- LocalLLM Embedded.

packages/core.gbapp/services/GBDeployer.ts

@@ -270,21 +270,18 @@ export class GBDeployer implements IGBDeployer {
    * Verifies if bot exists on bot catalog.
    */
   public async botExists(botId: string): Promise<boolean> {
-
     if (GBConfigService.get('GB_MODE') !== 'legacy') {
       const where = { botId: botId };
 
-      return await GuaribasInstance.findOne({
+      return (
-        where: where
+        (await GuaribasInstance.findOne({
-      }) !== null;
+          where: where
-
+        })) !== null
-    }
+      );
-    else {
+    } else {
-
       const service = await AzureDeployerService.createInstance(this);
 
       return await service.botExists(botId);
-
     }
   }
 
@@ -339,33 +336,68 @@ export class GBDeployer implements IGBDeployer {
   public async loadOrCreateEmptyVectorStore(min: GBMinInstance): Promise<HNSWLib> {
     let vectorStore: HNSWLib;
 
-    const azureOpenAIKey = await (min.core as any)['getParam'](min.instance, 'Azure Open AI Key', null, true);
+    // Get AI mode (default to 'azure' for backward compatibility)
-    const azureOpenAIVersion = await (min.core as any)['getParam'](min.instance, 'Azure Open AI Version', null, true);
+    const aiMode = (await (min.core as any)['getParam'](min.instance, 'AI Mode', 'azure', true)) || 'azure';
-    const azureOpenAIApiInstanceName = await (min.core as any)['getParam'](
-      min.instance,
-      'Azure Open AI Instance',
-      null,
-      true
-    );
-    const azureOpenAIEmbeddingModel = await (min.core as any)['getParam'](
-      min.instance,
-      'Azure Open AI Embedding Model',
-      null,
-      true
-    );
 
     let embedding;
-    if (!azureOpenAIEmbeddingModel) {
-      return;
-    }
 
-    embedding = new OpenAIEmbeddings({
+    if (aiMode === 'local') {
-      maxConcurrency: 5,
+      // Local embedding configuration
-      azureOpenAIApiKey: azureOpenAIKey,
+      const localEmbeddingEndpoint = await (min.core as any)['getParam'](
-      azureOpenAIApiDeploymentName: azureOpenAIEmbeddingModel,
+        min.instance,
-      azureOpenAIApiVersion: azureOpenAIVersion,
+        'Local Embedding Endpoint',
-      azureOpenAIApiInstanceName: azureOpenAIApiInstanceName
+        'http://localhost:5858/v1',
-    });
+        true
+      );
+      const localEmbeddingModel = await (min.core as any)['getParam'](
+        min.instance,
+        'Local Embedding Model',
+        'model',
+        true
+      );
+
+      if (!localEmbeddingEndpoint || !localEmbeddingModel) {
+        GBLogEx.error(min, 'Local embedding configuration incomplete. Please set Local Embedding Endpoint and Model.');
+        return;
+      }
+
+      embedding = new OpenAIEmbeddings({
+        maxConcurrency: 5,
+        openAIApiKey: 'null', // Required field but not used for local
+        modelName: localEmbeddingModel,
+        configuration: {
+          baseURL: localEmbeddingEndpoint
+        }
+      } as any);
+    } else {
+      // Azure OpenAI configuration (original code)
+      const azureOpenAIKey = await (min.core as any)['getParam'](min.instance, 'Azure Open AI Key', 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
+      );
+      const azureOpenAIEmbeddingModel = await (min.core as any)['getParam'](
+        min.instance,
+        'Azure Open AI Embedding Model',
+        null,
+        true
+      );
+
+      if (!azureOpenAIEmbeddingModel) {
+        return;
+      }
+
+      embedding = new OpenAIEmbeddings({
+        maxConcurrency: 5,
+        azureOpenAIApiKey: azureOpenAIKey,
+        azureOpenAIApiDeploymentName: azureOpenAIEmbeddingModel,
+        azureOpenAIApiVersion: azureOpenAIVersion,
+        azureOpenAIApiInstanceName: azureOpenAIApiInstanceName
+      });
+    }
 
