@n8n/n8n-nodes-langchain
Version:
397 lines • 14.6 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 transport_1 = require("../../transport");
const descriptions_1 = require("../descriptions");
const properties = [
descriptions_1.modelRLC,
{
displayName: 'Text Input',
name: 'text',
type: 'string',
placeholder: "e.g. What's in this image?",
default: "What's in this image?",
typeOptions: {
rows: 2,
},
},
{
displayName: 'Input Type',
name: 'inputType',
type: 'options',
default: 'binary',
options: [
{
name: 'Binary File(s)',
value: 'binary',
},
{
name: 'Image URL(s)',
value: 'url',
},
],
},
{
displayName: 'Input Data Field Name(s)',
name: 'binaryPropertyName',
type: 'string',
default: 'data',
placeholder: 'e.g. data',
hint: 'The name of the input field containing the binary file data to be processed',
description: 'Name of the binary field(s) which contains the image(s), separate multiple field names with commas',
displayOptions: {
show: {
inputType: ['binary'],
},
},
},
{
displayName: 'URL(s)',
name: 'imageUrls',
type: 'string',
placeholder: 'e.g. https://example.com/image.png',
description: 'URL(s) of the image(s) to analyze, multiple URLs can be added separated by comma',
default: '',
displayOptions: {
show: {
inputType: ['url'],
},
},
},
{
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: ['analyze'],
resource: ['image'],
},
};
exports.description = (0, n8n_workflow_1.updateDisplayOptions)(displayOptions, properties);
async function execute(i) {
const model = this.getNodeParameter('modelId', i, '', { extractValue: true });
const inputType = this.getNodeParameter('inputType', i, 'binary');
const text = this.getNodeParameter('text', i, '');
const simplify = this.getNodeParameter('simplify', i, true);
const options = this.getNodeParameter('options', i, {});
let images;
if (inputType === 'url') {
const urls = this.getNodeParameter('imageUrls', i, '');
const urlList = urls
.split(',')
.map((url) => url.trim())
.filter((url) => url);
const imagePromises = urlList.map(async (url) => {
const response = (await this.helpers.httpRequest({
method: 'GET',
url,
encoding: 'arraybuffer',
}));
return response.toString('base64');
});
images = await Promise.all(imagePromises);
}
else {
const binaryPropertyNames = this.getNodeParameter('binaryPropertyName', i, 'data');
const propertyNames = binaryPropertyNames
.split(',')
.map((name) => name.trim())
.filter((name) => name);
const imagePromises = propertyNames.map(async (binaryPropertyName) => {
const buffer = await this.helpers.getBinaryDataBuffer(i, binaryPropertyName);
return buffer.toString('base64');
});
images = await Promise.all(imagePromises);
}
const messages = [
{
role: 'user',
content: text,
images,
},
];
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,
options: processedOptions,
};
const response = await transport_1.apiRequest.call(this, 'POST', '/api/chat', {
body,
});
if (simplify) {
return [
{
json: { content: response.message.content },
pairedItem: { item: i },
},
];
}
return [
{
json: { ...response },
pairedItem: { item: i },
},
];
}
//# sourceMappingURL=analyze.operation.js.map