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

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'use strict';

import { HNSWLib } from '@langchain/community/vectorstores/hnswlib';
import { StringOutputParser } from "@langchain/core/output_parsers";
import { AIMessagePromptTemplate, ChatPromptTemplate, HumanMessagePromptTemplate, MessagesPlaceholder } from '@langchain/core/prompts';
import { RunnableSequence } from "@langchain/core/runnables";
import { convertToOpenAITool } from "@langchain/core/utils/function_calling";
import { ChatOpenAI } from "@langchain/openai";
import { GBLog, GBMinInstance } from 'botlib';
import * as Fs from 'fs';
import { jsonSchemaToZod } from "json-schema-to-zod";
import { BufferWindowMemory } from 'langchain/memory';
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 { z } from "zod";
import { DynamicStructuredTool } from "@langchain/core/tools";
import {
  BaseLLMOutputParser,
  OutputParserException,
} from "@langchain/core/output_parsers";
import { ChatGeneration, Generation } from "@langchain/core/outputs";

export interface CustomOutputParserFields { }
export type ExpectedOutput = string;

function isChatGeneration(
  llmOutput: ChatGeneration | Generation
): llmOutput is ChatGeneration {
  return "message" in llmOutput;
}

export class CustomLLMOutputParser extends BaseLLMOutputParser<ExpectedOutput> {
  lc_namespace = ["langchain", "output_parsers"];

  private toolChain: RunnableSequence
  private documentChain: RunnableSequence;

  constructor( toolChain: RunnableSequence,  documentChain: RunnableSequence) {
    super();
    this.toolChain = toolChain;
    this.documentChain = documentChain;
  }

  async parseResult(
    llmOutputs: ChatGeneration[] | Generation[]
  ): Promise<ExpectedOutput> {
    if (!llmOutputs.length) {
      throw new OutputParserException(
        "Output parser did not receive any generations."
      );
    }
    let parsedOutput;

    if (llmOutputs[0]['message'].lc_kwargs.additional_kwargs.tool_calls) {
      this.toolChain.invoke({func: llmOutputs[0]['message'].lc_kwargs.additional_kwargs.tool_calls});
    }

    if (isChatGeneration(llmOutputs[0])) {
      parsedOutput = llmOutputs[0].message.content;
    } else {
      parsedOutput = llmOutputs[0].text;
    }

    return this.documentChain.invoke(parsedOutput);

  }
}

export class ChatServices {

  private static async getRelevantContext(
    vectorStore: HNSWLib,
    sanitizedQuestion: string,
    numDocuments: number
  ): Promise<string> {
    if (sanitizedQuestion === '') {
      return '';
    }

    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, question: string, chatId) {

  }


  public static async answerByGPT(min: GBMinInstance, user, pid,
    question: 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')
    );


    const context = min['vectorStore'];

    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,
    });


    let tools = await ChatServices.getTools(min);
    let toolsAsText = ChatServices.getToolsAsText(tools);
    const modelWithTools = model.bind({
      tools: tools.map(convertToOpenAITool)
    });

    const questionGeneratorTemplate = ChatPromptTemplate.fromMessages([
      AIMessagePromptTemplate.fromTemplate(
        `Answer the question without calling any tool, but if there is a need to call:
         You have access to the following set of tools. Here are the names and descriptions for each tool:
          ${toolsAsText}
        `
      ),
      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 callToolChain = RunnableSequence.from([
      {
        func: async (output: object) =>{

          const name = output['func'][0].function.name;
          const args = JSON.parse(output['func'][0].function.arguments);

          GBLog.info(`Running .gbdialog '${name}' as GPT tool...`);
    
          const pid = GBVMService.createProcessInfo(null, min, 'gpt', null);

          return await GBVMService.callVM(name, min, false, pid, false, args);
        },
        chat_history: async () => {
          const { chat_history } = await memory.loadMemoryVariables({});
          return chat_history;
        },
      },
      new StringOutputParser()
    ]);



    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 ChatServices.getRelevantContext(context, output, 1);

        },
      },
      combineDocumentsPrompt,
      model,
      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 CustomLLMOutputParser(callToolChain, combineDocumentsChain)
    ]);

    const systemPrompt = user['systemPrompt'];

    let result = await conversationalQaChain.invoke({
      question,
    });

    if (result['name']) {
      const func = result['func'];
      await func.func(min, result['args']);

    } else {

      // await memory.saveContext(
      //   {
      //     input: query,
      //   },
      //   {
      //     output: result,
      //   }
      // ); 

      GBLog.info(`GPT Result: ${result.toString()}`);
      return { answer: result.toString(), questionId: 0 };

    }
  }

  private static getToolsAsText(tools) {
    return Object.keys(tools)
      .map((toolname) => `- ${tools[toolname].name}: ${tools[toolname].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 jsonFile = Path.join('work', path, `${script}.json`);

      if (Fs.existsSync(jsonFile)) {
        const funcJSON = JSON.parse(Fs.readFileSync(jsonFile, 'utf8'));
        const funcObj = funcJSON?.function;

        if (funcObj){
          
          // TODO: Use ajv.
          funcObj.schema = eval(jsonSchemaToZod(funcObj.parameters));
          functions.push(new DynamicStructuredTool(funcObj));
        }
        
      }

    });

    const multiplyTool = new DynamicStructuredTool({
      name: "multiply",
      description: "Multiply two integers together.",
      schema: z.object({
        firstInt: z.number(),
        secondInt: z.number(),
      }),
      func: async ({ firstInt, secondInt }) => {
        return (firstInt * secondInt).toString();
      },
    });

    functions.push(multiplyTool);


    return functions;
  }
}