     try {
       vectorStore = await HNSWLib.load(min['vectorStorePath'], embedding);
@@ -376,7 +408,7 @@ export class GBDeployer implements IGBDeployer {
         {}, // Optional metadata
         embedding,
         {
-          'space': 'cosine',
+          space: 'cosine'
         } as any
       );
       const dir = path.dirname(min['vectorStorePath']);
@@ -494,15 +526,16 @@ export class GBDeployer implements IGBDeployer {
     } else {
       return [];
     }
-    1
+    1;
     await asyncPromise.eachSeries(rows, async (line: any) => {
       if (line && line.length > 0) {
         const key = line[1];
         let value = line[2];
 
-
         if (key && value) {
-          if (value.text) { value = value.text };
+          if (value.text) {
+            value = value.text;
+          }
           obj[key] = value;
         }
       }
@@ -521,7 +554,8 @@ export class GBDeployer implements IGBDeployer {
     localPath: string,
     remotePath: string,
     baseUrl: string = null,
-    client = null, onlyTextFiles = false
+    client = null,
+    onlyTextFiles = false
   ): Promise<any> {
     const storageMode = process.env.GB_MODE;
 
@@ -531,7 +565,7 @@ export class GBDeployer implements IGBDeployer {
         port: parseInt(process.env.DRIVE_PORT || '9000', 10),
         useSSL: process.env.DRIVE_USE_SSL === 'true',
         accessKey: process.env.DRIVE_ACCESSKEY,
-        secretKey: process.env.DRIVE_SECRET,
+        secretKey: process.env.DRIVE_SECRET
       });
 
       const bucketName = (process.env.DRIVE_ORG_PREFIX + min.botId + '.gbai').toLowerCase();
@@ -566,7 +600,6 @@ export class GBDeployer implements IGBDeployer {
             }
           }
 
-
           if (download) {
             await minioClient.fGetObject(bucketName, obj.name, itemPath);
             await fs.utimes(itemPath, new Date(), new Date(obj.lastModified));
@@ -585,7 +618,7 @@ export class GBDeployer implements IGBDeployer {
           await fs.mkdir(pathBase, { recursive: true });
         }
 
-        await CollectionUtil.asyncForEach(parts, async (item) => {
+        await CollectionUtil.asyncForEach(parts, async item => {
           pathBase = path.join(pathBase, item);
           if (!(await GBUtil.exists(pathBase))) {
             await fs.mkdir(pathBase, { recursive: true });
@@ -609,7 +642,7 @@ export class GBDeployer implements IGBDeployer {
           return null;
         }
 
-        await CollectionUtil.asyncForEach(documents, async (item) => {
+        await CollectionUtil.asyncForEach(documents, async item => {
           const itemPath = path.join(localPath, remotePath, item.name);
 
           if (item.folder) {
@@ -639,7 +672,6 @@ export class GBDeployer implements IGBDeployer {
         });
       }
     }
-
   }
 
   /**
@@ -699,11 +731,9 @@ export class GBDeployer implements IGBDeployer {
           await GBUtil.copyIfNewerRecursive(filePath, packageWorkFolder, false);
         }
       } else {
-
         if (packageType === '.gbdrive' || packageType === '.gbdata') {
           await this.downloadFolder(min, path.join('work', `${gbai}`), packageName, undefined, undefined, true);
-        }
+        } else {
-        else {
           await this.downloadFolder(min, path.join('work', `${gbai}`), packageName);
         }
       }

packages/llm.gblib/services/ChatServices.ts

@@ -29,7 +29,7 @@
 \*****************************************************************************/
 
