mirror of
https://github.com/Abdulazizzn/n8n-enterprise-unlocked.git
synced 2025-12-16 17:46:45 +00:00
298 lines
8.3 KiB
TypeScript
298 lines
8.3 KiB
TypeScript
import type { BaseLanguageModel } from '@langchain/core/language_models/base';
|
||
import {
|
||
ChatPromptTemplate,
|
||
SystemMessagePromptTemplate,
|
||
HumanMessagePromptTemplate,
|
||
PromptTemplate,
|
||
} from '@langchain/core/prompts';
|
||
import type { BaseRetriever } from '@langchain/core/retrievers';
|
||
import { createStuffDocumentsChain } from 'langchain/chains/combine_documents';
|
||
import { createRetrievalChain } from 'langchain/chains/retrieval';
|
||
import { NodeConnectionTypes, NodeOperationError, parseErrorMetadata } from 'n8n-workflow';
|
||
import {
|
||
type INodeProperties,
|
||
type IExecuteFunctions,
|
||
type INodeExecutionData,
|
||
type INodeType,
|
||
type INodeTypeDescription,
|
||
} from 'n8n-workflow';
|
||
|
||
import { promptTypeOptions, textFromPreviousNode } from '@utils/descriptions';
|
||
import { getPromptInputByType, isChatInstance } from '@utils/helpers';
|
||
import { getTemplateNoticeField } from '@utils/sharedFields';
|
||
import { getTracingConfig } from '@utils/tracing';
|
||
|
||
const SYSTEM_PROMPT_TEMPLATE = `You are an assistant for question-answering tasks. Use the following pieces of retrieved context to answer the question.
|
||
If you don't know the answer, just say that you don't know, don't try to make up an answer.
|
||
----------------
|
||
Context: {context}`;
|
||
|
||
// Due to the refactoring in version 1.5, the variable name {question} needed to be changed to {input} in the prompt template.
|
||
const LEGACY_INPUT_TEMPLATE_KEY = 'question';
|
||
const INPUT_TEMPLATE_KEY = 'input';
|
||
|
||
const systemPromptOption: INodeProperties = {
|
||
displayName: 'System Prompt Template',
|
||
name: 'systemPromptTemplate',
|
||
type: 'string',
|
||
default: SYSTEM_PROMPT_TEMPLATE,
|
||
typeOptions: {
|
||
rows: 6,
|
||
},
|
||
};
|
||
|
||
export class ChainRetrievalQa implements INodeType {
|
||
description: INodeTypeDescription = {
|
||
displayName: 'Question and Answer Chain',
|
||
name: 'chainRetrievalQa',
|
||
icon: 'fa:link',
|
||
iconColor: 'black',
|
||
group: ['transform'],
|
||
version: [1, 1.1, 1.2, 1.3, 1.4, 1.5],
|
||
description: 'Answer questions about retrieved documents',
|
||
defaults: {
|
||
name: 'Question and Answer Chain',
|
||
color: '#909298',
|
||
},
|
||
codex: {
|
||
alias: ['LangChain'],
|
||
categories: ['AI'],
|
||
subcategories: {
|
||
AI: ['Chains', 'Root Nodes'],
|
||
},
|
||
resources: {
|
||
primaryDocumentation: [
|
||
{
|
||
url: 'https://docs.n8n.io/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.chainretrievalqa/',
|
||
},
|
||
],
|
||
},
|
||
},
|
||
// eslint-disable-next-line n8n-nodes-base/node-class-description-inputs-wrong-regular-node
|
||
inputs: [
|
||
NodeConnectionTypes.Main,
|
||
{
|
||
displayName: 'Model',
|
||
maxConnections: 1,
|
||
type: NodeConnectionTypes.AiLanguageModel,
|
||
required: true,
|
||
},
|
||
{
|
||
displayName: 'Retriever',
|
||
maxConnections: 1,
|
||
type: NodeConnectionTypes.AiRetriever,
|
||
required: true,
|
||
},
|
||
],
|
||
outputs: [NodeConnectionTypes.Main],
|
||
credentials: [],
|
||
properties: [
|
||
getTemplateNoticeField(1960),
|
||
{
|
||
displayName: 'Query',
|
||
name: 'query',
|
||
type: 'string',
|
||
required: true,
|
||
default: '={{ $json.input }}',
|
||
displayOptions: {
|
||
show: {
|
||
'@version': [1],
|
||
},
|
||
},
|
||
},
|
||
{
|
||
displayName: 'Query',
|
||
name: 'query',
|
||
type: 'string',
|
||
required: true,
|
||
default: '={{ $json.chat_input }}',
|
||
displayOptions: {
|
||
show: {
|
||
'@version': [1.1],
|
||
},
|
||
},
|
||
},
|
||
{
|
||
displayName: 'Query',
|
||
name: 'query',
|
||
type: 'string',
|
||
required: true,
|
||
default: '={{ $json.chatInput }}',
|
||
displayOptions: {
|
||
show: {
|
||
'@version': [1.2],
|
||
},
|
||
},
|
||
},
|
||
{
|
||
...promptTypeOptions,
|
||
displayOptions: {
|
||
hide: {
|
||
'@version': [{ _cnd: { lte: 1.2 } }],
|
||
},
|
||
},
|
||
},
|
||
{
|
||
...textFromPreviousNode,
|
||
displayOptions: { show: { promptType: ['auto'], '@version': [{ _cnd: { gte: 1.4 } }] } },
|
||
},
|
||
{
|
||
displayName: 'Prompt (User Message)',
|
||
name: 'text',
|
||
type: 'string',
|
||
required: true,
|
||
default: '',
|
||
placeholder: 'e.g. Hello, how can you help me?',
|
||
typeOptions: {
|
||
rows: 2,
|
||
},
|
||
displayOptions: {
|
||
show: {
|
||
promptType: ['define'],
|
||
},
|
||
},
|
||
},
|
||
{
|
||
displayName: 'Options',
