Feat: add German translations for all agent templates and optimized line breaks for template titles

agent/templates/advanced_ingestion_pipeline.json

@@ -2,10 +2,12 @@
     "id": 23,
     "title": {
         "en": "Advanced Ingestion Pipeline",
+        "de": "Erweiterte Ingestion Pipeline",
         "zh": "编排复杂的 Ingestion Pipeline"
     },
     "description": {
         "en": "This template demonstrates how to use an LLM to generate summaries, keywords, Q&A, and metadata for each chunk to support diverse retrieval needs.",
+        "de": "Diese Vorlage demonstriert, wie ein LLM verwendet wird, um Zusammenfassungen, Schlüsselwörter, Fragen & Antworten und Metadaten für jedes Segment zu generieren, um vielfältige Abrufanforderungen zu unterstützen.",
         "zh": "此模板演示如何利用大模型为切片生成摘要、关键词、问答及元数据，以满足多样化的召回需求。"
     },
     "canvas_type": "Ingestion Pipeline",

agent/templates/choose_your_knowledge_base_agent.json

@@ -2,9 +2,11 @@
     "id": 19,
     "title": {
 		"en": "Choose Your Knowledge Base Agent",
+		"de": "Wählen Sie Ihren Wissensdatenbank Agenten",
 		"zh": "选择知识库智能体"},
     "description": {
 		"en": "Select your desired knowledge base from the dropdown menu. The Agent will only retrieve from the selected knowledge base and use this content  to generate responses.",
+		"de": "Wählen Sie Ihre gewünschte Wissensdatenbank aus dem Dropdown-Menü. Der Agent ruft nur Informationen aus der ausgewählten Wissensdatenbank ab und verwendet diesen Inhalt zur Generierung von Antworten.",
 		"zh": "从下拉菜单中选择知识库，智能体将仅根据所选知识库内容生成回答。"},
 	"canvas_type": "Agent", 
     "dsl": {
@@ -417,5 +419,4 @@
 		"retrieval": []
 	},                                   
     "avatar": "data:image/png;base64,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"
-}
-
+}

agent/templates/choose_your_knowledge_base_workflow.json

@@ -2,9 +2,11 @@
     "id": 18,
     "title": {
 		"en": "Choose Your Knowledge Base Workflow",
+		"de": "Wählen Sie Ihren Wissensdatenbank Workflow",
 		"zh": "选择知识库工作流"},
     "description": {
 		"en": "Select your desired knowledge base from the dropdown menu. The retrieval assistant will only use data from your selected knowledge base to generate responses.",
+		"de": "Wählen Sie Ihre gewünschte Wissensdatenbank aus dem Dropdown-Menü. Der Abrufassistent verwendet nur Daten aus Ihrer ausgewählten Wissensdatenbank, um Antworten zu generieren.",
 		"zh": "从下拉菜单中选择知识库，工作流将仅根据所选知识库内容生成回答。"},
 	"canvas_type": "Other",
     "dsl": {
@@ -435,5 +437,4 @@
 		"retrieval": []
 	},
 	"avatar": 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"
-}
-
+}

agent/templates/chunk_summary.json

@@ -2,10 +2,12 @@
     "id": 24,
     "title": {
         "en": "Chunk Summary",
+        "de": "Segmentzusammenfassung",
         "zh": "总结切片"
     },
     "description": {
         "en": "This template uses an LLM to generate chunk summaries for building text and vector indexes. During retrieval, summaries enhance matching, and the original chunks are returned as results.",
+        "de": "Diese Vorlage nutzt ein LLM zur Generierung von Segmentzusammenfassungen für den Aufbau von Text- und Vektorindizes. Bei der Abfrage verbessern die Zusammenfassungen die Übereinstimmung, während die ursprünglichen Segmente als Ergebnisse zurückgegeben werden.",
         "zh": "此模板利用大模型生成切片摘要，并据此建立全文索引与向量。检索时以摘要提升匹配效果，最终召回对应的原文切片。"
     },
     "canvas_type": "Ingestion Pipeline",
@@ -350,7 +352,7 @@
                             ]
                         },
                         "label": "Tokenizer",
-                        "name": "Tokenizer"
+                        "name": "Indexer"
                     },
                     "dragging": false,
                     "id": "Tokenizer:EightRocketsAppear",