fix: 修复TypeScript配置错误并更新项目文档
详细说明: - 修复了@n8n/config包的TypeScript配置错误 - 移除了不存在的jest-expect-message类型引用 - 清理了所有TypeScript构建缓存 - 更新了可行性分析文档，添加了技术实施方案 - 更新了Agent prompt文档 - 添加了会展策划工作流文档 - 包含了n8n-chinese-translation子项目 - 添加了exhibition-demo展示系统框架
This commit is contained in:
Yep_Q
472
n8n-n8n-1.109.2/packages/@n8n/nodes-langchain/utils/logWrapper 2.ts
Executable file
472
n8n-n8n-1.109.2/packages/@n8n/nodes-langchain/utils/logWrapper 2.ts
Executable file
@@ -0,0 +1,472 @@
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import type { BaseChatMemory } from '@langchain/community/memory/chat_memory';
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import type { BaseCallbackConfig, Callbacks } from '@langchain/core/callbacks/manager';
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import type { BaseChatMessageHistory } from '@langchain/core/chat_history';
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import type { Document } from '@langchain/core/documents';
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import { Embeddings } from '@langchain/core/embeddings';
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import type { InputValues, MemoryVariables, OutputValues } from '@langchain/core/memory';
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import type { BaseMessage } from '@langchain/core/messages';
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import { BaseRetriever } from '@langchain/core/retrievers';
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import { BaseDocumentCompressor } from '@langchain/core/retrievers/document_compressors';
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import type { StructuredTool, Tool } from '@langchain/core/tools';
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import { VectorStore } from '@langchain/core/vectorstores';
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import { TextSplitter } from '@langchain/textsplitters';
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import type { BaseDocumentLoader } from 'langchain/dist/document_loaders/base';
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import type {
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IDataObject,
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IExecuteFunctions,
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INodeExecutionData,
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ISupplyDataFunctions,
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ITaskMetadata,
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NodeConnectionType,
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} from 'n8n-workflow';
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import {
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NodeOperationError,
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NodeConnectionTypes,
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parseErrorMetadata,
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deepCopy,
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} from 'n8n-workflow';
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import { logAiEvent, isToolsInstance, isBaseChatMemory, isBaseChatMessageHistory } from './helpers';
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import { N8nBinaryLoader } from './N8nBinaryLoader';
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import { N8nJsonLoader } from './N8nJsonLoader';
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export async function callMethodAsync<T>(
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this: T,
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parameters: {
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executeFunctions: IExecuteFunctions | ISupplyDataFunctions;
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connectionType: NodeConnectionType;
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currentNodeRunIndex: number;
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method: (...args: any[]) => Promise<unknown>;
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arguments: unknown[];
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},
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): Promise<unknown> {
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try {
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return await parameters.method.call(this, ...parameters.arguments);
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} catch (e) {
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const connectedNode = parameters.executeFunctions.getNode();
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const error = new NodeOperationError(connectedNode, e, {
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functionality: 'configuration-node',
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});
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const metadata = parseErrorMetadata(error);
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parameters.executeFunctions.addOutputData(
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parameters.connectionType,
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parameters.currentNodeRunIndex,
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error,
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metadata,
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);
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if (error.message) {
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if (!error.description) {
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error.description = error.message;
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}
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throw error;
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}
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throw new NodeOperationError(
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connectedNode,
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`Error on node "${connectedNode.name}" which is connected via input "${parameters.connectionType}"`,
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{ functionality: 'configuration-node' },
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);
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}
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}
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export function callMethodSync<T>(
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this: T,
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parameters: {
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executeFunctions: IExecuteFunctions;
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connectionType: NodeConnectionType;
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currentNodeRunIndex: number;
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method: (...args: any[]) => T;
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arguments: unknown[];
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},
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): unknown {
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try {
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return parameters.method.call(this, ...parameters.arguments);
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} catch (e) {
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const connectedNode = parameters.executeFunctions.getNode();
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const error = new NodeOperationError(connectedNode, e);
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parameters.executeFunctions.addOutputData(
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parameters.connectionType,
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parameters.currentNodeRunIndex,
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error,
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);
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throw new NodeOperationError(
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connectedNode,
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`Error on node "${connectedNode.name}" which is connected via input "${parameters.connectionType}"`,
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{ functionality: 'configuration-node' },
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);
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}
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}
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export function logWrapper<
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T extends
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| Tool
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| StructuredTool
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| BaseChatMemory
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| BaseChatMessageHistory
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| BaseRetriever
