dtamind-components
Version:
DTAmindai Components
205 lines (201 loc) • 8.81 kB
JavaScript
;
Object.defineProperty(exports, "__esModule", { value: true });
const agents_1 = require("langchain/agents");
const chains_1 = require("langchain/chains");
const handler_1 = require("../../../src/handler");
const Interface_1 = require("../../../src/Interface");
const utils_1 = require("../../../src/utils");
const core_1 = require("./core");
const Moderation_1 = require("../../moderation/Moderation");
const OutputParserHelpers_1 = require("../../outputparsers/OutputParserHelpers");
const src_1 = require("../../../src");
class CSV_Agents {
constructor() {
this.label = 'CSV Agent';
this.name = 'csvAgent';
this.version = 3.0;
this.type = 'AgentExecutor';
this.category = 'Agents';
this.icon = 'CSVagent.svg';
this.description = 'Agent used to answer queries on CSV data';
this.baseClasses = [this.type, ...(0, utils_1.getBaseClasses)(agents_1.AgentExecutor)];
this.inputs = [
{
label: 'Csv File',
name: 'csvFile',
type: 'file',
fileType: '.csv'
},
{
label: 'Language Model',
name: 'model',
type: 'BaseLanguageModel'
},
{
label: 'System Message',
name: 'systemMessagePrompt',
type: 'string',
rows: 4,
additionalParams: true,
optional: true,
placeholder: 'I want you to act as a document that I am having a conversation with. Your name is "AI Assistant". You will provide me with answers from the given info. If the answer is not included, say exactly "Hmm, I am not sure." and stop after that. Refuse to answer any question not about the info. Never break character.'
},
{
label: 'Input Moderation',
description: 'Detect text that could generate harmful output and prevent it from being sent to the language model',
name: 'inputModeration',
type: 'Moderation',
optional: true,
list: true
},
{
label: 'Custom Pandas Read_CSV Code',
description: 'Custom Pandas <a target="_blank" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html">read_csv</a> function. Takes in an input: "csv_data"',
name: 'customReadCSV',
default: 'read_csv(csv_data)',
type: 'code',
optional: true,
additionalParams: true
}
];
}
async init() {
// Not used
return undefined;
}
async run(nodeData, input, options) {
const csvFileBase64 = nodeData.inputs?.csvFile;
const model = nodeData.inputs?.model;
const systemMessagePrompt = nodeData.inputs?.systemMessagePrompt;
const moderations = nodeData.inputs?.inputModeration;
const _customReadCSV = nodeData.inputs?.customReadCSV;
if (moderations && moderations.length > 0) {
try {
// Use the output of the moderation chain as input for the CSV agent
input = await (0, Moderation_1.checkInputs)(moderations, input);
}
catch (e) {
await new Promise((resolve) => setTimeout(resolve, 500));
// if (options.shouldStreamResponse) {
// streamResponse(options.sseStreamer, options.chatId, e.message)
// }
return (0, OutputParserHelpers_1.formatResponse)(e.message);
}
}
const loggerHandler = new handler_1.ConsoleCallbackHandler(options.logger, options?.orgId);
const shouldStreamResponse = options.shouldStreamResponse;
const sseStreamer = options.sseStreamer;
const chatId = options.chatId;
const callbacks = await (0, handler_1.additionalCallbacks)(nodeData, options);
let files = [];
let base64String = '';
if (csvFileBase64.startsWith('FILE-STORAGE::')) {
const fileName = csvFileBase64.replace('FILE-STORAGE::', '');
if (fileName.startsWith('[') && fileName.endsWith(']')) {
files = JSON.parse(fileName);
}
else {
files = [fileName];
}
const orgId = options.orgId;
const chatflowid = options.chatflowid;
for (const file of files) {
if (!file)
continue;
const fileData = await (0, src_1.getFileFromStorage)(file, orgId, chatflowid);
base64String += fileData.toString('base64');
}
}
else {
if (csvFileBase64.startsWith('[') && csvFileBase64.endsWith(']')) {
files = JSON.parse(csvFileBase64);
}
else {
files = [csvFileBase64];
}
for (const file of files) {
if (!file)
continue;
const splitDataURI = file.split(',');
splitDataURI.pop();
base64String += splitDataURI.pop() ?? '';
}
}
const pyodide = await (0, core_1.LoadPyodide)();
// First load the csv file and get the dataframe dictionary of column types
// For example using titanic.csv: {'PassengerId': 'int64', 'Survived': 'int64', 'Pclass': 'int64', 'Name': 'object', 'Sex': 'object', 'Age': 'float64', 'SibSp': 'int64', 'Parch': 'int64', 'Ticket': 'object', 'Fare': 'float64', 'Cabin': 'object', 'Embarked': 'object'}
let dataframeColDict = '';
let customReadCSVFunc = _customReadCSV ? _customReadCSV : 'read_csv(csv_data)';
try {
const code = `import pandas as pd
import base64
from io import StringIO
import json
base64_string = "${base64String}"
decoded_data = base64.b64decode(base64_string)
csv_data = StringIO(decoded_data.decode('utf-8'))
df = pd.${customReadCSVFunc}
my_dict = df.dtypes.astype(str).to_dict()
print(my_dict)
json.dumps(my_dict)`;
dataframeColDict = await pyodide.runPythonAsync(code);
}
catch (error) {
throw new Error(error);
}
// Then tell GPT to come out with ONLY python code
// For example: len(df), df[df['SibSp'] > 3]['PassengerId'].count()
let pythonCode = '';
if (dataframeColDict) {
const chain = new chains_1.LLMChain({
llm: model,
prompt: Interface_1.PromptTemplate.fromTemplate(core_1.systemPrompt),
verbose: process.env.DEBUG === 'true' ? true : false
});
const inputs = {
dict: dataframeColDict,
question: input
};
const res = await chain.call(inputs, [loggerHandler, ...callbacks]);
pythonCode = res?.text;
// Regex to get rid of markdown code blocks syntax
pythonCode = pythonCode.replace(/^```[a-z]+\n|\n```$/gm, '');
}
// Then run the code using Pyodide
let finalResult = '';
if (pythonCode) {
try {
const code = `import pandas as pd\n${pythonCode}`;
// TODO: get print console output
finalResult = await pyodide.runPythonAsync(code);
}
catch (error) {
throw new Error(`Sorry, I'm unable to find answer for question: "${input}" using following code: "${pythonCode}"`);
}
}
// Finally, return a complete answer
if (finalResult) {
const chain = new chains_1.LLMChain({
llm: model,
prompt: Interface_1.PromptTemplate.fromTemplate(systemMessagePrompt ? `${systemMessagePrompt}\n${core_1.finalSystemPrompt}` : core_1.finalSystemPrompt),
verbose: process.env.DEBUG === 'true' ? true : false
});
const inputs = {
question: input,
answer: finalResult
};
if (options.shouldStreamResponse) {
const handler = new handler_1.CustomChainHandler(shouldStreamResponse ? sseStreamer : undefined, chatId);
const result = await chain.call(inputs, [loggerHandler, handler, ...callbacks]);
return result?.text;
}
else {
const result = await chain.call(inputs, [loggerHandler, ...callbacks]);
return result?.text;
}
}
return pythonCode;
}
}
module.exports = { nodeClass: CSV_Agents };
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