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fix: inserting chat history into the answer generation process

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This commit is contained in:
@jamaludinarifrohman6661
2025-03-06 11:06:45 +07:00
parent 15210a56ee
commit 3f5d0eb1cd
5 changed files with 198 additions and 150 deletions
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74
app/Http/Controllers/Api/ChatbotController.php
View File

@@ -7,6 +7,7 @@ use App\Services\OpenAIService;
use App\Http\Controllers\Controller; use App\Http\Controllers\Controller;
use Illuminate\Http\Response; use Illuminate\Http\Response;
use Illuminate\Support\Facades\DB; use Illuminate\Support\Facades\DB;
use Illuminate\Support\Facades\Log;
class ChatbotController extends Controller class ChatbotController extends Controller
{ {
@@ -19,7 +20,6 @@ class ChatbotController extends Controller
public function generateText(Request $request) public function generateText(Request $request)
{ {
info($request);
$request->validate([ $request->validate([
'tab_active' => 'required|string', 'tab_active' => 'required|string',
'prompt' => 'required|string', 'prompt' => 'required|string',
@@ -33,47 +33,29 @@ class ChatbotController extends Controller
default => "UNKNOWN", default => "UNKNOWN",
}; };
$chatHistory = $request->input('chatHistory');
// Log::info('Chat history sebelum disimpan:', ['history' => $chatHistory]);
if ($main_content === "UNKNOWN") { if ($main_content === "UNKNOWN") {
return response()->json(['response' => 'Invalid tab_active value.'], 400); return response()->json(['response' => 'Invalid tab_active value.'], 400);
} }
info($main_content); // info($main_content);
// Klasifikasi apakah pertanyaan butuh database atau bisa dijawab langsung $queryResponse = $this->openAIService->generateQueryBasedMainContent($request->input('prompt'), $main_content, $chatHistory);
$classifyResponse = $this->openAIService->generateClassifyMainContent($request->input('prompt'), $main_content);
$firstValidation = $this->openAIService->validateSyntaxQuery($queryResponse);
$secondValidation = $this->openAIService->validateSyntaxQuery($queryResponse);
if ($classifyResponse === "DATABASE") { $formattedResultQuery = "[]";
$queryResponse = $this->openAIService->generateQueryBasedMainContent($request->input('prompt'), $main_content); $queryResponse = str_replace(['```sql', '```'], '', $queryResponse);
if (is_array($queryResponse)) { $resultQuery = DB::select($queryResponse);
info('Query Response is an array: ', $queryResponse); $formattedResultQuery = json_encode($resultQuery, JSON_PRETTY_PRINT);
} else { // info($formattedResultQuery);
info('Query Response is a string: ' . $queryResponse);
} $nlpResult = $this->openAIService->generateNLPFromQuery($request->input('prompt'), $formattedResultQuery);
$finalGeneratedText =$this->openAIService->generateFinalText($nlpResult);
// Validasi query dua kali sebelum eksekusi return response()->json(['response' => $finalGeneratedText, 'nlpResponse' => $queryResponse]);
if (
$this->openAIService->validateSyntaxQuery($queryResponse) === "VALID" &&
$this->openAIService->validateSyntaxQuery($queryResponse) === "VALID"
) {
info($queryResponse);
$queryResponse = str_replace(['```sql', '```'], '', $queryResponse);
$resultQuery = DB::select($queryResponse);
$formattedResultQuery = json_encode($resultQuery, JSON_PRETTY_PRINT);
$nlpResult = $this->openAIService->generateNLPFromQuery($request->input('prompt'), $formattedResultQuery);
$finalGeneratedText =$this->openAIService->generateFinalText($nlpResult);
return response()->json(['response' => $finalGeneratedText]);
}
return response()->json(['response' => ''], 400);
}
if ($classifyResponse === "GENERAL") {
$nlpResult = $this->openAIService->generateGeneralText($request->input('prompt'), $main_content);
$finalGeneratedText =$this->openAIService->generateFinalText($nlpResult);
return response()->json(['response' => $finalGeneratedText]);
}
return response()->json(['response' => ''], 500);
} }
