new(gpt.gblib): GPT Tools and .gbdialog.
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e4a4c127c9
commit
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4 changed files with 69 additions and 80 deletions
1
.vscode/launch.json
vendored
1
.vscode/launch.json
vendored
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@ -6,6 +6,7 @@
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"request": "launch",
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"sourceMaps": true,
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"name": "Debug Program",
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"runtimeExecutable": "/root/.nvm/versions/node/v19.9.0/bin/node",
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"program": "${workspaceRoot}/boot.mjs",
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"cwd": "${workspaceRoot}",
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"env": {
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11
boot.mjs
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boot.mjs
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@ -1,24 +1,23 @@
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#!/usr/bin/env node
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import chalkAnimation from 'chalk-animation';
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import Fs from 'fs';
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import os from 'node:os';
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import Path from 'path';
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import { exec } from 'child_process';
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import pjson from './package.json' assert { type: 'json' };
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import { GBUtil } from './dist/src/util.js';
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// Displays version of Node JS being used at runtime and others attributes.
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console.log(`General Bots is loading source code files...`);
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process.stdout.write(`General Bots. BotServer@${pjson.version}, botlib@${pjson.dependencies.botlib}, botbuilder@${pjson.dependencies.botbuilder}, node@${process.version.replace('v', '')}, ${process.platform} ${process.arch} `);
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os.setPriority(process.pid, os.constants.priority.PRIORITY_HIGHEST);
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console.log(`\nLoading virtual machine source code files...`);
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var __dirname = process.env.PWD || process.cwd();
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try {
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var run = () => {
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import('./dist/src/app.js').then((gb)=> {
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console.log(`\n`);
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process.stdout.write(`${pjson.version}, botlib@${pjson.dependencies.botlib}, botbuilder@${pjson.dependencies.botbuilder}, node@${process.version.replace('v', '')}, ${process.platform} ${process.arch} `);
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console.log(`\n`);
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gb.GBServer.run()
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});
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};
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@ -56,8 +56,8 @@ import MicrosoftGraph from '@microsoft/microsoft-graph-client';
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import { GBLogEx } from './GBLogEx.js';
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import { DialogKeywords } from '../../basic.gblib/services/DialogKeywords.js';
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import { GBUtil } from '../../../src/util.js';
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import { HNSWLib } from 'langchain/vectorstores/hnswlib';
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import { OpenAIEmbeddings } from 'langchain/embeddings/openai';
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import { HNSWLib } from '@langchain/community/vectorstores/hnswlib';
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import { OpenAIEmbeddings } from '@langchain/openai';
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/**
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* Deployer service for bots, themes, ai and more.
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@ -48,16 +48,6 @@ import { GBConfigService } from '../../core.gbapp/services/GBConfigService.js';
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import { GuaribasSubject } from '../../kb.gbapp/models/index.js';
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import { z } from "zod";
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import { DynamicStructuredTool } from "@langchain/core/tools";
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import { JsonOutputToolsParser } from "langchain/output_parsers";
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import {
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RunnableLambda,
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RunnablePassthrough,
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} from "@langchain/core/runnables";
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import {
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CombiningOutputParser,
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} from "langchain/output_parsers";
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import {
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BaseLLMOutputParser,
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OutputParserException,
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@ -65,8 +55,6 @@ import {
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import { ChatGeneration, Generation } from "@langchain/core/outputs";
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export interface CustomOutputParserFields { }
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// This can be more generic, like Record<string, string>
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export type ExpectedOutput = string;
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function isChatGeneration(
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@ -78,8 +66,13 @@ function isChatGeneration(
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export class CustomLLMOutputParser extends BaseLLMOutputParser<ExpectedOutput> {
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lc_namespace = ["langchain", "output_parsers"];
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constructor(fields?: CustomOutputParserFields) {
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super(fields);
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private toolChain: RunnableSequence
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private documentChain: RunnableSequence;
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constructor( toolChain: RunnableSequence, documentChain: RunnableSequence) {
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super();
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this.toolChain = toolChain;
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this.documentChain = documentChain;
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}
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async parseResult(
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@ -92,15 +85,19 @@ export class CustomLLMOutputParser extends BaseLLMOutputParser<ExpectedOutput> {
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}
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let parsedOutput;
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if (llmOutputs[0]['message'].lc_kwargs.additional_kwargs.tool_calls) {
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this.toolChain.invoke({func: llmOutputs[0]['message'].lc_kwargs.additional_kwargs.tool_calls});
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}
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if (isChatGeneration(llmOutputs[0])) {
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parsedOutput = llmOutputs[0].message.content;
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} else {
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parsedOutput = llmOutputs[0].text;
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}
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let parsedText;
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parsedText = parsedOutput;
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return parsedText;
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this.documentChain.invoke(parsedOutput);
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return ``;
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}
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}
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@ -153,12 +150,8 @@ export class ChatServices {
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GBConfigService.get('DEFAULT_CONTENT_LANGUAGE')
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);
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let tools = await ChatServices.getTools(min);
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let toolsAsText = ChatServices.getToolsAsText(tools);
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const toolMap: Record<string, any> = {
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multiply: tools[0]
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};
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const context = min['vectorStore'];
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const memory = new BufferWindowMemory({
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returnMessages: true,
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@ -173,41 +166,17 @@ export class ChatServices {
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temperature: 0,
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});
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const context = min['vectorStore'];
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let tools = await ChatServices.getTools(min);
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let toolsAsText = ChatServices.getToolsAsText(tools);
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const modelWithTools = model.bind({
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tools: tools.map(convertToOpenAITool)
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});
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// Function for dynamically constructing the end of the chain based on the model-selected tool.
