botserver/packages/gpt.gblib/services/ChatServices.ts

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'use strict';

import { RunnableSequence } from "@langchain/core/runnables";
import { ChatOpenAI } from "@langchain/openai";
import { GBMinInstance } from 'botlib';
import * as Fs from 'fs';
import { formatXml } from "langchain/agents/format_scratchpad/xml";
import { jsonSchemaToZod } from "json-schema-to-zod";
import { renderTextDescription } from "langchain/tools/render";

import { AgentExecutor, AgentStep } from "langchain/agents";
import { BufferWindowMemory } from 'langchain/memory';
import { AIMessagePromptTemplate, ChatPromptTemplate, HumanMessagePromptTemplate, MessagesPlaceholder } from '@langchain/core/prompts';
import { Tool } from "langchain/tools";
import { HNSWLib } from '@langchain/community/vectorstores/hnswlib';
import Path from 'path';
import { CollectionUtil } from 'pragmatismo-io-framework';
import { DialogKeywords } from '../../basic.gblib/services/DialogKeywords.js';
import { GBVMService } from '../../basic.gblib/services/GBVMService.js';
import { GBConfigService } from '../../core.gbapp/services/GBConfigService.js';
import { GuaribasSubject } from '../../kb.gbapp/models/index.js';
import { XMLAgentOutputParser } from "langchain/agents/xml/output_parser";
import { StringOutputParser } from "@langchain/core/output_parsers";
import { convertToOpenAITool } from "@langchain/core/utils/function_calling";



export class ChatServices {

  private static async getRelevantContext(
    vectorStore: HNSWLib,
    sanitizedQuestion: string,
    numDocuments: number
  ): Promise<string> {
    const documents = await vectorStore.similaritySearch(sanitizedQuestion, numDocuments);
    return documents
      .map((doc) => doc.pageContent)
      .join(', ')
      .trim()
      .replaceAll('\n', ' ');
  }

  /**
   * Generate text
   *
   * CONTINUE keword.
   *
   * result = CONTINUE text
   *
   */
  public static async continue(min: GBMinInstance, text: string, chatId) {

  }


  public static async answerByGPT(min: GBMinInstance, pid,
    query: string,
    searchScore: number,
    subjects: GuaribasSubject[]
  ) {

    if (!process.env.OPENAI_API_KEY) {
      return { answer: undefined, questionId: 0 };
    }

    const contentLocale = min.core.getParam(
      min.instance,
      'Default Content Language',
      GBConfigService.get('DEFAULT_CONTENT_LANGUAGE')
    );

    let tools = await ChatServices.getTools(min);
    let toolsAsText = ChatServices.getToolsAsText(tools);

    const memory = new BufferWindowMemory({
      returnMessages: true,
      memoryKey: 'chat_history',
      inputKey: 'input',
      k: 2,
    });

    const model = new ChatOpenAI({
      openAIApiKey: process.env.OPENAI_API_KEY,
      modelName: "gpt-3.5-turbo-0125",
      temperature: 0,
    });
    
    const contextVectorStore = min['vectorStore'];

    let promptTemplate = `Answer in ${contentLocale}. 
    You have access to the context (RELEVANTDOCS) provided by the user.
    
    When answering think about whether the question in RELEVANTDOCS, but never mention
    to user about the source.
    Don’t justify your answers. Don't refer to yourself in any of the created content.
    Don´t prefix RESPONSE: when answering the user.
    RELEVANTDOCS: {context}

    QUESTION: """{input}"""

    You have the following tools that you can invoke based on the user inquiry. 
    Tools: 

    ${toolsAsText}

    `;


    const toolMap: Record<string, any> = {
      multiply: ()=>{},
    };

    const modelWithTools = model.bind({
      tools: tools.map(convertToOpenAITool),
    });

    const questionGeneratorTemplate = ChatPromptTemplate.fromMessages([
      AIMessagePromptTemplate.fromTemplate(
        "Given the following conversation about a codebase and a follow up question, rephrase the follow up question to be a standalone question."
      ),
      new MessagesPlaceholder("chat_history"),
      AIMessagePromptTemplate.fromTemplate(`Follow Up Input: {question} Standalone question:`),
    ]);

    const combineDocumentsPrompt = ChatPromptTemplate.fromMessages([
      AIMessagePromptTemplate.fromTemplate(
        "Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.\n\n{context}\n\n"
      ),
      new MessagesPlaceholder("chat_history"),
      HumanMessagePromptTemplate.fromTemplate("Question: {question}"),
    ]);

    const combineDocumentsChain = RunnableSequence.from([
      {
        question: (output: string) => output,
        chat_history: async () => {
          const { chat_history } = await memory.loadMemoryVariables({});
          return chat_history;
        },
        context: async (output: string) => {

          return await this.getRelevantContext(contextVectorStore, output, 1);
        },
      },
      combineDocumentsPrompt,
      modelWithTools,
      new StringOutputParser(),
    ]);

    const conversationalQaChain = RunnableSequence.from([
      {
        question: (i: { question: string }) => i.question,
        chat_history: async () => {
          const { chat_history } = await memory.loadMemoryVariables({});
          return chat_history;
        },
      },
      questionGeneratorTemplate,
      modelWithTools,
      new StringOutputParser(),
      combineDocumentsChain,
    ]);

    const question = "How can I initialize a ReAct agent?";
    let result = await conversationalQaChain.invoke({
      question,
    });

    return { answer: result.toString() , questionId: 0 };

  }


  private static getToolsAsText(tools) {
    return Object.keys(tools)
      .map((toolname) => `${tools[toolname].function.name}: ${tools[toolname].function.description}`)
      .join("\n");
  }

  private static async getTools(min: GBMinInstance) {
    let functions = [];

    // Adds .gbdialog as functions if any to GPT Functions.
    await CollectionUtil.asyncForEach(Object.keys(min.scriptMap), async (script) => {
      const path = DialogKeywords.getGBAIPath(min.botId, "gbdialog", null);
      const functionJSON = Path.join('work', path, `${script}.json`);

      if (Fs.existsSync(functionJSON)) {
        const func = JSON.parse(Fs.readFileSync(functionJSON, 'utf8'));
        func.schema =  jsonSchemaToZod(func.properties, { module: "esm" });
          func.func = async ()=>{
              const name = '';
              const pid = 1;
              const text = ''; // TODO:
              const result =  await GBVMService.callVM(name, min, false, pid,false, [text]);

          }

        functions.push(func);
      }

    });
    return functions;
  }
}