changes

app/db/qdrant.py

@@ -28,10 +28,13 @@ class QdrantDB:
                 raise ValueError("Missing Qdrant environment variables")
 
             self._client = AsyncQdrantClient(
-                url=self._url,
-                api_key=self._api_key,
-                prefer_grpc=False
-            )
+            url=self._url,
+            api_key=self._api_key,
+            prefer_grpc=False,
+            https=True,
+            verify=False   # 🔥 LOCAL SSL FIX
+        )
+
             logger.info("QdrantDB client created")
 
         return self._client

app/rag/pipeline.py

@@ -31,15 +31,19 @@ class RAGPipeline:
                 payload = chunk.get('payload', {})
                 content = payload.get('content', '').strip()
                 
-                if content:
-                    context_texts.append(content)
+                if len(content) > 120:   # 🔥 minimum semantic length
+                    context_texts.append(
+                    f"[Source: {payload.get('chapter')}]\n{content}"
+                )
+
                 
-                sources.append({
-                    'source_file': payload.get('source_file', 'unknown'),
-                    'chapter': payload.get('chapter', ''),
-                    'section': payload.get('section', 'Unknown'),
-                    'content': content[:500] + ("..." if len(content) > 500 else "")
-                })
+                if len(content) > 120:
+                    sources.append({
+                        'source_file': payload.get('source_file', 'unknown'),
+                        'chapter': payload.get('chapter', ''),
+                        'section': payload.get('section', 'Unknown'),
+                        'content': content[:500] + ("..." if len(content) > 500 else "")
+                    })
 
             # Remove duplicate sources
             seen = set()
@@ -58,9 +62,10 @@ class RAGPipeline:
                 }
 
             # Step 3: Combine context
-            combined_context = "\n\n".join(context_texts)
+            context_texts = context_texts[:5]   # top 5 only
+            combined_context = "\n\n---\n\n".join(context_texts)
 
-            # Step 4: Smart prompt for concise answer
+            # Step 4: Smart prompt for concise answerc     
             system_prompt = "You are a helpful course assistant for Physical AI & Humanoid Robotics. Answer the user's question using ONLY the provided course book content. Be concise, clear, and to-the-point. Use bullet points if needed. Do not add extra information."
 
             user_prompt = f"""Question: {question}