Update main.py

main.py

@@ -312,57 +312,56 @@ def _soft_match_score(a: str, b: str) -> float:
     # combine (weights tuned to prefer phrase overlaps slightly)
     return min(1.0, 0.65 * jacc + 0.45 * big)
 
-
 def _detect_next_intent(user_query: str) -> bool:
     q = _norm_text(user_query)
     keys = [
         'after','after this','what next','whats next','next step',
         'then what','following step','continue','subsequent','proceed',
-        # new entries
         'what to do','what should i do','how to proceed','how do i continue','proceed further','next?'
     ]
     return any(k in q for k in keys)
 
-
 def _resolve_next_steps(user_query: str, numbered_text: str, max_next: int = 8, min_score: float = 0.25):
     """
     Robust next-step resolver:
     1) Detect 'what next' intent.
     2) Stem & match query against each step using tokens + bigrams + synonyms.
     3) If a good anchor is found, return ONLY subsequent steps (window=max_next).
-       Else return None (fallback to full SOP rendering).
+    Else return None (fallback to full SOP rendering).
     """
     if not _detect_next_intent(user_query):
         return None
-
     steps = _split_sop_into_steps(numbered_text)
     if not steps:
         return None
-
     q_norm = _norm_text(user_query)
     q_tokens = [t for t in q_norm.split() if len(t) > 1]
-
     best_idx, best_score = -1, -1.0
     for idx, step in enumerate(steps):
-        # base fuzzy score
         s1 = _soft_match_score(user_query, step)
-        # synonym hits
         syn = _syn_hits(q_tokens, step)
-        # combined score (synonyms are discrete)
         score = s1 + 0.12 * syn
         if score > best_score:
             best_score, best_idx = score, idx
-
-    # Looser threshold to accept anchors with synonyms / tense differences
     if best_idx < 0 or best_score < min_score:
-        return None  # let caller fall back to the full SOP
-
+        return None
     start = best_idx + 1
     if start >= len(steps):
-        return []  # already at final step
-
+        return []
     end = min(start + max_next, len(steps))
-    return steps[start:end]
+    anchor_norm = _norm_text(steps[best_idx])
+    def _unique(seq):
+        seen = set()
+        out = []
+        for s in seq:
+            k = _norm_text(s)
+            if k == anchor_norm:
+                continue
+            if k not in seen:
+                seen.add(k)
+                out.append(s)
+        return out
+    return _unique(steps[start:end])
 
 def _syn_hits(q_tokens: List[str], step_line: str) -> int:
     """
@@ -936,43 +935,38 @@ async def chat_with_ai(input_data: ChatInput):
         context_preformatted = False
 
         if best_doc and detected_intent == "steps":
-            
-            sec = (top_meta or {}).get('section', '')
-            actions = kb_results.get('actions', [])
-            sec_low = (sec or '').lower()
-            wants_delete = ('delete' in actions) or ('remove' in actions) or ('delete' in sec_low) or ('remov' in sec_low)
-            if wants_delete and sec:
-               full_steps = get_section_text(best_doc, sec)
-            else:
-               full_steps = get_best_steps_section_text(best_doc)
-               if not full_steps and sec:
-                  full_steps = get_section_text(best_doc, sec)
+            full_steps = get_best_steps_section_text(best_doc)
+            if not full_steps:
+                sec = (top_meta or {}).get("section")
+                if sec:
+                    full_steps = get_section_text(best_doc, sec)
+
             if full_steps:
-               numbered_full = _ensure_numbering(full_steps)
-               next_only = _resolve_next_steps(
+                numbered_full = _ensure_numbering(full_steps)
+                next_only = _resolve_next_steps(
                     input_data.user_message,
                     numbered_full,
                     max_next=6,
-                    min_score=0.35)
-               if next_only is not None:
-                 if len(next_only) == 0:
-                   context = "You are at the final step of this SOP. No further steps."
-                   next_step_applied = True
-                   next_step_info = {"count": 0}
-                   context_preformatted = True
-                 else:
-                     
-                   context = _format_steps_as_numbered(next_only)
-                   next_step_applied = True
-                   next_step_info = {"count": len(next_only)}
-                   context_preformatted = True
-               else:
-                   context = full_steps
-                   context_preformatted = False
-                   
-               # clear filter info for debug clarity
-               filt_info = {'mode': None, 'matched_count': None, 'all_sentences': None}
-               context_found = True
+                    min_score=0.35
+                )
+                if next_only is not None:
+                    if len(next_only) == 0:
+                        context = "You are at the final step of this SOP. No further steps."
+                        next_step_applied = True
+                        next_step_info = {"count": 0}
+                        context_preformatted = True
+                    else:
+                        context = _format_steps_as_numbered(next_only)
+                        next_step_applied = True
+                        next_step_info = {"count": len(next_only)}
+                        context_preformatted = True
+                else:
+                    context = full_steps
+                    context_preformatted = False
+
+                # clear filter info for debug clarity
+                filt_info = {'mode': None, 'matched_count': None, 'all_sentences': None}
+                context_found = True
 
         elif best_doc and detected_intent == "errors":
             full_errors = get_best_errors_section_text(best_doc)