public function mainGenerateText(Request $request) public function mainGenerateText(Request $request)
@@ -104,7 +86,10 @@ class ChatbotController extends Controller
], 400); ], 400);
} }
$queryResponse = $this->openAIService->createMainQuery($classifyResponse, $request->input('prompt')); $chatHistory = $request->input('chatHistory');
Log::info('Chat history sebelum disimpan:', ['history' => $chatHistory]);
$queryResponse = $this->openAIService->createMainQuery($classifyResponse, $request->input('prompt'), $chatHistory);
info($queryResponse); info($queryResponse);
$firstValidation = $this->openAIService->validateSyntaxQuery($queryResponse); $firstValidation = $this->openAIService->validateSyntaxQuery($queryResponse);
@@ -112,18 +97,15 @@ class ChatbotController extends Controller
$formattedResultQuery = "[]"; $formattedResultQuery = "[]";
if($firstValidation === "VALID" && $secondValidation === "VALID") $queryResponse = str_replace(['```sql', '```'], '', $queryResponse);
{ $queryResult = DB::select($queryResponse);
$queryResponse = str_replace(['```sql', '```'], '', $queryResponse);
$queryResult = DB::select($queryResponse); $formattedResultQuery = json_encode($queryResult, JSON_PRETTY_PRINT);
info($queryResult);
$formattedResultQuery = json_encode($queryResult, JSON_PRETTY_PRINT);
info($formattedResultQuery);
}
$nlpResult = $this->openAIService->generateNLPFromQuery($request->input('prompt'), $formattedResultQuery); $nlpResult = $this->openAIService->generateNLPFromQuery($request->input('prompt'), $formattedResultQuery);
$finalGeneratedText =$this->openAIService->generateFinalText($nlpResult); $finalGeneratedText =$this->openAIService->generateFinalText($nlpResult);
return response()->json(['response' => $finalGeneratedText]); return response()->json(['response' => $finalGeneratedText, 'nlpResponse' => $queryResponse]);
} catch (\Exception $e) { } catch (\Exception $e) {
// Tangani error dan log exception // Tangani error dan log exception
\Log::error("Error generating text: " . $e->getMessage()); \Log::error("Error generating text: " . $e->getMessage());

216
app/Services/OpenAIService.php
View File

@@ -4,6 +4,7 @@ namespace App\Services;
use OpenAI; use OpenAI;
use Illuminate\Support\Facades\Storage; use Illuminate\Support\Facades\Storage;
use Illuminate\Support\Facades\Log;
class OpenAIService class OpenAIService
{ {
@@ -11,54 +12,11 @@ class OpenAIService
public function __construct() public function __construct()
{ {
// $this->client = OpenAI::client(env('OPENAI_API_KEY'));
$this->client = OpenAI::client(env('OPENAI_API_KEY')); $this->client = OpenAI::client(env('OPENAI_API_KEY'));
} }
public function generateGeneralText($prompt, $mainContent) public function generateQueryBasedMainContent($prompt, $mainContent, $chatHistory)
{
$response = $this->client->chat()->create([
'model' => 'gpt-4o-mini',
'messages' => [
[
'role' => 'system',
'content' => "You are an expert assistant. Your task is to generate a concise response based on the provided prompt and main content.
Guidelines:
- Summarize the key points in exactly 5 bullet points.
- Ensure the response is clear and relevant to the prompt.
- Use simple and professional language."
],
['role' => 'user', 'content' => "Prompt: $prompt \nMain Content: $mainContent"],
],
]);
return trim($response['choices'][0]['message']['content'] ?? 'No response');
}
public function generateClassifyMainContent($prompt, $mainContent)
{
$response = $this->client->chat()->create([
'model' => 'gpt-4o-mini',
'messages' => [
[
'role' => 'system',
'content' => "You are an expert assistant in classifying questions based on whether their answers must be retrieved from a database or can be explained generally.
Your task is to return one of the following two labels:
- \"DATABASE\" → If the question requires specific data that can only be obtained from a database.
- \"GENERAL\" → If the question can be answered without accessing a database.
Consider the following context: \"$mainContent\"
Respond with only one of the labels: \"DATABASE\" or \"GENERAL\"."
