diff --git a/comps/agent/langchain/src/strategy/planexec/planner.py b/comps/agent/langchain/src/strategy/planexec/planner.py
index c33c906f4..4d872e9e7 100644
--- a/comps/agent/langchain/src/strategy/planexec/planner.py
+++ b/comps/agent/langchain/src/strategy/planexec/planner.py
@@ -13,12 +13,12 @@
 from langchain_core.output_parsers.openai_tools import PydanticToolsParser
 from langchain_core.outputs import Generation
 from langchain_core.prompts import PromptTemplate
-from langchain_core.pydantic_v1 import BaseModel, Field
 from langchain_core.utils.json import parse_partial_json
 from langchain_huggingface import ChatHuggingFace
 from langgraph.checkpoint.memory import MemorySaver
 from langgraph.graph import END, START, StateGraph
 from langgraph.graph.message import add_messages
+from pydantic import BaseModel, Field
 
 from ...global_var import threads_global_kv
 from ...utils import has_multi_tool_inputs, tool_renderer
diff --git a/comps/agent/langchain/src/strategy/ragagent/planner.py b/comps/agent/langchain/src/strategy/ragagent/planner.py
index 8e3f27255..7f967e4ad 100644
--- a/comps/agent/langchain/src/strategy/ragagent/planner.py
+++ b/comps/agent/langchain/src/strategy/ragagent/planner.py
@@ -8,13 +8,13 @@
 from langchain_core.output_parsers import JsonOutputParser, StrOutputParser
 from langchain_core.output_parsers.openai_tools import PydanticToolsParser
 from langchain_core.prompts import PromptTemplate
-from langchain_core.pydantic_v1 import BaseModel, Field
 from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint
 from langchain_openai import ChatOpenAI
 from langgraph.checkpoint.memory import MemorySaver
 from langgraph.graph import END, START, StateGraph
 from langgraph.graph.message import add_messages
 from langgraph.prebuilt import ToolNode, tools_condition
+from pydantic import BaseModel, Field
 
 from ..base_agent import BaseAgent
 from .prompt import DOC_GRADER_PROMPT, RAG_PROMPT, QueryWriterLlamaPrompt
@@ -366,7 +366,7 @@ def __call__(self, state) -> Literal["generate", "rewrite"]:
         print("@@@@ Score: ", score)
 
         # if score.startswith("yes"):
-        if "yes" in score:
+        if "yes" in score.lower():
             print("---DECISION: DOCS RELEVANT---")
             return {"doc_score": "generate"}
 
diff --git a/comps/agent/langchain/src/tools.py b/comps/agent/langchain/src/tools.py
index 6d3af9ebe..9188f384e 100644
--- a/comps/agent/langchain/src/tools.py
+++ b/comps/agent/langchain/src/tools.py
@@ -7,11 +7,9 @@
 import sys
 
 import yaml
-
-# from pydantic import create_model, Field
-from langchain.pydantic_v1 import BaseModel, Field, create_model
 from langchain.tools import BaseTool, StructuredTool
 from langchain_community.agent_toolkits.load_tools import load_tools
+from pydantic import BaseModel, Field, create_model
 
 
 def generate_request_function(url):
diff --git a/tests/agent/test_agent_langchain_on_intel_hpu.sh b/tests/agent/test_agent_langchain_on_intel_hpu.sh
index a038e52cc..a8b791474 100644
--- a/tests/agent/test_agent_langchain_on_intel_hpu.sh
+++ b/tests/agent/test_agent_langchain_on_intel_hpu.sh
@@ -8,7 +8,7 @@ WORKPATH=$(dirname "$PWD")
 LOG_PATH="$WORKPATH/tests"
 ip_address=$(hostname -I | awk '{print $1}')
 tgi_port=8085
-tgi_volume=$WORKPATH/data #/data2/cache/hub/Meta-Llama-3.1-70B-Instruct #$HF_CACHE_DIR #
+tgi_volume=$WORKPATH/data
 
 export agent_image="opea/agent-langchain:comps"
 export agent_container_name="test-comps-agent-endpoint"
@@ -39,10 +39,9 @@ function build_docker_images() {
 }
 
 function start_tgi_service() {
-    # redis endpoint
     echo "token is ${HF_TOKEN}"
 
-    #single card
+    #multi cards
     echo "start tgi gaudi service"
     docker run -d --runtime=habana --name "test-comps-tgi-gaudi-service" -p $tgi_port:80 -v $tgi_volume:/data -e HF_TOKEN=$HF_TOKEN -e HABANA_VISIBLE_DEVICES=0,1,2,3 -e OMPI_MCA_btl_vader_single_copy_mechanism=none -e PT_HPU_ENABLE_LAZY_COLLECTIVES=true -e http_proxy=$http_proxy -e https_proxy=$https_proxy --cap-add=sys_nice --ipc=host ghcr.io/huggingface/tgi-gaudi:2.0.5 --model-id $model --max-input-tokens 4096 --max-total-tokens 8192 --sharded true --num-shard 4
     sleep 5s
@@ -62,7 +61,6 @@ function start_tgi_service() {
 
 function start_react_langchain_agent_service() {
     echo "Starting react_langchain agent microservice"
-    # docker run -d --runtime=runc --name="test-comps-agent-endpoint" -v $WORKPATH/comps/agent/langchain/tools:/home/user/comps/agent/langchain/tools -p 9095:9095 --ipc=host -e port=9095 -e HUGGINGFACEHUB_API_TOKEN=${HUGGINGFACEHUB_API_TOKEN} -e model=${model} -e strategy=react_langchain -e llm_endpoint_url=http://${ip_address}:${tgi_port} -e llm_engine=tgi -e recursion_limit=10 -e require_human_feedback=false -e tools=/home/user/comps/agent/langchain/tools/custom_tools.yaml opea/agent-langchain:comps
     docker compose -f $WORKPATH/tests/agent/react_langchain.yaml up -d
     sleep 5s
     docker logs test-comps-agent-endpoint
@@ -72,7 +70,6 @@ function start_react_langchain_agent_service() {
 
 function start_react_langgraph_agent_service() {
     echo "Starting react_langgraph agent microservice"
-    # docker run -d --runtime=runc --name="test-comps-agent-endpoint" -v $WORKPATH/comps/agent/langchain/tools:/home/user/comps/agent/langchain/tools -p 9095:9095 --ipc=host -e HUGGINGFACEHUB_API_TOKEN=${HUGGINGFACEHUB_API_TOKEN} -e model=${model} -e strategy=react_llama -e llm_endpoint_url=http://${ip_address}:${tgi_port} -e llm_engine=tgi -e recursion_limit=10 -e require_human_feedback=false -e tools=/home/user/comps/agent/langchain/tools/custom_tools.yaml opea/agent-langchain:comps
     docker compose -f $WORKPATH/tests/agent/reactllama.yaml up -d
     sleep 5s
     docker logs test-comps-agent-endpoint
@@ -90,7 +87,6 @@ function start_react_langgraph_agent_service_openai() {
 
 function start_ragagent_agent_service() {
     echo "Starting rag agent microservice"
-    # docker run -d --runtime=runc --name="test-comps-agent-endpoint" -v $WORKPATH/comps/agent/langchain/tools:/home/user/comps/agent/langchain/tools -p 9095:9095 --ipc=host -e HUGGINGFACEHUB_API_TOKEN=${HUGGINGFACEHUB_API_TOKEN} -e model=${model} -e strategy=rag_agent_llama -e llm_endpoint_url=http://${ip_address}:${tgi_port} -e llm_engine=tgi -e recursion_limit=10 -e require_human_feedback=false -e tools=/home/user/comps/agent/langchain/tools/custom_tools.yaml opea/agent-langchain:comps
     docker compose -f $WORKPATH/tests/agent/ragagent.yaml up -d
     sleep 5s
     docker logs test-comps-agent-endpoint