new(llm.gblib): Added Claude.

package.json

@@ -86,7 +86,9 @@
     "@google-cloud/translate": "8.5.0",
     "@hubspot/api-client": "11.2.0",
     "@koa/cors": "5.0.0",
+    "@langchain/anthropic": "^0.3.7",
     "@langchain/community": "0.2.31",
+    "@langchain/core": "^0.3.17",
     "@langchain/openai": "0.2.8",
     "@microsoft/microsoft-graph-client": "3.0.7",
     "@nlpjs/basic": "4.27.0",

packages/core.gbapp/services/GBConversationalService.ts

@@ -53,7 +53,6 @@ import fs from 'fs/promises';
 import twilio from 'twilio';
 import Nexmo from 'nexmo';
 import { join } from 'path';
-import path from 'path';
 import shell from 'any-shell-escape';
 import { exec } from 'child_process';
 import prism from 'prism-media';
@@ -66,7 +65,6 @@ import * as marked from 'marked';
 import Translate from '@google-cloud/translate';
 import { GBUtil } from '../../../src/util.js';
 import { GBLogEx } from './GBLogEx.js';
-import { DialogKeywords } from '../../basic.gblib/services/DialogKeywords.js';
 
 /**
  * Provides basic services for handling messages and dispatching to back-end

packages/core.gbapp/services/GBDeployer.ts

@@ -353,6 +353,7 @@ export class GBDeployer implements IGBDeployer {
     try {
       vectorStore = await HNSWLib.load(min['vectorStorePath'], embedding);
     } catch {
+      GBLogEx.info(min, 'Creating new store...');
       vectorStore = new HNSWLib(embedding, {
         space: 'cosine'
       });

packages/kb.gbapp/services/KBService.ts

@@ -47,6 +47,7 @@ import { CSVLoader } from '@langchain/community/document_loaders/fs/csv';
 import { DocxLoader } from '@langchain/community/document_loaders/fs/docx';
 import { EPubLoader } from '@langchain/community/document_loaders/fs/epub';
 import { PDFLoader } from '@langchain/community/document_loaders/fs/pdf';
+import { rimraf } from 'rimraf';
 
 import getColors from 'get-image-colors';
 import { Document } from 'langchain/document';
@@ -651,8 +652,8 @@ export class KBService implements IGBKBService {
       await min.conversationalService['playMarkdown'](min, answer, channel, step, GBMinService.userMobile(step));
     } else if (answer.endsWith('.ogg') && process.env.AUDIO_DISABLED !== 'true') {
       await this.playAudio(min, answer, channel, step, min.conversationalService);
-    } else if(answer.startsWith('![')){
+    } else if (answer.startsWith('![')) {
-      
+
       const url = answer.match(/\((.*?)\)/)[1];
       await this.showImage(min, min.conversationalService, step, url, channel)
     } else {
@@ -885,7 +886,7 @@ export class KBService implements IGBKBService {
   ): Promise<string[]> {
     try {
       if (
-        depth > maxDepth ||
+        (depth > maxDepth && !url.endsWith('pdf')) ||
         visited.has(url) ||
         url.endsWith('.jpg') ||
         url.endsWith('.png') ||
@@ -1029,6 +1030,14 @@ export class KBService implements IGBKBService {
     const websiteIgnoreUrls = min.core.getParam<[]>(min.instance, 'Website Ignore URLs', null);
     GBLogEx.info(min, `Website: ${website}, Max Depth: ${maxDepth}, Ignore URLs: ${websiteIgnoreUrls}`);
 
+    let vectorStore = min['vectorStore'];
+    if (vectorStore) {
+      rimraf.sync(min['vectorStorePath'])
+      
+      vectorStore = await min.deployService['loadOrCreateEmptyVectorStore'](min);
+      min['vectorStore'] = vectorStore;
+    }
+
     if (website) {
       // Removes last slash if any.
 
@@ -1099,19 +1108,20 @@ export class KBService implements IGBKBService {
 
       GBLogEx.info(min, `Vectorizing ${files.length} file(s)...`);
 
+
       await CollectionUtil.asyncForEach(files, async file => {
         let content = null;
 
         try {
           const document = await this.loadAndSplitFile(file);
           const flattenedDocuments = document.reduce((acc, val) => acc.concat(val), []);
-          const vectorStore = min['vectorStore'];
           await vectorStore.addDocuments(flattenedDocuments);
-          await vectorStore.save(min['vectorStorePath']);
         } catch (error) {
           GBLogEx.info(min, `Ignore processing of ${file}. ${GBUtil.toYAML(error)}`);
         }
       });
+
+
     }
 
     files = await walkPromise(urlJoin(localPath, 'docs'));
@@ -1123,13 +1133,16 @@ export class KBService implements IGBKBService {
 
         const document = await this.loadAndSplitFile(filePath);
         const flattenedDocuments = document.reduce((acc, val) => acc.concat(val), []);
-        const vectorStore = min['vectorStore'];
         await vectorStore.addDocuments(flattenedDocuments);
-        await vectorStore.save(min['vectorStorePath']);
       });
     }
+    await vectorStore.save(min['vectorStorePath']);
+    min['vectorStore'] = vectorStore;
+
   }
 
