mirror of
https://github.com/n8n-io/n8n.git
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595 lines
16 KiB
TypeScript
595 lines
16 KiB
TypeScript
import { ApplicationError, NodeConnectionType, NodeOperationError } from 'n8n-workflow';
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import type {
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IBinaryData,
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IDataObject,
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IExecuteFunctions,
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INodeExecutionData,
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INodeType,
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INodeTypeDescription,
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} from 'n8n-workflow';
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import type { BaseLanguageModel } from '@langchain/core/language_models/base';
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import {
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AIMessagePromptTemplate,
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PromptTemplate,
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SystemMessagePromptTemplate,
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HumanMessagePromptTemplate,
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ChatPromptTemplate,
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} from '@langchain/core/prompts';
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import type { BaseOutputParser } from '@langchain/core/output_parsers';
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import { CombiningOutputParser } from 'langchain/output_parsers';
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import { LLMChain } from 'langchain/chains';
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import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
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import { HumanMessage } from '@langchain/core/messages';
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import { ChatGoogleGenerativeAI } from '@langchain/google-genai';
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import { ChatOllama } from '@langchain/community/chat_models/ollama';
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import { getTemplateNoticeField } from '../../../utils/sharedFields';
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import {
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getOptionalOutputParsers,
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getPromptInputByType,
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isChatInstance,
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} from '../../../utils/helpers';
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import { getTracingConfig } from '../../../utils/tracing';
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interface MessagesTemplate {
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type: string;
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message: string;
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messageType: 'text' | 'imageBinary' | 'imageUrl';
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binaryImageDataKey?: string;
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imageUrl?: string;
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imageDetail?: 'auto' | 'low' | 'high';
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}
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async function getImageMessage(
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context: IExecuteFunctions,
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itemIndex: number,
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message: MessagesTemplate,
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) {
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if (message.messageType !== 'imageBinary' && message.messageType !== 'imageUrl') {
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// eslint-disable-next-line n8n-nodes-base/node-execute-block-wrong-error-thrown
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throw new NodeOperationError(
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context.getNode(),
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'Invalid message type. Only imageBinary and imageUrl are supported',
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);
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}
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const detail = message.imageDetail === 'auto' ? undefined : message.imageDetail;
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if (message.messageType === 'imageUrl' && message.imageUrl) {
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return new HumanMessage({
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content: [
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{
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type: 'image_url',
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image_url: {
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url: message.imageUrl,
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detail,
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},
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},
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],
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});
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}
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const binaryDataKey = message.binaryImageDataKey ?? 'data';
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const inputData = context.getInputData()[itemIndex];
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const binaryData = inputData.binary?.[binaryDataKey] as IBinaryData;
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if (!binaryData) {
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throw new NodeOperationError(context.getNode(), 'No binary data set.');
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}
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const bufferData = await context.helpers.getBinaryDataBuffer(itemIndex, binaryDataKey);
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const model = (await context.getInputConnectionData(
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NodeConnectionType.AiLanguageModel,
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0,
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)) as BaseLanguageModel;
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const dataURI = `data:image/jpeg;base64,${bufferData.toString('base64')}`;
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const directUriModels = [ChatGoogleGenerativeAI, ChatOllama];
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const imageUrl = directUriModels.some((i) => model instanceof i)
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? dataURI
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: { url: dataURI, detail };
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return new HumanMessage({
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content: [
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{
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type: 'image_url',
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image_url: imageUrl,
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},
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],
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});
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}
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async function getChainPromptTemplate(
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context: IExecuteFunctions,
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itemIndex: number,
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llm: BaseLanguageModel | BaseChatModel,
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messages?: MessagesTemplate[],
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formatInstructions?: string,
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query?: string,
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) {
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const queryTemplate = new PromptTemplate({
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template: `{query}${formatInstructions ? '\n{formatInstructions}' : ''}`,
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inputVariables: ['query'],
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partialVariables: formatInstructions ? { formatInstructions } : undefined,
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});
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if (isChatInstance(llm)) {
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const parsedMessages = await Promise.all(
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(messages ?? []).map(async (message) => {
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const messageClass = [
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SystemMessagePromptTemplate,
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AIMessagePromptTemplate,
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HumanMessagePromptTemplate,
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].find((m) => m.lc_name() === message.type);
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if (!messageClass) {
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// eslint-disable-next-line n8n-nodes-base/node-execute-block-wrong-error-thrown
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throw new ApplicationError('Invalid message type', {
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extra: { messageType: message.type },
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});
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}
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if (messageClass === HumanMessagePromptTemplate && message.messageType !== 'text') {
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const test = await getImageMessage(context, itemIndex, message);
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return test;
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}
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const res = messageClass.fromTemplate(
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// Since we're using the message as template, we need to escape any curly braces
