labels.popular_word_word_enabled=Respons Kata Populer
labels.supported_search_web=Web
labels.supported_search_none=Tidak Tersedia
labels.purge_search_log_day=Hapus log pencarian lama
labels.purge_job_log_day=Hapus log job lama
labels.purge_user_info_day=Hapus log pengguna lama
labels.purge_by_bots=Nama bot untuk menghapus log
labels.log_level=Level Log
labels.csv_file_encoding=Encoding Berkas CSV
labels.notification_to=Email Notifikasi

                .addAssistantMessage("Hi there!")
                .addUserMessage("How are you?")
                .setModel("llama3")
                .setMaxTokens(500)
                .setTemperature(0.5)
                .setStream(false);

        assertEquals(4, request.getMessages().size());
        assertEquals("llama3", request.getModel());
        assertEquals(Integer.valueOf(500), request.getMaxTokens());

    }

    @Test
    public void test_setModel() {
        final LlmChatResponse response = new LlmChatResponse();

        response.setModel("llama3");
        assertEquals("llama3", response.getModel());

        response.setModel("gpt-3.5-turbo");
        assertEquals("gpt-3.5-turbo", response.getModel());
    }

    @Test
    public void test_nullValues() {

package org.codelibs.fess.llm;

import java.util.List;
import java.util.Map;

/**
 * Interface for LLM (Large Language Model) clients.
 * Implementations provide integration with different LLM providers
 * such as Ollama, OpenAI, and Google Gemini.
 *
 * In addition to low-level chat operations, this interface defines
 * high-level RAG workflow methods that allow each provider to optimize

        assertEquals("ollama", request.getAttribute(Constants.SEARCH_LOG_ACCESS_TYPE));
    }

    @Test
    public void test_mockRequest_attributeOverwrite() {
        final MockHttpServletRequest request = new MockHttpServletRequest();

        request.setAttribute(Constants.SEARCH_LOG_ACCESS_TYPE, "ollama");

    }

    @Test
    public void test_addSearchLog_accessType_ollama() {
        final SearchLog searchLog = callCreateSearchLogAndGetResult("ollama");
        assertEquals("ollama", searchLog.getAccessType());
    }

    @Test
    public void test_addSearchLog_accessType_openai() {
        final SearchLog searchLog = callCreateSearchLogAndGetResult("openai");

    /**
     * Gets the configured LLM type from the system configuration.
     *
     * @return The LLM type string from configuration (e.g., "ollama", "openai", "gemini")
     */
    protected String getLlmType() {
        return ComponentUtil.getFessConfig().getSystemProperty("rag.llm.name", "ollama");
    }

    /**
     * Checks if RAG chat feature is enabled.
     *
     * @return true if RAG chat is enabled, false otherwise

            // Set LLM type name as Access Type for search log
            request.setAttribute(Constants.SEARCH_LOG_ACCESS_TYPE,
                    ComponentUtil.getFessConfig().getSystemProperty("rag.llm.name", "ollama"));

            final Map<String, String[]> fields = parseFieldFilters(request);
            final String[] extraQueries = parseExtraQueries(request);
            final ChatResult result;

errors.failed_to_download_mapping_file = Eşleme dosyası indirilemedi.
errors.failed_to_upload_mapping_file = Eşleme dosyası yüklenemedi.
errors.invalid_kuromoji_token={0} bir belirteç olarak geçersiz.
errors.invalid_kuromoji_segmentation={0} ve {1} için segmentasyon sayısı farklı.
errors.invalid_str_is_included = {1}, {0} için geçersiz.
errors.blank_password = Şifre gereklidir.

# -------------------
constraints.CreditCardNumber.message = {item} adalah nomor kartu kredit yang tidak valid.
constraints.EAN.message = {item} adalah barcode {type} yang tidak valid.
constraints.Email.message = {item} bukan alamat email yang valid.
constraints.Length.message = Panjang {item} harus antara {min} dan {max}.
constraints.LuhnCheck.message = Checksum Luhn Modulo 11 dari {value} tidak benar.