],
['role' => 'user', 'content' => $prompt],
],
]);
return trim($response['choices'][0]['message']['content'] ?? 'No response');
}
public function generateQueryBasedMainContent($prompt, $mainContent)
{ {
// Load file JSON // Load file JSON
$jsonPath = public_path('templates/contentTemplatePrompt.json'); // Sesuaikan path $jsonPath = public_path('templates/contentTemplatePrompt.json'); // Sesuaikan path
@@ -72,17 +30,59 @@ class OpenAIService
// Ambil template berdasarkan kategori // Ambil template berdasarkan kategori
$promptTemplate = $jsonData[$mainContent]['prompt']; $promptTemplate = $jsonData[$mainContent]['prompt'];
// Menyusun pesan untuk OpenAI
$messages = [
['role' => 'system', 'content' => $promptTemplate],
];
// Menambahkan chat history sebagai konteks
foreach ($chatHistory as $chat) {
if (isset($chat['user'])) {
$messages[] = ['role' => 'user', 'content' => $chat['user']];
}
if (isset($chat['rawBotResponse'])) {
$messages[] = ['role' => 'assistant', 'content' => $chat['rawBotResponse']];
}
}
// Tambahkan prompt terbaru user
$messages[] = ['role' => 'user', 'content' => $prompt];
// Kirim request ke OpenAI API
$response = $this->client->chat()->create([ $response = $this->client->chat()->create([
'model' => 'gpt-4o-mini', 'model' => 'gpt-4o-mini',
'messages' => [ 'messages' => $messages,
['role' => 'system', 'content' => $promptTemplate],
['role' => 'user', 'content' => $prompt],
],
]); ]);
return trim($response['choices'][0]['message']['content'] ?? 'No response'); return trim($response['choices'][0]['message']['content'] ?? 'No response');
} }
// public function generateQueryBasedMainContent($prompt, $mainContent, $chatHistory)
// {
// // Load file JSON
// $jsonPath = public_path('templates/contentTemplatePrompt.json'); // Sesuaikan path
// $jsonData = json_decode(file_get_contents($jsonPath), true);
// // Periksa apakah kategori ada dalam JSON
// if (!isset($jsonData[$mainContent])) {
// return "Template prompt tidak ditemukan.";
// }
// // Ambil template berdasarkan kategori
// $promptTemplate = $jsonData[$mainContent]['prompt'];
// $response = $this->client->chat()->create([
// 'model' => 'gpt-4o-mini',
// 'messages' => [
// ['role' => 'system', 'content' => $promptTemplate],
// ['role' => 'user', 'content' => $prompt],
// ],
// ]);
// return trim($response['choices'][0]['message']['content'] ?? 'No response');
// }
public function validateSyntaxQuery($queryResponse) public function validateSyntaxQuery($queryResponse)
{ {
@@ -181,8 +181,8 @@ class OpenAIService
return trim($response['choices'][0]['message']['content'] ?? 'No response'); return trim($response['choices'][0]['message']['content'] ?? 'No response');
} }
public function createMainQuery($classify, $prompt) public function createMainQuery($classify, $prompt, $chatHistory)
{ {
// Load file JSON // Load file JSON
$jsonPath = public_path('templates/table_config.json'); $jsonPath = public_path('templates/table_config.json');
@@ -197,54 +197,80 @@ class OpenAIService
$tableName = $jsonConfig[$classify]['table_name']; $tableName = $jsonConfig[$classify]['table_name'];
$columns = implode(', ', $jsonConfig[$classify]['list_column']); $columns = implode(', ', $jsonConfig[$classify]['list_column']);
// Konversi chatHistory ke dalam format messages
$messages = [
[
'role' => 'system',
'content' => "You are an AI assistant that generates only valid MariaDB queries based on user requests.
Use the following table information to construct the SQL query:
- Table Name: $tableName
- Available Columns: $columns
Generate only the SQL query without any explanation or additional text.
The query should include `LIMIT 10` to restrict the results."
]
];
// Menambahkan chat history sebagai konteks
foreach ($chatHistory as $chat) {
if (isset($chat['user'])) {
$messages[] = ['role' => 'user', 'content' => $chat['user']];
}
if (isset($chat['rawBotResponse'])) {
$messages[] = ['role' => 'assistant', 'content' => $chat['rawBotResponse']];
}
}
// Tambahkan prompt utama pengguna
$messages[] = ['role' => 'user', 'content' => $prompt];
// Kirim permintaan ke model AI
$response = $this->client->chat()->create([ $response = $this->client->chat()->create([
'model' => 'gpt-4o-mini', 'model' => 'gpt-4o-mini',
'messages' => [ 'messages' => $messages
[
'role' => 'system',
'content' => "You are an AI assistant that generates only valid MariaDB queries based on user requests.
Use the following table information to construct the SQL query:
- Table Name: $tableName
- Available Columns: $columns
Generate only the SQL query without any explanation or additional text."
