Update services/chat_service.py

services/chat_service.py

@@ -67,29 +67,78 @@ class ChatService:
         )
 
     def construct_prompt(
-        self, 
-        user_input: str, 
-        context: str, 
-        chat_history: List[Tuple[str, str]], 
-        max_history_turns: int = 1
-    ) -> str:
-        """Constructs the full prompt."""
-        # System message
-        system_message = self.construct_system_prompt(context)
-
-        # Start with system message
-        prompt = f"<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n{system_message}<|eot_id|>"
-
-        # Add chat history (limit to last `max_history_turns` interactions)
-        for user_msg, assistant_msg in chat_history[-max_history_turns:]:
-            prompt += f"<|start_header_id|>user<|end_header_id|>\n\n{user_msg}<|eot_id|>"
-            prompt += f"<|start_header_id|>assistant<|end_header_id|>\n\n{assistant_msg}<|eot_id|>"
-
-        # Add the current user input
-        prompt += f"<|start_header_id|>user<|end_header_id|>\n\n{user_input}<|eot_id|>"
-        prompt += "<|start_header_id|>assistant<|end_header_id|>\n\n"
+            self, 
+            user_input: str, 
+            context: str, 
+            chat_history: List[Tuple[str, str]], 
+            max_history_turns: int = 1
+        ) -> str:
+            """Constructs the full prompt."""
+            # System message
+            system_message = self.construct_system_prompt(context)
+    
+            # Start with system message
+            prompt = f"<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n{system_message}<|eot_id|>"
+    
+            # Add chat history (limit to last `max_history_turns` interactions)
+            for user_msg, assistant_msg in chat_history[-max_history_turns:]:
+                prompt += f"<|start_header_id|>user<|end_header_id|>\n\n{user_msg}<|eot_id|>"
+                prompt += f"<|start_header_id|>assistant<|end_header_id|>\n\n{assistant_msg}<|eot_id|>"
+    
+            # Add the current user input
+            prompt += f"<|start_header_id|>user<|end_header_id|>\n\n{user_input}<|eot_id|>"
+            prompt += "<|start_header_id|>assistant<|end_header_id|>\n\n"
+    
+            return prompt
 
-        return prompt
+    def build_context(
+            self,
+            search_results: Dict[str, List[Dict[str, Any]]],
+            chat_history: List[Dict[str, Any]]
+        ) -> str:
+            """Build context for the model from search results and chat history"""
+            context_parts = []
+            
+            # Add relevant products
+            if search_results.get('products'):
+                products = search_results['products'][:2]  # Limit to top 2 products
+                for product in products:
+                    context_parts.append(
+                        f"Produkt: {product['Name']}\n"
+                        f"Beschreibung: {product['Description']}\n"
+                        f"Preis: {product['Price']}€\n"
+                        f"Kategorie: {product['ProductCategory']}"
+                    )
+            
+            # Add relevant PDF content
+            if search_results.get('documents'):
+                docs = search_results['documents'][:2]
+                for doc in docs:
+                    context_parts.append(
+                        f"Aus Dokument '{doc['source']}' (Seite {doc['page']}):\n"
+                        f"{doc['text']}"
+                    )
+            
+            # Add relevant FAQs
+            if search_results.get('faqs'):
+                faqs = search_results['faqs'][:2]
+                for faq in faqs:
+                    context_parts.append(
+                        f"FAQ:\n"
+                        f"Frage: {faq['question']}\n"
+                        f"Antwort: {faq['answer']}"
+                    )
+            
+            # Add recent chat history
+            if chat_history:
+                recent_history = chat_history[-3:]  # Last 3 interactions
+                history_text = "\n".join(
+                    f"User: {h['user_input']}\nAssistant: {h['response']}"
+                    for h in recent_history
+                )
+                context_parts.append(f"Letzte Interaktionen:\n{history_text}")
+            
+            return "\n\n".join(context_parts)
 
     async def chat(
         self,
@@ -174,54 +223,6 @@ class ChatService:
             logger.error(f"Error searching sources: {e}")
             return {'products': [], 'documents': [], 'faqs': []}
 
-    def build_context(
-        self,
-        search_results: Dict[str, List[Dict[str, Any]]],
-        chat_history: List[Dict[str, Any]]
-    ) -> str:
-        """Build context for the model from search results and chat history"""
-        context_parts = []
-        
-        # Add relevant products
-        if search_results.get('products'):
-            products = search_results['products'][:2]  # Limit to top 2 products
-            for product in products:
-                context_parts.append(
-                    f"Produkt: {product['Name']}\n"
-                    f"Beschreibung: {product['Description']}\n"
-                    f"Preis: {product['Price']}€\n"
-                    f"Kategorie: {product['ProductCategory']}"
-                )
-        
-        # Add relevant PDF content
-        if search_results.get('documents'):
-            docs = search_results['documents'][:2]
-            for doc in docs:
-                context_parts.append(
-                    f"Aus Dokument '{doc['source']}' (Seite {doc['page']}):\n"
-                    f"{doc['text']}"
-                )
-        
-        # Add relevant FAQs
-        if search_results.get('faqs'):
-            faqs = search_results['faqs'][:2]
-            for faq in faqs:
-                context_parts.append(
-                    f"FAQ:\n"
-                    f"Frage: {faq['question']}\n"
-                    f"Antwort: {faq['answer']}"
-                )
-        
-        # Add recent chat history
-        if chat_history:
-            recent_history = chat_history[-3:]  # Last 3 interactions
-            history_text = "\n".join(
-                f"User: {h['user_input']}\nAssistant: {h['response']}"
-                for h in recent_history
-            )
-            context_parts.append(f"Letzte Interaktionen:\n{history_text}")
-        
-        return "\n\n".join(context_parts)
 
     async def generate_response(
         self,