From 7ffaf247f74393cf0860b849e98da3dca0f639a4 Mon Sep 17 00:00:00 2001
From: "chen, suyue" <suyue.chen@intel.com>
Date: Fri, 16 Aug 2024 19:58:57 +0800
Subject: [PATCH 01/11] adapt GenAIExample test structure refine (#510)

Signed-off-by: chensuyue <suyue.chen@intel.com>
---
 .github/workflows/pr-examples-test.yml | 19 +++++++++----------
 1 file changed, 9 insertions(+), 10 deletions(-)

diff --git a/.github/workflows/pr-examples-test.yml b/.github/workflows/pr-examples-test.yml
index 92354c5c2..a1f41f9f6 100644
--- a/.github/workflows/pr-examples-test.yml
+++ b/.github/workflows/pr-examples-test.yml
@@ -41,26 +41,25 @@ jobs:
         env:
           HUGGINGFACEHUB_API_TOKEN: ${{ secrets.HUGGINGFACEHUB_API_TOKEN }}
         run: |
+          cd ../ && rm -rf GenAIExamples
           git clone https://github.com/opea-project/GenAIExamples.git
-          cd ${{ github.workspace }}/GenAIExamples/ChatQnA/docker/gaudi
-          sed -i "s#:latest#:comps#g" compose.yaml
-          cat compose.yaml
+          cd GenAIExamples/ChatQnA/docker
+          cp -r ${{ github.workspace }}/../GenAIComps .
 
-          cd ${{ github.workspace }}/GenAIExamples/ChatQnA/tests
-          GenAIComps_dir=${{github.workspace}}
+          cd ../tests
           sed -i '/GenAIComps.git/d' test_chatqna_on_gaudi.sh
-          sed -i "s#cd GenAIComps#cd ${GenAIComps_dir}#g" test_chatqna_on_gaudi.sh
-          sed -i "s#docker build -t#docker build --no-cache -q -t#g" test_chatqna_on_gaudi.sh
-          sed -i "s#:latest#:comps#g" test_chatqna_on_gaudi.sh
           cat test_chatqna_on_gaudi.sh
 
           echo "Run test..."
+          export IMAGE_TAG="comps"
           timeout 50m bash test_chatqna_on_gaudi.sh
 
+          echo "LOG_PATH=$(pwd)/*.log" >> $GITHUB_ENV
+
       - name: Clean up container
         if: cancelled() || failure()
         run: |
-          cd ${{ github.workspace }}/GenAIExamples/ChatQnA/docker/gaudi
+          cd ${{ github.workspace }}/../GenAIExamples/ChatQnA/docker/gaudi
           docker compose stop && docker compose rm -f
           docker system prune -f
 
@@ -69,4 +68,4 @@ jobs:
         uses: actions/upload-artifact@v4
         with:
           name: "Examples-Test-Logs"
-          path: ${{ github.workspace }}/GenAIExamples/ChatQnA/tests/*.log
+          path: ${{ env.LOG_PATH }}

From 10433364bb425c53fa96b302435bb3d6ebf61575 Mon Sep 17 00:00:00 2001
From: "chen, suyue" <suyue.chen@intel.com>
Date: Fri, 16 Aug 2024 20:11:30 +0800
Subject: [PATCH 02/11] add sudo in wf remove (#511)

Signed-off-by: chensuyue <suyue.chen@intel.com>
---
 .github/workflows/pr-examples-test.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/pr-examples-test.yml b/.github/workflows/pr-examples-test.yml
index a1f41f9f6..302d6f8e1 100644
--- a/.github/workflows/pr-examples-test.yml
+++ b/.github/workflows/pr-examples-test.yml
@@ -41,7 +41,7 @@ jobs:
         env:
           HUGGINGFACEHUB_API_TOKEN: ${{ secrets.HUGGINGFACEHUB_API_TOKEN }}
         run: |
-          cd ../ && rm -rf GenAIExamples
+          cd ../ && sudo rm -rf GenAIExamples
           git clone https://github.com/opea-project/GenAIExamples.git
           cd GenAIExamples/ChatQnA/docker
           cp -r ${{ github.workspace }}/../GenAIComps .

From 22075037a0c9b24bfd14fe8fc9f56a2830cc088a Mon Sep 17 00:00:00 2001
From: "Sun, Xuehao" <xuehao.sun@intel.com>
Date: Fri, 16 Aug 2024 20:53:49 +0800
Subject: [PATCH 03/11] Fix requirement actions (#500)

Signed-off-by: Sun, Xuehao <xuehao.sun@intel.com>
---
 .github/workflows/manual-freeze-requirements.yml |  2 +-
 .github/workflows/scripts/freeze_requirements.sh | 16 ++++++++++------
 comps/chathistory/mongo/requirements.txt         |  2 +-
 .../dataprep/redis/llama_index/requirements.txt  |  2 +-
 4 files changed, 13 insertions(+), 9 deletions(-)

diff --git a/.github/workflows/manual-freeze-requirements.yml b/.github/workflows/manual-freeze-requirements.yml
index ff958bbb2..8f00ca423 100644
--- a/.github/workflows/manual-freeze-requirements.yml
+++ b/.github/workflows/manual-freeze-requirements.yml
@@ -34,5 +34,5 @@ jobs:
       - name: Commit changes
         run: |
           git add .
-          git commit -m "Freeze requirements"
+          git commit -s -m "Freeze requirements"
           git push
diff --git a/.github/workflows/scripts/freeze_requirements.sh b/.github/workflows/scripts/freeze_requirements.sh
index 7c3b1d18a..431dadaef 100644
--- a/.github/workflows/scripts/freeze_requirements.sh
+++ b/.github/workflows/scripts/freeze_requirements.sh
@@ -15,16 +15,20 @@ function freeze() {
         --output-file "$folder/freeze.txt" \
         "$file"
     echo "::endgroup::"
+
     if [[ -e "$folder/freeze.txt" ]]; then
         if [[ "$keep_origin_packages" == "true" ]]; then
-            sed -i '/^\s*#/d; s/#.*//; /^\s*$/d' "$file"
-            sed -i '/^\s*#/d; s/#.*//; /^\s*$/d' "$folder/freeze.txt"
-
-            packages1=$(cut -d'=' -f1 "$file" | tr '[:upper:]' '[:lower:]' | sed 's/[-_]/-/g')
+            # fix corner cases
+            sed -i '/^\s*#/d; s/#.*//; /^\s*$/d; s/ //g' "$file"
+            sed -i '/^\s*#/d; s/#.*//; /^\s*$/d; s/ //g; s/huggingface-hub\[inference\]/huggingface-hub/g; s/uvicorn\[standard\]/uvicorn/g' "$folder/freeze.txt"
+            if grep -q '^transformers$' $file && ! grep -q '^transformers\[sentencepiece\]$' $file; then
+                sed -i "s/transformers\[sentencepiece\]/transformers/" "$folder/freeze.txt"
+            fi
+            packages1=$(tr '><' '=' <"$file" | cut -d'=' -f1 | tr '[:upper:]' '[:lower:]' | sed 's/[-_]/-/g')
             packages2=$(cut -d'=' -f1 "$folder/freeze.txt" | tr '[:upper:]' '[:lower:]' | sed 's/[-_]/-/g')
             common_packages=$(comm -12 <(echo "$packages2" | sort) <(echo "$packages1" | sort))
-
-            rm "$file"
+            grep '^git\+' "$file" >temp_file || touch temp_file
+            rm -rf "$file" && mv temp_file "$file"
             while IFS= read -r line; do
                 package=$(echo "$line" | cut -d'=' -f1)
                 package_transformed=$(echo "$package" | tr '[:upper:]' '[:lower:]' | sed 's/[_-]/-/g')
diff --git a/comps/chathistory/mongo/requirements.txt b/comps/chathistory/mongo/requirements.txt
index b0dec78c0..aa08f761a 100644
--- a/comps/chathistory/mongo/requirements.txt
+++ b/comps/chathistory/mongo/requirements.txt
@@ -1 +1 @@
-motor==3.4.0
+motor==3.4.0
diff --git a/comps/dataprep/redis/llama_index/requirements.txt b/comps/dataprep/redis/llama_index/requirements.txt
index bb2f38b44..ad75869c1 100644
--- a/comps/dataprep/redis/llama_index/requirements.txt
+++ b/comps/dataprep/redis/llama_index/requirements.txt
@@ -2,7 +2,7 @@ docarray[full]
 fastapi
 huggingface_hub
 langsmith
-llama-index 
+llama-index
 llama-index-embeddings-huggingface==0.2.0
 llama-index-readers-file
 llama-index-vector-stores-redis

From 72a255303630d011c72f358b0a513f1161d2a589 Mon Sep 17 00:00:00 2001
From: Harsha Ramayanam <harsha.ramayanam@intel.com>
Date: Fri, 16 Aug 2024 09:14:09 -0700
Subject: [PATCH 04/11] Fix image in docker compose yaml to use the built
 docker image tag from the README (#498)

* Update README.md

The `ray_serve:habana` doesn't exist (yet) in docker hub

* Fixed the image in docker compose yaml

Signed-off-by: Harsha Ramayanam <harsha.ramayanam@intel.com>

---------

Signed-off-by: Harsha Ramayanam <harsha.ramayanam@intel.com>
Co-authored-by: chen, suyue <suyue.chen@intel.com>
---
 comps/llms/README.md                                   | 2 +-
 comps/llms/text-generation/tgi/docker_compose_llm.yaml | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/comps/llms/README.md b/comps/llms/README.md
index 584f2ba12..9c3867f03 100644
--- a/comps/llms/README.md
+++ b/comps/llms/README.md
@@ -40,7 +40,7 @@ docker run -it --name vllm_service -p 8008:80 -e HF_TOKEN=${HUGGINGFACEHUB_API_T
 ```bash
 export HUGGINGFACEHUB_API_TOKEN=${your_hf_api_token}
 export TRUST_REMOTE_CODE=True
-docker run -it --runtime=habana --name ray_serve_service -e OMPI_MCA_btl_vader_single_copy_mechanism=none --cap-add=sys_nice --ipc=host -p 8008:80 -e HUGGINGFACEHUB_API_TOKEN=$HUGGINGFACEHUB_API_TOKEN -e TRUST_REMOTE_CODE=$TRUST_REMOTE_CODE ray_serve:habana /bin/bash -c "ray start --head && python api_server_openai.py --port_number 80 --model_id_or_path ${your_hf_llm_model} --chat_processor ${your_hf_chatprocessor}"
+docker run -it --runtime=habana --name ray_serve_service -e OMPI_MCA_btl_vader_single_copy_mechanism=none --cap-add=sys_nice --ipc=host -p 8008:80 -e HUGGINGFACEHUB_API_TOKEN=$HUGGINGFACEHUB_API_TOKEN -e TRUST_REMOTE_CODE=$TRUST_REMOTE_CODE opea/llm-ray:latest /bin/bash -c "ray start --head && python api_server_openai.py --port_number 80 --model_id_or_path ${your_hf_llm_model} --chat_processor ${your_hf_chatprocessor}"
 ```
 
 ## 1.3 Verify the LLM Service
diff --git a/comps/llms/text-generation/tgi/docker_compose_llm.yaml b/comps/llms/text-generation/tgi/docker_compose_llm.yaml
index c1ab98dcc..9551979a7 100644
--- a/comps/llms/text-generation/tgi/docker_compose_llm.yaml
+++ b/comps/llms/text-generation/tgi/docker_compose_llm.yaml
@@ -14,7 +14,7 @@ services:
     shm_size: 1g
     command: --model-id ${LLM_MODEL_ID}
   llm:
-    image: opea/gen-ai-comps:llm-tgi-server
+    image: opea/llm-tgi:latest
     container_name: llm-tgi-server
     ports:
       - "9000:9000"

From 5bd8bda9466d91d1c43cae2a0c2c4942abce7985 Mon Sep 17 00:00:00 2001
From: David Kinder <david.b.kinder@intel.com>
Date: Fri, 16 Aug 2024 20:54:51 -0700
Subject: [PATCH 05/11] doc: remove use of unknown highlight language (#489)

This file use ```log  but there's no such syntax highlight language.
Leave it empty instead.

Signed-off-by: David B. Kinder <david.b.kinder@intel.com>
---
 comps/vectorstores/langchain/chroma/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/comps/vectorstores/langchain/chroma/README.md b/comps/vectorstores/langchain/chroma/README.md
index d7399b8fb..c64b094f6 100644
--- a/comps/vectorstores/langchain/chroma/README.md
+++ b/comps/vectorstores/langchain/chroma/README.md
@@ -18,7 +18,7 @@ docker compose up -d
 
 Upon starting the server, you should see log outputs similar to the following:
 
-```log
+```
 server-1  | Starting 'uvicorn chromadb.app:app' with args: --workers 1 --host 0.0.0.0 --port 8000 --proxy-headers --log-config chromadb/log_config.yml --timeout-keep-alive 30
 server-1  | INFO:     [02-08-2024 07:03:19] Set chroma_server_nofile to 65536
 server-1  | INFO:     [02-08-2024 07:03:19] Anonymized telemetry enabled. See                     https://docs.trychroma.com/telemetry for more information.

From 8eb8b6a44ed42c325441ef257b2ebdf0e3225404 Mon Sep 17 00:00:00 2001
From: "Wang, Kai Lawrence" <109344418+wangkl2@users.noreply.github.com>
Date: Sat, 17 Aug 2024 23:33:24 +0800
Subject: [PATCH 06/11] Add cmds to restart ollama service and add proxy
 settings while launching docker (#438)

Signed-off-by: Wang, Kai Lawrence <kai.lawrence.wang@intel.com>
---
 comps/llms/text-generation/ollama/README.md | 28 +++++++++++++--------
 1 file changed, 18 insertions(+), 10 deletions(-)

diff --git a/comps/llms/text-generation/ollama/README.md b/comps/llms/text-generation/ollama/README.md
index 1ad636098..fc7de914e 100644
--- a/comps/llms/text-generation/ollama/README.md
+++ b/comps/llms/text-generation/ollama/README.md
@@ -15,17 +15,25 @@ Follow [these instructions](https://github.com/ollama/ollama) to set up and run
 Note:
 Special settings are necessary to pull models behind the proxy.
 
-```bash
-sudo vim /etc/systemd/system/ollama.service
-```
+- Step1: Modify the ollama service configure file.
 
-Add your proxy to the above configure file.
+  ```bash
+  sudo vim /etc/systemd/system/ollama.service
+  ```
 
-```markdown
-[Service]
-Environment="http_proxy=${your_proxy}"
-Environment="https_proxy=${your_proxy}"
-```
+  Add your proxy to the above configure file.
+
+  ```markdown
+  [Service]
+  Environment="http_proxy=${your_proxy}"
+  Environment="https_proxy=${your_proxy}"
+  ```
+
+- Step2: Restart the ollama service.
+  ```bash
+  sudo systemctl daemon-reload
+  sudo systemctl restart ollama
+  ```
 
 ## Usage
 
@@ -56,7 +64,7 @@ docker build --no-cache -t opea/llm-ollama:latest --build-arg https_proxy=$https
 # Run the Ollama Microservice
 
 ```bash
-docker run --network host opea/llm-ollama:latest
+docker run --network host -e http_proxy=$http_proxy -e https_proxy=$https_proxy opea/llm-ollama:latest
 ```
 
 # Consume the Ollama Microservice

From 2b14c637f41cdbaf4fdc117ede157355c96c3bab Mon Sep 17 00:00:00 2001
From: ZePan110 <ze.pan@intel.com>
Date: Sat, 17 Aug 2024 23:35:16 +0800
Subject: [PATCH 07/11] Fix waring. (#504)

