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 { Serialized } from "@langchain/core/load/serializable";
import { BaseCallbackHandler } from "@langchain/core/callbacks/base";
import { pdfToPng, PngPageOutput } from 'pdf-to-png-converter';
import { DynamicStructuredTool } from "@langchain/core/tools";
import { WikipediaQueryRun } from "@langchain/community/tools/wikipedia_query_run";
import {
  BaseLLMOutputParser,
  OutputParserException,
} from "@langchain/core/output_parsers";
import { ChatGeneration, Generation } from "@langchain/core/outputs";
import { GBAdminService } from '../../admin.gbapp/services/GBAdminService.js';
import { GBServer } from '../../../src/app.js';
import urlJoin from 'url-join';
import { getDocument } from "pdfjs-dist/legacy/build/pdf.mjs";


export interface CustomOutputParserFields { }
export type ExpectedOutput = string;

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

class CustomHandler extends BaseCallbackHandler {
  name = "custom_handler";

  handleLLMNewToken(token: string) {
    GBLog.info(`LLM: token: ${JSON.stringify(token)}`);
  }

  handleLLMStart(llm: Serialized, _prompts: string[]) {
    GBLog.info(`LLM: handleLLMStart ${JSON.stringify(llm)}, Prompts: ${_prompts.join('\n')}`);
  }

  handleChainStart(chain: Serialized) {
    GBLog.info(`LLM: handleChainStart: ${JSON.stringify(chain)}`);
  }

  handleToolStart(tool: Serialized) {
    GBLog.info(`LLM: handleToolStart: ${JSON.stringify(tool)}`);
  }
}

const logHandler = new CustomHandler();

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

  private toolChain: RunnableSequence
  private documentChain: RunnableSequence;
  private min;

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

  }

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

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

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

    const naiveJSONFromText = (text) => {
      const match = text.match(/\{[\s\S]*\}/);
      if (!match) return null;

      try {
        return {metadata: JSON.parse(match[0]),
           text: text.replace(match, '')};
      } catch {
        return null;
      }
    };

    if (result) {
       const res = naiveJSONFromText(result);

      if (res) {
        const {metadata, text} = res;
        const {url} = await ChatServices.pdfPageAsImage(this.min, metadata.filename,
          text);
        result = `![alt text](${url})
         ${result}`;
      }
    }

    return result;
  }
}

export class ChatServices {
  
  public static async pdfPageAsImage(min, filename, pageNumber) {
    
    const gbaiName = DialogKeywords.getGBAIPath(min.botId, 'gbkb');
    const localName = Path.join('work', gbaiName, 'docs', filename);

    // Converts the PDF to PNG.

    const pngPages: PngPageOutput[] = await pdfToPng(localName, {
      disableFontFace: true,
      useSystemFonts: true,
      viewportScale: 2.0,
      pagesToProcess: [pageNumber],
      strictPagesToProcess: false,
      verbosityLevel: 0
    });


    // Prepare an image on cache and return the GBFILE information.

    if (pngPages.length > 0) {
      const buffer = pngPages[0].content;
      const gbaiName = DialogKeywords.getGBAIPath(min.botId, null);
      const localName = Path.join('work', gbaiName, 'cache', `img${GBAdminService.getRndReadableIdentifier()}.png`);
      const url = urlJoin(GBServer.globals.publicAddress, min.botId, 'cache', Path.basename(localName));
      Fs.writeFileSync(localName, buffer, { encoding: null });
      return { localName: localName, url: url, data: buffer };
    }
  }

  private static async getRelevantContext(
    vectorStore: HNSWLib,
    sanitizedQuestion: string,
    numDocuments: number = 10
  ): Promise<string> {

    if (sanitizedQuestion === '') {
      return '';
    }

    const documents = await vectorStore.similaritySearch(sanitizedQuestion, numDocuments);
    let output = '';

    await CollectionUtil.asyncForEach(documents, async (doc) => {

      const metadata = doc.metadata;
      const filename = Path.basename(metadata.source);
      const page = await ChatServices.findPageForText(doc.metadata.source,
        doc.pageContent);

      output = `${output}\n\n\n\nThe following context is coming from ${filename} at page: ${page}, 
      memorize this block among document information and return when you are refering this part of content:\n\n\n\n ${doc.pageContent} \n\n\n\n.`;
    });
    return output;
  }


  private static async findPageForText(pdfPath, searchText) {
    const data = new Uint8Array(Fs.readFileSync(pdfPath));
    const pdf = await getDocument({ data }).promise;

    searchText = searchText.replace(/\s/g, '')

    for (let i = 1; i <= pdf.numPages; i++) {
        const page = await pdf.getPage(i);
        const textContent = await page.getTextContent();
        const text = textContent.items.map(item => item['str']).join('').replace(/\s/g, '');

        if (text.includes(searchText)) return i;
    }

    return -1; // Texto não encontrado
}

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

  }

  private static memoryMap = {};
  public static userSystemPrompt = {};

