === "object" && result !== null ? JSON.stringify(result) : String(result);
} catch (error) {
toolResponse = `Error executing tool: ${error instanceof Error ? error.message : "Unknown error"}`;
}
break;
}
}
if (!toolFound) {
toolResponse = `Error: Tool '${toolCall.function.name}' not found`;
}
messages.push({
role: "tool",
content: toolResponse,
tool_name: toolCall.function.name
});
}
const updatedBody = {
...body,
messages
};
response = await import_transport.apiRequest.call(this, "POST", "/api/chat", {
body: updatedBody
});
}
if (simplify) {
return [
{
json: { content: response.message.content },
pairedItem: { item: i }
}
];
}
return [
{
json: { ...response },
pairedItem: { item: i }
}
];
}
async function getTools() {
let connectedTools = [];
const nodeInputs = this.getNodeInputs();
if (nodeInputs.some((input) => input.type === "ai_tool")) {
connectedTools = await (0, import_helpers.getConnectedTools)(this, true);
}
const tools = connectedTools.map((tool) => ({
type: "function",
function: {
name: tool.name,
description: tool.description,
parameters: (0, import_zod_to_json_schema.zodToJsonSchema)(tool.schema)
}
}));
return { tools, connectedTools };
}
// Annotate the CommonJS export names for ESM import in node:
0 && (module.exports = {
description,
execute
});
//# sourceMappingURL=message.operation.js.map