 'use strict';
-import { ChatAnthropic } from "@langchain/anthropic";
+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';
@@ -60,8 +60,8 @@ import { DialogKeywords } from '../../basic.gblib/services/DialogKeywords.js';
 import { GBVMService } from '../../basic.gblib/services/GBVMService.js';
 import { GBLogEx } from '../../core.gbapp/services/GBLogEx.js';
 import { GBUtil } from '../../../src/util.js';
-import { GBConfigService } from "../../core.gbapp/services/GBConfigService.js";
+import { GBConfigService } from '../../core.gbapp/services/GBConfigService.js';
-export interface CustomOutputParserFields { }
+export interface CustomOutputParserFields {}
 export type ExpectedOutput = any;
 
 function isChatGeneration(llmOutput: ChatGeneration | Generation): llmOutput is ChatGeneration {
@@ -135,16 +135,13 @@ export class GBLLMOutputParser extends BaseLLMOutputParser<ExpectedOutput> {
     let securityEnabled = false;
 
     if (!sources) {
-
       GBLogEx.verbose(this.min, `LLM JSON output sources is NULL.`);
-    }
+    } else {
-    else {
       await CollectionUtil.asyncForEach(sources, async source => {
         let found = false;
 
         if (securityEnabled) {
           GBLogEx.info(this.min, `LLM JSON output security enabled.`);
-          
         }
 
         if (source && source.file.endsWith('.pdf')) {
@@ -156,11 +153,14 @@ export class GBLLMOutputParser extends BaseLLMOutputParser<ExpectedOutput> {
 
             if (!isNaN(this.user.userSystemId)) {
               await this.min.whatsAppDirectLine.sendFileToDevice(
-                this.user.userSystemId, pngs[0].url,
+                this.user.userSystemId,
-                localName, null, undefined, true);
+                pngs[0].url,
-
+                localName,
-            }
+                null,
-            else {
+                undefined,
+                true
+              );
+            } else {
               text = `![alt text](${pngs[0].url})
              ${text}`;
             }
@@ -179,8 +179,6 @@ export class GBLLMOutputParser extends BaseLLMOutputParser<ExpectedOutput> {
 }
 
 export class ChatServices {
-
-
   private static async getRelevantContext(
     vectorStore: HNSWLib,
     sanitizedQuestion: string,
@@ -189,7 +187,7 @@ export class ChatServices {
     if (sanitizedQuestion === '' || !vectorStore) {
       return '';
     }
-    let documents = await vectorStore.similaritySearch(sanitizedQuestion, numDocuments );
+    let documents = await vectorStore.similaritySearch(sanitizedQuestion, numDocuments);
     const uniqueDocuments = {};
     const MAX_DOCUMENTS = numDocuments;
 
@@ -219,16 +217,14 @@ export class ChatServices {
         page = await ChatServices.findPageForText(metadata.source, doc.pageContent);
       }
 
-
+      output = `${output}\n\n\n\nUse also the following context which is coming from Source Document: ${filename} at page: ${
-
+        page ? page : 'entire document'
-      output = `${output}\n\n\n\nUse also the following context which is coming from Source Document: ${filename} at page: ${page ? page : 'entire document'
+      }
-        } 
       (you will fill the JSON sources collection field later),
       Use other page if  this block is an index or table of contents (TOC).
       And memorize this block (if it is not an Index or TOC) among document
        information and return when you
-       are refering this part of content:\n\n\n\n ${doc.pageContent
+       are refering this part of content:\n\n\n\n ${doc.pageContent} \n\n\n\n.`;
-        } \n\n\n\n.`;
     }
     return output;
   }
@@ -258,7 +254,7 @@ export class ChatServices {
 
     model = await ChatServices.getModel(min);
 
-    return await model .invoke(text);
+    return await model.invoke(text);
   }
 
   public static memoryMap = {};
@@ -266,29 +262,41 @@ export class ChatServices {
   public static usersMode = {};
 