  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 LLMMode = min.core.getParam(
      min.instance,
      'Answer Mode', 'direct'
    );

    const docsContext = min['vectorStore'];

    if (!this.memoryMap[user.userSystemId]) {
      this.memoryMap[user.userSystemId] = new BufferWindowMemory({
        returnMessages: true,
        memoryKey: 'chat_history',
        inputKey: 'input',
        k: 2,
      })
    }
    const memory = this.memoryMap[user.userSystemId];
    const systemPrompt = this.userSystemPrompt[user.userSystemId];

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


    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}

          Do not use any previous tools output in the chat_history. 
        `
      ),
      new MessagesPlaceholder("chat_history"),
      AIMessagePromptTemplate.fromTemplate(`Follow Up Input: {question}
    Standalone question:`),
    ]);

    const toolsResultPrompt = ChatPromptTemplate.fromMessages([
      AIMessagePromptTemplate.fromTemplate(
        `The tool just returned value in last call. Using {chat_history}
        rephrase the answer to the user using this tool output.
        `
      ),
      new MessagesPlaceholder("chat_history"),
      AIMessagePromptTemplate.fromTemplate(`Tool output: {tool_output} 
    Standalone question:`),
    ]);

    const combineDocumentsPrompt = ChatPromptTemplate.fromMessages([
      AIMessagePromptTemplate.fromTemplate(
        `
        This is a segmented context.

        VERY IMPORTANT: When responding, include the following information at the end of your message as JSON: 'file' indicating the PDF filename and 'page' indicating the page number. Example JSON format: "file": "filename.pdf", "page": 3, return valid JSON with brackets. Avoid explaining the context directly to the user; instead, refer to the document source.
        
        \n\n{context}\n\n
        
        And based on \n\n{chat_history}\n\n
        rephrase the response to the user using the aforementioned context. If you're unsure of the answer, simply state that you don't know. Do not invent an answer. Utilize any relevant context provided to answer the question effectively.
        `
      ),
      new MessagesPlaceholder("chat_history"),
      HumanMessagePromptTemplate.fromTemplate("Question: {question}"),
    ]);

    const callToolChain = RunnableSequence.from([
      {
        tool_output: 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;
        },

      },
      toolsResultPrompt,
      model,
      new StringOutputParser()
    ]);

    const combineDocumentsChain = RunnableSequence.from([
      {
        question: (question: string) => question,
        chat_history: async () => {
          const { chat_history } = await memory.loadMemoryVariables({});
          return chat_history;
        },
        context: async (output: string) => {
          const c = await ChatServices.getRelevantContext(docsContext, output);
          return `${systemPrompt} \n ${c ? 'Use this context to answer:\n' + c : 'answer just with user question.'}`;

        },
      },
      combineDocumentsPrompt,
      model,
      new GBLLMOutputParser(min, null, null)
    ]);

    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 GBLLMOutputParser(min, callToolChain, docsContext?.docstore?._docs.length > 0 ? combineDocumentsChain : null),
      new StringOutputParser()
    ]);

    const conversationalToolChain = RunnableSequence.from([
      {
        question: (i: { question: string }) => i.question,
        chat_history: async () => {
          const { chat_history } = await memory.loadMemoryVariables({});
          return chat_history;
        },
      },
      questionGeneratorTemplate,
      modelWithTools,
      new GBLLMOutputParser(min, callToolChain, docsContext?.docstore?._docs.length > 0 ? combineDocumentsChain : null),
      new StringOutputParser()
    ]);

    let result;


    // Choose the operation mode of answer generation, based on 
    // .gbot switch LLMMode and choose the corresponding chain.

    if (LLMMode === "direct") {
      result = await (tools.length > 0 ? modelWithTools : model).invoke(`
      ${systemPrompt}
      
      ${question}`);

      result = result.content;
    }
    else if (LLMMode === "document") {

      result = await combineDocumentsChain.invoke(question);

    } else if (LLMMode === "function") {

      result = await conversationalToolChain.invoke({
        question,
      });
    }
    else if (LLMMode === "full") {

      throw new Error('Not implemented.'); // TODO: #407.
    }

    else {
      GBLog.info(`Invalid Answer Mode in Config.xlsx: ${LLMMode}.`);
    }

    await memory.saveContext(
      {
        input: question,
      },
      {
        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) && script.toLowerCase() !== 'start.vbs') {

        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 tool = new WikipediaQueryRun({
      topKResults: 3,
      maxDocContentLength: 4000,
    });
    functions.push(tool);

    return functions;
  }
}