feat: chat 命令

Author: crazyurus

.github/workflows/publish.yaml

@@ -13,8 +13,8 @@ jobs:
         registry-url: 'https://registry.npmjs.org'
     - name: Install dependencies
       run: |
-        npm install [email protected] -g
-        pnpm install --frozen-lockfile
+        corepack install
+        corepack pnpm install --frozen-lockfile
     - name: Build
       run: npm run build
     - name: Publish

.prettierrc.cjs

@@ -8,15 +8,6 @@ module.exports = {
   bracketSameLine: true,
   useTabs: false,
   plugins: [
-    '@ianvs/prettier-plugin-sort-imports',
     'prettier-plugin-packagejson'
-  ],
-  importOrder: [
-    '<BUILTIN_MODULES>',
-    '<THIRD_PARTY_MODULES>',
-    '',
-    '^@/(.*)$',
-    '',
-    '^[./]'
   ]
 };

README.md

@@ -42,6 +42,12 @@ Finally, tell us the content that needs to be detected and we can proceed.
 $ aigc-detector detect [CONTENT]
 ```
 
+Also, You can chat with the large model with the following command:
+
+```sh
+$ aigc-detector chat
+```
+
 For more ways to use `aigc-detector`, please refer to the help command.
 
 ```sh

package.json

@@ -1,6 +1,6 @@
 {
   "name": "aigc-detector",
-  "version": "1.0.2",
+  "version": "1.0.3",
   "description": "Detect if content is generated by AI",
   "keywords": [
     "aigc",
@@ -58,7 +58,6 @@
     "@babel/core": "^7.24.4",
     "@commitlint/cli": "^19.2.2",
     "@commitlint/config-conventional": "^19.2.2",
-    "@ianvs/prettier-plugin-sort-imports": "^4.2.1",
     "@oclif/prettier-config": "^0.2.1",
     "@types/inquirer": "^9.0.7",
     "@types/node": "^18.11.9",
@@ -77,5 +76,5 @@
   "engines": {
     "node": ">=18.0.0"
   },
-  "packageManager": "pnpm@9.0.6"
+  "packageManager": "pnpm@9.1.0"
 }

pnpm-lock.yaml

@@ -0,0 +1,94 @@
+import { AIMessage, HumanMessage } from '@langchain/core/messages';
+import chalk from 'chalk';
+import { ChatMessageHistory } from 'langchain/stores/message/in_memory';
+import readline from 'node:readline';
+
+import type { Platform } from '../../platform';
+
+import { AIGC } from '../../core';
+import BaseCommand from '../extends/command';
+
+enum PromptRole {
+  AI = 'ai',
+  USER = 'user'
+}
+
+const promptMessageMap = {
+  [PromptRole.AI]: AIMessage,
+  [PromptRole.USER]: HumanMessage
+};
+const promptRoleDisplayMap = {
+  [PromptRole.AI]: {
+    color: 'yellow',
+    name: 'AI'
+  },
+  [PromptRole.USER]: {
+    color: 'green',
+    name: 'You'
+  }
+} as const;
+
+const reader = readline.createInterface({
+  input: process.stdin,
+  output: process.stdout
+});
+
+class ChatCommand extends BaseCommand {
+  static args = {};
+
+  static description = 'Chat with the LLM';
+
+  static examples = [];
+
+  static flags = {};
+
+  private lastMessage = 'How can I help you today?';
+
+  private messages = new ChatMessageHistory();
+
+  async run(): Promise<void> {
+    const config = await this.configManager.getAll();
+
+    if (Object.keys(config).length > 0) {
+      const detector = new AIGC({
+        apiKey: config.apiKey,
+        platform: config.platform as unknown as Platform
+      });
+      const userDisplay = this.getDisplayContent(PromptRole.USER);
+
+      // eslint-disable-next-line no-constant-condition
+      while (true) {
+        const aiMessage = await this.addMessage(PromptRole.AI, this.lastMessage);
+        const userMessage = await this.getUserMessage(aiMessage + `\n${userDisplay}`);
+        const answer = await detector.chat(userMessage, await this.messages.getMessages());
+
+        await this.addMessage(PromptRole.USER, userMessage);
+        this.lastMessage = answer;
+      }
+    } else {
+      this.showHelp();
+    }
+  }
+
+  private async addMessage(role: PromptRole, content: string): Promise<string> {
+    const Message = promptMessageMap[role];
+
+    await this.messages.addMessage(new Message(content));
+
+    return this.getDisplayContent(role) + content;
+  }
+
+  private getDisplayContent(role: PromptRole): string {
+    const roleDisplay = promptRoleDisplayMap[role];
+
+    return chalk[roleDisplay.color](`[${roleDisplay.name}] `);
+  }
+
+  private getUserMessage(aiMessage: string): Promise<string> {
+    return new Promise<string>((resolve) => {
+      reader.question(aiMessage, resolve);
+    });
+  }
+}
+
+export default ChatCommand;

src/cli/commands/chat.ts

@@ -2,8 +2,9 @@ import { Args, Flags } from '@oclif/core';
 import chalk from 'chalk';
 import ora from 'ora';
 
