hi

app.py

@@ -208,8 +208,6 @@ class RagPipeline:
                 self.state.sentences = data["sentences"]
                 self.state.meta = data["meta"]
 
-            log.info("載入 BM25 索引...")
-
             log.info("載入 BM25 索引...")
             with open(BM25_PKL, "rb") as f:
                 # 載入整個字典，然後取 'bm25' 這個鍵
@@ -255,7 +253,11 @@ class RagPipeline:
                 temperature=config["temperature"],
                 max_tokens=config["max_tokens"],
             )
-            return response.choices[0].message.content
+            content = response.choices[0].message.content
+            # [MODIFIED] 確保回傳值為非空字串
+            if not isinstance(content, str) or not content.strip():
+                raise ValueError("LLM response content is empty or not a string.")
+            return content
         except Exception as e:
             log.error(f"LLM API 呼叫失敗: {e}")
             raise
@@ -266,27 +268,30 @@ class RagPipeline:
         try:
             drug_ids = self._find_drug_ids_from_name(q_orig)
             if not drug_ids:
+                log.info("找不到藥品 ID，無法回答。")
                 return f"抱歉，資料庫中找不到該藥品。請確認藥品名稱，或直接諮詢醫師/藥師。{DISCLAIMER}"
-            log.info(f"找到藥品 ID: {drug_ids}")
+            log.info(f"步驟 1/5: 找到藥品 ID: {drug_ids}")
 
             analysis = self._analyze_query(q_orig)
             sub_queries, intents = analysis.get("sub_queries", [q_orig]), analysis.get("intents", [])
-            log.info(f"子問題: {sub_queries}, 意圖: {intents}")
+            log.info(f"步驟 2/5: 意圖分析完成。子問題: {sub_queries}, 意圖: {intents}")
 
             all_candidates = self._retrieve_candidates_for_all_queries(drug_ids, sub_queries, intents)
-            log.info(f"所有子查詢共找到 {len(all_candidates)} 個不重複候選 chunks。")
+            log.info(f"步驟 3/5: 檢索完成。所有子查詢共找到 {len(all_candidates)} 個不重複候選 chunks。")
 
             reranked_results = self._rerank_with_crossencoder(q_orig, all_candidates)
-            log.info(f"Reranker 最終選出 {len(reranked_results)} 個高品質候選。")
+            log.info(f"步驟 4/5: Reranker 最終選出 {len(reranked_results)} 個高品質候選。")
 
             context = self._build_context(reranked_results)
             if not context:
+                log.info("沒有足夠的上下文來回答問題。")
                 return f"根據您的問題，找不到相關的具體說明。建議您直接諮詢醫師或藥師以獲得最準確的資訊。{DISCLAIMER}"
                 
             prompt = self._make_final_prompt(q_orig, context, intents)
             answer = self._llm_call([{"role": "user", "content": prompt}])
             
             final_answer = f"{answer.strip()}\n\n{DISCLAIMER}"
+            log.info(f"步驟 5/5: 答案生成完成。答案長度: {len(answer.strip())} 字。")
             log.info(f"===== 查詢處理完成，總耗時: {time.time() - start_time:.2f} 秒 =====")
             return final_answer