add

sug_v6_1_2_5.py

@@ -320,7 +320,7 @@ def add_step(context: RunContext, step_name: str, step_type: str, data: dict):
     return step
 
 
-def add_query_to_graph(context: RunContext, query_state: QueryState, iteration: int, evaluation_reason: str = "", is_selected: bool = True):
+def add_query_to_graph(context: RunContext, query_state: QueryState, iteration: int, evaluation_reason: str = "", is_selected: bool = True, parent_level: int | None = None):
     """添加Query节点到演化图
 
     Args:
@@ -329,8 +329,10 @@ def add_query_to_graph(context: RunContext, query_state: QueryState, iteration:
         iteration: 迭代次数
         evaluation_reason: 评估原因（可选）
         is_selected: 是否被选中进入处理队列（默认True）
+        parent_level: 父节点的层级（用于构造parent_id）
     """
-    query_id = query_state.query  # 直接使用query作为ID
+    # 使用 "query_level" 格式作为节点ID
+    query_id = f"{query_state.query}_{query_state.level}"
 
     # 初始化图结构
     if "nodes" not in context.query_graph:
@@ -354,8 +356,9 @@ def add_query_to_graph(context: RunContext, query_state: QueryState, iteration:
     }
 
     # 添加边（父子关系）
-    if query_state.parent_query:
-        parent_id = query_state.parent_query
+    if query_state.parent_query and parent_level is not None:
+        # 构造父节点ID: parent_query_parent_level
+        parent_id = f"{query_state.parent_query}_{parent_level}"
         if parent_id in context.query_graph["nodes"]:
             context.query_graph["edges"].append({
                 "from": parent_id,
@@ -371,8 +374,15 @@ def add_query_to_graph(context: RunContext, query_state: QueryState, iteration:
     context.query_graph["iterations"][iteration].append(query_id)
 
 
-def add_note_to_graph(context: RunContext, query: str, note: dict):
-    """添加Note节点到演化图，并连接到对应的Query"""
+def add_note_to_graph(context: RunContext, query: str, query_level: int, note: dict):
+    """添加Note节点到演化图，并连接到对应的Query
+
+    Args:
+        context: 运行上下文
+        query: query文本
+        query_level: query所在层级
+        note: 帖子数据
+    """
     note_id = note["note_id"]
 
     # 初始化图结构
@@ -396,10 +406,11 @@ def add_note_to_graph(context: RunContext, query: str, note: dict):
         "found_by_query": query
     }
 
-    # 添加边：Query → Note
-    if query in context.query_graph["nodes"]:
+    # 添加边：Query → Note，使用 query_level 格式的ID
+    query_id = f"{query}_{query_level}"
+    if query_id in context.query_graph["nodes"]:
         context.query_graph["edges"].append({
-            "from": query,
+            "from": query_id,
             "to": note_id,
             "edge_type": "query_to_note",
             "match_level": note["evaluation"]["match_level"],
@@ -539,7 +550,7 @@ async def process_suggestions(
     new_queries = []
     suggestion_evaluations = []
 
-    for sug in suggestions[:5]:  # 限制处理数量
+    for sug in suggestions:  # 处理所有建议
         # 评估sug与原始需求的相关度（注意：这里是与原始需求original_need对比，而非当前query）
         # 这样可以确保生成的suggestion始终围绕用户的核心需求
         sug_eval = await evaluate_query_relevance(sug, original_need, query_state.relevance_score, context)
@@ -558,7 +569,7 @@ async def process_suggestions(
             level=query_state.level + 1,
             relevance_score=sug_eval.relevance_score,
             parent_query=query,
-            strategy="direct_sug"
+            strategy="调用sug"
         )
 
         # 判断是否比当前query更好（只有提升的才加入待处理队列）
@@ -570,7 +581,8 @@ async def process_suggestions(
             sug_state,
             iteration,
             evaluation_reason=sug_eval.reason,
-            is_selected=is_selected
+            is_selected=is_selected,
+            parent_level=query_state.level  # 父节点的层级
         )
 
         if is_selected:
@@ -621,7 +633,7 @@ async def process_suggestions(
             level=query_state.level + 1,
             relevance_score=rewrite_eval.relevance_score,
             parent_query=query,
-            strategy="rewrite_abstract"
+            strategy="抽象改写"
         )
 
         # 添加到演化图（无论是否提升）
@@ -630,7 +642,8 @@ async def process_suggestions(
             new_state,
             iteration,
             evaluation_reason=rewrite_eval.reason,
-            is_selected=rewrite_eval.is_improved
+            is_selected=rewrite_eval.is_improved,
+            parent_level=query_state.level  # 父节点的层级
         )
 
         if rewrite_eval.is_improved:
@@ -681,7 +694,7 @@ async def process_suggestions(
             level=query_state.level + 1,
             relevance_score=rewrite_syn_eval.relevance_score,
             parent_query=query,
-            strategy="rewrite_synonym"
+            strategy="同义改写"
         )
 