+import type { Platform } from '../../platform';
+
 import { AIGC } from '../../core';
-import { type Platform } from '../../platform';
 import BaseCommand from '../extends/command';
 
 class DetectCommand extends BaseCommand {

src/cli/commands/detect.ts

@@ -1,3 +1,6 @@
+import type { BaseMessage } from '@langchain/core/messages';
+
+import { PROMPT } from '../const';
 import { getPlatform, type Platform } from '../platform';
 import { getEnvConfig } from './env';
 import { getDetectResult } from './utils';
@@ -19,9 +22,20 @@ export class AIGC {
     this.platform = (env.platform as unknown as Platform) || options.platform;
   }
 
+  public async chat(content: string, messages: BaseMessage[]) {
+    const platform = getPlatform(this.platform);
+    const result = await platform.invoke(
+      'You are a helpful assistant. Answer all questions to the best of your ability.',
+      { content, messages },
+      this.apiKey
+    );
+
+    return result;
+  }
+
   public async detect(content: string): Promise<ReturnType<typeof getDetectResult>> {
     const platform = getPlatform(this.platform);
-    const result = await platform.invoke(content, this.apiKey);
+    const result = await platform.invoke(PROMPT, { content }, this.apiKey);
 
     return getDetectResult(result);
   }

src/core/index.ts

@@ -1,9 +1,10 @@
-import { type BaseLanguageModel } from '@langchain/core/language_models/base';
+import type { BaseLanguageModel } from '@langchain/core/language_models/base';
+
 import { ChatPromptTemplate, HumanMessagePromptTemplate, SystemMessagePromptTemplate } from '@langchain/core/prompts';
 import { ChatOpenAI } from '@langchain/openai';
 import { LLMChain } from 'langchain/chains';
 
-import { PROMPT } from '../const';
+type InvokeParameter = Parameters<InstanceType<typeof LLMChain>['invoke']>[0];
 
 abstract class Platform {
   protected temperature = 0.7;
@@ -20,22 +21,20 @@ abstract class Platform {
     });
   }
 
-  protected getPrompt(): ChatPromptTemplate {
+  protected getPrompt(prompt: string): ChatPromptTemplate {
     return ChatPromptTemplate.fromMessages([
-      SystemMessagePromptTemplate.fromTemplate(PROMPT),
+      SystemMessagePromptTemplate.fromTemplate(prompt),
       HumanMessagePromptTemplate.fromTemplate('Here is what needs to be evaluated: \n{content}')
     ]);
   }
 
-  public async invoke(content?: string, apiKey?: string): Promise<string> {
-    const prompt = this.getPrompt();
+  public async invoke(prompt: string, params: InvokeParameter, apiKey?: string): Promise<string> {
+    const promptTemplate = this.getPrompt(prompt);
     const chain = new LLMChain({
       llm: this.getChatModel(apiKey),
-      prompt
-    });
-    const result = await chain.invoke({
-      content
+      prompt: promptTemplate
     });
+    const result = await chain.invoke(params);
 
     return result.text;
   }

src/platform/base.ts

@@ -1,5 +1,6 @@
+import type { BaseLanguageModel } from '@langchain/core/language_models/base';
+
 import { ChatMinimax } from '@langchain/community/chat_models/minimax';
-import { type BaseLanguageModel } from '@langchain/core/language_models/base';
 
 import Platform from './base';
 

src/platform/minimax.ts

@@ -1,5 +1,6 @@
+import type { BaseLanguageModel } from '@langchain/core/language_models/base';
+
 import { ChatAlibabaTongyi } from '@langchain/community/chat_models/alibaba_tongyi';
-import { type BaseLanguageModel } from '@langchain/core/language_models/base';
 
 import Platform from './base';
 

src/platform/tongyi.ts

@@ -1,5 +1,6 @@
+import type { BaseLanguageModel } from '@langchain/core/language_models/base';
+
 import { ChatZhipuAI } from '@langchain/community/chat_models/zhipuai';
-import { type BaseLanguageModel } from '@langchain/core/language_models/base';
 
 import Platform from './base';