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| BaseDocumentCompressor
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| Embeddings
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| Document[]
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| Document
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| BaseDocumentLoader
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| TextSplitter
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| VectorStore
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| N8nBinaryLoader
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| N8nJsonLoader,
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>(originalInstance: T, executeFunctions: IExecuteFunctions | ISupplyDataFunctions): T {
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return new Proxy(originalInstance, {
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get: (target, prop) => {
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let connectionType: NodeConnectionType | undefined;
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// ========== BaseChatMemory ==========
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if (isBaseChatMemory(originalInstance)) {
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if (prop === 'loadMemoryVariables' && 'loadMemoryVariables' in target) {
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return async (values: InputValues): Promise<MemoryVariables> => {
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connectionType = NodeConnectionTypes.AiMemory;
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const { index } = executeFunctions.addInputData(connectionType, [
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[{ json: { action: 'loadMemoryVariables', values } }],
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]);
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const response = (await callMethodAsync.call(target, {
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executeFunctions,
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connectionType,
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currentNodeRunIndex: index,
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method: target[prop],
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arguments: [values],
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})) as MemoryVariables;
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const chatHistory = (response?.chat_history as BaseMessage[]) ?? response;
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executeFunctions.addOutputData(connectionType, index, [
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[{ json: { action: 'loadMemoryVariables', chatHistory } }],
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]);
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return response;
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};
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} else if (prop === 'saveContext' && 'saveContext' in target) {
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return async (input: InputValues, output: OutputValues): Promise<MemoryVariables> => {
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connectionType = NodeConnectionTypes.AiMemory;
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const { index } = executeFunctions.addInputData(connectionType, [
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[{ json: { action: 'saveContext', input, output } }],
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]);
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const response = (await callMethodAsync.call(target, {
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executeFunctions,
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connectionType,
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currentNodeRunIndex: index,
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method: target[prop],
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arguments: [input, output],
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})) as MemoryVariables;
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const chatHistory = await target.chatHistory.getMessages();
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executeFunctions.addOutputData(connectionType, index, [
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[{ json: { action: 'saveContext', chatHistory } }],
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]);
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return response;
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};
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}
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}
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// ========== BaseChatMessageHistory ==========
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if (isBaseChatMessageHistory(originalInstance)) {
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if (prop === 'getMessages' && 'getMessages' in target) {
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return async (): Promise<BaseMessage[]> => {
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connectionType = NodeConnectionTypes.AiMemory;
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const { index } = executeFunctions.addInputData(connectionType, [
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[{ json: { action: 'getMessages' } }],
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]);
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const response = (await callMethodAsync.call(target, {
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executeFunctions,
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connectionType,
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currentNodeRunIndex: index,
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method: target[prop],
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arguments: [],
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})) as BaseMessage[];
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const payload = { action: 'getMessages', response };
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executeFunctions.addOutputData(connectionType, index, [[{ json: payload }]]);
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logAiEvent(executeFunctions, 'ai-messages-retrieved-from-memory', { response });
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return response;
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};
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} else if (prop === 'addMessage' && 'addMessage' in target) {
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return async (message: BaseMessage): Promise<void> => {
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connectionType = NodeConnectionTypes.AiMemory;
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const payload = { action: 'addMessage', message };
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const { index } = executeFunctions.addInputData(connectionType, [[{ json: payload }]]);
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await callMethodAsync.call(target, {
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executeFunctions,
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connectionType,
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currentNodeRunIndex: index,
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method: target[prop],
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arguments: [message],
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});
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logAiEvent(executeFunctions, 'ai-message-added-to-memory', { message });
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executeFunctions.addOutputData(connectionType, index, [[{ json: payload }]]);
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};
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}
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}
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// ========== BaseRetriever ==========
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if (originalInstance instanceof BaseRetriever) {
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if (prop === 'getRelevantDocuments' && 'getRelevantDocuments' in target) {
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return async (
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query: string,
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config?: Callbacks | BaseCallbackConfig,
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): Promise<Document[]> => {
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connectionType = NodeConnectionTypes.AiRetriever;
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const { index } = executeFunctions.addInputData(connectionType, [
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[{ json: { query, config } }],