],
[
'role' => 'user',
'content' => $prompt
],
],
]); ]);
return trim($response['choices'][0]['message']['content'] ?? 'No response'); return trim($response['choices'][0]['message']['content'] ?? 'No response');
} }
public function mainGenerateText($prompt) {
$response = $this->client->chat()->create([
'model' => 'gpt-4o-mini',
'messages' => [
[
'role' => 'system',
'content' => "You are an expert assistant with deep knowledge in multiple fields, including programming, data science, and business strategy. Provide clear, concise, and accurate responses."
],
[
'role' => 'user',
'content' => $prompt
],
],
]);
return trim($response['choices'][0]['message']['content'] ?? 'No response');
}
// 1. Buat fungsi untuk akses data advertisements
// 2. Buat fungsi untuk akses data business_or_industries
// 3. Buat fungsi untuk akses data customers
// 4. Buat fungsi untuk akses data pbg_task
// 5. Buat fungsi untuk akses data pbg_task_retribution
// 6. Buat fungsi untuk akses data spatial_plannings
// 7. Buat fungsi untuk akses data tourisms
// 8. Buat fungsi untuk akses data umkms
// public function createMainQuery($classify, $prompt)
// {
// // Load file JSON
// $jsonPath = public_path('templates/table_config.json');
// $jsonConfig = json_decode(file_get_contents($jsonPath), true);
// // Pastikan kategori tersedia dalam konfigurasi
// if (!isset($jsonConfig[$classify])) {
// return "Error: Kategori tidak ditemukan dalam konfigurasi.";
// }
// // Ambil nama tabel dan kolom
// $tableName = $jsonConfig[$classify]['table_name'];
// $columns = implode(', ', $jsonConfig[$classify]['list_column']);
// $response = $this->client->chat()->create([
// 'model' => 'gpt-4o-mini',
// 'messages' => [
// [
// 'role' => 'system',
// 'content' => "You are an AI assistant that generates only valid MariaDB queries based on user requests.
// Use the following table information to construct the SQL query:
// - Table Name: $tableName
// - Available Columns: $columns
// Generate only the SQL query without any explanation or additional text
// The query should include `LIMIT 10` to restrict the results."
// ],
// [
// 'role' => 'user',
// 'content' => $prompt
// ],
// ],
// ]);
// return trim($response['choices'][0]['message']['content'] ?? 'No response');
// }
} }

6
public/templates/contentTemplatePrompt.json
View File

@@ -1,11 +1,11 @@
{ {
"RETRIBUTION": { "RETRIBUTION": {
"prompt": "You are a MariaDB SQL expert. Your task is to generate an efficient and optimized SQL query based on user input.\n\n The query should retrieve data from the view table `v_pbg_task_with_retributions`, specifically selecting the following columns:\n\n - nilai_retribusi_bangunan\n - land_certificate_phase\n - due_date\n - consultation_type\n - function_type\n - slf_status_name\n - slf_status\n - status_name\n - status\n - address\n - document_number\n - registration_number\n - application_type_name\n - application_type\n - owner_name\n - name\n\n Ensure the query is well-structured, uses best indexing practices, and avoids performance bottlenecks.\n\n Consider the following context: \"$mainContent\".\n\n Always return only the SQL query without any additional explanation." "prompt": "You are a MariaDB SQL expert. Your task is to generate an efficient and optimized SQL query based on user input.\n\n The query should retrieve data from the view table `v_pbg_task_with_retributions`, specifically selecting the following columns:\n\n - nilai_retribusi_bangunan\n - land_certificate_phase\n - due_date\n - consultation_type\n - function_type\n - slf_status_name\n - slf_status\n - status_name\n - status\n - address\n - document_number\n - registration_number\n - application_type_name\n - application_type\n - owner_name\n - name\n\n Ensure the query is well-structured, uses best indexing practices, and avoids performance bottlenecks.\n\n Consider the following context: \"$mainContent\".\n\n Always return only the SQL query without any additional explanation.\n\n The query should include `LIMIT 5` to restrict the results."