         # 添加到演化图（无论是否提升）
@@ -690,7 +703,8 @@ async def process_suggestions(
             new_state,
             iteration,
             evaluation_reason=rewrite_syn_eval.reason,
-            is_selected=rewrite_syn_eval.is_improved
+            is_selected=rewrite_syn_eval.is_improved,
+            parent_level=query_state.level  # 父节点的层级
         )
 
         if rewrite_syn_eval.is_improved:
@@ -741,7 +755,7 @@ async def process_suggestions(
             level=query_state.level + 1,
             relevance_score=insertion_eval.relevance_score,
             parent_query=query,
-            strategy="add_word"
+            strategy="加词"
         )
 
         # 添加到演化图（无论是否提升）
@@ -750,7 +764,8 @@ async def process_suggestions(
             new_state,
             iteration,
             evaluation_reason=insertion_eval.reason,
-            is_selected=insertion_eval.is_improved
+            is_selected=insertion_eval.is_improved,
+            parent_level=query_state.level  # 父节点的层级
         )
 
         if insertion_eval.is_improved:
@@ -766,7 +781,7 @@ async def process_suggestions(
         "query_relevance": query_state.relevance_score,
         "suggestions_count": len(suggestions),
         "suggestions_evaluated": len(suggestion_evaluations),
-        "suggestion_evaluations": suggestion_evaluations[:10],  # 只保存前10个
+        "suggestion_evaluations": suggestion_evaluations,  # 保存所有评估
         "agent_calls": agent_calls,  # 所有Agent调用的详细记录
         "new_queries_generated": len(new_queries),
         "new_queries": [{"query": nq.query, "score": nq.relevance_score, "strategy": nq.strategy} for nq in new_queries],
@@ -826,7 +841,7 @@ async def process_search_results(
     satisfied_notes = []
     partial_notes = []
 
-    for note in notes[:10]:  # 限制评估数量
+    for note in notes:  # 评估所有帖子
         note_data = process_note_data(note)
         title = note_data["title"] or ""
         desc = note_data["desc"] or ""
@@ -858,7 +873,7 @@ async def process_search_results(
             "input": {
                 "note_id": note_data.get("note_id"),
                 "title": title,
-                "desc": desc[:200] if len(desc) > 200 else desc  # 限制长度
+                "desc": desc  # 完整描述
             },
             "output": {
                 "match_level": evaluation.match_level,
@@ -877,7 +892,7 @@ async def process_search_results(
         }
 
         # 将所有评估过的帖子添加到演化图（包括satisfied、partial、unsatisfied）
-        add_note_to_graph(context, query, note_data)
+        add_note_to_graph(context, query, query_state.level, note_data)
 
         if evaluation.match_level == "satisfied":
             satisfied_notes.append(note_data)
@@ -928,7 +943,7 @@ async def process_search_results(
 </当前Query>
 
 <缺失的方面>
-{', '.join(set(all_missing[:5]))}
+{', '.join(set(all_missing))}
 </缺失的方面>
 
 请改造query使其包含这些缺失的内容。
@@ -942,7 +957,7 @@ async def process_search_results(
                 "action": "基于缺失方面改造Query",
                 "input": {
                     "query": query,
-                    "missing_aspects": list(set(all_missing[:5]))
+                    "missing_aspects": list(set(all_missing))
                 },
                 "output": {
                     "improved_query": improvement.improved_query,
@@ -961,7 +976,7 @@ async def process_search_results(
                 level=query_state.level + 1,
                 relevance_score=improved_eval.relevance_score,
                 parent_query=query,
-                strategy="improve_from_partial"
+                strategy="基于部分匹配改进"
             )
 
             # 添加到演化图（无论是否提升）
@@ -970,7 +985,8 @@ async def process_search_results(
                 new_state,
                 iteration,
                 evaluation_reason=improved_eval.reason,
-                is_selected=improved_eval.is_improved
+                is_selected=improved_eval.is_improved,
+                parent_level=query_state.level  # 父节点的层级
             )
 
             if improved_eval.is_improved:
@@ -1025,7 +1041,7 @@ async def process_search_results(
                 level=query_state.level + 1,
                 relevance_score=rewrite_eval.relevance_score,
                 parent_query=query,
-                strategy="result_rewrite_abstract"
+                strategy="结果分支-抽象改写"
             )
 
