Change language support

.gitignore

@@ -153,9 +153,15 @@ dmypy.json
 # Cython debug symbols
 cython_debug/
 
+
 # PyCharm
 #  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
 #  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
 #  and can be added to the global gitignore or merged into this file.  For a more nuclear
 #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
 #.idea/
+
+#docker-override.yml
+.override.yml
+docker-compose.override.yml
+docker-compose.override.yaml

.vscode/settings.json

@@ -0,0 +1,5 @@
+{
+    "python-envs.defaultEnvManager": "ms-python.python:conda",
+    "python-envs.defaultPackageManager": "ms-python.python:conda",
+    "python-envs.pythonProjects": []
+}

app/api/chat.py

@@ -32,9 +32,10 @@
 
 import logging
 import uuid
+import os
 from fastapi import APIRouter, HTTPException, Request
 from fastapi.responses import StreamingResponse
-from typing import Dict, Any
+from typing import Dict, Any, Optional
 
 from app.models.chat import ChatCompletionRequest, ModelsResponse
 from app.services.llms import azure_openai_provider, echo_provider
@@ -120,6 +121,9 @@ async def chat_completion(req: ChatCompletionRequest, request: Request):
     """
     logger.info(f"Chat completion request for model: {req.model}, streaming: {req.stream}")
 
+    # Use system config language specified in environment variable or default to German
+    response_language = os.getenv("RESPONSE_LANGUAGE", "German")
+
     # Get the provider for the requested model
     provider = MODEL_MAPPING.get(req.model)
 

app/logic/agent_controller.py

@@ -26,6 +26,7 @@
 from app.models.chat import Message, ChatCompletionRequest
 from app.services.llms.azure_openai_provider import AzureOpenAIProvider
 from app.prompts.prompt_manager import PromptManager
+import os
 
 """
 -----------------------------------------------------------------
@@ -113,7 +114,7 @@ async def run_agent(self, messages: List[Message]) -> str:
         except Exception as e:
             logger.error(f"Agent execution failed: {str(e)}")
 
-            return f"I encountered an error while processing your request: {str(e)}"
+            return f"Es gab einen Fehler bei der Verarbeitung Ihrer Anfrage: {str(e)}"
 
     async def _simple_planner(self, messages: List[Message]) -> str:
         """
@@ -129,7 +130,7 @@ async def _simple_planner(self, messages: List[Message]) -> str:
         if not self.azure_service.is_available():
             # Fallback if Azure OpenAI is not available
             user_message = next((m.content for m in messages if m.role == "user"), "")
-            fallback_goal = f"Help the user with: {user_message}"
+            fallback_goal = f"Hilf dem Nutzer mit: {user_message}"
 
             return fallback_goal
 
@@ -214,18 +215,24 @@ async def _generate_final_response(self, messages: List[Message], goal: str, rea
         Returns:
             Final response as a string
         """
-
+
+        # Get configured language or use default
+        response_language = os.getenv("RESPONSE_LANGUAGE", "German")
+
         if not self.azure_service.is_available():
             # Fallback if Azure OpenAI is not available
             user_message = next((m.content for m in messages if m.role == "user"), "No message")
-            fallback_response = f"I understand you want help with: {user_message}. However, I'm currently running in limited mode. Please ensure Azure OpenAI is configured for full functionality."
-
-            return fallback_response
+            fallback_response = f"Ich verstehe, dass Sie Hilfe benötigen bei: {user_message}. Allerdings scheint mein Antwortgenerierungsdienst derzeit nicht verfügbar zu sein. Bitte versuchen Sie es später erneut."
 
+            return fallback_response
+
         final_messages = [
             Message(
                 role="system",
-                content=PromptManager.get_prompt("final_response_generator", goal=goal, reasoning=reasoning)
+                content=PromptManager.get_prompt("final_response_generator", 
+                    goal=goal, 
+                    reasoning=reasoning,
+                    language=response_language)
             )
         ] + messages
 

app/logic/rag_agent_controller.py

@@ -197,20 +197,21 @@ async def _generate_streaming_response_with_context(
         # Build system prompt with context
         system_prompt = f"""You are a helpful assistant that answers questions based on the provided Confluence documentation context.
 
-Instructions:
-- Use the provided document context to answer the user's question
-- Be accurate and specific based on the documentation
-- If the context doesn't contain enough information to fully answer the question, say so
-- Provide a clear and helpful response
-- Do not make up information not present in the context
-- Provide also the link to the source document if available
+                        Instructions:
+                        - Use the provided document context to answer the user's question
+                        - Be accurate and specific based on the documentation
+                        - If the context doesn't contain enough information to fully answer the question, say so
+                        - Provide a clear and helpful response
+                        - Do not make up information not present in the context
+                        - Provide also the link to the source document if available
+                        - IMPORTANT: Always respond only in {response_language}
 
-User Query: {query}
+                        User Query: {query}
 
-Document Context:
-{context}
+                        Document Context:
+                        {context}
 
-Please provide a comprehensive answer based on the available documentation."""
+                        Please provide a comprehensive answer based on the available documentation."""
 
         # Create messages for LLM
         rag_messages = [
@@ -413,6 +414,9 @@ async def _generate_response_with_context(
         Returns:
             Generated response with source citations
         """
+        # Get configured language or use default
+        response_language = os.getenv("RESPONSE_LANGUAGE", "German")
+
         # Build context from retrieved documents
         context_parts = []
         sources = []
@@ -437,23 +441,24 @@ async def _generate_response_with_context(
             logger.warning("LLM provider not available, returning context-only response")
             return self._generate_context_only_response(query, documents, sources)
 
-        # Build system prompt with context
+        # Build system prompt with context and language instruction
         system_prompt = f"""You are a helpful assistant that answers questions based on the provided Confluence documentation context.
 