Signed-off-by: zepan <ze.pan@intel.com>
---
 tests/test_agent_langchain.sh                    | 2 +-
 tests/test_asr.sh                                | 4 ++--
 tests/test_chathistory_mongo.sh                  | 2 +-
 tests/test_dataprep_milvus.sh                    | 4 ++--
 tests/test_dataprep_pgvector.sh                  | 2 +-
 tests/test_dataprep_pinecone.sh                  | 2 +-
 tests/test_dataprep_qdrant_langchain.sh          | 2 +-
 tests/test_dataprep_redis_langchain.sh           | 2 +-
 tests/test_dataprep_redis_langchain_ray.sh       | 2 +-
 tests/test_dataprep_redis_llama_index.sh         | 2 +-
 tests/test_embeddings_langchain-mosec.sh         | 4 ++--
 tests/test_embeddings_langchain.sh               | 2 +-
 tests/test_embeddings_llama_index.sh             | 2 +-
 tests/test_guardrails_llama_guard.sh             | 2 +-
 tests/test_guardrails_pii_detection.sh           | 2 +-
 tests/test_llms_faq-generation_tgi.sh            | 2 +-
 tests/test_llms_summarization_tgi.sh             | 2 +-
 tests/test_llms_text-generation_native.sh        | 2 +-
 tests/test_llms_text-generation_tgi.sh           | 2 +-
 tests/test_llms_text-generation_vllm-openvino.sh | 2 +-
 tests/test_llms_text-generation_vllm-ray.sh      | 4 ++--
 tests/test_llms_text-generation_vllm.sh          | 4 ++--
 tests/test_lvms_llava.sh                         | 4 ++--
 tests/test_lvms_tgi_llava_next.sh                | 4 ++--
 tests/test_prompt_registry_mongo.sh              | 2 +-
 tests/test_reranks_fastrag.sh                    | 2 +-
 tests/test_reranks_langchain-mosec.sh            | 4 ++--
 tests/test_reranks_tei.sh                        | 2 +-
 tests/test_retrievers_haystack_qdrant.sh         | 2 +-
 tests/test_retrievers_langchain_pgvector.sh      | 2 +-
 tests/test_retrievers_langchain_pinecone.sh      | 2 +-
 tests/test_retrievers_langchain_redis.sh         | 2 +-
 tests/test_tts.sh                                | 4 ++--
 tests/test_web_retrievers_langchain_chroma.sh    | 2 +-
 34 files changed, 43 insertions(+), 43 deletions(-)

diff --git a/tests/test_agent_langchain.sh b/tests/test_agent_langchain.sh
index c08024847..0c4db337e 100644
--- a/tests/test_agent_langchain.sh
+++ b/tests/test_agent_langchain.sh
@@ -13,7 +13,7 @@ function build_docker_images() {
     cd $WORKPATH
     echo $WORKPATH
     docker build --no-cache -t opea/comps-agent-langchain:comps -f comps/agent/langchain/docker/Dockerfile .
-    if $? ; then
+    if [ $? -ne 0 ]; then
         echo "opea/comps-agent-langchain built fail"
         exit 1
     else
diff --git a/tests/test_asr.sh b/tests/test_asr.sh
index 305202c10..7334a18f1 100644
--- a/tests/test_asr.sh
+++ b/tests/test_asr.sh
@@ -11,14 +11,14 @@ function build_docker_images() {
     cd $WORKPATH
     echo $(pwd)
     docker build --no-cache -t opea/whisper:comps -f comps/asr/whisper/Dockerfile .
-    if $? ; then
+    if [ $? -ne 0 ]; then
         echo "opea/whisper built fail"
         exit 1
     else
         echo "opea/whisper built successful"
     fi
     docker build --no-cache -t opea/asr:comps -f comps/asr/Dockerfile .
-    if $? ; then
+    if [ $? -ne 0 ]; then
         echo "opea/asr built fail"
         exit 1
     else
diff --git a/tests/test_chathistory_mongo.sh b/tests/test_chathistory_mongo.sh
index 41116f886..c821fc05e 100755
--- a/tests/test_chathistory_mongo.sh
+++ b/tests/test_chathistory_mongo.sh
@@ -18,7 +18,7 @@ function build_docker_images() {
     docker run -d -p 27017:27017 --name=test-comps-mongo mongo:latest
 
     docker build --no-cache -t opea/chathistory-mongo-server:comps --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/chathistory/mongo/docker/Dockerfile .
-    if $? ; then
+    if [ $? -ne 0 ]; then
         echo "opea/chathistory-mongo-server built fail"
         exit 1
     else
diff --git a/tests/test_dataprep_milvus.sh b/tests/test_dataprep_milvus.sh
index 23f275712..727ef81e1 100644
--- a/tests/test_dataprep_milvus.sh
+++ b/tests/test_dataprep_milvus.sh
@@ -13,7 +13,7 @@ function build_docker_images() {
     echo $(pwd)
     # langchain mosec embedding image
     docker build --no-cache -t opea/langchain-mosec:comps --build-arg http_proxy=$http_proxy --build-arg https_proxy=$https_proxy -f comps/embeddings/langchain-mosec/mosec-docker/Dockerfile .
-    if $? ; then
+    if [ $? -ne 0 ]; then
         echo "opea/langchain-mosec built fail"
         exit 1
     else
@@ -21,7 +21,7 @@ function build_docker_images() {
     fi
     # dataprep milvus image
     docker build --no-cache -t opea/dataprep-milvus:comps --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/dataprep/milvus/docker/Dockerfile .
-    if $? ; then
+    if [ $? -ne 0 ]; then
         echo "opea/dataprep-milvus built fail"
         exit 1
     else
diff --git a/tests/test_dataprep_pgvector.sh b/tests/test_dataprep_pgvector.sh
index 3ea3df9f8..ca5649fe9 100755
--- a/tests/test_dataprep_pgvector.sh
+++ b/tests/test_dataprep_pgvector.sh
@@ -17,7 +17,7 @@ function build_docker_images() {
 
     # build dataprep image for pgvector
     docker build --no-cache -t opea/dataprep-pgvector:comps --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f $WORKPATH/comps/dataprep/pgvector/langchain/docker/Dockerfile .
-    if $? ; then
+    if [ $? -ne 0 ]; then
         echo "opea/dataprep-pgvector built fail"
         exit 1
     else
diff --git a/tests/test_dataprep_pinecone.sh b/tests/test_dataprep_pinecone.sh
index 1930d2798..4d0f64fac 100755
--- a/tests/test_dataprep_pinecone.sh
+++ b/tests/test_dataprep_pinecone.sh
@@ -11,7 +11,7 @@ function build_docker_images() {
 
     # build dataprep image for pinecone
     docker build --no-cache -t opea/dataprep-pinecone:comps --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f $WORKPATH/comps/dataprep/pinecone/docker/Dockerfile .
-    if $? ; then
+    if [ $? -ne 0 ]; then
         echo "opea/dataprep-pinecone built fail"
         exit 1
     else
diff --git a/tests/test_dataprep_qdrant_langchain.sh b/tests/test_dataprep_qdrant_langchain.sh
index e112438fd..632d7e06f 100644
--- a/tests/test_dataprep_qdrant_langchain.sh
+++ b/tests/test_dataprep_qdrant_langchain.sh
@@ -13,7 +13,7 @@ function build_docker_images() {
 
     # dataprep qdrant image
     docker build --no-cache -t opea/dataprep-qdrant:comps --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/dataprep/qdrant/docker/Dockerfile .
-    if $? ; then
+    if [ $? -ne 0 ]; then
         echo "opea/dataprep-qdrant built fail"
         exit 1
     else
diff --git a/tests/test_dataprep_redis_langchain.sh b/tests/test_dataprep_redis_langchain.sh
index 1a9831dc5..82c25b9d3 100644
--- a/tests/test_dataprep_redis_langchain.sh
+++ b/tests/test_dataprep_redis_langchain.sh
@@ -12,7 +12,7 @@ function build_docker_images() {
     cd $WORKPATH
     echo $(pwd)
     docker build --no-cache -t opea/dataprep-redis:comps --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/dataprep/redis/langchain/docker/Dockerfile .
-    if $? ; then
+    if [ $? -ne 0 ]; then
         echo "opea/dataprep-redis built fail"
         exit 1
     else
diff --git a/tests/test_dataprep_redis_langchain_ray.sh b/tests/test_dataprep_redis_langchain_ray.sh
index 0980eafc1..9b1303d90 100644
--- a/tests/test_dataprep_redis_langchain_ray.sh
+++ b/tests/test_dataprep_redis_langchain_ray.sh
@@ -12,7 +12,7 @@ function build_docker_images() {
     echo "Building the docker images"
     cd $WORKPATH
     docker build --no-cache -t opea/dataprep-on-ray-redis:comps --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/dataprep/redis/langchain_ray/docker/Dockerfile .
-    if $? ; then
+    if [ $? -ne 0 ]; then
         echo "opea/dataprep-on-ray-redis built fail"
         exit 1
     else
diff --git a/tests/test_dataprep_redis_llama_index.sh b/tests/test_dataprep_redis_llama_index.sh
index 13f0daa26..a7d20160b 100644
--- a/tests/test_dataprep_redis_llama_index.sh
+++ b/tests/test_dataprep_redis_llama_index.sh
@@ -12,7 +12,7 @@ function build_docker_images() {
     cd $WORKPATH
     echo $(pwd)
     docker build --no-cache -t opea/dataprep-redis-llama-index:comps --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/dataprep/redis/llama_index/docker/Dockerfile .
-    if $? ; then
+    if [ $? -ne 0 ]; then
         echo "opea/dataprep-redis-llama-index built fail"
         exit 1
     else
diff --git a/tests/test_embeddings_langchain-mosec.sh b/tests/test_embeddings_langchain-mosec.sh
index 0b6e6cc47..95858118b 100644
--- a/tests/test_embeddings_langchain-mosec.sh
+++ b/tests/test_embeddings_langchain-mosec.sh
@@ -11,7 +11,7 @@ function build_mosec_docker_images() {
     cd $WORKPATH
     echo $(pwd)
     docker build --build-arg http_proxy=$http_proxy --build-arg https_proxy=$https_proxy --no-cache -t opea/embedding-langchain-mosec-endpoint:comps -f comps/embeddings/langchain-mosec/mosec-docker/Dockerfile .
-    if $? ; then
+    if [ $? -ne 0 ]; then
         echo "opea/embedding-langchain-mosec-endpoint built fail"
         exit 1
     else
@@ -23,7 +23,7 @@ function build_docker_images() {
     cd $WORKPATH
     echo $(pwd)
     docker build --build-arg http_proxy=$http_proxy --build-arg https_proxy=$https_proxy --no-cache -t opea/embedding-langchain-mosec:comps -f comps/embeddings/langchain-mosec/docker/Dockerfile .
-    if $? ; then
+    if [ $? -ne 0 ]; then
         echo "opea/embedding-langchain-mosec built fail"
         exit 1
     else
diff --git a/tests/test_embeddings_langchain.sh b/tests/test_embeddings_langchain.sh
index caa7301ea..6c6241226 100644
--- a/tests/test_embeddings_langchain.sh
+++ b/tests/test_embeddings_langchain.sh
@@ -11,7 +11,7 @@ function build_docker_images() {
     cd $WORKPATH
     echo $(pwd)
     docker build --no-cache -t opea/embedding-tei:comps -f comps/embeddings/langchain/docker/Dockerfile .
-    if $? ; then
+    if [ $? -ne 0 ]; then
         echo "opea/embedding-tei built fail"
         exit 1
     else
diff --git a/tests/test_embeddings_llama_index.sh b/tests/test_embeddings_llama_index.sh
index 8e47e8bb6..048726044 100644
--- a/tests/test_embeddings_llama_index.sh
+++ b/tests/test_embeddings_llama_index.sh
@@ -12,7 +12,7 @@ function build_docker_images() {
     cd $WORKPATH
     echo $(pwd)
     docker build --no-cache -t opea/embedding-tei-llamaindex:comps --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/embeddings/llama_index/docker/Dockerfile .
-    if $? ; then
+    if [ $? -ne 0 ]; then
         echo "opea/embedding-tei-llamaindex built fail"
         exit 1
     else
diff --git a/tests/test_guardrails_llama_guard.sh b/tests/test_guardrails_llama_guard.sh
index 515ce2a69..0e7980384 100644
--- a/tests/test_guardrails_llama_guard.sh
+++ b/tests/test_guardrails_llama_guard.sh
@@ -12,7 +12,7 @@ function build_docker_images() {
     cd $WORKPATH
     docker pull ghcr.io/huggingface/tgi-gaudi:2.0.1
     docker build --no-cache -t opea/guardrails-tgi:comps --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/guardrails/llama_guard/docker/Dockerfile .
-    if $? ; then
+    if [ $? -ne 0 ]; then
         echo "opea/guardrails-tgi built fail"
         exit 1
     else
diff --git a/tests/test_guardrails_pii_detection.sh b/tests/test_guardrails_pii_detection.sh
index fef024a3d..178b6ea23 100644
--- a/tests/test_guardrails_pii_detection.sh
+++ b/tests/test_guardrails_pii_detection.sh
@@ -11,7 +11,7 @@ function build_docker_images() {
     echo "Start building docker images for microservice"
     cd $WORKPATH
     docker build --no-cache -t opea/guardrails-pii-detection:comps --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/guardrails/pii_detection/docker/Dockerfile .
-    if $? ; then
+    if [ $? -ne 0 ]; then
         echo "opea/guardrails-pii-detection built fail"
         exit 1
     else
diff --git a/tests/test_llms_faq-generation_tgi.sh b/tests/test_llms_faq-generation_tgi.sh
index c36ea3430..de5ec3466 100755
--- a/tests/test_llms_faq-generation_tgi.sh
+++ b/tests/test_llms_faq-generation_tgi.sh
@@ -11,7 +11,7 @@ LOG_PATH="$WORKPATH/tests"
 function build_docker_images() {
     cd $WORKPATH
     docker build --no-cache -t opea/llm-faqgen-tgi:comps --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/llms/faq-generation/tgi/Dockerfile .
-    if $? ; then
+    if [ $? -ne 0 ]; then
         echo "opea/llm-faqgen-tgi built fail"
         exit 1
     else
diff --git a/tests/test_llms_summarization_tgi.sh b/tests/test_llms_summarization_tgi.sh
index bfab7a9f3..9d463d321 100644
--- a/tests/test_llms_summarization_tgi.sh
+++ b/tests/test_llms_summarization_tgi.sh
@@ -11,7 +11,7 @@ LOG_PATH="$WORKPATH/tests"
 function build_docker_images() {
     cd $WORKPATH
     docker build --no-cache -t opea/llm-tgi:comps --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/llms/summarization/tgi/Dockerfile .
-    if $? ; then
+    if [ $? -ne 0 ]; then
         echo "opea/llm-tgi built fail"
         exit 1
     else
diff --git a/tests/test_llms_text-generation_native.sh b/tests/test_llms_text-generation_native.sh
index 69517327e..f1e7fff63 100644
--- a/tests/test_llms_text-generation_native.sh
+++ b/tests/test_llms_text-generation_native.sh
@@ -14,7 +14,7 @@ function build_docker_images() {
         --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy \
         -t opea/llm-native:comps \
         -f comps/llms/text-generation/native/docker/Dockerfile .
-    if $? ; then
+    if [ $? -ne 0 ]; then
         echo "opea/llm-native built fail"
         exit 1
     else
diff --git a/tests/test_llms_text-generation_tgi.sh b/tests/test_llms_text-generation_tgi.sh
index dc8594811..b2956b12b 100644
--- a/tests/test_llms_text-generation_tgi.sh
+++ b/tests/test_llms_text-generation_tgi.sh
@@ -10,7 +10,7 @@ ip_address=$(hostname -I | awk '{print $1}')
 function build_docker_images() {
     cd $WORKPATH
     docker build --no-cache --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -t opea/llm-tgi:comps -f comps/llms/text-generation/tgi/Dockerfile .
-    if $? ; then
+    if [ $? -ne 0 ]; then
         echo "opea/llm-tgi built fail"
         exit 1
     else
diff --git a/tests/test_llms_text-generation_vllm-openvino.sh b/tests/test_llms_text-generation_vllm-openvino.sh
index dc460d0aa..ac57b29d8 100755
--- a/tests/test_llms_text-generation_vllm-openvino.sh
+++ b/tests/test_llms_text-generation_vllm-openvino.sh
@@ -21,7 +21,7 @@ function build_container() {
       . \
       --build-arg https_proxy=$https_proxy \
       --build-arg http_proxy=$http_proxy
-    if $? ; then
+    if [ $? -ne 0 ]; then
         echo "vllm-openvino built fail"
         exit 1
     else
diff --git a/tests/test_llms_text-generation_vllm-ray.sh b/tests/test_llms_text-generation_vllm-ray.sh
index ae9a42728..41433b27f 100644
--- a/tests/test_llms_text-generation_vllm-ray.sh
+++ b/tests/test_llms_text-generation_vllm-ray.sh
@@ -13,7 +13,7 @@ function build_docker_images() {
     docker build \
         -f comps/llms/text-generation/vllm-ray/docker/Dockerfile.vllmray  \
         --no-cache -t opea/vllm_ray-habana:comps --network=host .
-    if $? ; then
+    if [ $? -ne 0 ]; then
         echo "opea/vllm_ray-habana built fail"
         exit 1
     else
@@ -25,7 +25,7 @@ function build_docker_images() {
     docker build \
         --no-cache -t opea/llm-vllm-ray:comps \
         -f comps/llms/text-generation/vllm-ray/docker/Dockerfile.microservice .
-    if $? ; then
+    if [ $? -ne 0 ]; then
         echo "opea/llm-vllm-ray built fail"
         exit 1
     else
diff --git a/tests/test_llms_text-generation_vllm.sh b/tests/test_llms_text-generation_vllm.sh
index 1aae114e8..0210f5075 100644
--- a/tests/test_llms_text-generation_vllm.sh
+++ b/tests/test_llms_text-generation_vllm.sh
@@ -14,7 +14,7 @@ function build_docker_images() {
         -f docker/Dockerfile.hpu \
         --no-cache -t opea/vllm-hpu:comps \
         --shm-size=128g .
-    if $? ; then
+    if [ $? -ne 0 ]; then
         echo "opea/vllm-hpu built fail"
         exit 1
     else
@@ -26,7 +26,7 @@ function build_docker_images() {
     docker build  \
         --no-cache -t opea/llm-vllm:comps \
         -f comps/llms/text-generation/vllm/docker/Dockerfile.microservice .
-    if $? ; then
+    if [ $? -ne 0 ]; then
         echo "opea/llm-vllm built fail"
         exit 1
     else
diff --git a/tests/test_lvms_llava.sh b/tests/test_lvms_llava.sh
index 2e8f3cbd5..08f138e2f 100644
--- a/tests/test_lvms_llava.sh
+++ b/tests/test_lvms_llava.sh
@@ -11,14 +11,14 @@ function build_docker_images() {
     cd $WORKPATH
     echo $(pwd)
     docker build --no-cache -t opea/llava:comps -f comps/lvms/llava/Dockerfile .
-    if $? ; then
+    if [ $? -ne 0 ]; then
         echo "opea/llava built fail"
         exit 1
     else
         echo "opea/llava built successful"
     fi
     docker build --no-cache -t opea/lvm:comps -f comps/lvms/Dockerfile .
-    if $? ; then
+    if [ $? -ne 0 ]; then
         echo "opea/lvm built fail"
         exit 1
     else
diff --git a/tests/test_lvms_tgi_llava_next.sh b/tests/test_lvms_tgi_llava_next.sh
index 5a654b4ec..c9b28f6d5 100644
--- a/tests/test_lvms_tgi_llava_next.sh
+++ b/tests/test_lvms_tgi_llava_next.sh
@@ -12,7 +12,7 @@ function build_docker_images() {
     echo $(pwd)
     git clone https://github.com/yuanwu2017/tgi-gaudi.git && cd tgi-gaudi && git checkout v2.0.4
     docker build --no-cache -t opea/llava-tgi:comps .
-    if $? ; then
+    if [ $? -ne 0 ]; then
         echo "opea/llava-tgi built fail"
         exit 1
     else
@@ -21,7 +21,7 @@ function build_docker_images() {
 