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]);
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const response = (await callMethodAsync.call(target, {
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executeFunctions,
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connectionType,
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currentNodeRunIndex: index,
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method: target[prop],
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arguments: [query, config],
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})) as Array<Document<Record<string, any>>>;
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const executionId: string | undefined = response[0]?.metadata?.executionId as string;
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const workflowId: string | undefined = response[0]?.metadata?.workflowId as string;
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const metadata: ITaskMetadata = {};
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if (executionId && workflowId) {
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metadata.subExecution = {
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executionId,
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workflowId,
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};
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}
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logAiEvent(executeFunctions, 'ai-documents-retrieved', { query });
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executeFunctions.addOutputData(
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connectionType,
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index,
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[[{ json: { response } }]],
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metadata,
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);
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return response;
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};
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}
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}
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// ========== Embeddings ==========
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if (originalInstance instanceof Embeddings) {
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// Docs -> Embeddings
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if (prop === 'embedDocuments' && 'embedDocuments' in target) {
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return async (documents: string[]): Promise<number[][]> => {
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connectionType = NodeConnectionTypes.AiEmbedding;
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const { index } = executeFunctions.addInputData(connectionType, [
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[{ json: { documents } }],
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]);
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const response = (await callMethodAsync.call(target, {
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executeFunctions,
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connectionType,
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currentNodeRunIndex: index,
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method: target[prop],
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arguments: [documents],
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})) as number[][];
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logAiEvent(executeFunctions, 'ai-document-embedded');
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executeFunctions.addOutputData(connectionType, index, [[{ json: { response } }]]);
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return response;
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};
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}
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// Query -> Embeddings
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if (prop === 'embedQuery' && 'embedQuery' in target) {
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return async (query: string): Promise<number[]> => {
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connectionType = NodeConnectionTypes.AiEmbedding;
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const { index } = executeFunctions.addInputData(connectionType, [
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[{ json: { query } }],
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]);
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const response = (await callMethodAsync.call(target, {
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executeFunctions,
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connectionType,
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currentNodeRunIndex: index,
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method: target[prop],
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arguments: [query],
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})) as number[];
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logAiEvent(executeFunctions, 'ai-query-embedded');
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executeFunctions.addOutputData(connectionType, index, [[{ json: { response } }]]);
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return response;
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};
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}
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}
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// ========== Rerankers ==========
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if (originalInstance instanceof BaseDocumentCompressor) {
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if (prop === 'compressDocuments' && 'compressDocuments' in target) {
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return async (documents: Document[], query: string): Promise<Document[]> => {
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connectionType = NodeConnectionTypes.AiReranker;
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const { index } = executeFunctions.addInputData(connectionType, [
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[{ json: { query, documents } }],
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]);
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const response = (await callMethodAsync.call(target, {
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executeFunctions,
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connectionType,
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currentNodeRunIndex: index,
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method: target[prop],
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// compressDocuments mutates the original object
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// messing up the input data logging
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arguments: [deepCopy(documents), query],
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})) as Document[];
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logAiEvent(executeFunctions, 'ai-document-reranked', { query });
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executeFunctions.addOutputData(connectionType, index, [[{ json: { response } }]]);
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return response;
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};
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}
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}
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// ========== N8n Loaders Process All ==========
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if (
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originalInstance instanceof N8nJsonLoader ||
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originalInstance instanceof N8nBinaryLoader
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) {
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// Process All
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if (prop === 'processAll' && 'processAll' in target) {
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return async (items: INodeExecutionData[]): Promise<number[]> => {
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connectionType = NodeConnectionTypes.AiDocument;
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const { index } = executeFunctions.addInputData(connectionType, [items]);
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const response = (await callMethodAsync.call(target, {
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executeFunctions,
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connectionType,
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currentNodeRunIndex: index,
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method: target[prop],