|
||
name: 'options',
|
||
type: 'collection',
|
||
default: {},
|
||
placeholder: 'Add Option',
|
||
options: [
|
||
{
|
||
...systemPromptOption,
|
||
description: `Template string used for the system prompt. This should include the variable \`{context}\` for the provided context. For text completion models, you should also include the variable \`{${LEGACY_INPUT_TEMPLATE_KEY}}\` for the user’s query.`,
|
||
displayOptions: {
|
||
show: {
|
||
'@version': [{ _cnd: { lt: 1.5 } }],
|
||
},
|
||
},
|
||
},
|
||
{
|
||
...systemPromptOption,
|
||
description: `Template string used for the system prompt. This should include the variable \`{context}\` for the provided context. For text completion models, you should also include the variable \`{${INPUT_TEMPLATE_KEY}}\` for the user’s query.`,
|
||
displayOptions: {
|
||
show: {
|
||
'@version': [{ _cnd: { gte: 1.5 } }],
|
||
},
|
||
},
|
||
},
|
||
],
|
||
},
|
||
],
|
||
};
|
||
|
||
async execute(this: IExecuteFunctions): Promise<INodeExecutionData[][]> {
|
||
this.logger.debug('Executing Retrieval QA Chain');
|
||
|
||
const items = this.getInputData();
|
||
const returnData: INodeExecutionData[] = [];
|
||
|
||
// Run for each item
|
||
for (let itemIndex = 0; itemIndex < items.length; itemIndex++) {
|
||
try {
|
||
const model = (await this.getInputConnectionData(
|
||
NodeConnectionTypes.AiLanguageModel,
|
||
0,
|
||
)) as BaseLanguageModel;
|
||
|
||
const retriever = (await this.getInputConnectionData(
|
||
NodeConnectionTypes.AiRetriever,
|
||
0,
|
||
)) as BaseRetriever;
|
||
|
||
let query;
|
||
|
||
if (this.getNode().typeVersion <= 1.2) {
|
||
query = this.getNodeParameter('query', itemIndex) as string;
|
||
} else {
|
||
query = getPromptInputByType({
|
||
ctx: this,
|
||
i: itemIndex,
|
||
inputKey: 'text',
|
||
promptTypeKey: 'promptType',
|
||
});
|
||
}
|
||
|
||
if (query === undefined) {
|
||
throw new NodeOperationError(this.getNode(), 'The ‘query‘ parameter is empty.');
|
||
}
|
||
|
||
const options = this.getNodeParameter('options', itemIndex, {}) as {
|
||
systemPromptTemplate?: string;
|
||
};
|
||
|
||
let templateText = options.systemPromptTemplate ?? SYSTEM_PROMPT_TEMPLATE;
|
||
|
||
// Replace legacy input template key for versions 1.4 and below
|
||
if (this.getNode().typeVersion < 1.5) {
|
||
templateText = templateText.replace(
|
||
`{${LEGACY_INPUT_TEMPLATE_KEY}}`,
|
||
`{${INPUT_TEMPLATE_KEY}}`,
|
||
);
|
||
}
|
||
|
||
// Create prompt template based on model type and user configuration
|
||
let promptTemplate;
|
||
if (isChatInstance(model)) {
|
||
// For chat models, create a chat prompt template with system and human messages
|
||
const messages = [
|
||
SystemMessagePromptTemplate.fromTemplate(templateText),
|
||
HumanMessagePromptTemplate.fromTemplate('{input}'),
|
||
];
|
||
promptTemplate = ChatPromptTemplate.fromMessages(messages);
|
||
} else {
|
||
// For non-chat models, create a text prompt template with Question/Answer format
|
||
const questionSuffix =
|
||
options.systemPromptTemplate === undefined ? '\n\nQuestion: {input}\nAnswer:' : '';
|
||
|
||
promptTemplate = new PromptTemplate({
|
||
template: templateText + questionSuffix,
|
||
inputVariables: ['context', 'input'],
|
||
});
|
||
}
|
||
|
||
// Create the document chain that combines the retrieved documents
|
||
const combineDocsChain = await createStuffDocumentsChain({
|
||
llm: model,
|
||
prompt: promptTemplate,
|
||
});
|
||
|
||
// Create the retrieval chain that handles the retrieval and then passes to the combine docs chain
|
||
const retrievalChain = await createRetrievalChain({
|
||
combineDocsChain,
|
||
retriever,
|
||
});
|
||
|
||
// Execute the chain with tracing config
|
||
const tracingConfig = getTracingConfig(this);
|
||
const response = await retrievalChain
|
||
.withConfig(tracingConfig)
|
||
.invoke({ input: query }, { signal: this.getExecutionCancelSignal() });
|
||
|
||
// Get the answer from the response
|
||
const answer: string = response.answer;
|
||
if (this.getNode().typeVersion >= 1.5) {
|
||
returnData.push({ json: { response: answer } });
|
||
} else {
|
||
// Legacy format for versions 1.4 and below is { text: string }
|
||
returnData.push({ json: { response: { text: answer } } });
|
||
}
|
||
} catch (error) {
|
||
if (this.continueOnFail()) {
|
||
const metadata = parseErrorMetadata(error);
|
||
returnData.push({
|
||
json: { error: error.message },
|
||
pairedItem: { item: itemIndex },
|
||
metadata,
|
||
});
|
||
continue;
|
||
}
|
||
|
||
throw error;
|
||
}
|
||
}
|
||
return [returnData];
|
||
}
|
||
}
|