     cd ..
     docker build --no-cache -t opea/lvm-tgi:comps -f comps/lvms/Dockerfile_tgi .
-    if $? ; then
+    if [ $? -ne 0 ]; then
         echo "opea/lvm-tgi built fail"
         exit 1
     else
diff --git a/tests/test_prompt_registry_mongo.sh b/tests/test_prompt_registry_mongo.sh
index 66d0a5757..b5d976999 100644
--- a/tests/test_prompt_registry_mongo.sh
+++ b/tests/test_prompt_registry_mongo.sh
@@ -18,7 +18,7 @@ function build_docker_images() {
     docker run -d -p 27017:27017 --name=test-comps-mongo mongo:latest
 
     docker build --no-cache -t opea/promptregistry-mongo-server:comps --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/prompt_registry/mongo/docker/Dockerfile .
-    if $? ; then
+    if [ $? -ne 0 ]; then
         echo "opea/promptregistry-mongo-server built fail"
         exit 1
     else
diff --git a/tests/test_reranks_fastrag.sh b/tests/test_reranks_fastrag.sh
index 0be7489af..7b0575523 100644
--- a/tests/test_reranks_fastrag.sh
+++ b/tests/test_reranks_fastrag.sh
@@ -9,7 +9,7 @@ ip_address=$(hostname -I | awk '{print $1}')
 function build_docker_images() {
     cd $WORKPATH
     docker build --no-cache --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -t opea/reranking-fastrag:comps -f comps/reranks/fastrag/docker/Dockerfile .
-    if $? ; then
+    if [ $? -ne 0 ]; then
         echo "opea/reranking-fastrag built fail"
         exit 1
     else
diff --git a/tests/test_reranks_langchain-mosec.sh b/tests/test_reranks_langchain-mosec.sh
index e5d166516..d34957a4c 100644
--- a/tests/test_reranks_langchain-mosec.sh
+++ b/tests/test_reranks_langchain-mosec.sh
@@ -11,7 +11,7 @@ function build_mosec_docker_images() {
     cd $WORKPATH
     echo $(pwd)
     docker build --build-arg http_proxy=$http_proxy --build-arg https_proxy=$https_proxy --no-cache -t opea/reranking-langchain-mosec-endpoint:comps -f comps/reranks/langchain-mosec/mosec-docker/Dockerfile .
-    if $? ; then
+    if [ $? -ne 0 ]; then
         echo "opea/reranking-langchain-mosec-endpoint built fail"
         exit 1
     else
@@ -23,7 +23,7 @@ function build_docker_images() {
     cd $WORKPATH
     echo $(pwd)
     docker build --build-arg http_proxy=$http_proxy --build-arg https_proxy=$https_proxy --no-cache -t opea/reranking-langchain-mosec:comps -f comps/reranks/langchain-mosec/docker/Dockerfile .
-    if $? ; then
+    if [ $? -ne 0 ]; then
         echo "opea/reranking-langchain-mosec built fail"
         exit 1
     else
diff --git a/tests/test_reranks_tei.sh b/tests/test_reranks_tei.sh
index 611e4d437..0b146d81e 100644
--- a/tests/test_reranks_tei.sh
+++ b/tests/test_reranks_tei.sh
@@ -9,7 +9,7 @@ ip_address=$(hostname -I | awk '{print $1}')
 function build_docker_images() {
     cd $WORKPATH
     docker build --no-cache -t opea/reranking-tei:comps -f comps/reranks/tei/docker/Dockerfile .
-    if $? ; then
+    if [ $? -ne 0 ]; then
         echo "opea/reranking-tei built fail"
         exit 1
     else
diff --git a/tests/test_retrievers_haystack_qdrant.sh b/tests/test_retrievers_haystack_qdrant.sh
index 364f63450..4fdfb13d6 100644
--- a/tests/test_retrievers_haystack_qdrant.sh
+++ b/tests/test_retrievers_haystack_qdrant.sh
@@ -9,7 +9,7 @@ ip_address=$(hostname -I | awk '{print $1}')
 function build_docker_images() {
     cd $WORKPATH
     docker build --no-cache -t opea/retriever-qdrant:comps --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/retrievers/haystack/qdrant/docker/Dockerfile .
-    if $? ; then
+    if [ $? -ne 0 ]; then
         echo "opea/retriever-qdrant built fail"
         exit 1
     else
diff --git a/tests/test_retrievers_langchain_pgvector.sh b/tests/test_retrievers_langchain_pgvector.sh
index b28fb632e..41295eb5e 100755
--- a/tests/test_retrievers_langchain_pgvector.sh
+++ b/tests/test_retrievers_langchain_pgvector.sh
@@ -9,7 +9,7 @@ ip_address=$(hostname -I | awk '{print $1}')
 function build_docker_images() {
     cd $WORKPATH
     docker build --no-cache -t opea/retriever-pgvector:comps --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/retrievers/langchain/pgvector/docker/Dockerfile .
-    if $? ; then
+    if [ $? -ne 0 ]; then
         echo "opea/retriever-pgvector built fail"
         exit 1
     else
diff --git a/tests/test_retrievers_langchain_pinecone.sh b/tests/test_retrievers_langchain_pinecone.sh
index 7fb105a94..d370fa92a 100755
--- a/tests/test_retrievers_langchain_pinecone.sh
+++ b/tests/test_retrievers_langchain_pinecone.sh
@@ -9,7 +9,7 @@ ip_address=$(hostname -I | awk '{print $1}')
 function build_docker_images() {
     cd $WORKPATH
     docker build --no-cache -t opea/retriever-pinecone:comps --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/retrievers/langchain/pinecone/docker/Dockerfile .
-    if $? ; then
+    if [ $? -ne 0 ]; then
         echo "opea/retriever-pinecone built fail"
         exit 1
     else
diff --git a/tests/test_retrievers_langchain_redis.sh b/tests/test_retrievers_langchain_redis.sh
index 5f4460d2c..9d367ce25 100644
--- a/tests/test_retrievers_langchain_redis.sh
+++ b/tests/test_retrievers_langchain_redis.sh
@@ -11,7 +11,7 @@ ip_address=$(hostname -I | awk '{print $1}')
 function build_docker_images() {
     cd $WORKPATH
     docker build --no-cache -t opea/retriever-redis:comps --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/retrievers/langchain/redis/docker/Dockerfile .
-    if $? ; then
+    if [ $? -ne 0 ]; then
         echo "opea/retriever-redis built fail"
         exit 1
     else
diff --git a/tests/test_tts.sh b/tests/test_tts.sh
index 3ae6f8c1d..e4039d956 100644
--- a/tests/test_tts.sh
+++ b/tests/test_tts.sh
@@ -11,14 +11,14 @@ function build_docker_images() {
     cd $WORKPATH
     echo $(pwd)
     docker build --no-cache -t opea/speecht5:comps -f comps/tts/speecht5/Dockerfile .
-    if $? ; then
+    if [ $? -ne 0 ]; then
         echo "opea/speecht5 built fail"
         exit 1
     else
         echo "opea/speecht5 built successful"
     fi
     docker build --no-cache -t opea/tts:comps -f comps/tts/Dockerfile .
-    if $? ; then
+    if [ $? -ne 0 ]; then
         echo "opea/tts built fail"
         exit 1
     else
diff --git a/tests/test_web_retrievers_langchain_chroma.sh b/tests/test_web_retrievers_langchain_chroma.sh
index 37e0dd4ea..c9e20aa08 100644
--- a/tests/test_web_retrievers_langchain_chroma.sh
+++ b/tests/test_web_retrievers_langchain_chroma.sh
@@ -9,7 +9,7 @@ ip_address=$(hostname -I | awk '{print $1}')
 function build_docker_images() {
     cd $WORKPATH
     docker build --no-cache -t opea/web-retriever-chroma:comps --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/web_retrievers/langchain/chroma/docker/Dockerfile .
-    if $? ; then
+    if [ $? -ne 0 ]; then
         echo "opea/web-retriever-chroma built fail"
         exit 1
     else

From b271739838fbcc5be87494f7b27c49865ac259ac Mon Sep 17 00:00:00 2001
From: Harsha Ramayanam <harsha.ramayanam@intel.com>
Date: Sat, 17 Aug 2024 08:38:46 -0700
Subject: [PATCH 08/11] Update README.md (#482)

Co-authored-by: Abolfazl Shahbazi <abolfazl.shahbazi@intel.com>

From 77e0e7be1c596679a7e19a97ac33fbee2acf7cf7 Mon Sep 17 00:00:00 2001
From: David Kinder <david.b.kinder@intel.com>
Date: Sat, 17 Aug 2024 08:43:12 -0700
Subject: [PATCH 09/11] doc: fix multiple H1 headings (#481)

Only one H1 heading (first for the title) allowed.

Signed-off-by: David B. Kinder <david.b.kinder@intel.com>
Co-authored-by: chen, suyue <suyue.chen@intel.com>
---
 comps/agent/langchain/README.md               | 16 ++---
 comps/asr/README.md                           | 24 +++----
 comps/chathistory/mongo/README.md             |  8 +--
 comps/dataprep/README.md                      | 10 +--
 comps/dataprep/milvus/README.md               | 18 +++---
 comps/dataprep/pgvector/README.md             | 30 ++++-----
 comps/dataprep/pinecone/README.md             | 22 +++----
 comps/dataprep/qdrant/README.md               | 22 +++----
 comps/dataprep/redis/README.md                | 34 +++++-----
 comps/embeddings/README.md                    | 30 ++++-----
 comps/embeddings/langchain-mosec/README.md    |  8 +--
 comps/guardrails/llama_guard/README.md        | 26 ++++----
 comps/guardrails/pii_detection/README.md      | 22 +++----
 comps/knowledgegraphs/README.md               | 18 +++---
 comps/llms/README.md                          | 64 +++++++++----------
 comps/llms/faq-generation/tgi/README.md       | 16 ++---
 comps/llms/summarization/tgi/README.md        | 26 ++++----
 comps/llms/text-generation/ollama/README.md   | 16 ++---
 comps/llms/text-generation/tgi/README.md      | 28 ++++----
 comps/llms/text-generation/vllm-xft/README.md | 14 ++--
 comps/lvms/README.md                          | 24 +++----
 comps/prompt_registry/mongo/README.md         |  8 +--
 comps/reranks/README.md                       | 26 ++++----
 comps/reranks/fastrag/README.md               | 22 +++----
 comps/reranks/langchain-mosec/README.md       |  8 +--
 comps/retrievers/haystack/qdrant/README.md    | 24 +++----
 comps/retrievers/langchain/README.md          |  6 +-
 comps/retrievers/langchain/milvus/README.md   | 22 +++----
 comps/retrievers/langchain/pgvector/README.md | 28 ++++----
 comps/retrievers/langchain/redis/README.md    | 28 ++++----
 comps/retrievers/llamaindex/README.md         | 24 +++----
 comps/tts/README.md                           | 16 ++---
 comps/vectorstores/README.md                  |  8 +--
 comps/vectorstores/langchain/chroma/README.md |  6 +-
 .../web_retrievers/langchain/chroma/README.md | 10 +--
 35 files changed, 357 insertions(+), 355 deletions(-)

diff --git a/comps/agent/langchain/README.md b/comps/agent/langchain/README.md
index 286e95508..b48e393eb 100644
--- a/comps/agent/langchain/README.md
+++ b/comps/agent/langchain/README.md
@@ -4,32 +4,32 @@ The langchain agent model refers to a framework that integrates the reasoning ca
 
 ![Architecture Overview](agent_arch.jpg)
 
-# 🚀1. Start Microservice with Python（Option 1）
+## 🚀1. Start Microservice with Python（Option 1）
 
-## 1.1 Install Requirements
+### 1.1 Install Requirements
 
 ```bash
 cd comps/agent/langchain/
 pip install -r requirements.txt
 ```
 
-## 1.2 Start Microservice with Python Script
+### 1.2 Start Microservice with Python Script
 
 ```bash
 cd comps/agent/langchain/
 python agent.py
 ```
 
-# 🚀2. Start Microservice with Docker (Option 2)
+## 🚀2. Start Microservice with Docker (Option 2)
 
-## Build Microservices
+### Build Microservices
 
 ```bash
 cd GenAIComps/ # back to GenAIComps/ folder
 docker build -t opea/comps-agent-langchain:latest -f comps/agent/langchain/docker/Dockerfile .
 ```
 
-## start microservices
+### start microservices
 
 ```bash
 export ip_address=$(hostname -I | awk '{print $1}')
@@ -56,7 +56,7 @@ docker logs comps-langchain-agent-endpoint
 > docker run --rm --runtime=runc --name="comps-langchain-agent-endpoint" -v ./comps/agent/langchain/:/home/user/comps/agent/langchain/ -p 9090:9090 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e HUGGINGFACEHUB_API_TOKEN=${HUGGINGFACEHUB_API_TOKEN} --env-file ${agent_env} opea/comps-agent-langchain:latest
 > ```
 
-# 🚀3. Validate Microservice
+## 🚀3. Validate Microservice
 
 Once microservice starts, user can use below script to invoke.
 
@@ -73,7 +73,7 @@ data: [DONE]
 
 ```
 
-# 🚀4. Provide your own tools
+## 🚀4. Provide your own tools
 
 - Define tools
 
diff --git a/comps/asr/README.md b/comps/asr/README.md
index 4cf78cbf1..10cac9421 100644
--- a/comps/asr/README.md
+++ b/comps/asr/README.md
@@ -2,17 +2,17 @@
 
 ASR (Audio-Speech-Recognition) microservice helps users convert speech to text. When building a talking bot with LLM, users will need to convert their audio inputs (What they talk, or Input audio from other sources) to text, so the LLM is able to tokenize the text and generate an answer. This microservice is built for that conversion stage.
 
-# 🚀1. Start Microservice with Python (Option 1)
+## 🚀1. Start Microservice with Python (Option 1)
 
 To start the ASR microservice with Python, you need to first install python packages.
 