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const callSelectedTool = RunnableLambda.from(
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(toolInvocation: Record<string, any>) => {
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const selectedTool = toolMap[toolInvocation.type];
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if (!selectedTool) {
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throw new Error(
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`No matching tool available for requested type "${toolInvocation.type}".`
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);
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}
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const toolCallChain = RunnableSequence.from([
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(toolInvocation) => toolInvocation.args,
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selectedTool,
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]);
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// We use `RunnablePassthrough.assign` here to return the intermediate `toolInvocation` params
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// as well, but you can omit if you only care about the answer.
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return RunnablePassthrough.assign({
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output: toolCallChain,
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});
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},
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);
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const questionGeneratorTemplate = ChatPromptTemplate.fromMessages([
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AIMessagePromptTemplate.fromTemplate(
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`Answer the question without calling any tool, but if there is a need to call:
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You have access to the following set of tools. Here are the names and descriptions for each tool:
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You have access to the following set of tools. Here are the names and descriptions for each tool:
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${toolsAsText}
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`
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),
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@ -227,6 +196,26 @@ export class ChatServices {
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HumanMessagePromptTemplate.fromTemplate("Question: {question}"),
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]);
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const callToolChain = RunnableSequence.from([
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{
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func: async (output: object) =>{
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const pid = 1;
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const name = output['func'][0].function.name;
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const args = JSON.parse(output['func'][0].function.arguments);
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return await GBVMService.callVM(name, min, false, pid, false, args);
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},
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chat_history: async () => {
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const { chat_history } = await memory.loadMemoryVariables({});
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return chat_history;
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},
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},
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new StringOutputParser()
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]);
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const combineDocumentsChain = RunnableSequence.from([
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{
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question: (output: string) => output,
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@ -236,14 +225,14 @@ export class ChatServices {
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},
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context: async (output: string) => {
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return await ChatServices.getRelevantContext(context, output, 1);
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},
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},
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combineDocumentsPrompt,
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modelWithTools,
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]);
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const conversationalQaChain = RunnableSequence.from([
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{
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question: (i: { question: string }) => i.question,
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@ -254,7 +243,7 @@ export class ChatServices {
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},
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questionGeneratorTemplate,
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modelWithTools,
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new CustomLLMOutputParser()
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new CustomLLMOutputParser(callToolChain, combineDocumentsChain)
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]);
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const systemPrompt = user['systemPrompt'];
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@ -262,20 +251,28 @@ export class ChatServices {
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let result = await conversationalQaChain.invoke({
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question,
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});
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// await memory.saveContext(
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// {
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// input: query,
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// },
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// {
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// output: result,
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// }
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// );
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GBLog.info(`GPT Result: ${result.toString()}`);
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return { answer: result.toString(), questionId: 0 };
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if (result['name']) {
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const func = result['func'];
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await func.func(min, result['args']);
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} else {
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// await memory.saveContext(
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// {
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// input: query,
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// },
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// {
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// output: result,
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// }
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// );
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GBLog.info(`GPT Result: ${result.toString()}`);
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return { answer: result.toString(), questionId: 0 };
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}
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}
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private static getToolsAsText(tools) {
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return Object.keys(tools)
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.map((toolname) => `- ${tools[toolname].name}: ${tools[toolname].description}`)
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@ -293,14 +290,6 @@ export class ChatServices {
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if (Fs.existsSync(functionJSON)) {
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const func = JSON.parse(Fs.readFileSync(functionJSON, 'utf8'));
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func.schema = jsonSchemaToZod(func.properties, { module: "esm" });
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func.func = async () => {
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const name = '';
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const pid = 1;
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const text = ''; // TODO:
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const result = await GBVMService.callVM(name, min, false, pid, false, [text]);
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}
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functions.push(func);
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}
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