}, },
"DOCUMENT VALIDATION": { "DOCUMENT VALIDATION": {
"prompt": "You are a MariaDB SQL expert. Your task is to generate an efficient and optimized SQL query based on user input.\n\n The query should retrieve data from the view table `pbg_task`, specifically selecting the following columns:\n\n - name\n - owner_name\n - application_type\n - application_type_name\n - registration_number\n - document_number\n - address\n - status_name\n - slf_status\n - slf_status_name\n - function_type\n - consultation_type\n - function_type\n - consultation_type\n - due_date\n - land_certificate_phase\n\n Ensure the query is well-structured, uses best indexing practices, and avoids performance bottlenecks.\n\n Consider the following context: \"$mainContent\".\n\n Always return only the SQL query without any additional explanation." "prompt": "You are a MariaDB SQL expert. Your task is to generate an efficient and optimized SQL query based on user input.\n\n The query should retrieve data from the view table `pbg_task`, specifically selecting the following columns:\n\n - name\n - owner_name\n - application_type\n - application_type_name\n - registration_number\n - document_number\n - address\n - status_name\n - slf_status\n - slf_status_name\n - function_type\n - consultation_type\n - function_type\n - consultation_type\n - due_date\n - land_certificate_phase\n\n Ensure the query is well-structured, uses best indexing practices, and avoids performance bottlenecks.\n\n Consider the following context: \"$mainContent\".\n\n Always return only the SQL query without any additional explanation.\n\n The query should include `LIMIT 5` to restrict the results."
}, },
"DATA SUMMARY": { "DATA SUMMARY": {
"prompt": "You are a MariaDB SQL expert. Your task is to generate an efficient and optimized SQL query based on user input.\n\n The query should retrieve data from the view table `bigdata_resumes`, specifically selecting the following columns:\n\n - potention_count\n - potention_sum\n - non_verified_count\n - non_verified_sum\n - verified_sum\n - verified_count\n - business_count\n - business_sum\n - non_business_count\n - non_business_sum\n - spatial_count\n - spatial_sum\n - updated_at\n\n Ensure the query is well-structured, uses best indexing practices, and avoids performance bottlenecks.\n\n Consider the following context: \"$mainContent\".\n\n Always return only the SQL query without any additional explanation." "prompt": "You are a MariaDB SQL expert. Your task is to generate an efficient and optimized SQL query based on user input.\n\n The query should retrieve data from the view table `bigdata_resumes`, specifically selecting the following columns:\n\n - potention_count\n - potention_sum\n - non_verified_count\n - non_verified_sum\n - verified_sum\n - verified_count\n - business_count\n - business_sum\n - non_business_count\n - non_business_sum\n - spatial_count\n - spatial_sum\n - updated_at\n\n Ensure the query is well-structured, uses best indexing practices, and avoids performance bottlenecks.\n\n Consider the following context: \"$mainContent\".\n\n Always return only the SQL query without any additional explanation.\n\n The query should include `LIMIT 5` to restrict the results."
} }
} }

13
resources/js/chatbot-pimpinan/index.js
View File

@@ -10,6 +10,7 @@ document.addEventListener("DOMContentLoaded", function () {
const textarea = document.getElementById("user-message"); const textarea = document.getElementById("user-message");
const sendButton = document.getElementById("send"); const sendButton = document.getElementById("send");
const conversationArea = document.querySelector(".row.flex-grow"); const conversationArea = document.querySelector(".row.flex-grow");
const chatHistory = [];
// Fungsi untuk mengirim pesan // Fungsi untuk mengirim pesan
async function sendMessage() { async function sendMessage() {
@@ -30,7 +31,7 @@ document.addEventListener("DOMContentLoaded", function () {
} }
// Panggil API untuk mendapatkan respons dari bot // Panggil API untuk mendapatkan respons dari bot
const botResponse = await getBotResponse(userText); const botResponse = await getBotResponse(userText, chatHistory);
// Perbarui pesan bot dengan respons yang sebenarnya // Perbarui pesan bot dengan respons yang sebenarnya
if (messageTextContainer) { if (messageTextContainer) {
@@ -124,12 +125,12 @@ document.addEventListener("DOMContentLoaded", function () {
} }
// Fungsi untuk memanggil API // Fungsi untuk memanggil API
async function getBotResponse(userText) { async function getBotResponse(userText, historyChat) {
try { try {