-## 1.1 Install Requirements
+### 1.1 Install Requirements
 
 ```bash
 pip install -r requirements.txt
 ```
 
-## 1.2 Start Whisper Service/Test
+### 1.2 Start Whisper Service/Test
 
 - Xeon CPU
 
@@ -40,7 +40,7 @@ nohup python whisper_server.py --device=hpu &
 python check_whisper_server.py
 ```
 
-## 1.3 Start ASR Service/Test
+### 1.3 Start ASR Service/Test
 
 ```bash
 cd ../
@@ -54,13 +54,13 @@ While the Whisper service is running, you can start the ASR service. If the ASR
 {'id': '0e686efd33175ce0ebcf7e0ed7431673', 'text': 'who is pat gelsinger'}
 ```
 
-# 🚀2. Start Microservice with Docker (Option 2)
+## 🚀2. Start Microservice with Docker (Option 2)
 
 Alternatively, you can also start the ASR microservice with Docker.
 
-## 2.1 Build Images
+### 2.1 Build Images
 
-### 2.1.1 Whisper Server Image
+#### 2.1.1 Whisper Server Image
 
 - Xeon CPU
 
@@ -76,15 +76,15 @@ cd ../..
 docker build -t opea/whisper-gaudi:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/asr/whisper/Dockerfile_hpu .
 ```
 
-### 2.1.2 ASR Service Image
+#### 2.1.2 ASR Service Image
 
 ```bash
 docker build -t opea/asr:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/asr/Dockerfile .
 ```
 
-## 2.2 Start Whisper and ASR Service
+### 2.2 Start Whisper and ASR Service
 
-### 2.2.1 Start Whisper Server
+#### 2.2.1 Start Whisper Server
 
 - Xeon
 
@@ -98,7 +98,7 @@ docker run -p 7066:7066 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$htt
 docker run -p 7066:7066 --runtime=habana -e HABANA_VISIBLE_DEVICES=all -e OMPI_MCA_btl_vader_single_copy_mechanism=none --cap-add=sys_nice --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy opea/whisper-gaudi:latest
 ```
 
-### 2.2.2 Start ASR service
+#### 2.2.2 Start ASR service
 
 ```bash
 ip_address=$(hostname -I | awk '{print $1}')
@@ -106,7 +106,7 @@ ip_address=$(hostname -I | awk '{print $1}')
 docker run -d -p 9099:9099 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e ASR_ENDPOINT=http://$ip_address:7066 opea/asr:latest
 ```
 
-### 2.2.3 Test
+#### 2.2.3 Test
 
 ```bash
 # Use curl or python
diff --git a/comps/chathistory/mongo/README.md b/comps/chathistory/mongo/README.md
index 2eaa62e55..6f3f7a93a 100644
--- a/comps/chathistory/mongo/README.md
+++ b/comps/chathistory/mongo/README.md
@@ -17,16 +17,16 @@ export DB_NAME=${DB_NAME}
 export COLLECTION_NAME=${COLLECTION_NAME}
 ```
 
-# 🚀Start Microservice with Docker
+## 🚀Start Microservice with Docker
 
-## Build Docker Image
+### Build Docker Image
 
 ```bash
 cd ../../../../
 docker build -t opea/chathistory-mongo-server:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/chathistory/mongo/docker/Dockerfile .
 ```
 
-## Run Docker with CLI
+### Run Docker with CLI
 
 - Run mongoDB image
 
@@ -40,7 +40,7 @@ docker run -d -p 27017:27017 --name=mongo mongo:latest
 docker run -d --name="chathistory-mongo-server" -p 6013:6013 -p 6012:6012 -p 6014:6014 -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e no_proxy=$no_proxy -e MONGO_HOST=${MONGO_HOST} -e MONGO_PORT=${MONGO_PORT} -e DB_NAME=${DB_NAME} -e COLLECTION_NAME=${COLLECTION_NAME} opea/chathistory-mongo-server:latest
 ```
 
-# Invoke Microservice
+## Invoke Microservice
 
 Once chathistory service is up and running, users can update the database by using the below API endpoint. The API returns a unique UUID for the saved conversation.
 
diff --git a/comps/dataprep/README.md b/comps/dataprep/README.md
index 46e5e3c0f..7c3873794 100644
--- a/comps/dataprep/README.md
+++ b/comps/dataprep/README.md
@@ -17,22 +17,22 @@ Occasionally unstructured data will contain image data, to convert the image dat
 export SUMMARIZE_IMAGE_VIA_LVM=1
 ```
 
-# Dataprep Microservice with Redis
+## Dataprep Microservice with Redis
 
 For details, please refer to this [readme](redis/README.md)
 
-# Dataprep Microservice with Milvus
+## Dataprep Microservice with Milvus
 
 For details, please refer to this [readme](milvus/README.md)
 
-# Dataprep Microservice with Qdrant
+## Dataprep Microservice with Qdrant
 
 For details, please refer to this [readme](qdrant/README.md)
 
-# Dataprep Microservice with Pinecone
+## Dataprep Microservice with Pinecone
 
 For details, please refer to this [readme](pinecone/README.md)
 
-# Dataprep Microservice with PGVector
+## Dataprep Microservice with PGVector
 
 For details, please refer to this [readme](pgvector/README.md)
diff --git a/comps/dataprep/milvus/README.md b/comps/dataprep/milvus/README.md
index 738869a82..9941dbaa6 100644
--- a/comps/dataprep/milvus/README.md
+++ b/comps/dataprep/milvus/README.md
@@ -1,8 +1,8 @@
 # Dataprep Microservice with Milvus
 
-# 🚀Start Microservice with Python
+## 🚀Start Microservice with Python
 
-## Install Requirements
+### Install Requirements
 
 ```bash
 pip install -r requirements.txt
@@ -11,11 +11,11 @@ apt-get install libtesseract-dev -y
 apt-get install poppler-utils -y
 ```
 
-## Start Milvus Server
+### Start Milvus Server
 
 Please refer to this [readme](../../../vectorstores/langchain/milvus/README.md).
 
-## Setup Environment Variables
+### Setup Environment Variables
 
 ```bash
 export no_proxy=${your_no_proxy}
@@ -27,7 +27,7 @@ export COLLECTION_NAME=${your_collection_name}
 export MOSEC_EMBEDDING_ENDPOINT=${your_embedding_endpoint}
 ```
 
-## Start Document Preparation Microservice for Milvus with Python Script
+### Start Document Preparation Microservice for Milvus with Python Script
 
 Start document preparation microservice for Milvus with below command.
 
@@ -35,22 +35,22 @@ Start document preparation microservice for Milvus with below command.
 python prepare_doc_milvus.py
 ```
 
-# 🚀Start Microservice with Docker
+## 🚀Start Microservice with Docker
 
-## Build Docker Image
+### Build Docker Image
 
 ```bash
 cd ../../../../
 docker build -t opea/dataprep-milvus:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy --build-arg no_proxy=$no_proxy -f comps/dataprep/milvus/docker/Dockerfile .
 ```
 
-## Run Docker with CLI
+### Run Docker with CLI
 
 ```bash
 docker run -d --name="dataprep-milvus-server" -p 6010:6010 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e no_proxy=$no_proxy -e MOSEC_EMBEDDING_ENDPOINT=${your_embedding_endpoint} -e MILVUS=${your_milvus_host_ip} opea/dataprep-milvus:latest
 ```
 
-# Invoke Microservice
+## Invoke Microservice
 
 Once document preparation microservice for Milvus is started, user can use below command to invoke the microservice to convert the document to embedding and save to the database.
 
diff --git a/comps/dataprep/pgvector/README.md b/comps/dataprep/pgvector/README.md
index af25ae56d..1a7772eb8 100644
--- a/comps/dataprep/pgvector/README.md
+++ b/comps/dataprep/pgvector/README.md
@@ -1,14 +1,14 @@
 # Dataprep Microservice with PGVector
 
-# 🚀1. Start Microservice with Python（Option 1）
+## 🚀1. Start Microservice with Python（Option 1）
 
-## 1.1 Install Requirements
+### 1.1 Install Requirements
 
 ```bash
 pip install -r requirements.txt
 ```
 
-## 1.2 Setup Environment Variables
+### 1.2 Setup Environment Variables
 
 ```bash
 export PG_CONNECTION_STRING=postgresql+psycopg2://testuser:testpwd@${your_ip}:5432/vectordb
@@ -18,11 +18,11 @@ export LANGCHAIN_API_KEY=${your_langchain_api_key}
 export LANGCHAIN_PROJECT="opea/gen-ai-comps:dataprep"
 ```
 
-## 1.3 Start PGVector
+### 1.3 Start PGVector
 
 Please refer to this [readme](../../vectorstores/langchain/pgvector/README.md).
 
-## 1.4 Start Document Preparation Microservice for PGVector with Python Script
+### 1.4 Start Document Preparation Microservice for PGVector with Python Script
 
 Start document preparation microservice for PGVector with below command.
 
@@ -30,13 +30,13 @@ Start document preparation microservice for PGVector with below command.
 python prepare_doc_pgvector.py
 ```
 
-# 🚀2. Start Microservice with Docker (Option 2)
+## 🚀2. Start Microservice with Docker (Option 2)
 
-## 2.1 Start PGVector
+### 2.1 Start PGVector
 
 Please refer to this [readme](../../vectorstores/langchain/pgvector/README.md).
 
-## 2.2 Setup Environment Variables
+### 2.2 Setup Environment Variables
 
 ```bash
 export PG_CONNECTION_STRING=postgresql+psycopg2://testuser:testpwd@${your_ip}:5432/vectordb
@@ -46,29 +46,29 @@ export LANGCHAIN_API_KEY=${your_langchain_api_key}
 export LANGCHAIN_PROJECT="opea/dataprep"
 ```
 
-## 2.3 Build Docker Image
+### 2.3 Build Docker Image
 
 ```bash
 cd GenAIComps
 docker build -t opea/dataprep-pgvector:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/dataprep/pgvector/langchain/docker/Dockerfile .
 ```
 
-## 2.4 Run Docker with CLI (Option A)
+### 2.4 Run Docker with CLI (Option A)
 
 ```bash
 docker run  --name="dataprep-pgvector" -p 6007:6007 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e PG_CONNECTION_STRING=$PG_CONNECTION_STRING  -e INDEX_NAME=$INDEX_NAME -e TEI_ENDPOINT=$TEI_ENDPOINT opea/dataprep-pgvector:latest
 ```
 
-## 2.5 Run with Docker Compose (Option B)
+### 2.5 Run with Docker Compose (Option B)
 
 ```bash
 cd comps/dataprep/langchain/pgvector/docker
 docker compose -f docker-compose-dataprep-pgvector.yaml up -d
 ```
 
-# 🚀3. Consume Microservice
+## 🚀3. Consume Microservice
 
-## 3.1 Consume Upload API
+### 3.1 Consume Upload API
 
 Once document preparation microservice for PGVector is started, user can use below command to invoke the microservice to convert the document to embedding and save to the database.
 
@@ -79,7 +79,7 @@ curl -X POST \
     http://localhost:6007/v1/dataprep
 ```
 
-## 3.2 Consume get_file API
+### 3.2 Consume get_file API
 
 To get uploaded file structures, use the following command:
 
@@ -108,7 +108,7 @@ Then you will get the response JSON like this:
 ]
 ```
 
-## 4.3 Consume delete_file API
+### 4.3 Consume delete_file API
 
 To delete uploaded file/link, use the following command.
 
diff --git a/comps/dataprep/pinecone/README.md b/comps/dataprep/pinecone/README.md
index 3a9f6fc30..42e3d048a 100644
--- a/comps/dataprep/pinecone/README.md
+++ b/comps/dataprep/pinecone/README.md
@@ -1,18 +1,18 @@
 # Dataprep Microservice with Pinecone
 
-# 🚀Start Microservice with Python
+## 🚀Start Microservice with Python
 
-## Install Requirements
+### Install Requirements
 
 ```bash
 pip install -r requirements.txt
 ```
 
-## Start Pinecone Server
+### Start Pinecone Server
 
 Please refer to this [readme](../../../vectorstores/langchain/pinecone/README.md).
 
-## Setup Environment Variables
+### Setup Environment Variables
 
 ```bash
 export http_proxy=${your_http_proxy}
@@ -21,7 +21,7 @@ export PINECONE_API_KEY=${PINECONE_API_KEY}
 export PINECONE_INDEX_NAME=${PINECONE_INDEX_NAME}
 ```
 
-## Start Document Preparation Microservice for Pinecone with Python Script
+### Start Document Preparation Microservice for Pinecone with Python Script
 
 Start document preparation microservice for Pinecone with below command.
 
@@ -29,22 +29,22 @@ Start document preparation microservice for Pinecone with below command.
 python prepare_doc_pinecone.py
 ```
 
-# 🚀Start Microservice with Docker
+## 🚀Start Microservice with Docker
 
-## Build Docker Image
+### Build Docker Image
 
 ```bash
 cd ../../../../
 docker build -t opea/dataprep-pinecone:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/dataprep/pinecone/docker/Dockerfile .
 ```
 
-## Run Docker with CLI
+### Run Docker with CLI
 
 ```bash
 docker run -d --name="dataprep-pinecone-server" -p 6000:6000 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy opea/dataprep-pinecone:latest
 ```
 
-## Setup Environment Variables
+### Setup Environment Variables
 
 ```bash
 export http_proxy=${your_http_proxy}
@@ -53,14 +53,14 @@ export PINECONE_API_KEY=${PINECONE_API_KEY}
 export PINECONE_INDEX_NAME=${PINECONE_INDEX_NAME}
 ```
 
-## Run Docker with Docker Compose
+### Run Docker with Docker Compose
 
 ```bash
 cd comps/dataprep/pinecone/docker
 docker compose -f docker-compose-dataprep-pinecone.yaml up -d
 ```
 
-# Invoke Microservice
+## Invoke Microservice
 
 Once document preparation microservice for Pinecone is started, user can use below command to invoke the microservice to convert the document to embedding and save to the database.
 
diff --git a/comps/dataprep/qdrant/README.md b/comps/dataprep/qdrant/README.md
index 30aefb43d..4b52eaeeb 100644
--- a/comps/dataprep/qdrant/README.md
+++ b/comps/dataprep/qdrant/README.md
@@ -1,8 +1,8 @@
 # Dataprep Microservice with Qdrant
 
-# 🚀Start Microservice with Python
+## 🚀Start Microservice with Python
 
-## Install Requirements
+### Install Requirements
 
 ```bash
 pip install -r requirements.txt
@@ -11,11 +11,11 @@ apt-get install libtesseract-dev -y
 apt-get install poppler-utils -y
 ```
 
-## Start Qdrant Server
+### Start Qdrant Server
 
 Please refer to this [readme](../../vectorstores/langchain/qdrant/README.md).
 
-## Setup Environment Variables
+### Setup Environment Variables
 
 ```bash
 export no_proxy=${your_no_proxy}
@@ -27,7 +27,7 @@ export COLLECTION_NAME=${your_collection_name}
 export PYTHONPATH=${path_to_comps}
 ```
 
-## Start Document Preparation Microservice for Qdrant with Python Script
+### Start Document Preparation Microservice for Qdrant with Python Script
 
 Start document preparation microservice for Qdrant with below command.
 
@@ -35,22 +35,22 @@ Start document preparation microservice for Qdrant with below command.
 python prepare_doc_qdrant.py
 ```
 
-# 🚀Start Microservice with Docker
+## 🚀Start Microservice with Docker
 
-## Build Docker Image
+### Build Docker Image
 
 ```bash
 cd ../../../../
 docker build -t opea/dataprep-qdrant:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/dataprep/qdrant/docker/Dockerfile .
 ```
 
-## Run Docker with CLI
+### Run Docker with CLI
 
 ```bash
 docker run -d --name="dataprep-qdrant-server" -p 6007:6007 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy opea/dataprep-qdrant:latest
 ```
 
-## Setup Environment Variables
+### Setup Environment Variables
 
 ```bash
 export http_proxy=${your_http_proxy}
@@ -60,14 +60,14 @@ export QDRANT_PORT=6333
 export COLLECTION_NAME=${your_collection_name}
 ```
 
-## Run Docker with Docker Compose
+### Run Docker with Docker Compose
 
 ```bash
 cd comps/dataprep/qdrant/docker
 docker compose -f docker-compose-dataprep-qdrant.yaml up -d
 ```
 
-# Invoke Microservice
+## Invoke Microservice
 
 Once document preparation microservice for Qdrant is started, user can use below command to invoke the microservice to convert the document to embedding and save to the database.
 
diff --git a/comps/dataprep/redis/README.md b/comps/dataprep/redis/README.md
index 0845254df..4617dfa25 100644
--- a/comps/dataprep/redis/README.md
+++ b/comps/dataprep/redis/README.md
@@ -4,9 +4,9 @@ For dataprep microservice, we provide two frameworks: `Langchain` and `LlamaInde
 
 We organized these two folders in the same way, so you can use either framework for dataprep microservice with the following constructions.
 
-# 🚀1. Start Microservice with Python（Option 1）
+## 🚀1. Start Microservice with Python（Option 1）
 
-## 1.1 Install Requirements
+### 1.1 Install Requirements
 
 - option 1: Install Single-process version (for 1-10 files processing)
 
@@ -29,11 +29,11 @@ pip install -r requirements.txt
 cd langchain_ray; pip install -r requirements_ray.txt
 ```
 
-## 1.2 Start Redis Stack Server
+### 1.2 Start Redis Stack Server
 
 Please refer to this [readme](../../vectorstores/langchain/redis/README.md).
 
-## 1.3 Setup Environment Variables
+### 1.3 Setup Environment Variables
 
 ```bash
 export REDIS_URL="redis://${your_ip}:6379"
@@ -41,7 +41,7 @@ export INDEX_NAME=${your_index_name}
 export PYTHONPATH=${path_to_comps}
 ```
 
-## 1.4 Start Embedding Service
+### 1.4 Start Embedding Service
 
 First, you need to start a TEI service.
 
@@ -67,7 +67,7 @@ After checking that it works, set up environment variables.
 export TEI_ENDPOINT="http://localhost:$your_port"
 ```
 
-## 1.4 Start Document Preparation Microservice for Redis with Python Script
+### 1.4 Start Document Preparation Microservice for Redis with Python Script
 
 Start document preparation microservice for Redis with below command.
 
@@ -83,13 +83,13 @@ python prepare_doc_redis.py
 python prepare_doc_redis_on_ray.py
 ```
 
-# 🚀2. Start Microservice with Docker (Option 2)
+## 🚀2. Start Microservice with Docker (Option 2)
 
-## 2.1 Start Redis Stack Server
+### 2.1 Start Redis Stack Server
 
 Please refer to this [readme](../../vectorstores/langchain/redis/README.md).
 