-Instructions:
-- Use the provided document context to answer the user's question
-- Be accurate and specific based on the documentation
-- If the context doesn't contain enough information to fully answer the question, say so
-- Provide a clear and helpful response
-- Do not make up information not present in the context
-- Provide also the link to the source document if available
+                        Instructions:
+                        - Use the provided document context to answer the user's question
+                        - Be accurate and specific based on the documentation
+                        - If the context doesn't contain enough information to fully answer the question, say so
+                        - Provide a clear and helpful response
+                        - Do not make up information not present in the context
+                        - Provide also the link to the source document if available
+                        - IMPORTANT: Always respond only in {response_language}
 
-User Query: {query}
+                        User Query: {query}
 
-Document Context:
-{context}
+                        Document Context:
+                        {context}
 
-Please provide a comprehensive answer based on the available documentation."""
+                        Please provide a comprehensive answer based on the available documentation."""
 
         # Create messages for LLM
         rag_messages = [
@@ -539,13 +544,11 @@ def _generate_no_documents_response(self, query: str) -> str:
             Appropriate response for no documents found
         """
         return (
-            f"I couldn't find any relevant information in our Confluence documentation "
-            f"to answer your question about: {query}\n\n"
-            f"This could mean:\n"
-            f"- The information hasn't been documented yet\n"
-            f"- The question might be outside the scope of our current documentation\n"
-            f"- Try rephrasing your question with different keywords\n\n"
-            f"You might want to check the Confluence space directly or reach out to the appropriate team for more information."
+            f"Ich konnte leider keine relevanten Informationen zum Thema '{query}' in meinen Unterlagen finden\n\n "
+            f"Das könnte folgende Gründe haben:\n"
+            f"- Die Informationen wurden noch nicht dokumentiert\n"
+            f"- Die Frage könnte außerhalb des Umfangs unserer aktuellen Dokumentation liegen\n"
+            f"- Versuchen Sie, Ihre Frage mit anderen Schlüsselwörtern umzuformulieren\n\n"
         )
 
     def _generate_context_only_response(
@@ -566,8 +569,8 @@ def _generate_context_only_response(
             Context-based response without LLM generation
         """
         response_parts = [
-            f"I found some relevant information about your query: {query}\n",
-            "Here are the most relevant sections from our documentation:\n"
+            f"Ich habe relevante Informationen zu Ihrer Anfrage gefunden: {query}\n",
+            "Hier sind die relevantesten Abschnitte aus unserer Datenbank:\n"
         ]
 
         for i, doc in enumerate(documents[:3], 1):  # Limit to top 3 documents
@@ -580,8 +583,8 @@ def _generate_context_only_response(
             response_parts.extend(["\n**Sources:**", "\n".join(sources)])
 
         response_parts.append(
-            "\n*Note: AI response generation is currently unavailable. "
-            "The above information is directly from our documentation.*"
+            "\n*Note: KI Antwortgenerierung ist aktuell nicht verfügbar. "
+            "Daher ist die obige Information möglicherweise unvollständig.*"
         )
 
         return "\n".join(response_parts)
@@ -600,13 +603,12 @@ def _generate_fallback_response(self, messages: List[Message], error: str) -> st
         user_query = self._extract_user_query(messages)
 
         return (
-            f"I apologize, but I encountered an issue while searching for information about: {user_query}\n\n"
-            f"This might be due to:\n"
-            f"- Temporary service unavailability\n"
-            f"- Configuration issues with the knowledge base\n"
-            f"- Network connectivity problems\n\n"
-            f"Please try again in a few moments, or reach out to support if the issue persists.\n\n"
-            f"Technical details: {error}"
+            f"Entschuldigung aber es gab einen Fehler beim Versuch etwas über '{user_query}' zu finden.\n\n"
+            f"Das könnte folgende Gründe haben:\n"
+            f"- Temporäre Dienstunterbrechung\n"
+            f"- Konfigurationsprobleme mit der Wissensdatenbank\n"
+            f"- Netzwerkverbindungsprobleme\n\n"
+            f"Bitte versuchen Sie es in ein paar Minuten erneut oder wenden Sie sich an den Support, wenn das Problem weiterhin besteht.\n\n"
         )
 
 

app/prompts/prompt_manager.py

@@ -55,6 +55,11 @@ def get_prompt(template: str, **kwargs) -> str:
 
             # Render the template
             jinja_template = env.from_string(post.content)
+
+            # Always include language from environment variables if not provided
+            if 'language' not in kwargs:
+                kwargs['language'] = os.getenv("RESPONSE_LANGUAGE", "German")
+
             return jinja_template.render(**kwargs)
 
         except TemplateNotFound:

app/prompts/templates/final_response_generator.j2

@@ -1,14 +1,17 @@
 ---
 description: "System prompt for generating final comprehensive responses based on goal and reasoning"
-author: "Marinho Krieg"
-version: "1.0"
-variables: ["goal", "reasoning"]
+authors: ["Marinho Krieg", "Gilbert Lynsche"]
+version: "1.1"
+variables: ["goal", "reasoning", "language"]
 ---
 You are a helpful AI assistant. Based on the following context, provide a comprehensive and helpful response:
 
 GOAL: {{ goal }}
 
 REASONING: {{ reasoning }}
 
+LANGUAGE: {{ language }}
+
 Now respond to the user's request in a helpful, informative, and well-structured way. 
-Make sure your response directly addresses their needs and follows logical steps if applicable. Antworte nur auf Bayrisch!
+Make sure your response directly addresses their needs and follows logical steps if applicable. 
+IMPORTANT: Always respond only in {{ language }}.