+
+
   defaultRecursiveCharacterTextSplitter = new RecursiveCharacterTextSplitter({
     chunkSize: 700,
     chunkOverlap: 50
@@ -1496,7 +1509,11 @@ export class KBService implements IGBKBService {
 
         return filePath; // Return the saved file path
       } else {
-        await page.goto(url, { waitUntil: 'networkidle2' });
+        await page.goto(url, {
+          waitUntil: 'networkidle2',
+          timeout: 60000 // Timeout after 1 minute (60,000 ms)
+        });
+
 
         const parsedUrl = new URL(url);
 

packages/llm.gblib/services/ChatServices.ts

@@ -29,6 +29,7 @@
 \*****************************************************************************/
 
 'use strict';
+import { ChatAnthropic } from "@langchain/anthropic";
 import { PromptTemplate } from '@langchain/core/prompts';
 import { WikipediaQueryRun } from '@langchain/community/tools/wikipedia_query_run';
 import { HNSWLib } from '@langchain/community/vectorstores/hnswlib';
@@ -244,6 +245,16 @@ export class ChatServices {
   public static async invokeLLM(min: GBMinInstance, text: string) {
     let model;
 
+    model = await ChatServices.getModel(min);
+
+    return await model.invoke(text);
+  }
+
+  public static memoryMap = {};
+  public static userSystemPrompt = {};
+  public static usersMode = {};
+
+  private static async getModel(min: GBMinInstance) {
     const azureOpenAIKey = await (min.core as any)['getParam'](min.instance, 'Azure Open AI Key', null, true);
     const azureOpenAILLMModel = await (min.core as any)['getParam'](
       min.instance,
@@ -259,23 +270,33 @@ export class ChatServices {
       true
     );
 
-    model = new ChatOpenAI({
+    const provider = await (min.core as any)['getParam'](
-      azureOpenAIApiKey: azureOpenAIKey,
+      min.instance,
-      azureOpenAIApiInstanceName: azureOpenAIApiInstanceName,
+      'LLM Provider',
-
+      null,
-      azureOpenAIApiDeploymentName: azureOpenAILLMModel,
+      'openai'
-      azureOpenAIApiVersion: azureOpenAIVersion,
+    );
-      temperature: 0,
+    let model;
-      callbacks: [logHandler]
+    if (provider === 'claude') {
-    });
+      model = new ChatAnthropic({
-
+        model: "claude-3-haiku-20240307",
-    return await model.invoke(text);
+        temperature: 0,
+        maxTokens: undefined,
+        maxRetries: 2,
+      });
+    } else {
+      model = new ChatOpenAI({
+        azureOpenAIApiKey: azureOpenAIKey,
+        azureOpenAIApiInstanceName: azureOpenAIApiInstanceName,
+        azureOpenAIApiDeploymentName: azureOpenAILLMModel,
+        azureOpenAIApiVersion: azureOpenAIVersion,
+        temperature: 0,
+        callbacks: [logHandler]
+      });
+    }
+    return model;
   }
 
-  public static memoryMap = {};
-  public static userSystemPrompt = {};
-  public static usersMode = {};
-
   public static async answerByLLM(pid: number, min: GBMinInstance, user, question: string, mode = null) {
     const answerMode = this.usersMode[user.userSystemId]
       ? this.usersMode[user.userSystemId]
@@ -308,32 +329,7 @@ export class ChatServices {
 
     const systemPrompt = securityPrompt + (user ? this.userSystemPrompt[user.userSystemId] : '');
 
-    let model;
+    let model = await ChatServices.getModel(min);
-
-    const azureOpenAIKey = await (min.core as any)['getParam'](min.instance, 'Azure Open AI Key', null, true);
-    const azureOpenAILLMModel = await (min.core as any)['getParam'](
-      min.instance,
-      'Azure Open AI LLM Model',
-      null,
-      true
-    );
-    const azureOpenAIVersion = await (min.core as any)['getParam'](min.instance, 'Azure Open AI Version', null, true);
-    const azureOpenAIApiInstanceName = await (min.core as any)['getParam'](
-      min.instance,
-      'Azure Open AI Instance',
-      null,
-      true
-    );
-
-    model = new ChatOpenAI({
-      azureOpenAIApiKey: azureOpenAIKey,
-      azureOpenAIApiInstanceName: azureOpenAIApiInstanceName,
-
-      azureOpenAIApiDeploymentName: azureOpenAILLMModel,
-      azureOpenAIApiVersion: azureOpenAIVersion,
-      temperature: 0,
-      callbacks: [logHandler]
-    });
 
     let tools = await ChatServices.getTools(min);
     let toolsAsText = ChatServices.getToolsAsText(tools);

templates/crawler.gbai/crawler.gbot/config.csv

@@ -1,4 +1,5 @@
 name,value
-Website,https://www.dgti.uerj.br/
+Website,https://www.oabgo.org.br/
 Answer Mode,document
-Theme Color,purple    
+Theme Color,purple
+LLM Provider,claude