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arguments: [items],
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})) as number[];
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executeFunctions.addOutputData(connectionType, index, [[{ json: { response } }]]);
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return response;
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};
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}
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// Process Each
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if (prop === 'processItem' && 'processItem' in target) {
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return async (item: INodeExecutionData, itemIndex: number): Promise<number[]> => {
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connectionType = NodeConnectionTypes.AiDocument;
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const { index } = executeFunctions.addInputData(connectionType, [[item]]);
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const response = (await callMethodAsync.call(target, {
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executeFunctions,
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connectionType,
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currentNodeRunIndex: index,
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method: target[prop],
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arguments: [item, itemIndex],
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})) as number[];
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logAiEvent(executeFunctions, 'ai-document-processed');
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executeFunctions.addOutputData(connectionType, index, [
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[{ json: { response }, pairedItem: { item: itemIndex } }],
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]);
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return response;
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};
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}
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}
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// ========== TextSplitter ==========
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if (originalInstance instanceof TextSplitter) {
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if (prop === 'splitText' && 'splitText' in target) {
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return async (text: string): Promise<string[]> => {
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connectionType = NodeConnectionTypes.AiTextSplitter;
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const { index } = executeFunctions.addInputData(connectionType, [
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[{ json: { textSplitter: text } }],
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]);
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const response = (await callMethodAsync.call(target, {
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executeFunctions,
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connectionType,
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currentNodeRunIndex: index,
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method: target[prop],
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arguments: [text],
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})) as string[];
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logAiEvent(executeFunctions, 'ai-text-split');
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executeFunctions.addOutputData(connectionType, index, [[{ json: { response } }]]);
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return response;
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};
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}
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}
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// ========== Tool ==========
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if (isToolsInstance(originalInstance)) {
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if (prop === '_call' && '_call' in target) {
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return async (query: string): Promise<string> => {
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connectionType = NodeConnectionTypes.AiTool;
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const inputData: IDataObject = { query };
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if (target.metadata?.isFromToolkit) {
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inputData.tool = {
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name: target.name,
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description: target.description,
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};
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}
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const { index } = executeFunctions.addInputData(connectionType, [
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[{ json: inputData }],
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]);
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const response = (await callMethodAsync.call(target, {
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executeFunctions,
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connectionType,
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currentNodeRunIndex: index,
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method: target[prop],
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arguments: [query],
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})) as string;
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logAiEvent(executeFunctions, 'ai-tool-called', { ...inputData, response });
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executeFunctions.addOutputData(connectionType, index, [[{ json: { response } }]]);
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if (typeof response === 'string') return response;
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return JSON.stringify(response);
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};
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}
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}
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// ========== VectorStore ==========
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if (originalInstance instanceof VectorStore) {
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if (prop === 'similaritySearch' && 'similaritySearch' in target) {
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return async (
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query: string,
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k?: number,
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filter?: BiquadFilterType,
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_callbacks?: Callbacks,
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): Promise<Document[]> => {
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connectionType = NodeConnectionTypes.AiVectorStore;
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const { index } = executeFunctions.addInputData(connectionType, [
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[{ json: { query, k, filter } }],
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]);
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const response = (await callMethodAsync.call(target, {
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executeFunctions,
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connectionType,
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currentNodeRunIndex: index,
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method: target[prop],
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arguments: [query, k, filter, _callbacks],
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})) as Array<Document<Record<string, any>>>;
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logAiEvent(executeFunctions, 'ai-vector-store-searched', { query });
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executeFunctions.addOutputData(connectionType, index, [[{ json: { response } }]]);
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return response;
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};
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}
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}
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// eslint-disable-next-line @typescript-eslint/no-unsafe-return
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return (target as any)[prop];
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},
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});
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}
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