-## 2.2 Setup Environment Variables
+### 2.2 Setup Environment Variables
 
 ```bash
 export EMBEDDING_MODEL_ID="BAAI/bge-base-en-v1.5"
@@ -99,7 +99,7 @@ export INDEX_NAME=${your_index_name}
 export HUGGINGFACEHUB_API_TOKEN=${your_hf_api_token}
 ```
 
-## 2.3 Build Docker Image
+### 2.3 Build Docker Image
 
 - Build docker image with langchain
 
@@ -124,7 +124,7 @@ cd ../../../../
 docker build -t opea/dataprep-on-ray-redis:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/dataprep/redis/langchain_ray/docker/Dockerfile .
 ```
 
-## 2.4 Run Docker with CLI (Option A)
+### 2.4 Run Docker with CLI (Option A)
 
 - option 1: Start single-process version (for 1-10 files processing)
 
@@ -138,7 +138,7 @@ docker run -d --name="dataprep-redis-server" -p 6007:6007 --runtime=runc --ipc=h
 docker run -d --name="dataprep-redis-server" -p 6007:6007 --runtime=runc --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e REDIS_URL=$REDIS_URL -e INDEX_NAME=$INDEX_NAME -e TEI_ENDPOINT=$TEI_ENDPOINT -e HUGGINGFACEHUB_API_TOKEN=$HUGGINGFACEHUB_API_TOKEN -e TIMEOUT_SECONDS=600 opea/dataprep-on-ray-redis:latest
 ```
 
-## 2.5 Run with Docker Compose (Option B - deprecated, will move to genAIExample in future)
+### 2.5 Run with Docker Compose (Option B - deprecated, will move to genAIExample in future)
 
 ```bash
 # for langchain
@@ -148,15 +148,15 @@ cd comps/dataprep/redis/llama_index/docker
 docker compose -f docker-compose-dataprep-redis.yaml up -d
 ```
 
-# 🚀3. Status Microservice
+## 🚀3. Status Microservice
 
 ```bash
 docker container logs -f dataprep-redis-server
 ```
 
-# 🚀4. Consume Microservice
+## 🚀4. Consume Microservice
 
-## 4.1 Consume Upload API
+### 4.1 Consume Upload API
 
 Once document preparation microservice for Redis is started, user can use below command to invoke the microservice to convert the document to embedding and save to the database.
 
@@ -236,7 +236,7 @@ except requests.exceptions.RequestException as e:
     print("An error occurred:", e)
 ```
 
-## 4.2 Consume get_file API
+### 4.2 Consume get_file API
 
 To get uploaded file structures, use the following command:
 
@@ -265,7 +265,7 @@ Then you will get the response JSON like this:
 ]
 ```
 
-## 4.3 Consume delete_file API
+### 4.3 Consume delete_file API
 
 To delete uploaded file/link, use the following command.
 
diff --git a/comps/embeddings/README.md b/comps/embeddings/README.md
index edf164b48..407644ada 100644
--- a/comps/embeddings/README.md
+++ b/comps/embeddings/README.md
@@ -14,7 +14,7 @@ Key Features:
 
 Users are albe to configure and build embedding-related services according to their actual needs.
 
-# 🚀1. Start Microservice with Python (Option 1)
+## 🚀1. Start Microservice with Python (Option 1)
 
 Currently, we provide two ways to implement the embedding service:
 
@@ -24,7 +24,7 @@ Currently, we provide two ways to implement the embedding service:
 
 For both of the implementations, you need to install requirements first.
 
-## 1.1 Install Requirements
+### 1.1 Install Requirements
 
 ```bash
 # run with langchain
@@ -33,11 +33,11 @@ pip install -r langchain/requirements.txt
 pip install -r llama_index/requirements.txt
 ```
 
-## 1.2 Start Embedding Service
+### 1.2 Start Embedding Service
 
 You can select one of following ways to start the embedding service:
 
-### Start Embedding Service with TEI
+#### Start Embedding Service with TEI
 
 First, you need to start a TEI service.
 
@@ -69,7 +69,7 @@ export TEI_EMBEDDING_MODEL_NAME="BAAI/bge-large-en-v1.5"
 python embedding_tei.py
 ```
 
-### Start Embedding Service with Local Model
+#### Start Embedding Service with Local Model
 
 ```bash
 # run with langchain
@@ -79,9 +79,9 @@ cd llama_index
 python local_embedding.py
 ```
 
-# 🚀2. Start Microservice with Docker (Optional 2)
+## 🚀2. Start Microservice with Docker (Optional 2)
 
-## 2.1 Start Embedding Service with TEI
+### 2.1 Start Embedding Service with TEI
 
 First, you need to start a TEI service.
 
@@ -108,23 +108,23 @@ export TEI_EMBEDDING_ENDPOINT="http://localhost:$yourport"
 export TEI_EMBEDDING_MODEL_NAME="BAAI/bge-large-en-v1.5"
 ```
 
-## 2.2 Build Docker Image
+### 2.2 Build Docker Image
 
-### Build Langchain Docker (Option a)
+#### Build Langchain Docker (Option a)
 
 ```bash
 cd ../../
 docker build -t opea/embedding-tei:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/embeddings/langchain/docker/Dockerfile .
 ```
 
-### Build LlamaIndex Docker (Option b)
+#### Build LlamaIndex Docker (Option b)
 
 ```bash
 cd ../../
 docker build -t opea/embedding-tei-llama-index:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/embeddings/llama_index/docker/Dockerfile .
 ```
 
-## 2.3 Run Docker with CLI
+### 2.3 Run Docker with CLI
 
 ```bash
 # run with langchain docker
@@ -133,16 +133,16 @@ docker run -d --name="embedding-tei-server" -p 6000:6000 --ipc=host -e http_prox
 docker run -d --name="embedding-tei-llama-index-server" -p 6000:6000 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e TEI_EMBEDDING_ENDPOINT=$TEI_EMBEDDING_ENDPOINT -e TEI_EMBEDDING_MODEL_NAME=$TEI_EMBEDDING_MODEL_NAME opea/embedding-tei-llama-index:latest
 ```
 
-## 2.4 Run Docker with Docker Compose
+### 2.4 Run Docker with Docker Compose
 
 ```bash
 cd docker
 docker compose -f docker_compose_embedding.yaml up -d
 ```
 
-# 🚀3. Consume Embedding Service
+## 🚀3. Consume Embedding Service
 
-## 3.1 Check Service Status
+### 3.1 Check Service Status
 
 ```bash
 curl http://localhost:6000/v1/health_check\
@@ -150,7 +150,7 @@ curl http://localhost:6000/v1/health_check\
   -H 'Content-Type: application/json'
 ```
 
-## 3.2 Consume Embedding Service
+### 3.2 Consume Embedding Service
 
 ```bash
 curl http://localhost:6000/v1/embeddings\
diff --git a/comps/embeddings/langchain-mosec/README.md b/comps/embeddings/langchain-mosec/README.md
index 624fcf6a2..4fd265829 100644
--- a/comps/embeddings/langchain-mosec/README.md
+++ b/comps/embeddings/langchain-mosec/README.md
@@ -4,26 +4,26 @@
 docker build --build-arg http_proxy=$http_proxy --build-arg https_proxy=$https_proxy -t opea/embedding-langchain-mosec-endpoint:latest -f comps/embeddings/langchain-mosec/mosec-docker/Dockerfile .
 ```
 
-# build embedding microservice docker image
+## build embedding microservice docker image
 
 ```
 docker build --build-arg http_proxy=$http_proxy --build-arg https_proxy=$https_proxy -t opea/embedding-langchain-mosec:latest -f comps/embeddings/langchain-mosec/docker/Dockerfile .
 ```
 
-# launch Mosec endpoint docker container
+## launch Mosec endpoint docker container
 
 ```
 docker run -d --name="embedding-langchain-mosec-endpoint" -p 6001:8000  opea/embedding-langchain-mosec-endpoint:latest
 ```
 
-# launch embedding microservice docker container
+## launch embedding microservice docker container
 
 ```
 export MOSEC_EMBEDDING_ENDPOINT=http://{mosec_embedding_host_ip}:6001
 docker run -d --name="embedding-langchain-mosec-server" -e http_proxy=$http_proxy -e https_proxy=$https_proxy -p 6000:6000 --ipc=host -e MOSEC_EMBEDDING_ENDPOINT=$MOSEC_EMBEDDING_ENDPOINT opea/embedding-langchain-mosec:latest
 ```
 
-# run client test
+## run client test
 
 ```
 curl localhost:6000/v1/embeddings \
diff --git a/comps/guardrails/llama_guard/README.md b/comps/guardrails/llama_guard/README.md
index 084711125..019aab5e3 100644
--- a/comps/guardrails/llama_guard/README.md
+++ b/comps/guardrails/llama_guard/README.md
@@ -20,17 +20,17 @@ Any content that is detected in the following categories is determined as unsafe
 - Regulated or Controlled Substances
 - Suicide & Self Harm
 
-# 🚀1. Start Microservice with Python (Option 1)
+## 🚀1. Start Microservice with Python (Option 1)
 
 To start the Guardrails microservice, you need to install python packages first.
 
-## 1.1 Install Requirements
+### 1.1 Install Requirements
 
 ```bash
 pip install -r requirements.txt
 ```
 
-## 1.2 Start TGI Gaudi Service
+### 1.2 Start TGI Gaudi Service
 
 ```bash
 export HF_TOKEN=${your_hf_api_token}
@@ -43,7 +43,7 @@ docker pull ghcr.io/huggingface/tgi-gaudi:2.0.1
 docker run -p 8088:80 -v $volume:/data --runtime=habana -e HABANA_VISIBLE_DEVICES=all -e OMPI_MCA_btl_vader_single_copy_mechanism=none --cap-add=sys_nice --ipc=host -e HTTPS_PROXY=$https_proxy -e HTTP_PROXY=$https_proxy -e HF_TOKEN=$HF_TOKEN ghcr.io/huggingface/tgi-gaudi:2.0.1 --model-id $model_id --max-input-length 1024 --max-total-tokens 2048
 ```
 
-## 1.3 Verify the TGI Gaudi Service
+### 1.3 Verify the TGI Gaudi Service
 
 ```bash
 curl 127.0.0.1:8088/generate \
@@ -52,7 +52,7 @@ curl 127.0.0.1:8088/generate \
   -H 'Content-Type: application/json'
 ```
 
-## 1.4 Start Guardrails Service
+### 1.4 Start Guardrails Service
 
 Optional: If you have deployed a Guardrails model with TGI Gaudi Service other than default model (i.e., `meta-llama/Meta-Llama-Guard-2-8B`) [from section 1.2](## 1.2 Start TGI Gaudi Service), you will need to add the eviornment variable `SAFETY_GUARD_MODEL_ID` containing the model id. For example, the following informs the Guardrails Service the deployed model used LlamaGuard2:
 
@@ -65,11 +65,11 @@ export SAFETY_GUARD_ENDPOINT="http://${your_ip}:8088"
 python langchain/guardrails_tgi.py
 ```
 
-# 🚀2. Start Microservice with Docker (Option 2)
+## 🚀2. Start Microservice with Docker (Option 2)
 
 If you start an Guardrails microservice with docker, the `docker_compose_guardrails.yaml` file will automatically start a TGI gaudi service with docker.
 
-## 2.1 Setup Environment Variables
+### 2.1 Setup Environment Variables
 
 In order to start TGI and LLM services, you need to setup the following environment variables first.
 
@@ -79,29 +79,29 @@ export SAFETY_GUARD_ENDPOINT="http://${your_ip}:8088"
 export LLM_MODEL_ID=${your_hf_llm_model}
 ```
 
-## 2.2 Build Docker Image
+### 2.2 Build Docker Image
 
 ```bash
 cd ../../
 docker build -t opea/guardrails-tgi:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/guardrails/llama_guard/docker/Dockerfile .
 ```
 
-## 2.3 Run Docker with CLI
+### 2.3 Run Docker with CLI
 
 ```bash
 docker run -d --name="guardrails-tgi-server" -p 9090:9090 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e no_proxy=$no_proxy -e SAFETY_GUARD_ENDPOINT=$SAFETY_GUARD_ENDPOINT -e HUGGINGFACEHUB_API_TOKEN=$HUGGINGFACEHUB_API_TOKEN opea/guardrails-tgi:latest
 ```
 
-## 2.4 Run Docker with Docker Compose
+### 2.4 Run Docker with Docker Compose
 
 ```bash
 cd langchain/docker
 docker compose -f docker_compose_guardrails.yaml up -d
 ```
 
-# 🚀3. Consume Guardrails Service
+## 🚀3. Consume Guardrails Service
 
-## 3.1 Check Service Status
+### 3.1 Check Service Status
 
 ```bash
 curl http://localhost:9090/v1/health_check\
@@ -109,7 +109,7 @@ curl http://localhost:9090/v1/health_check\
   -H 'Content-Type: application/json'
 ```
 
-## 3.2 Consume Guardrails Service
+### 3.2 Consume Guardrails Service
 
 ```bash
 curl http://localhost:9090/v1/guardrails\
diff --git a/comps/guardrails/pii_detection/README.md b/comps/guardrails/pii_detection/README.md
index 3c1f1180e..152fa96db 100644
--- a/comps/guardrails/pii_detection/README.md
+++ b/comps/guardrails/pii_detection/README.md
@@ -19,7 +19,7 @@ The classifiler model is used together with an embedding model to make predictio
 
 Currently this strategy can detect both personal sensitive and business sensitive information such as financial figures and performance reviews. Please refer to the [model card](<(https://huggingface.co/Intel/business_safety_logistic_regression_classifier)>) to see the performance of our demo model on the Patronus EnterprisePII dataset.
 
-# Input and output
+## Input and output
 
 Users can send a list of files, a list of text strings, or a list of urls to the microservice, and the microservice will return a list of True or False for each piece of text following the original sequence.
 
@@ -27,15 +27,15 @@ For a concrete example of what input should look like, please refer to [Consume
 
 The output will be a list of booleans, which can be parsed and used as conditions in a bigger application.
 
-# 🚀1. Start Microservice with Python（Option 1）
+## 🚀1. Start Microservice with Python（Option 1）
 
-## 1.1 Install Requirements
+### 1.1 Install Requirements
 
 ```bash
 pip install -r requirements.txt
 ```
 
-## 1.2 Start PII Detection Microservice with Python Script
+### 1.2 Start PII Detection Microservice with Python Script
 
 Start pii detection microservice with below command.
 
@@ -43,24 +43,24 @@ Start pii detection microservice with below command.
 python pii_detection.py
 ```
 
-# 🚀2. Start Microservice with Docker (Option 2)
+## 🚀2. Start Microservice with Docker (Option 2)
 
-## 2.1 Prepare PII detection model
+### 2.1 Prepare PII detection model
 
 export HUGGINGFACEHUB_API_TOKEN=${HP_TOKEN}
 
-## 2.1.1 use LLM endpoint (will add later)
+#### 2.1.1 use LLM endpoint (will add later)
 
 intro placeholder
 
-## 2.2 Build Docker Image
+### 2.2 Build Docker Image
 
 ```bash
 cd ../../../ # back to GenAIComps/ folder
 docker build -t opea/guardrails-pii-detection:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/guardrails/pii_detection/docker/Dockerfile .
 ```
 
-## 2.3 Run Docker with CLI
+### 2.3 Run Docker with CLI
 
 ```bash
 docker run -d --rm --runtime=runc --name="guardrails-pii-detection-endpoint" -p 6357:6357 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e HUGGINGFACEHUB_API_TOKEN=${HUGGINGFACEHUB_API_TOKEN} -e HF_TOKEN=${HUGGINGFACEHUB_API_TOKEN} opea/guardrails-pii-detection:latest
@@ -72,13 +72,13 @@ docker run -d --rm --runtime=runc --name="guardrails-pii-detection-endpoint" -p
 docker run --rm --runtime=runc --name="guardrails-pii-detection-endpoint" -p 6357:6357 -v ./comps/guardrails/pii_detection/:/home/user/comps/guardrails/pii_detection/ --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e HUGGINGFACEHUB_API_TOKEN=${HUGGINGFACEHUB_API_TOKEN}  -e HF_TOKEN=${HUGGINGFACEHUB_API_TOKEN} opea/guardrails-pii-detection:latest
 ```
 
-# 🚀3. Get Status of Microservice
+## 🚀3. Get Status of Microservice
 
 ```bash
 docker container logs -f guardrails-pii-detection-endpoint
 ```
 
-# 🚀4. Consume Microservice
+## 🚀4. Consume Microservice
 
 Once microservice starts, user can use below script to invoke the microservice for pii detection.
 
diff --git a/comps/knowledgegraphs/README.md b/comps/knowledgegraphs/README.md
index 248e46eba..db8140b9c 100755
--- a/comps/knowledgegraphs/README.md
+++ b/comps/knowledgegraphs/README.md
@@ -16,9 +16,9 @@ A prerequisite for using this microservice is that users must have a knowledge g
 
 Overall, this microservice provides efficient support for applications related with graph dataset, especially for answering multi-part questions, or any other conditions including comples relationship between entities.
 
-# 🚀1. Start Microservice with Docker
+## 🚀1. Start Microservice with Docker
 
-## 1.1 Setup Environment Variables
+### 1.1 Setup Environment Variables
 
 ```bash
 export NEO4J_ENDPOINT="neo4j://${your_ip}:7687"
@@ -30,7 +30,7 @@ export LLM_MODEL="meta-llama/Llama-2-7b-hf"
 export AGENT_LLM="HuggingFaceH4/zephyr-7b-beta"
 ```
 
-## 1.2 Start Neo4j Service
+### 1.2 Start Neo4j Service
 
 ```bash
 docker pull neo4j
@@ -43,7 +43,7 @@ docker run --rm \
     neo4j
 ```
 
-## 1.3 Start LLM Service for "rag"/"query" mode
+### 1.3 Start LLM Service for "rag"/"query" mode
 
 You can start any LLM microserve, here we take TGI as an example.
 
@@ -70,7 +70,7 @@ curl $LLM_ENDPOINT/generate \
   -H 'Content-Type: application/json'
 ```
 
-## 1.4 Start Microservice
+### 1.4 Start Microservice
 
 ```bash
 cd ../..
@@ -93,9 +93,9 @@ docker run --rm \
     opea/knowledge_graphs:latest
 ```
 
-# 🚀2. Consume Knowledge Graph Service
+## 🚀2. Consume Knowledge Graph Service
 
-## 2.1 Cypher mode
+### 2.1 Cypher mode
 
 ```bash
 curl http://${your_ip}:8060/v1/graphs \
@@ -107,7 +107,7 @@ curl http://${your_ip}:8060/v1/graphs \
 Example output:
 ![Cypher Output](doc/output_cypher.png)
 
-## 2.2 Rag mode
+### 2.2 Rag mode
 
 ```bash
 curl http://${your_ip}:8060/v1/graphs \
@@ -119,7 +119,7 @@ curl http://${your_ip}:8060/v1/graphs \
 Example output:
 ![Cypher Output](doc/output_rag.png)
 
-## 2.3 Query mode
+### 2.3 Query mode
 
 First example:
 
diff --git a/comps/llms/README.md b/comps/llms/README.md
index 9c3867f03..d6ea6dbb6 100644
--- a/comps/llms/README.md
+++ b/comps/llms/README.md
@@ -6,19 +6,19 @@ A prerequisite for using this microservice is that users must have a LLM text ge
 
 Overall, this microservice offers a streamlined way to integrate large language model inference into applications, requiring minimal setup from the user beyond initiating a TGI/vLLM/Ray service and configuring the necessary environment variables. This allows for the seamless processing of queries and documents to generate intelligent, context-aware responses.
 