   private static async getModel(min: GBMinInstance) {
-    const provider = await (min.core as any)['getParam'](
+    const provider = await (min.core as any)['getParam'](min.instance, 'LLM Provider', null, 'openai');
-      min.instance,
-      'LLM Provider',
-      null,
-      'openai'
-    );
     let model;
     if (provider === 'claude') {
       model = new ChatAnthropic({
-        model: "claude-3-haiku-20240307",
+        model: 'claude-3-haiku-20240307',
         temperature: 0,
         maxTokens: undefined,
-        maxRetries: 2,
+        maxRetries: 2
       });
-    } else {
+    } else if (process.env.AI_MODE === 'local') {
-
-
-
       const azureOpenAIKey = process.env.AZURE_OPEN_AI_KEY;
       const azureOpenAILLMModel = process.env.AZURE_OPEN_AI_LLM_MODEL;
       const azureOpenAIVersion = process.env.AZURE_OPEN_AI_VERSION;
-      const azureOpenAIApiInstanceName =  process.env.AZURE_OPEN_AI_INSTANCE;
+      const azureOpenAIApiInstanceName = process.env.AZURE_OPEN_AI_INSTANCE;
-      const azureOpenAIEndPoint =  process.env.AZURE_OPEN_AI_ENDPOINT;
+      const azureOpenAIEndPoint = process.env.AZURE_OPEN_AI_ENDPOINT;
+
+      model = new ChatOpenAI({
+        model: process.env.LOCAL_LLM_MODEL,
+        apiKey: 'empty',
+        azureOpenAIApiDeploymentName: 'v1',
+        azureOpenAIApiInstanceName: 'v1',
+        azureOpenAIApiKey: 'empty',
+        azureOpenAIApiVersion: 'empty',
+        azureOpenAIBasePath: process.env.LOCAL_LLM_ENDPOINT,
+        openAIApiKey: 'empty',
+        configuration: {
+          baseURL: process.env.LOCAL_LLM_ENDPOINT
+        }
+      });
+    } else {
+      const azureOpenAIKey = process.env.AZURE_OPEN_AI_KEY;
+      const azureOpenAILLMModel = process.env.AZURE_OPEN_AI_LLM_MODEL;
+      const azureOpenAIVersion = process.env.AZURE_OPEN_AI_VERSION;
+      const azureOpenAIApiInstanceName = process.env.AZURE_OPEN_AI_INSTANCE;
+      const azureOpenAIEndPoint = process.env.AZURE_OPEN_AI_ENDPOINT;
 
       model = new ChatOpenAI({
         azureOpenAIApiKey: azureOpenAIKey,
@@ -296,7 +304,7 @@ export class ChatServices {
         azureOpenAIApiDeploymentName: azureOpenAILLMModel,
         azureOpenAIApiVersion: azureOpenAIVersion,
         azureOpenAIBasePath: azureOpenAIEndPoint,
-        temperature: 0,
+        temperature: 0
       });
     }
     return model;
@@ -313,7 +321,6 @@ export class ChatServices {
 
     const LLMMode = (mode ?? answerMode).toLowerCase();
 
-
     let memory;
     if (user && !this.memoryMap[user.userSystemId]) {
       memory = new BufferWindowMemory({
@@ -406,7 +413,6 @@ export class ChatServices {
     Folowing answer:`)
     ] as any);
 
-
     const jsonInformation = `
       RESPONSE FORMAT: Return only a single valid JSON object with no surrounding text. Structure:
       {{"text": "Complete response as a single string, using \\n for all line breaks, \n1. bullets and; \n2.lists.", "sources": [{{"file": "filename", "page": number}}]}}
@@ -431,7 +437,6 @@ export class ChatServices {
 
       ERROR IF: Line breaks in JSON, text outside JSON, invalid format`;
 
-
     const combineDocumentsPrompt = ChatPromptTemplate.fromMessages([
       AIMessagePromptTemplate.fromTemplate(
         `
@@ -496,10 +501,10 @@ export class ChatServices {
           const { chat_history } = await memory.loadMemoryVariables({});
           return chat_history;
         },
-        context: (async (output: string) => {
+        context: async (output: string) => {
           const c = await ChatServices.getRelevantContext(min['vectorStore'], output);
           return `${systemPrompt} \n ${c ? 'Use this context to answer:\n' + c : 'answer just with user question.'}`;
-        })
+        }
       },
       combineDocumentsPrompt,
       model,
@@ -516,7 +521,12 @@ export class ChatServices {
       },
       questionGeneratorTemplate,
       modelWithTools,
-      new GBLLMOutputParser(min, user, callToolChain, min['vectorStore']?.docstore?._docs.length > 0 ? combineDocumentsChain : null),
+      new GBLLMOutputParser(
+        min,
+        user,
+        callToolChain,
+        min['vectorStore']?.docstore?._docs.length > 0 ? combineDocumentsChain : null
+      ),
       new StringOutputParser()
     ] as any);
 