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// so LangChain doesn't try to parse them as variables
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(message.message || '').replace(/[{}]/g, (match) => match + match),
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);
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return res;
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}),
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);
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const lastMessage = parsedMessages[parsedMessages.length - 1];
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// If the last message is a human message and it has an array of content, we need to add the query to the last message
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if (lastMessage instanceof HumanMessage && Array.isArray(lastMessage.content)) {
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const humanMessage = new HumanMessagePromptTemplate(queryTemplate);
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const test = await humanMessage.format({ query });
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lastMessage.content.push({ text: test.content.toString(), type: 'text' });
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} else {
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parsedMessages.push(new HumanMessagePromptTemplate(queryTemplate));
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}
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return ChatPromptTemplate.fromMessages(parsedMessages);
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}
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return queryTemplate;
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}
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async function createSimpleLLMChain(
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context: IExecuteFunctions,
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llm: BaseLanguageModel,
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query: string,
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prompt: ChatPromptTemplate | PromptTemplate,
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): Promise<string[]> {
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const chain = new LLMChain({
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llm,
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prompt,
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}).withConfig(getTracingConfig(context));
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const response = (await chain.invoke({
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query,
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signal: context.getExecutionCancelSignal(),
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})) as string[];
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return Array.isArray(response) ? response : [response];
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}
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async function getChain(
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context: IExecuteFunctions,
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itemIndex: number,
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query: string,
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llm: BaseLanguageModel,
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outputParsers: BaseOutputParser[],
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messages?: MessagesTemplate[],
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): Promise<unknown[]> {
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const chatTemplate: ChatPromptTemplate | PromptTemplate = await getChainPromptTemplate(
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context,
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itemIndex,
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llm,
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messages,
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undefined,
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query,
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);
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// If there are no output parsers, create a simple LLM chain and execute the query
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if (!outputParsers.length) {
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return await createSimpleLLMChain(context, llm, query, chatTemplate);
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}
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// If there's only one output parser, use it; otherwise, create a combined output parser
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const combinedOutputParser =
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outputParsers.length === 1 ? outputParsers[0] : new CombiningOutputParser(...outputParsers);
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const formatInstructions = combinedOutputParser.getFormatInstructions();
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// Create a prompt template incorporating the format instructions and query
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const prompt = await getChainPromptTemplate(
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context,
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itemIndex,
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llm,
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messages,
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formatInstructions,
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query,
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);
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const chain = prompt.pipe(llm).pipe(combinedOutputParser);
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const response = (await chain.withConfig(getTracingConfig(context)).invoke({ query })) as
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| string
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| string[];
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return Array.isArray(response) ? response : [response];
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}
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function getInputs(parameters: IDataObject) {
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const hasOutputParser = parameters?.hasOutputParser;
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const inputs = [
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{ displayName: '', type: NodeConnectionType.Main },
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{
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displayName: 'Model',
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maxConnections: 1,
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type: NodeConnectionType.AiLanguageModel,
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required: true,
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},
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];
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// If `hasOutputParser` is undefined it must be version 1.3 or earlier so we
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// always add the output parser input
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if (hasOutputParser === undefined || hasOutputParser === true) {
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inputs.push({ displayName: 'Output Parser', type: NodeConnectionType.AiOutputParser });
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}
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return inputs;
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}
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export class ChainLlm implements INodeType {
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description: INodeTypeDescription = {
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displayName: 'Basic LLM Chain',
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name: 'chainLlm',
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icon: 'fa:link',
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group: ['transform'],
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version: [1, 1.1, 1.2, 1.3, 1.4],
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description: 'A simple chain to prompt a large language model',
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defaults: {
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name: 'Basic LLM Chain',
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color: '#909298',
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},
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codex: {
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alias: ['LangChain'],
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categories: ['AI'],
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subcategories: {
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AI: ['Chains', 'Root Nodes'],
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},
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resources: {
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primaryDocumentation: [
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{
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url: 'https://docs.n8n.io/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.chainllm/',
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},
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],
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},
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},
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inputs: `={{ ((parameter) => { ${getInputs.toString()}; return getInputs(parameter) })($parameter) }}`,
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outputs: [NodeConnectionType.Main],
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credentials: [],
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properties: [
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getTemplateNoticeField(1978),
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{
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displayName: 'Prompt',
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name: 'prompt',
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type: 'string',
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required: true,