-# 🚀1. Start Microservice with Python (Option 1)
+## 🚀1. Start Microservice with Python (Option 1)
 
 To start the LLM microservice, you need to install python packages first.
 
-## 1.1 Install Requirements
+### 1.1 Install Requirements
 
 ```bash
 pip install -r requirements.txt
 ```
 
-## 1.2 Start LLM Service
+### 1.2 Start LLM Service
 
-### 1.2.1 Start TGI Service
+#### 1.2.1 Start TGI Service
 
 ```bash
 export HF_TOKEN=${your_hf_api_token}
@@ -28,14 +28,14 @@ export LANGCHAIN_PROJECT="opea/gen-ai-comps:llms"
 docker run -p 8008:80 -v ./data:/data --name tgi_service --shm-size 1g ghcr.io/huggingface/text-generation-inference:1.4 --model-id ${your_hf_llm_model}
 ```
 
-### 1.2.2 Start vLLM Service
+#### 1.2.2 Start vLLM Service
 
 ```bash
 export HUGGINGFACEHUB_API_TOKEN=${your_hf_api_token}
 docker run -it --name vllm_service -p 8008:80 -e HF_TOKEN=${HUGGINGFACEHUB_API_TOKEN} -v ./data:/data opea/vllm:cpu /bin/bash -c "cd / && export VLLM_CPU_KVCACHE_SPACE=40 && python3 -m vllm.entrypoints.openai.api_server --model ${your_hf_llm_model} --port 80"
 ```
 
-## 1.2.3 Start Ray Service
+### 1.2.3 Start Ray Service
 
 ```bash
 export HUGGINGFACEHUB_API_TOKEN=${your_hf_api_token}
@@ -43,9 +43,9 @@ export TRUST_REMOTE_CODE=True
 docker run -it --runtime=habana --name ray_serve_service -e OMPI_MCA_btl_vader_single_copy_mechanism=none --cap-add=sys_nice --ipc=host -p 8008:80 -e HUGGINGFACEHUB_API_TOKEN=$HUGGINGFACEHUB_API_TOKEN -e TRUST_REMOTE_CODE=$TRUST_REMOTE_CODE opea/llm-ray:latest /bin/bash -c "ray start --head && python api_server_openai.py --port_number 80 --model_id_or_path ${your_hf_llm_model} --chat_processor ${your_hf_chatprocessor}"
 ```
 
-## 1.3 Verify the LLM Service
+### 1.3 Verify the LLM Service
 
-### 1.3.1 Verify the TGI Service
+#### 1.3.1 Verify the TGI Service
 
 ```bash
 curl http://${your_ip}:8008/generate \
@@ -54,7 +54,7 @@ curl http://${your_ip}:8008/generate \
   -H 'Content-Type: application/json'
 ```
 
-### 1.3.2 Verify the vLLM Service
+#### 1.3.2 Verify the vLLM Service
 
 ```bash
 curl http://${your_ip}:8008/v1/completions \
@@ -67,7 +67,7 @@ curl http://${your_ip}:8008/v1/completions \
   }'
 ```
 
-### 1.3.3 Verify the Ray Service
+#### 1.3.3 Verify the Ray Service
 
 ```bash
 curl http://${your_ip}:8008/v1/chat/completions \
@@ -83,34 +83,34 @@ curl http://${your_ip}:8008/v1/chat/completions \
   }'
 ```
 
-## 1.4 Start LLM Service with Python Script
+### 1.4 Start LLM Service with Python Script
 
-### 1.4.1 Start the TGI Service
+#### 1.4.1 Start the TGI Service
 
 ```bash
 export TGI_LLM_ENDPOINT="http://${your_ip}:8008"
 python text-generation/tgi/llm.py
 ```
 
-### 1.4.2 Start the vLLM Service
+#### 1.4.2 Start the vLLM Service
 
 ```bash
 export vLLM_LLM_ENDPOINT="http://${your_ip}:8008"
 python text-generation/vllm/llm.py
 ```
 
-### 1.4.3 Start the Ray Service
+#### 1.4.3 Start the Ray Service
 
 ```bash
 export RAY_Serve_ENDPOINT="http://${your_ip}:8008"
 python text-generation/ray_serve/llm.py
 ```
 
-# 🚀2. Start Microservice with Docker (Option 2)
+## 🚀2. Start Microservice with Docker (Option 2)
 
 If you start an LLM microservice with docker, the `docker_compose_llm.yaml` file will automatically start a TGI/vLLM service with docker.
 
-## 2.1 Setup Environment Variables
+### 2.1 Setup Environment Variables
 
 In order to start TGI and LLM services, you need to setup the following environment variables first.
 
@@ -144,16 +144,16 @@ export LANGCHAIN_PROJECT="opea/llms"
 export CHAT_PROCESSOR="ChatModelLlama"
 ```
 
-## 2.2 Build Docker Image
+### 2.2 Build Docker Image
 
-### 2.2.1 TGI
+#### 2.2.1 TGI
 
 ```bash
 cd ../../
 docker build -t opea/llm-tgi:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/llms/text-generation/tgi/Dockerfile .
 ```
 
-### 2.2.2 vLLM
+#### 2.2.2 vLLM
 
 Build vllm docker.
 
@@ -168,7 +168,7 @@ cd ../../../../
 docker build -t opea/llm-vllm:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/llms/text-generation/vllm/docker/Dockerfile.microservice .
 ```
 
-### 2.2.3 Ray Serve
+#### 2.2.3 Ray Serve
 
 Build Ray Serve docker.
 
@@ -190,15 +190,15 @@ To start a docker container, you have two options:
 
 You can choose one as needed.
 
-## 2.3 Run Docker with CLI (Option A)
+### 2.3 Run Docker with CLI (Option A)
 
-### 2.3.1 TGI
+#### 2.3.1 TGI
 
 ```bash
 docker run -d --name="llm-tgi-server" -p 9000:9000 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e TGI_LLM_ENDPOINT=$TGI_LLM_ENDPOINT -e HF_TOKEN=$HF_TOKEN opea/llm-tgi:latest
 ```
 
-### 2.3.2 vLLM
+#### 2.3.2 vLLM
 
 Start vllm endpoint.
 
@@ -212,7 +212,7 @@ Start vllm microservice.
 docker run --name="llm-vllm-server" -p 9000:9000 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e no_proxy=${no_proxy} -e vLLM_LLM_ENDPOINT=$vLLM_LLM_ENDPOINT -e HUGGINGFACEHUB_API_TOKEN=$HUGGINGFACEHUB_API_TOKEN -e LLM_MODEL_ID=$LLM_MODEL_ID opea/llm-vllm:latest
 ```
 
-### 2.3.3 Ray Serve
+#### 2.3.3 Ray Serve
 
 Start Ray Serve endpoint.
 
@@ -226,32 +226,32 @@ Start Ray Serve microservice.
 docker run -d --name="llm-ray-server" -p 9000:9000 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e RAY_Serve_ENDPOINT=$RAY_Serve_ENDPOINT -e HUGGINGFACEHUB_API_TOKEN=$HUGGINGFACEHUB_API_TOKEN -e LLM_MODEL=$LLM_MODEL opea/llm-ray:latest
 ```
 
-## 2.4 Run Docker with Docker Compose (Option B)
+### 2.4 Run Docker with Docker Compose (Option B)
 
-### 2.4.1 TGI
+#### 2.4.1 TGI
 
 ```bash
 cd text-generation/tgi
 docker compose -f docker_compose_llm.yaml up -d
 ```
 
-### 2.4.2 vLLM
+#### 2.4.2 vLLM
 
 ```bash
 cd text-generation/vllm
 docker compose -f docker_compose_llm.yaml up -d
 ```
 
-### 2.4.3 Ray Serve
+#### 2.4.3 Ray Serve
 
 ```bash
 cd text-genetation/ray_serve
 docker compose -f docker_compose_llm.yaml up -d
 ```
 
-# 🚀3. Consume LLM Service
+## 🚀3. Consume LLM Service
 
-## 3.1 Check Service Status
+### 3.1 Check Service Status
 
 ```bash
 curl http://${your_ip}:9000/v1/health_check\
@@ -259,7 +259,7 @@ curl http://${your_ip}:9000/v1/health_check\
   -H 'Content-Type: application/json'
 ```
 
-## 3.2 Consume LLM Service
+### 3.2 Consume LLM Service
 
 You can set the following model parameters according to your actual needs, such as `max_new_tokens`, `streaming`.
 
@@ -279,7 +279,7 @@ curl http://${your_ip}:9000/v1/chat/completions \
   -H 'Content-Type: application/json'
 ```
 
-## 4. Validated Model
+### 4. Validated Model
 
 | Model                     | TGI-Gaudi | vLLM-CPU | vLLM-Gaudi | Ray |
 | ------------------------- | --------- | -------- | ---------- | --- |
diff --git a/comps/llms/faq-generation/tgi/README.md b/comps/llms/faq-generation/tgi/README.md
index 45f1ad1bd..9673ee7fe 100644
--- a/comps/llms/faq-generation/tgi/README.md
+++ b/comps/llms/faq-generation/tgi/README.md
@@ -2,11 +2,11 @@
 
 This microservice interacts with the TGI LLM server to generate FAQs from Input Text.[Text Generation Inference](https://github.com/huggingface/text-generation-inference) (TGI) is a toolkit for deploying and serving Large Language Models (LLMs). TGI enables high-performance text generation for the most popular open-source LLMs, including Llama, Falcon, StarCoder, BLOOM, GPT-NeoX, and more.
 
-# 🚀1. Start Microservice with Docker
+## 🚀1. Start Microservice with Docker
 
 If you start an LLM microservice with docker, the `docker_compose_llm.yaml` file will automatically start a TGI service with docker.
 
-## 1.1 Setup Environment Variables
+### 1.1 Setup Environment Variables
 
 In order to start TGI and LLM services, you need to setup the following environment variables first.
 
@@ -18,7 +18,7 @@ export LANGCHAIN_TRACING_V2=true
 export LANGCHAIN_API_KEY=${your_langchain_api_key}
 ```
 
-## 1.2 Build Docker Image
+### 1.2 Build Docker Image
 
 ```bash
 cd ../../../../
@@ -32,7 +32,7 @@ To start a docker container, you have two options:
 
 You can choose one as needed.
 
-## 1.3 Run Docker with CLI (Option A)
+### 1.3 Run Docker with CLI (Option A)
 
 ```bash
 docker run -d -p 8008:80 -v ./data:/data --name tgi_service --shm-size 1g ghcr.io/huggingface/text-generation-inference:1.4 --model-id ${LLM_MODEL_ID}
@@ -42,16 +42,16 @@ docker run -d -p 8008:80 -v ./data:/data --name tgi_service --shm-size 1g ghcr.i
 docker run -d --name="llm-faqgen-server" -p 9000:9000 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e TGI_LLM_ENDPOINT=$TGI_LLM_ENDPOINT -e HUGGINGFACEHUB_API_TOKEN=$HF_TOKEN opea/llm-faqgen-tgi:latest
 ```
 
-## 1.4 Run Docker with Docker Compose (Option B)
+### 1.4 Run Docker with Docker Compose (Option B)
 
 ```bash
 cd text-generation/tgi
 docker compose -f docker_compose_llm.yaml up -d
 ```
 
-# 🚀3. Consume LLM Service
+## 🚀3. Consume LLM Service
 
-## 3.1 Check Service Status
+### 3.1 Check Service Status
 
 ```bash
 curl http://${your_ip}:9000/v1/health_check\
@@ -59,7 +59,7 @@ curl http://${your_ip}:9000/v1/health_check\
   -H 'Content-Type: application/json'
 ```
 
-## 3.2 Consume FAQGen LLM Service
+### 3.2 Consume FAQGen LLM Service
 
 ```bash
 curl http://${your_ip}:9000/v1/faqgen \
diff --git a/comps/llms/summarization/tgi/README.md b/comps/llms/summarization/tgi/README.md
index 9e5858b4b..c8cc9c44a 100644
--- a/comps/llms/summarization/tgi/README.md
+++ b/comps/llms/summarization/tgi/README.md
@@ -3,24 +3,24 @@
 In this microservice, we utilize LangChain to implement summarization strategies and facilitate LLM inference using Text Generation Inference on Intel Xeon and Gaudi2 processors.
 [Text Generation Inference](https://github.com/huggingface/text-generation-inference) (TGI) is a toolkit for deploying and serving Large Language Models (LLMs). TGI enables high-performance text generation for the most popular open-source LLMs, including Llama, Falcon, StarCoder, BLOOM, GPT-NeoX, and more.
 
-# 🚀1. Start Microservice with Python (Option 1)
+## 🚀1. Start Microservice with Python (Option 1)
 
 To start the LLM microservice, you need to install python packages first.
 
-## 1.1 Install Requirements
+### 1.1 Install Requirements
 
 ```bash
 pip install -r requirements.txt
 ```
 
-## 1.2 Start LLM Service
+### 1.2 Start LLM Service
 
 ```bash
 export HF_TOKEN=${your_hf_api_token}
 docker run -p 8008:80 -v ./data:/data --name llm-docsum-tgi --shm-size 1g ghcr.io/huggingface/text-generation-inference:2.1.0 --model-id ${your_hf_llm_model}
 ```
 
-## 1.3 Verify the TGI Service
+### 1.3 Verify the TGI Service
 
 ```bash
 curl http://${your_ip}:8008/generate \
@@ -29,18 +29,18 @@ curl http://${your_ip}:8008/generate \
   -H 'Content-Type: application/json'
 ```
 
-## 1.4 Start LLM Service with Python Script
+### 1.4 Start LLM Service with Python Script
 
 ```bash
 export TGI_LLM_ENDPOINT="http://${your_ip}:8008"
 python llm.py
 ```
 
-# 🚀2. Start Microservice with Docker (Option 2)
+## 🚀2. Start Microservice with Docker (Option 2)
 
 If you start an LLM microservice with docker, the `docker_compose_llm.yaml` file will automatically start a TGI/vLLM service with docker.
 
-## 2.1 Setup Environment Variables
+### 2.1 Setup Environment Variables
 
 In order to start TGI and LLM services, you need to setup the following environment variables first.
 
@@ -50,7 +50,7 @@ export TGI_LLM_ENDPOINT="http://${your_ip}:8008"
 export LLM_MODEL_ID=${your_hf_llm_model}
 ```
 
-## 2.2 Build Docker Image
+### 2.2 Build Docker Image
 
 ```bash
 cd ../../
@@ -64,21 +64,21 @@ To start a docker container, you have two options:
 
 You can choose one as needed.
 
