Update app/app.py

app/app.py

@@ -1,3 +1,4 @@
+//app.py
 import os
 import json
 import asyncio
@@ -100,6 +101,20 @@ class Feedback(BaseModel):
     feedback: str
     comment: str | None = None
 
+# -----------------------------
+# ✅ Helper for metadata-based filtering
+# -----------------------------
+def is_personnel_related(metadata: dict) -> bool:
+    # Keywords indicating personnel or HR related policy sections
+    personnel_keywords = [
+        "ii", "personnel", "recruitment", "resignation", "hr", "promotion",
+        "employee", "staff", "service", "termination", "transfer"
+    ]
+    section = str(metadata.get("section", "")).lower()
+    title = str(metadata.get("title", "")).lower()
+    # Return True if any keyword is found in section or title
+    return any(kw in section for kw in personnel_keywords) or any(kw in title for kw in personnel_keywords)
+
 # -----------------------------
 # ✅ Endpoints
 # -----------------------------
@@ -170,13 +185,23 @@ async def chat(query: Query, request: Request):
             "answer": "Sorry, I could not find a relevant policy to answer that question. Please try rephrasing."
         }
     
+    # Post-search metadata-based filtering for personnel/HR queries
+    if any(keyword in question_lower for keyword in ["personnel", "hr", "recruitment", "resignation",
+                                                     "promotion", "employee", "termination", "transfer"]):
+        filtered_results = [res for res in search_results if is_personnel_related(res.get('metadata', {}))]
+        if filtered_results:
+            adapter.info(f"Filtered {len(search_results) - len(filtered_results)} irrelevant chunks for personnel query.")
+            search_results = filtered_results
+        else:
+            adapter.info("No personnel-related chunks found after filtering; using unfiltered results.")
+
     scores = [f"{result['relevance_score']:.4f}" for result in search_results]
     adapter.info(f"Found {len(search_results)} relevant chunks with scores: {scores}")
 
     # 2. Prepare Context
     context_chunks = [result['text'] for result in search_results[:TOP_K_CONTEXT]]
     context = "\n---\n".join(context_chunks)
-    
+
     # 3. Build Prompt with Separator Instruction
     prompt = f"""<|system|>
 You are a precise and factual assistant for NEEPCO's Delegation of Powers (DoP) policy.
@@ -187,6 +212,7 @@ Your task is to answer the user's question based ONLY on the provided context.
 </s>
 <|user|>
 ### Relevant Context:
+
 ```
 {context}
 ```
@@ -212,14 +238,10 @@ Your task is to answer the user's question based ONLY on the provided context.
         # Check if the model used the pipe separator, indicating a list.
         if '|' in raw_answer:
             adapter.info("Pipe separator found. Formatting response as a bulleted list.")
-            # Split the string into a list of items
             items = raw_answer.split('|')
-            # Clean up each item and format it as a bullet point
             cleaned_items = [f"* {item.strip()}" for item in items if item.strip()]
-            # Join them back together with newlines
             answer = "\n".join(cleaned_items)
         else:
-            # If no separator, use the answer as is.
             answer = raw_answer
 
     except asyncio.TimeoutError: