diff --git a/eqa_search.py b/eqa_search.py
index 186de10..b3bb83e 100644
--- a/eqa_search.py
+++ b/eqa_search.py
@@ -24,21 +24,18 @@ class EnhancedQASearchConfig:
         # config for sentence_embedding
         self.model = 'all-MiniLM-L6-v2'
         self.openai_api_key = None
-        self.normalize_vec = True
-        self.device = -1
+        self.embedding_device = -1
         # config for search_milvus
         self.host = '127.0.0.1'
         self.port = '19530'
         self.collection_name = 'chatbot'
         self.top_k = 5
-        self.metric_type='IP'
-        self.output_fields=['sentence']
         self.user = None
         self.password = None
         # config for similarity evaluation
         self.threshold = 0.6
-        # self.similarity_evaluation = 'score_filter'
-
+        # config for llm
+        self.llm_device = -1
 
 _hf_models = ops.sentence_embedding.transformers().get_op().supported_model_names()
 _sbert_models = ops.sentence_embedding.sbert().get_op().supported_model_names()
@@ -48,13 +45,10 @@ _openai_models = ['text-embedding-ada-002', 'text-similarity-davinci-001',
 
 
 def _get_embedding_op(config):
-    if config.device == -1:
+    if config.embedding_device == -1:
         device = 'cpu'
     else:
-        device = config.device
-
-    if config.customize_embedding_op is not None:
-        return True, config.customize_embedding_op
+        device = config.embedding_device
 
     if config.model in _hf_models:
         return True, ops.sentence_embedding.transformers(
@@ -75,7 +69,6 @@ def _get_embedding_op(config):
 
 
 def _get_similarity_evaluation_op(config):
-    # if config.similarity_evaluation == 'score_filter':
     return lambda x: [i for i in x if i[1] >= config.threshold]
 
 
@@ -84,8 +77,8 @@ def enhanced_qa_search_pipe(config):
     allow_triton, sentence_embedding_op = _get_embedding_op(config)
     sentence_embedding_config = {}
     if allow_triton:
-        if config.device >= 0:
-            sentence_embedding_config = AutoConfig.TritonGPUConfig(device_ids=[config.device], max_batch_size=128)
+        if config.embedding_device >= 0:
+            sentence_embedding_config = AutoConfig.TritonGPUConfig(device_ids=[config.embedding_device], max_batch_size=128)
         else:
             sentence_embedding_config = AutoConfig.TritonCPUConfig()
 
@@ -94,8 +87,8 @@ def enhanced_qa_search_pipe(config):
         port=config.port,
         collection_name=config.collection_name,
         limit=config.top_k,
-        output_fields=config.output_fields,
-        metric_type=config.metric_type,
+        output_fields=['text'],
+        metric_type='IP',
         user=config.user,
         password=config.password,
     )
@@ -103,16 +96,18 @@ def enhanced_qa_search_pipe(config):
     p = (
         pipe.input('question', 'history')
             .map('question', 'embedding', sentence_embedding_op, config=sentence_embedding_config)
+            .map('embedding', 'embedding', ops.towhee.np_normalize())
+            .map('embedding', 'result', search_milvus_op)
     )
 
-    if config.normalize_vec:
-        p = p.map('embedding', 'embedding', ops.towhee.np_normalize())
-    
-    p = p.map('embedding', 'result', search_milvus_op)
-
     # if config.similarity_evaluation:
     if config.threshold:
         sim_eval_op = _get_similarity_evaluation_op(config)
         p = p.map('result', 'result', sim_eval_op)
+    
+    p = (
+        p.map('result', 'docs', lambda x:[i[2] for i in x])
+            .map(('question', 'docs', 'history'), 'prompt', ops.prompt.question_answer())
+    )
 
-    return p.output('question', 'history', 'result')
+    return p.output('question', 'history', 'docs', 'prompt')