@n8n/n8n-nodes-langchain
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428 lines • 16.4 kB
JavaScript
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
exports.description = void 0;
exports.execute = execute;
const n8n_workflow_1 = require("n8n-workflow");
const zod_to_json_schema_1 = require("zod-to-json-schema");
const helpers_1 = require("../../../../../utils/helpers");
const transport_1 = require("../../transport");
const descriptions_1 = require("../descriptions");
const properties = [
descriptions_1.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: 0.0,
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: 0.0,
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: 0.0,
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'],
},
};
exports.description = (0, n8n_workflow_1.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 transport_1.apiRequest.call(this, 'POST', '/api/chat', {
body,
});
if (response.prompt_eval_count != null || response.eval_count != null) {
(0, n8n_workflow_1.accumulateTokenUsage)(this, response.prompt_eval_count ?? 0, response.eval_count ?? 0);
}
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 transport_1.apiRequest.call(this, 'POST', '/api/chat', {
body: updatedBody,
});
if (response.prompt_eval_count != null || response.eval_count != null) {
(0, n8n_workflow_1.accumulateTokenUsage)(this, response.prompt_eval_count ?? 0, response.eval_count ?? 0);
}
}
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, helpers_1.getConnectedTools)(this, true);
}
const tools = connectedTools.map((tool) => ({
type: 'function',
function: {
name: tool.name,
description: tool.description,
parameters: (0, zod_to_json_schema_1.zodToJsonSchema)(tool.schema),
},
}));
return { tools, connectedTools };
}
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