492 lines
17 KiB
TypeScript
492 lines
17 KiB
TypeScript
/*****************************************************************************\
|
|
| █████ █████ ██ █ █████ █████ ████ ██ ████ █████ █████ ███ ® |
|
|
| ██ █ ███ █ █ ██ ██ ██ ██ ██ ██ █ ██ ██ █ █ |
|
|
| ██ ███ ████ █ ██ █ ████ █████ ██████ ██ ████ █ █ █ ██ |
|
|
| ██ ██ █ █ ██ █ █ ██ ██ ██ ██ ██ ██ █ ██ ██ █ █ |
|
|
| █████ █████ █ ███ █████ ██ ██ ██ ██ █████ ████ █████ █ ███ |
|
|
| |
|
|
| General Bots Copyright (c) pragmatismo.com.br. All rights reserved. |
|
|
| Licensed under the AGPL-3.0. |
|
|
| |
|
|
| According to our dual licensing model, this program can be used either |
|
|
| under the terms of the GNU Affero General Public License, version 3, |
|
|
| or under a proprietary license. |
|
|
| |
|
|
| The texts of the GNU Affero General Public License with an additional |
|
|
| permission and of our proprietary license can be found at and |
|
|
| in the LICENSE file you have received along with this program. |
|
|
| |
|
|
| This program is distributed in the hope that it will be useful, |
|
|
| but WITHOUT ANY WARRANTY, without even the implied warranty of |
|
|
| MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the |
|
|
| GNU Affero General Public License for more details. |
|
|
| |
|
|
| "General Bots" is a registered trademark of pragmatismo.com.br. |
|
|
| The licensing of the program under the AGPLv3 does not imply a |
|
|
| trademark license. Therefore any rights, title and interest in |
|
|
| our trademarks remain entirely with us. |
|
|
| |
|
|
\*****************************************************************************/
|
|
|
|
'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.file,
|
|
metadata.page);
|
|
result = `
|
|
${result}`;
|
|
}
|
|
}
|
|
|
|
return result;
|
|
}
|
|
}
|
|
|
|
export class ChatServices {
|
|
|
|
public static async pdfPageAsImage(min, filename, pageNumber) {
|
|
|
|
const gbaiName = DialogKeywords.getGBAIPath(min.botId, 'gbkb');
|
|
const localName = Path.join('work', gbaiName, 'docs', filename);
|
|
|
|
// Converts the PDF to PNG.
|
|
|
|
const pngPages: PngPageOutput[] = await pdfToPng(localName, {
|
|
disableFontFace: true,
|
|
useSystemFonts: true,
|
|
viewportScale: 2.0,
|
|
pagesToProcess: [pageNumber],
|
|
strictPagesToProcess: false,
|
|
verbosityLevel: 0
|
|
});
|
|
|
|
|
|
// Prepare an image on cache and return the GBFILE information.
|
|
|
|
if (pngPages.length > 0) {
|
|
const buffer = pngPages[0].content;
|
|
const gbaiName = DialogKeywords.getGBAIPath(min.botId, null);
|
|
const localName = Path.join('work', gbaiName, 'cache', `img${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:
|
|
|
|
${toolsAsText}
|
|
|
|
Do not use any previous tools output in the chat_history.
|
|
`
|
|
),
|
|
new MessagesPlaceholder("chat_history"),
|
|
AIMessagePromptTemplate.fromTemplate(`Follow Up Input: {question}
|
|
Standalone question:`),
|
|
]);
|
|
|
|
const toolsResultPrompt = ChatPromptTemplate.fromMessages([
|
|
AIMessagePromptTemplate.fromTemplate(
|
|
`The tool just returned value in last call. Using {chat_history}
|
|
rephrase the answer to the user using this tool output.
|
|
`
|
|
),
|
|
new MessagesPlaceholder("chat_history"),
|
|
AIMessagePromptTemplate.fromTemplate(`Tool output: {tool_output}
|
|
Standalone question:`),
|
|
]);
|
|
|
|
const combineDocumentsPrompt = ChatPromptTemplate.fromMessages([
|
|
AIMessagePromptTemplate.fromTemplate(
|
|
`
|
|
This is a segmented context.
|
|
|
|
VERY IMPORTANT: When responding, ALWAYS, I said, You must always include the following information at the end of your message as a VALID standard JSON: 'file' indicating the PDF filename and 'page' indicating the page number. Example JSON format: "file": "filename.pdf", "page": 3, return valid JSON with brackets. Avoid explaining the context directly to the user; instead, refer to the document source.
|
|
|
|
\n\n{context}\n\n
|
|
|
|
And based on \n\n{chat_history}\n\n
|
|
rephrase the response to the user using the aforementioned context. If you're unsure of the answer, utilize any relevant context provided to answer the question effectively.
|
|
`
|
|
),
|
|
new MessagesPlaceholder("chat_history"),
|
|
HumanMessagePromptTemplate.fromTemplate("Question: {question}"),
|
|
]);
|
|
|
|
const callToolChain = RunnableSequence.from([
|
|
{
|
|
tool_output: async (output: object) => {
|
|
|
|
const name = output['func'][0].function.name;
|
|
const args = JSON.parse(output['func'][0].function.arguments);
|
|
GBLog.info(`Running .gbdialog '${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;
|
|
}
|
|
}
|