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default: '={{ $json.input }}',
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displayOptions: {
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show: {
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'@version': [1],
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},
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},
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},
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{
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displayName: 'Prompt',
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name: 'prompt',
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type: 'string',
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required: true,
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default: '={{ $json.chat_input }}',
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displayOptions: {
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show: {
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'@version': [1.1, 1.2],
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},
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},
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},
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{
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displayName: 'Prompt',
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name: 'prompt',
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type: 'string',
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required: true,
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default: '={{ $json.chatInput }}',
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displayOptions: {
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show: {
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'@version': [1.3],
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},
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},
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},
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{
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displayName: 'Prompt',
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name: 'promptType',
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type: 'options',
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options: [
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{
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// eslint-disable-next-line n8n-nodes-base/node-param-display-name-miscased
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name: 'Take from previous node automatically',
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value: 'auto',
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description: 'Looks for an input field called chatInput',
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},
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{
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// eslint-disable-next-line n8n-nodes-base/node-param-display-name-miscased
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name: 'Define below',
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value: 'define',
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description:
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'Use an expression to reference data in previous nodes or enter static text',
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},
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],
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displayOptions: {
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hide: {
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'@version': [1, 1.1, 1.2, 1.3],
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},
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},
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default: 'auto',
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},
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{
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displayName: 'Text',
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name: 'text',
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type: 'string',
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required: true,
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default: '',
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placeholder: 'e.g. Hello, how can you help me?',
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typeOptions: {
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rows: 2,
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},
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displayOptions: {
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show: {
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promptType: ['define'],
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},
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},
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},
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{
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displayName: 'Require Specific Output Format',
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name: 'hasOutputParser',
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type: 'boolean',
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default: false,
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noDataExpression: true,
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displayOptions: {
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hide: {
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'@version': [1, 1.1, 1.3],
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},
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},
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},
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{
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displayName: 'Chat Messages (if Using a Chat Model)',
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name: 'messages',
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type: 'fixedCollection',
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typeOptions: {
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multipleValues: true,
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},
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default: {},
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placeholder: 'Add prompt',
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options: [
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{
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name: 'messageValues',
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displayName: 'Prompt',
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values: [
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{
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displayName: 'Type Name or ID',
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name: 'type',
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type: 'options',
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options: [
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{
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name: 'AI',
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value: AIMessagePromptTemplate.lc_name(),
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},
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{
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name: 'System',
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value: SystemMessagePromptTemplate.lc_name(),
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},
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{
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name: 'User',
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value: HumanMessagePromptTemplate.lc_name(),
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},
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],
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default: SystemMessagePromptTemplate.lc_name(),
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},
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{
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displayName: 'Message Type',
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name: 'messageType',
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type: 'options',
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displayOptions: {
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show: {
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type: [HumanMessagePromptTemplate.lc_name()],
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},
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},
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options: [
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{
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name: 'Text',
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value: 'text',
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description: 'Simple text message',
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},
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{
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name: 'Image (Binary)',
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value: 'imageBinary',
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description: 'Process the binary input from the previous node',
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},
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{
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name: 'Image (URL)',
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value: 'imageUrl',
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description: 'Process the image from the specified URL',
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},
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],
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default: 'text',
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},
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{
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displayName: 'Image Data Field Name',
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name: 'binaryImageDataKey',
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type: 'string',
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default: 'data',
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required: true,