const url = `${GlobalConfig.apiHost}/api/main-generate-text`; const url = `${GlobalConfig.apiHost}/api/main-generate-text`;
const response = await fetch(url, { const response = await fetch(url, {
method: "POST", method: "POST",
body: JSON.stringify({prompt: userText }), body: JSON.stringify({prompt: userText, chatHistory: historyChat}),
headers: { headers: {
Authorization: `Bearer ${document Authorization: `Bearer ${document
.querySelector('meta[name="api-token"]') .querySelector('meta[name="api-token"]')
@@ -139,6 +140,12 @@ document.addEventListener("DOMContentLoaded", function () {
}); });
const data = await response.json(); const data = await response.json();
const rawBotResponse = data.nlpResponse;
// Tambahkan ke chatHistory
chatHistory.push({
user: userText,
rawBotResponse: rawBotResponse,
});
return data.response || "Maaf, saya tidak mengerti."; return data.response || "Maaf, saya tidak mengerti.";
} catch (error) { } catch (error) {
console.error("Error fetching bot response:", error); console.error("Error fetching bot response:", error);

39
resources/js/chatbot/index.js
View File

@@ -32,6 +32,32 @@ document.addEventListener("DOMContentLoaded", function () {
setTimeout(() => { setTimeout(() => {
const tab_active = getActiveTabId(); const tab_active = getActiveTabId();
console.log("Active Tab ID:", tab_active); console.log("Active Tab ID:", tab_active);
// Hapus semua chat kecuali pesan default bot
conversationArea.innerHTML = `
<div class="row flex-grow overflow-auto align-items-start">
<!-- Avatar -->
<div class="col-auto alignpe-0">
<img class="rounded-circle" width="45" src="/images/iconchatbot.jpeg" alt="avatar-3">
</div>
<!-- Nama dan Bubble Chat -->
<div class="col-9 w-auto">
<!-- Nama Bot -->
<p class="fw-bolder mb-1">Neng Bedas</p>
<!-- Bubble Chat -->
<div class="bot-response p-2 bg-light rounded mb-2 d-inline-block">
<p class="mb-0">Halo! Ada yang bisa saya bantu?</p>
<!-- Waktu (Tetap di Dalam Bubble Chat) -->
<div class="sending-message-time text-end mt-1">
<p class="text-muted small mb-0">Now</p>
</div>
</div>
</div>
</div>
`;
}, 100); // Timeout untuk memastikan class `active` sudah diperbarui }, 100); // Timeout untuk memastikan class `active` sudah diperbarui
}); });
}); });
@@ -39,6 +65,7 @@ document.addEventListener("DOMContentLoaded", function () {
const textarea = document.getElementById("user-message"); const textarea = document.getElementById("user-message");
const sendButton = document.getElementById("send"); const sendButton = document.getElementById("send");
const conversationArea = document.querySelector(".row.flex-grow"); const conversationArea = document.querySelector(".row.flex-grow");
const chatHistory = [];
// Fungsi untuk mengirim pesan // Fungsi untuk mengirim pesan
async function sendMessage() { async function sendMessage() {
@@ -62,7 +89,7 @@ document.addEventListener("DOMContentLoaded", function () {
} }
// Panggil API untuk mendapatkan respons dari bot // Panggil API untuk mendapatkan respons dari bot
const botResponse = await getBotResponse(currentTab, userText); const botResponse = await getBotResponse(currentTab, userText, chatHistory);
// Perbarui pesan bot dengan respons yang sebenarnya // Perbarui pesan bot dengan respons yang sebenarnya
if (messageTextContainer) { if (messageTextContainer) {
@@ -156,12 +183,12 @@ document.addEventListener("DOMContentLoaded", function () {
} }
// Fungsi untuk memanggil API // Fungsi untuk memanggil API
async function getBotResponse(tab_active, userText) { async function getBotResponse(tab_active, userText, historyChat) {
try { try {
const url = `${GlobalConfig.apiHost}/api/generate-text`; const url = `${GlobalConfig.apiHost}/api/generate-text`;
const response = await fetch(url, { const response = await fetch(url, {
method: "POST", method: "POST",
body: JSON.stringify({tab_active:tab_active, prompt: userText }), body: JSON.stringify({tab_active:tab_active, prompt: userText, chatHistory: historyChat }),
headers: { headers: {
Authorization: `Bearer ${document Authorization: `Bearer ${document
.querySelector('meta[name="api-token"]') .querySelector('meta[name="api-token"]')
@@ -171,6 +198,12 @@ document.addEventListener("DOMContentLoaded", function () {
}); });
const data = await response.json(); const data = await response.json();
const rawBotResponse = data.nlpResponse;
// Tambahkan ke chatHistory
chatHistory.push({
user: userText,
rawBotResponse: rawBotResponse,
});
return data.response || "Maaf, saya tidak mengerti."; return data.response || "Maaf, saya tidak mengerti.";
} catch (error) { } catch (error) {
console.error("Error fetching bot response:", error); console.error("Error fetching bot response:", error);