@@ -544,15 +554,13 @@ export class ChatServices {
       let tables = con['storageTables'];
       tables = tables ? tables.split(';') : null;
 
-      const answerSource = await (min.core as any)['getParam'](min.instance,
+      const answerSource = await (min.core as any)['getParam'](min.instance, 'Answer Source', 'server');
-        'Answer Source', 'server');
 
       GBLogEx.info(min, `Answer Source = ${answerSource}.`);
 
       let dataSource;
       if (answerSource === 'cache') {
-        let sqliteFilePath =
+        let sqliteFilePath = path.join('work', GBUtil.getGBAIPath(min.botId), `${con['name']}.sqlite`);
-          path.join('work', GBUtil.getGBAIPath(min.botId), `${con['name']}.sqlite`);
         GBLogEx.info(min, `Using data from cache: Path.basename(${sqliteFilePath}).`);
 
         dataSource = new DataSource({
@@ -562,8 +570,6 @@ export class ChatServices {
           logging: true
         });
       } else {
-
-
         if (dialect === 'sqlite') {
           const storageFile = con['storageFile'];
           dataSource = new DataSource({
@@ -572,9 +578,7 @@ export class ChatServices {
             synchronize: false,
             logging: true
           });
-
+        } else {
-        }
-        else {
           const host = con['storageServer'];
           const port = con['storagePort'];
           const storageName = con['storageName'];
@@ -624,7 +628,7 @@ export class ChatServices {
           schema: async () => db.getTableInfo(tables),
           question: (input: { question: string }) => input.question,
           top_k: () => 10,
-          table_info: () => 'any',
+          table_info: () => 'any'
         },
         prompt,
         model,
@@ -672,8 +676,7 @@ export class ChatServices {
           table_names_to_use: () => tables
         },
         {
-          result: finalResponsePrompt.pipe(model).pipe(
+          result: finalResponsePrompt.pipe(model).pipe(new StringOutputParser() as any),
-            new StringOutputParser() as any),
 
           // Pipe the query through here unchanged so it gets logged alongside the result.
           sql: previousStepResult => previousStepResult.query
@@ -752,7 +755,7 @@ export class ChatServices {
       const packagePath = GBUtil.getGBAIPath(min.botId, 'gbdialog', null);
       const jsonFile = path.join('work', packagePath, `${script}.json`);
 
-      if (await GBUtil.exists(jsonFile) && script.toLowerCase() !== 'start.vbs') {
+      if ((await GBUtil.exists(jsonFile)) && script.toLowerCase() !== 'start.vbs') {
         const funcJSON = JSON.parse(await fs.readFile(jsonFile, 'utf8'));
         const funcObj = funcJSON?.function;
 

templates/ai-search.gbai/ai-search.gbkb/docs/README.md

@@ -0,0 +1 @@
+O céu é azul-lilás e pronto.

templates/marketing.gbai/marketing.gbdialog/broadcast.bas

@@ -0,0 +1,40 @@
+TALK "For favor, digite a mensagem que deseja enviar:"
+HEAR message
+
+TALK "Analizando template ... (antes de mandar para a META)"
+report = LLM "Esta mensagem vai ser aprovada pelo WhatsApp META como Template? Tem recomendação? Se estiver OK, responda o texto: OK. Do contrário, avalie o que deve ser feito."
+
+IF report <> "OK" THEN
+    TALK "A mensagem não será aprovada pela Meta. " + report
+END IF
+
+TALK "Envie agora o arquivo de imagem de cabefalho:"
+HEAR plan AS FILE
+
+TALK "É para um arquivo ou todos?"
+HEAR in AS FILE
+
+PUBLISH
+
+IF in.isValid THEN
+    list = FIND in.filename, "Perfil=" + grupos
+ELSE
+    list = GET "broadcast"
+END IF
+
+SET MAX LINES 2020
+
+index = 1
+
+DO WHILE index < UBOUND(list)
+    row = list[index]
+
+    SEND TEMPLATE TO row.telefone. filename
+
+    WAIT 0.1
+
+    index = index + 1
+
+LOOP
+
+TALK "OK, o envio foi realizado. Para saber mais, digite /report."