             # 添加到演化图（无论是否提升）
@@ -1034,7 +1050,8 @@ async def process_search_results(
                 new_state,
                 iteration,
                 evaluation_reason=rewrite_eval.reason,
-                is_selected=rewrite_eval.is_improved
+                is_selected=rewrite_eval.is_improved,
+                parent_level=query_state.level  # 父节点的层级
             )
 
             if rewrite_eval.is_improved:
@@ -1085,7 +1102,7 @@ async def process_search_results(
                 level=query_state.level + 1,
                 relevance_score=rewrite_syn_eval.relevance_score,
                 parent_query=query,
-                strategy="result_rewrite_synonym"
+                strategy="结果分支-同义改写"
             )
 
             # 添加到演化图（无论是否提升）
@@ -1094,7 +1111,8 @@ async def process_search_results(
                 new_state,
                 iteration,
                 evaluation_reason=rewrite_syn_eval.reason,
-                is_selected=rewrite_syn_eval.is_improved
+                is_selected=rewrite_syn_eval.is_improved,
+                parent_level=query_state.level  # 父节点的层级
             )
 
             if rewrite_syn_eval.is_improved:
@@ -1120,7 +1138,7 @@ async def process_search_results(
                 "score": note["evaluation"]["relevance_score"],
                 "match_level": note["evaluation"]["match_level"]
             }
-            for note in satisfied_notes[:10]  # 只保存前10个
+            for note in satisfied_notes  # 保存所有满足的帖子
         ],
         "agent_calls": agent_calls,  # 所有Agent调用的详细记录
         "new_queries_generated": len(new_queries),
@@ -1158,7 +1176,7 @@ async def iterative_search_loop(
         query=context.q,
         level=0,
         relevance_score=1.0,  # 原始问题本身相关度为1.0
-        strategy="root"
+        strategy="根节点"
     )
     add_query_to_graph(context, root_query_state, 0, evaluation_reason="原始问题，作为搜索的根节点", is_selected=True)
     print(f"[根节点] 原始问题: {context.q}")
@@ -1183,30 +1201,30 @@ async def iterative_search_loop(
         })
         print(f"  {word}: {eval_result.relevance_score:.2f}")
 
-    # 按相关度排序，选择top 3
+    # 按相关度排序，使用所有分词
     word_scores.sort(key=lambda x: x['score'], reverse=True)
-    selected_words = word_scores[:3]
+    selected_words = word_scores  # 使用所有分词
 
-    # 将所有分词添加到演化图（包括未被选中的）
+    # 将所有分词添加到演化图（全部被选中）
     for item in word_scores:
-        is_selected = item in selected_words
+        is_selected = True  # 所有分词都被选中
         query_state = QueryState(
             query=item['word'],
             level=1,
             relevance_score=item['score'],
-            strategy="initial",
+            strategy="初始分词",
             parent_query=context.q  # 父节点是原始问题
         )
 
         # 添加到演化图（会自动创建从parent_query到该query的边）
-        add_query_to_graph(context, query_state, 0, evaluation_reason=item['eval'].reason, is_selected=is_selected)
+        add_query_to_graph(context, query_state, 0, evaluation_reason=item['eval'].reason, is_selected=is_selected, parent_level=0)  # 父节点是根节点（level 0）
 
         # 只有被选中的才加入队列
         if is_selected:
             query_queue.append(query_state)
 
-    print(f"\n初始query队列（按相关度选择）: {[(q.query, f'{q.relevance_score:.2f}') for q in query_queue]}")
-    print(f"  (共评估了 {len(word_scores)} 个分词，选择了前 {len(query_queue)} 个)")
+    print(f"\n初始query队列（按相关度排序）: {[(q.query, f'{q.relevance_score:.2f}') for q in query_queue]}")
+    print(f"  (共评估了 {len(word_scores)} 个分词，全部加入队列)")
 
     # 3. API实例
     xiaohongshu_api = XiaohongshuSearchRecommendations()
@@ -1276,9 +1294,8 @@ async def iterative_search_loop(
         # 更新队列
         all_new_queries = new_queries_from_sug + new_queries_from_result
 
-        # 将新生成的queries添加到演化图
-        for new_q in all_new_queries:
-            add_query_to_graph(context, new_q, iteration)
+        # 注意：不需要在这里再次添加到演化图，因为在 process_suggestions 和 process_search_results 中已经添加过了
+        # 如果在这里再次调用 add_query_to_graph，会覆盖之前设置的 evaluation_reason 等字段
 
         query_queue.extend(all_new_queries)
 
@@ -1390,7 +1407,7 @@ async def main(input_dir: str, max_iterations: int = 20, visualize: bool = False
 
     if satisfied_notes:
         output += "【满足需求的帖子】\n\n"
-        for idx, note in enumerate(satisfied_notes[:10], 1):
+        for idx, note in enumerate(satisfied_notes, 1):
             output += f"{idx}. {note['title']}\n"
             output += f"   相关度: {note['evaluation']['relevance_score']:.2f}\n"
             output += f"   URL: {note['note_url']}\n\n"