-## 2.3 Run Docker with CLI (Option A)
+### 2.3 Run Docker with CLI (Option A)
 
 ```bash
 docker run -d --name="llm-docsum-tgi-server" -p 9000:9000 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e TGI_LLM_ENDPOINT=$TGI_LLM_ENDPOINT -e HF_TOKEN=$HF_TOKEN opea/llm-docsum-tgi:latest
 ```
 
-## 2.4 Run Docker with Docker Compose (Option B)
+### 2.4 Run Docker with Docker Compose (Option B)
 
 ```bash
 docker compose -f docker_compose_llm.yaml up -d
 ```
 
-# 🚀3. Consume LLM Service
+## 🚀3. Consume LLM Service
 
-## 3.1 Check Service Status
+### 3.1 Check Service Status
 
 ```bash
 curl http://${your_ip}:9000/v1/health_check\
@@ -86,7 +86,7 @@ curl http://${your_ip}:9000/v1/health_check\
   -H 'Content-Type: application/json'
 ```
 
-## 3.2 Consume LLM Service
+### 3.2 Consume LLM Service
 
 ```bash
 curl http://${your_ip}:9000/v1/chat/docsum \
diff --git a/comps/llms/text-generation/ollama/README.md b/comps/llms/text-generation/ollama/README.md
index fc7de914e..5a86e8c61 100644
--- a/comps/llms/text-generation/ollama/README.md
+++ b/comps/llms/text-generation/ollama/README.md
@@ -2,9 +2,9 @@
 
 [Ollama](https://github.com/ollama/ollama) allows you to run open-source large language models, such as Llama 3, locally. Ollama bundles model weights, configuration, and data into a single package, defined by a Modelfile. Ollama is a lightweight, extensible framework for building and running language models on the local machine. It provides a simple API for creating, running, and managing models, as well as a library of pre-built models that can be easily used in a variety of applications. It's the best choice to deploy large language models on AIPC locally.
 
-# Get Started
+## Get Started
 
-## Setup
+### Setup
 
 Follow [these instructions](https://github.com/ollama/ollama) to set up and run a local Ollama instance.
 
@@ -35,15 +35,15 @@ Special settings are necessary to pull models behind the proxy.
   sudo systemctl restart ollama
   ```
 
-## Usage
+### Usage
 
 Here are a few ways to interact with pulled local models:
 
-### In the terminal
+#### In the terminal
 
 All of your local models are automatically served on localhost:11434. Run ollama run <name-of-model> to start interacting via the command line directly.
 
-### API access
+#### API access
 
 Send an application/json request to the API endpoint of Ollama to interact.
 
@@ -54,20 +54,20 @@ curl http://localhost:11434/api/generate -d '{
 }'
 ```
 
-# Build Docker Image
+## Build Docker Image
 
 ```bash
 cd GenAIComps/
 docker build --no-cache -t opea/llm-ollama:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/llms/text-generation/ollama/Dockerfile .
 ```
 
-# Run the Ollama Microservice
+## Run the Ollama Microservice
 
 ```bash
 docker run --network host -e http_proxy=$http_proxy -e https_proxy=$https_proxy opea/llm-ollama:latest
 ```
 
-# Consume the Ollama Microservice
+## Consume the Ollama Microservice
 
 ```bash
 curl http://127.0.0.1:9000/v1/chat/completions  -X POST   -d '{"model": "llama3", "query":"What is Deep Learning?","max_new_tokens":32,"top_k":10,"top_p":0.95,"typical_p":0.95,"temperature":0.01,"repetition_penalty":1.03,"streaming":true}'   -H 'Content-Type: application/json'
diff --git a/comps/llms/text-generation/tgi/README.md b/comps/llms/text-generation/tgi/README.md
index 57f476720..cca4d1fa4 100644
--- a/comps/llms/text-generation/tgi/README.md
+++ b/comps/llms/text-generation/tgi/README.md
@@ -2,17 +2,17 @@
 
 [Text Generation Inference](https://github.com/huggingface/text-generation-inference) (TGI) is a toolkit for deploying and serving Large Language Models (LLMs). TGI enables high-performance text generation for the most popular open-source LLMs, including Llama, Falcon, StarCoder, BLOOM, GPT-NeoX, and more.
 
-# 🚀1. Start Microservice with Python (Option 1)
+## 🚀1. Start Microservice with Python (Option 1)
 
 To start the LLM microservice, you need to install python packages first.
 
-## 1.1 Install Requirements
+### 1.1 Install Requirements
 
 ```bash
 pip install -r requirements.txt
 ```
 
-## 1.2 Start LLM Service
+### 1.2 Start LLM Service
 
 ```bash
 export HF_TOKEN=${your_hf_api_token}
@@ -22,7 +22,7 @@ export LANGCHAIN_PROJECT="opea/gen-ai-comps:llms"
 docker run -p 8008:80 -v ./data:/data --name tgi_service --shm-size 1g ghcr.io/huggingface/text-generation-inference:2.1.0 --model-id ${your_hf_llm_model}
 ```
 
-## 1.3 Verify the TGI Service
+### 1.3 Verify the TGI Service
 
 ```bash
 curl http://${your_ip}:8008/generate \
@@ -31,18 +31,18 @@ curl http://${your_ip}:8008/generate \
   -H 'Content-Type: application/json'
 ```
 
-## 1.4 Start LLM Service with Python Script
+### 1.4 Start LLM Service with Python Script
 
 ```bash
 export TGI_LLM_ENDPOINT="http://${your_ip}:8008"
 python text-generation/tgi/llm.py
 ```
 
-# 🚀2. Start Microservice with Docker (Option 2)
+## 🚀2. Start Microservice with Docker (Option 2)
 
 If you start an LLM microservice with docker, the `docker_compose_llm.yaml` file will automatically start a TGI/vLLM service with docker.
 
-## 2.1 Setup Environment Variables
+### 2.1 Setup Environment Variables
 
 In order to start TGI and LLM services, you need to setup the following environment variables first.
 
@@ -55,7 +55,7 @@ export LANGCHAIN_API_KEY=${your_langchain_api_key}
 export LANGCHAIN_PROJECT="opea/llms"
 ```
 
-## 2.2 Build Docker Image
+### 2.2 Build Docker Image
 
 ```bash
 cd ../../
@@ -69,22 +69,22 @@ To start a docker container, you have two options:
 
 You can choose one as needed.
 
-## 2.3 Run Docker with CLI (Option A)
+### 2.3 Run Docker with CLI (Option A)
 
 ```bash
 docker run -d --name="llm-tgi-server" -p 9000:9000 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e TGI_LLM_ENDPOINT=$TGI_LLM_ENDPOINT -e HF_TOKEN=$HF_TOKEN opea/llm-tgi:latest
 ```
 
-## 2.4 Run Docker with Docker Compose (Option B)
+### 2.4 Run Docker with Docker Compose (Option B)
 
 ```bash
 cd text-generation/tgi
 docker compose -f docker_compose_llm.yaml up -d
 ```
 
-# 🚀3. Consume LLM Service
+## 🚀3. Consume LLM Service
 
-## 3.1 Check Service Status
+### 3.1 Check Service Status
 
 ```bash
 curl http://${your_ip}:9000/v1/health_check\
@@ -92,7 +92,7 @@ curl http://${your_ip}:9000/v1/health_check\
   -H 'Content-Type: application/json'
 ```
 
-## 3.2 Consume LLM Service
+### 3.2 Consume LLM Service
 
 You can set the following model parameters according to your actual needs, such as `max_new_tokens`, `streaming`.
 
@@ -118,7 +118,7 @@ curl http://${your_ip}:9000/v1/chat/completions \
   -H 'Content-Type: application/json'
 ```
 
-## 4. Validated Model
+### 4. Validated Model
 
 | Model                     | TGI |
 | ------------------------- | --- |
diff --git a/comps/llms/text-generation/vllm-xft/README.md b/comps/llms/text-generation/vllm-xft/README.md
index 68931d4ca..4b39709a8 100644
--- a/comps/llms/text-generation/vllm-xft/README.md
+++ b/comps/llms/text-generation/vllm-xft/README.md
@@ -1,24 +1,26 @@
+# vLLM-xFT
+
 vLLM-xFT is a fork of vLLM to integrate the xfastertransformer backend, maintaining compatibility with most of the official vLLM's features.
 For usage of vllm-xFT, please refer to [xFasterTransformer/vllm-xft](https://github.com/intel/xFasterTransformer/blob/main/serving/vllm-xft.md)
 
-# 🚀 Start Microservice with Docker
+## 🚀 Start Microservice with Docker
 
-## 1 Build Docker Image
+### 1 Build Docker Image
 
 ```bash
 cd ../../../
 docker build -t opea/llm-vllm-xft:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/llms/text-generation/vllm-xft/docker/Dockerfile .
 ```
 
-## 2 Run Docker with CLI
+### 2 Run Docker with CLI
 
 ```bash
 docker run -it -p 9000:9000  -v /home/sdp/Qwen2-7B-Instruct/:/Qwen2-7B-Instruct/   -e vLLM_LLM_ENDPOINT="http://localhost:18688" -e HF_DATASET_DIR="/Qwen2-7B-Instruct/" -e OUTPUT_DIR="./output" -e TOKEN_PATH="/Qwen2-7B-Instruct/" -e https_proxy=$https_proxy -e http_proxy=$http_proxy -e no_proxy=$no_proxy --ipc=host opea/llm-vllm-xft:latest
 ```
 
-# 🚀3. Consume LLM Service
+## 🚀3. Consume LLM Service
 
-## 3.1 Check Service Status
+### 3.1 Check Service Status
 
 ```bash
 curl http://${your_ip}:9000/v1/health_check\
@@ -26,7 +28,7 @@ curl http://${your_ip}:9000/v1/health_check\
   -H 'Content-Type: application/json'
 ```
 
-## 3.2 Consume LLM Service
+### 3.2 Consume LLM Service
 
 You can set the following model parameters according to your actual needs, such as `max_new_tokens`, `streaming`.
 
diff --git a/comps/lvms/README.md b/comps/lvms/README.md
index 8f8237180..12648e99b 100644
--- a/comps/lvms/README.md
+++ b/comps/lvms/README.md
@@ -2,15 +2,15 @@
 
 Visual Question and Answering is one of the multimodal tasks empowered by LVMs (Large Visual Models). This microservice supports visual Q&A by using LLaVA as the base large visual model. It accepts two inputs: a prompt and an image. It outputs the answer to the prompt about the image.
 
-# 🚀1. Start Microservice with Python (Option 1)
+## 🚀1. Start Microservice with Python (Option 1)
 
-## 1.1 Install Requirements
+### 1.1 Install Requirements
 
 ```bash
 pip install -r requirements.txt
 ```
 
-## 1.2 Start LLaVA Service/Test
+### 1.2 Start LLaVA Service/Test
 
 - Xeon CPU
 
@@ -37,7 +37,7 @@ nohup python llava_server.py &
 python check_llava_server.py
 ```
 
-## 1.3 Start Image To Text Service/Test
+### 1.3 Start Image To Text Service/Test
 
 ```bash
 cd ..
@@ -47,11 +47,11 @@ python lvm.py
 python check_lvm.py
 ```
 
-# 🚀2. Start Microservice with Docker (Option 2)
+## 🚀2. Start Microservice with Docker (Option 2)
 
-## 2.1 Build Images
+### 2.1 Build Images
 
-### 2.1.1 LLaVA Server Image
+#### 2.1.1 LLaVA Server Image
 
 - Xeon CPU
 
@@ -67,16 +67,16 @@ cd ../..
 docker build -t opea/llava:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/lvms/llava/Dockerfile_hpu .
 ```
 
-### 2.1.2 LVM Service Image
+#### 2.1.2 LVM Service Image
 
 ```bash
 cd ../..
 docker build -t opea/lvm:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/lvms/Dockerfile .
 ```
 
-## 2.2 Start LLaVA and LVM Service
+### 2.2 Start LLaVA and LVM Service
 
-### 2.2.1 Start LLaVA server
+#### 2.2.1 Start LLaVA server
 
 - Xeon
 
@@ -90,7 +90,7 @@ docker run -p 8399:8399 -e http_proxy=$http_proxy --ipc=host -e https_proxy=$htt
 docker run -p 8399:8399 --runtime=habana -e HABANA_VISIBLE_DEVICES=all -e OMPI_MCA_btl_vader_single_copy_mechanism=none --cap-add=sys_nice --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy opea/llava:latest
 ```
 
-### 2.2.2 Start LVM service
+#### 2.2.2 Start LVM service
 
 ```bash
 ip_address=$(hostname -I | awk '{print $1}')
@@ -98,7 +98,7 @@ ip_address=$(hostname -I | awk '{print $1}')
 docker run -p 9399:9399 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e LVM_ENDPOINT=http://$ip_address:8399 opea/lvm:latest
 ```
 
-### 2.2.3 Test
+#### 2.2.3 Test
 
 ```bash
 # Use curl/python
diff --git a/comps/prompt_registry/mongo/README.md b/comps/prompt_registry/mongo/README.md
index 0cbfd6f99..86baaaf27 100644
--- a/comps/prompt_registry/mongo/README.md
+++ b/comps/prompt_registry/mongo/README.md
@@ -21,16 +21,16 @@ Start document preparation microservice for Milvus with below command.
 python prompt.py
 ```
 
-# 🚀Start Microservice with Docker
+## 🚀Start Microservice with Docker
 
-## Build Docker Image
+### Build Docker Image
 
 ```bash
 cd ~/GenAIComps
 docker build -t opea/promptregistry-mongo-server:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/prompt_registry/mongo/docker/Dockerfile .
 ```
 
-## Run Docker with CLI
+### Run Docker with CLI
 
 1. Run mongoDB image
 
@@ -44,7 +44,7 @@ docker run -d -p 27017:27017 --name=mongo mongo:latest
 docker run -d --name="promptregistry-mongo-server" -p 6012:6012 -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e no_proxy=$no_proxy -e MONGO_HOST=${MONGO_HOST} -e MONGO_PORT=${MONGO_PORT} -e DB_NAME=${DB_NAME} -e COLLECTION_NAME=${COLLECTION_NAME} opea/promptregistry-mongo-server:latest
 ```
 
-## Invoke Microservice
+### Invoke Microservice
 
 Once prompt_registry service is up and running, users can access the database by using API endpoint below. Each API serves different purpose and return appropriate response.
 
diff --git a/comps/reranks/README.md b/comps/reranks/README.md
index ecec38272..9b5dc9042 100644
--- a/comps/reranks/README.md
+++ b/comps/reranks/README.md
@@ -2,17 +2,17 @@
 
 The Reranking Microservice, fueled by reranking models, stands as a straightforward yet immensely potent tool for semantic search. When provided with a query and a collection of documents, reranking swiftly indexes the documents based on their semantic relevance to the query, arranging them from most to least pertinent. This microservice significantly enhances overall accuracy. In a text retrieval system, either a dense embedding model or a sparse lexical search index is often employed to retrieve relevant text documents based on the input. However, a reranking model can further refine this process by rearranging potential candidates into a final, optimized order.
 
-# 🚀1. Start Microservice with Python (Option 1)
+## 🚀1. Start Microservice with Python (Option 1)
 
 To start the Reranking microservice, you must first install the required python packages.
 
-## 1.1 Install Requirements
+### 1.1 Install Requirements
 
 ```bash
 pip install -r requirements.txt
 ```
 
-## 1.2 Start TEI Service
+### 1.2 Start TEI Service
 
 ```bash
 export HF_TOKEN=${your_hf_api_token}
@@ -25,7 +25,7 @@ volume=$PWD/data
 docker run -d -p 6060:80 -v $volume:/data -e http_proxy=$http_proxy -e https_proxy=$https_proxy --pull always ghcr.io/huggingface/text-embeddings-inference:cpu-1.2 --model-id $RERANK_MODEL_ID --revision $revision --hf-api-token $HF_TOKEN
 ```
 
-## 1.3 Verify the TEI Service
+### 1.3 Verify the TEI Service
 
 ```bash
 curl 127.0.0.1:6060/rerank \
@@ -34,18 +34,18 @@ curl 127.0.0.1:6060/rerank \
     -H 'Content-Type: application/json'
 ```
 
-## 1.4 Start Reranking Service with Python Script
+### 1.4 Start Reranking Service with Python Script
 
 ```bash
 export TEI_RERANKING_ENDPOINT="http://${your_ip}:6060"
 python reranking_tei_xeon.py
 ```
 
-# 🚀2. Start Microservice with Docker (Option 2)
+## 🚀2. Start Microservice with Docker (Option 2)
 
 If you start an Reranking microservice with docker, the `docker_compose_reranking.yaml` file will automatically start a TEI service with docker.
 
-## 2.1 Setup Environment Variables
+### 2.1 Setup Environment Variables
 
 ```bash
 export HF_TOKEN=${your_hf_api_token}
@@ -55,7 +55,7 @@ export LANGCHAIN_PROJECT="opea/reranks"
 export TEI_RERANKING_ENDPOINT="http://${your_ip}:8808"
 ```
 
-## 2.2 Build Docker Image
+### 2.2 Build Docker Image
 
 ```bash
 cd ../../
@@ -69,22 +69,22 @@ To start a docker container, you have two options:
 
 You can choose one as needed.
 