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description:
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'The name of the field in the chain’s input that contains the binary image file to be processed',
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displayOptions: {
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show: {
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messageType: ['imageBinary'],
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},
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},
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},
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{
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displayName: 'Image URL',
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name: 'imageUrl',
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type: 'string',
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default: '',
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required: true,
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description: 'URL to the image to be processed',
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displayOptions: {
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show: {
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messageType: ['imageUrl'],
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},
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},
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},
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{
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displayName: 'Image Details',
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description:
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'Control how the model processes the image and generates its textual understanding',
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name: 'imageDetail',
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type: 'options',
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displayOptions: {
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show: {
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type: [HumanMessagePromptTemplate.lc_name()],
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messageType: ['imageBinary', 'imageUrl'],
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},
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},
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options: [
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{
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name: 'Auto',
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value: 'auto',
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description:
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'Model will use the auto setting which will look at the image input size and decide if it should use the low or high setting',
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},
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{
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name: 'Low',
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value: 'low',
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description:
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'The model will receive a low-res 512px x 512px version of the image, and represent the image with a budget of 65 tokens. This allows the API to return faster responses and consume fewer input tokens for use cases that do not require high detail.',
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},
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{
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name: 'High',
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value: 'high',
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description:
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'Allows the model to see the low res image and then creates detailed crops of input images as 512px squares based on the input image size. Each of the detailed crops uses twice the token budget (65 tokens) for a total of 129 tokens.',
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},
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],
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default: 'auto',
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},
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{
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displayName: 'Message',
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name: 'message',
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type: 'string',
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required: true,
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displayOptions: {
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hide: {
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messageType: ['imageBinary', 'imageUrl'],
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},
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},
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default: '',
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},
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],
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},
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],
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},
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{
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displayName: `Connect an <a data-action='openSelectiveNodeCreator' data-action-parameter-connectiontype='${NodeConnectionType.AiOutputParser}'>output parser</a> on the canvas to specify the output format you require`,
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name: 'notice',
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type: 'notice',
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default: '',
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displayOptions: {
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show: {
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hasOutputParser: [true],
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},
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},
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},
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],
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};
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async execute(this: IExecuteFunctions): Promise<INodeExecutionData[][]> {
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this.logger.verbose('Executing LLM Chain');
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const items = this.getInputData();
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const returnData: INodeExecutionData[] = [];
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const llm = (await this.getInputConnectionData(
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NodeConnectionType.AiLanguageModel,
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0,
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)) as BaseLanguageModel;
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const outputParsers = await getOptionalOutputParsers(this);
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for (let itemIndex = 0; itemIndex < items.length; itemIndex++) {
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try {
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let prompt: string;
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if (this.getNode().typeVersion <= 1.3) {
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prompt = this.getNodeParameter('prompt', itemIndex) as string;
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} else {
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prompt = getPromptInputByType({
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ctx: this,
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i: itemIndex,
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inputKey: 'text',
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promptTypeKey: 'promptType',
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});
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}
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const messages = this.getNodeParameter(
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'messages.messageValues',
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itemIndex,
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[],
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) as MessagesTemplate[];
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if (prompt === undefined) {
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throw new NodeOperationError(this.getNode(), "The 'prompt' parameter is empty.");
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}
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const responses = await getChain(this, itemIndex, prompt, llm, outputParsers, messages);
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responses.forEach((response) => {
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let data: IDataObject;
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if (typeof response === 'string') {
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data = {
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response: {
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text: response.trim(),
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},
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};
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} else if (Array.isArray(response)) {
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data = {
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data: response,
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};
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} else if (response instanceof Object) {
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data = response as IDataObject;
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} else {
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data = {
|
||
response: {
|
||
text: response,
|
||
},
|
||
};
|
||
}
|
||
|
||
returnData.push({
|
||
json: data,
|
||
});
|
||
});
|
||
} catch (error) {
|
||
if (this.continueOnFail(error)) {
|
||
returnData.push({ json: { error: error.message }, pairedItem: { item: itemIndex } });
|
||
continue;
|
||
}
|
||
|
||
throw error;
|
||
}
|
||
}
|
||
|
||
return [returnData];
|
||
}
|
||
}
|