diff --git a/rag/nlp/search.py b/rag/nlp/search.py
index d2129e77f..36fb88843 100644
--- a/rag/nlp/search.py
+++ b/rag/nlp/search.py
@@ -619,6 +619,8 @@ class Dealer:
                 chunks[id2idx[cid]]["similarity"] += sim
                 continue
             chunk = self.dataStore.get(cid, idx_nms, kb_ids)
+            if not chunk:
+                continue
             d = {
                 "chunk_id": cid,
                 "content_ltks": chunk["content_ltks"],
diff --git a/rag/prompts/generator.py b/rag/prompts/generator.py
index de047f17e..5a11294ba 100644
--- a/rag/prompts/generator.py
+++ b/rag/prompts/generator.py
@@ -827,6 +827,11 @@ async def relevant_chunks_with_toc(query: str, toc:list[dict], chat_mdl, topn: i
 META_DATA = load_prompt("meta_data")
 async def gen_metadata(chat_mdl, schema:dict, content:str):
     template = PROMPT_JINJA_ENV.from_string(META_DATA)
+    for k, desc in schema.items():
+        if "enum" in desc and not desc.get("enum"):
+            del desc["enum"]
+        if desc.get("enum"):
+            desc["description"] += "\n** Extracted values must strictly match the given list specified by `enum`. **"
     system_prompt = template.render(content=content, schema=schema)
     user_prompt = "Output: "
     _, msg = message_fit_in(form_message(system_prompt, user_prompt), chat_mdl.max_length)
diff --git a/rag/svr/task_executor.py b/rag/svr/task_executor.py
index ee041da1e..371bd8185 100644
--- a/rag/svr/task_executor.py
+++ b/rag/svr/task_executor.py
@@ -374,13 +374,13 @@ async def build_chunks(task, progress_callback):
         chat_mdl = LLMBundle(task["tenant_id"], LLMType.CHAT, llm_name=task["llm_id"], lang=task["language"])
 
         async def gen_metadata_task(chat_mdl, d):
-            cached = get_llm_cache(chat_mdl.llm_name, d["content_with_weight"], "metadata", {})
+            cached = get_llm_cache(chat_mdl.llm_name, d["content_with_weight"], "metadata", task["parser_config"]["metadata"])
             if not cached:
                 async with chat_limiter:
                     cached = await gen_metadata(chat_mdl,
                                                 metadata_schema(task["parser_config"]["metadata"]),
                                                 d["content_with_weight"])
-                set_llm_cache(chat_mdl.llm_name, d["content_with_weight"], cached, "metadata", {})
+                set_llm_cache(chat_mdl.llm_name, d["content_with_weight"], cached, "metadata", task["parser_config"]["metadata"])
             if cached:
                 d["metadata_obj"] = cached
         tasks = []