-## 2.3 Run Docker with CLI (Option A)
+### 2.3 Run Docker with CLI (Option A)
 
 ```bash
 docker run -d --name="reranking-tei-server" -p 8000:8000 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e TEI_RERANKING_ENDPOINT=$TEI_RERANKING_ENDPOINT -e HF_TOKEN=$HF_TOKEN -e LANGCHAIN_API_KEY=$LANGCHAIN_API_KEY opea/reranking-tei:latest
 ```
 
-## 2.4 Run Docker with Docker Compose (Option B)
+### 2.4 Run Docker with Docker Compose (Option B)
 
 ```bash
 cd langchain/docker
 docker compose -f docker_compose_reranking.yaml up -d
 ```
 
-# 🚀3. Consume Reranking Service
+## 🚀3. Consume Reranking Service
 
-## 3.1 Check Service Status
+### 3.1 Check Service Status
 
 ```bash
 curl http://localhost:8000/v1/health_check \
@@ -92,7 +92,7 @@ curl http://localhost:8000/v1/health_check \
   -H 'Content-Type: application/json'
 ```
 
-## 3.2 Consume Reranking Service
+### 3.2 Consume Reranking Service
 
 ```bash
 curl http://localhost:8000/v1/reranking \
diff --git a/comps/reranks/fastrag/README.md b/comps/reranks/fastrag/README.md
index fac481860..025799d05 100644
--- a/comps/reranks/fastrag/README.md
+++ b/comps/reranks/fastrag/README.md
@@ -2,17 +2,17 @@
 
 The Reranking Microservice, fueled by reranking models, stands as a straightforward yet immensely potent tool for semantic search. When provided with a query and a collection of documents, reranking swiftly indexes the documents based on their semantic relevance to the query, arranging them from most to least pertinent. This microservice significantly enhances overall accuracy. In a text retrieval system, either a dense embedding model or a sparse lexical search index is often employed to retrieve relevant text documents based on the input. However, a reranking model can further refine this process by rearranging potential candidates into a final, optimized order.
 
-# 🚀1. Start Microservice with Python (Option 1)
+## 🚀1. Start Microservice with Python (Option 1)
 
 To start the Reranking microservice, you must first install the required python packages.
 
-## 1.1 Install Requirements
+### 1.1 Install Requirements
 
 ```bash
 pip install -r requirements.txt
 ```
 
-## 1.2 Install fastRAG
+### 1.2 Install fastRAG
 
 ```bash
 git clone https://github.com/IntelLabs/fastRAG.git
@@ -21,37 +21,37 @@ pip install .
 pip install .[intel]
 ```
 
-## 1.3 Start Reranking Service with Python Script
+### 1.3 Start Reranking Service with Python Script
 
 ```bash
 export EMBED_MODEL="Intel/bge-small-en-v1.5-rag-int8-static"
 python local_reranking.py
 ```
 
-# 🚀2. Start Microservice with Docker (Option 2)
+## 🚀2. Start Microservice with Docker (Option 2)
 
-## 2.1 Setup Environment Variables
+### 2.1 Setup Environment Variables
 
 ```bash
 export EMBED_MODEL="Intel/bge-small-en-v1.5-rag-int8-static"
 ```
 
-## 2.2 Build Docker Image
+### 2.2 Build Docker Image
 
 ```bash
 cd ../../
 docker build -t opea/reranking-fastrag:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/reranks/fastrag/docker/Dockerfile .
 ```
 
-## 2.3 Run Docker
+### 2.3 Run Docker
 
 ```bash
 docker run -d --name="reranking-fastrag-server" -p 8000:8000 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e EMBED_MODEL=$EMBED_MODEL opea/reranking-fastrag:latest
 ```
 
-# 🚀3. Consume Reranking Service
+## 🚀3. Consume Reranking Service
 
-## 3.1 Check Service Status
+### 3.1 Check Service Status
 
 ```bash
 curl http://localhost:8000/v1/health_check \
@@ -59,7 +59,7 @@ curl http://localhost:8000/v1/health_check \
   -H 'Content-Type: application/json'
 ```
 
-## 3.2 Consume Reranking Service
+### 3.2 Consume Reranking Service
 
 ```bash
 curl http://localhost:8000/v1/reranking \
diff --git a/comps/reranks/langchain-mosec/README.md b/comps/reranks/langchain-mosec/README.md
index 59592a4ba..cd7e36ce0 100644
--- a/comps/reranks/langchain-mosec/README.md
+++ b/comps/reranks/langchain-mosec/README.md
@@ -4,26 +4,26 @@
 docker build --build-arg http_proxy=$http_proxy --build-arg https_proxy=$https_proxy -t opea/reranking-langchain-mosec-endpoint:latest -f comps/reranks/langchain-mosec/mosec-docker/Dockerfile .
 ```
 
-# build reranking microservice docker image
+## build reranking microservice docker image
 
 ```
 docker build --build-arg http_proxy=$http_proxy --build-arg https_proxy=$https_proxy -t opea/reranking-langchain-mosec:latest -f comps/reranks/langchain-mosec/docker/Dockerfile .
 ```
 
-# launch Mosec endpoint docker container
+## launch Mosec endpoint docker container
 
 ```
 docker run -d --name="reranking-langchain-mosec-endpoint" -p 6001:8000  opea/reranking-langchain-mosec-endpoint:latest
 ```
 
-# launch embedding microservice docker container
+## launch embedding microservice docker container
 
 ```
 export MOSEC_RERANKING_ENDPOINT=http://127.0.0.1:6001
 docker run -d --name="reranking-langchain-mosec-server" -e http_proxy=$http_proxy -e https_proxy=$https_proxy -p 6000:8000 --ipc=host -e MOSEC_RERANKING_ENDPOINT=$MOSEC_RERANKING_ENDPOINT opea/reranking-langchain-mosec:latest
 ```
 
-# run client test
+## run client test
 
 ```
 curl http://localhost:6000/v1/reranking \
diff --git a/comps/retrievers/haystack/qdrant/README.md b/comps/retrievers/haystack/qdrant/README.md
index 66da3c627..a7653e8ce 100644
--- a/comps/retrievers/haystack/qdrant/README.md
+++ b/comps/retrievers/haystack/qdrant/README.md
@@ -1,18 +1,18 @@
 # Retriever Microservice with Qdrant
 
-# 1. 🚀Start Microservice with Python (Option 1)
+## 1. 🚀Start Microservice with Python (Option 1)
 
-## 1.1 Install Requirements
+### 1.1 Install Requirements
 
 ```bash
 pip install -r requirements.txt
 ```
 
-## 1.2 Start Qdrant Server
+### 1.2 Start Qdrant Server
 
 Please refer to this [readme](../../../vectorstores/langchain/qdrant/README.md).
 
-## 1.3 Setup Environment Variables
+### 1.3 Setup Environment Variables
 
 ```bash
 export QDRANT_HOST=${your_qdrant_host_ip}
@@ -21,16 +21,16 @@ export EMBED_DIMENSION=${your_embedding_dimension}
 export INDEX_NAME=${your_index_name}
 ```
 
-## 1.4 Start Retriever Service
+### 1.4 Start Retriever Service
 
 ```bash
 export TEI_EMBEDDING_ENDPOINT="http://${your_ip}:6060"
 python haystack/qdrant/retriever_qdrant.py
 ```
 
-# 2. 🚀Start Microservice with Docker (Option 2)
+## 2. 🚀Start Microservice with Docker (Option 2)
 
-## 2.1 Setup Environment Variables
+### 2.1 Setup Environment Variables
 
 ```bash
 export QDRANT_HOST=${your_qdrant_host_ip}
@@ -38,22 +38,22 @@ export QDRANT_PORT=6333
 export TEI_EMBEDDING_ENDPOINT="http://${your_ip}:6060"
 ```
 
-## 2.2 Build Docker Image
+### 2.2 Build Docker Image
 
 ```bash
 cd ../../
 docker build -t opea/retriever-qdrant:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/retrievers/haystack/qdrant/docker/Dockerfile .
 ```
 
-## 2.3 Run Docker with CLI
+### 2.3 Run Docker with CLI
 
 ```bash
 docker run -d --name="retriever-qdrant-server" -p 7000:7000 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e TEI_EMBEDDING_ENDPOINT=$TEI_EMBEDDING_ENDPOINT -e QDRANT_HOST=$QDRANT_HOST -e QDRANT_PORT=$QDRANT_PORT opea/retriever-qdrant:latest
 ```
 
-# 🚀3. Consume Retriever Service
+## 🚀3. Consume Retriever Service
 
-## 3.1 Check Service Status
+### 3.1 Check Service Status
 
 ```bash
 curl http://${your_ip}:7000/v1/health_check \
@@ -61,7 +61,7 @@ curl http://${your_ip}:7000/v1/health_check \
   -H 'Content-Type: application/json'
 ```
 
-## 3.2 Consume Embedding Service
+### 3.2 Consume Embedding Service
 
 To consume the Retriever Microservice, you can generate a mock embedding vector of length 768 with Python.
 
diff --git a/comps/retrievers/langchain/README.md b/comps/retrievers/langchain/README.md
index 3de5cab21..300fbc099 100644
--- a/comps/retrievers/langchain/README.md
+++ b/comps/retrievers/langchain/README.md
@@ -6,14 +6,14 @@ The service primarily utilizes similarity measures in vector space to rapidly re
 
 Overall, this microservice provides robust backend support for applications requiring efficient similarity searches, playing a vital role in scenarios such as recommendation systems, information retrieval, or any other context where precise measurement of document similarity is crucial.
 
-# Retriever Microservice with Redis
+## Retriever Microservice with Redis
 
 For details, please refer to this [readme](redis/README.md)
 
-# Retriever Microservice with Milvus
+## Retriever Microservice with Milvus
 
 For details, please refer to this [readme](milvus/README.md)
 
-# Retriever Microservice with PGVector
+## Retriever Microservice with PGVector
 
 For details, please refer to this [readme](pgvector/README.md)
diff --git a/comps/retrievers/langchain/milvus/README.md b/comps/retrievers/langchain/milvus/README.md
index d1bbc80da..6b9fe2045 100644
--- a/comps/retrievers/langchain/milvus/README.md
+++ b/comps/retrievers/langchain/milvus/README.md
@@ -1,18 +1,18 @@
 # Retriever Microservice with Milvus
 
-# 🚀Start Microservice with Python
+## 🚀Start Microservice with Python
 
-## Install Requirements
+### Install Requirements
 
 ```bash
 pip install -r requirements.txt
 ```
 
-## Start Milvus Server
+### Start Milvus Server
 
 Please refer to this [readme](../../../vectorstores/langchain/milvus/README.md).
 
-## Setup Environment Variables
+### Setup Environment Variables
 
 ```bash
 export no_proxy=${your_no_proxy}
@@ -24,31 +24,31 @@ export COLLECTION_NAME=${your_collection_name}
 export MOSEC_EMBEDDING_ENDPOINT=${your_emdding_endpoint}
 ```
 
-## Start Retriever Service
+### Start Retriever Service
 
 ```bash
 export MOSEC_EMBEDDING_ENDPOINT="http://${your_ip}:6060"
 python langchain/retriever_redis.py
 ```
 
-# 🚀Start Microservice with Docker
+## 🚀Start Microservice with Docker
 
-## Build Docker Image
+### Build Docker Image
 
 ```bash
 cd ../../
 docker build -t opea/retriever-milvus:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/retrievers/langchain/milvus/docker/Dockerfile .
 ```
 
-## Run Docker with CLI
+### Run Docker with CLI
 
 ```bash
 docker run -d --name="retriever-milvus-server" -p 7000:7000 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e MOSEC_EMBEDDING_ENDPOINT=${your_emdding_endpoint} -e MILVUS=${your_milvus_host_ip}  opea/retriever-milvus:latest
 ```
 
-# 🚀3. Consume Retriever Service
+## 🚀3. Consume Retriever Service
 
-## 3.1 Check Service Status
+### 3.1 Check Service Status
 
 ```bash
 curl http://${your_ip}:7000/v1/health_check \
@@ -56,7 +56,7 @@ curl http://${your_ip}:7000/v1/health_check \
   -H 'Content-Type: application/json'
 ```
 
-## 3.2 Consume Embedding Service
+### 3.2 Consume Embedding Service
 
 To consume the Retriever Microservice, you can generate a mock embedding vector of length 768 with Python.
 
diff --git a/comps/retrievers/langchain/pgvector/README.md b/comps/retrievers/langchain/pgvector/README.md
index a0febb7fb..4914e8cb4 100644
--- a/comps/retrievers/langchain/pgvector/README.md
+++ b/comps/retrievers/langchain/pgvector/README.md
@@ -6,17 +6,17 @@ The service primarily utilizes similarity measures in vector space to rapidly re
 
 Overall, this microservice provides robust backend support for applications requiring efficient similarity searches, playing a vital role in scenarios such as recommendation systems, information retrieval, or any other context where precise measurement of document similarity is crucial.
 
-# 🚀1. Start Microservice with Python (Option 1)
+## 🚀1. Start Microservice with Python (Option 1)
 
 To start the retriever microservice, you must first install the required python packages.
 
-## 1.1 Install Requirements
+### 1.1 Install Requirements
 
 ```bash
 pip install -r requirements.txt
 ```
 
-## 1.2 Start TEI Service
+### 1.2 Start TEI Service
 
 ```bash
 export LANGCHAIN_TRACING_V2=true
@@ -28,7 +28,7 @@ volume=$PWD/data
 docker run -d -p 6060:80 -v $volume:/data -e http_proxy=$http_proxy -e https_proxy=$https_proxy --pull always ghcr.io/huggingface/text-embeddings-inference:cpu-1.2 --model-id $model --revision $revision
 ```
 
-## 1.3 Verify the TEI Service
+### 1.3 Verify the TEI Service
 
 ```bash
 curl 127.0.0.1:6060/rerank \
@@ -37,7 +37,7 @@ curl 127.0.0.1:6060/rerank \
     -H 'Content-Type: application/json'
 ```
 
-## 1.4 Setup VectorDB Service
+### 1.4 Setup VectorDB Service
 
 You need to setup your own VectorDB service (PGvector in this example), and ingest your knowledge documents into the vector database.
 
@@ -52,16 +52,16 @@ export POSTGRES_DB=vectordb
 docker run --name vectorstore-postgres -e POSTGRES_USER=${POSTGRES_USER} -e POSTGRES_HOST_AUTH_METHOD=trust -e POSTGRES_DB=${POSTGRES_DB} -e POSTGRES_PASSWORD=${POSTGRES_PASSWORD} -d -v ./init.sql:/docker-entrypoint-initdb.d/init.sql -p 5432:5432 pgvector/pgvector:0.7.0-pg16
 ```
 
-## 1.5 Start Retriever Service
+### 1.5 Start Retriever Service
 
 ```bash
 export TEI_EMBEDDING_ENDPOINT="http://${your_ip}:6060"
 python retriever_pgvector.py
 ```
 
-# 🚀2. Start Microservice with Docker (Option 2)
+## 🚀2. Start Microservice with Docker (Option 2)
 
-## 2.1 Setup Environment Variables
+### 2.1 Setup Environment Variables
 
 ```bash
 export RETRIEVE_MODEL_ID="BAAI/bge-base-en-v1.5"
@@ -73,7 +73,7 @@ export LANGCHAIN_API_KEY=${your_langchain_api_key}
 export LANGCHAIN_PROJECT="opea/retrievers"
 ```
 
-## 2.2 Build Docker Image
+### 2.2 Build Docker Image
 
 ```bash
 cd comps/retrievers/langchain/pgvector/docker
@@ -87,22 +87,22 @@ To start a docker container, you have two options:
 
 You can choose one as needed.
 
-## 2.3 Run Docker with CLI (Option A)
+### 2.3 Run Docker with CLI (Option A)
 
 ```bash
 docker run -d --name="retriever-pgvector" -p 7000:7000 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e PG_CONNECTION_STRING=$PG_CONNECTION_STRING  -e INDEX_NAME=$INDEX_NAME -e TEI_ENDPOINT=$TEI_ENDPOINT opea/retriever-pgvector:latest
 ```
 
-## 2.4 Run Docker with Docker Compose (Option B)
+### 2.4 Run Docker with Docker Compose (Option B)
 
 ```bash
 cd comps/retrievers/langchain/pgvector/docker
 docker compose -f docker_compose_retriever.yaml up -d
 ```
 
-# 🚀3. Consume Retriever Service
+## 🚀3. Consume Retriever Service
 
-## 3.1 Check Service Status
+### 3.1 Check Service Status
 
 ```bash
 curl http://localhost:7000/v1/health_check \
@@ -110,7 +110,7 @@ curl http://localhost:7000/v1/health_check \
   -H 'Content-Type: application/json'
 ```
 
-## 3.2 Consume Embedding Service
+### 3.2 Consume Embedding Service
 
 To consume the Retriever Microservice, you can generate a mock embedding vector of length 768 with Python.
 
diff --git a/comps/retrievers/langchain/redis/README.md b/comps/retrievers/langchain/redis/README.md
index 5330e7870..17c37ed4c 100644
--- a/comps/retrievers/langchain/redis/README.md
+++ b/comps/retrievers/langchain/redis/README.md
@@ -6,17 +6,17 @@ The service primarily utilizes similarity measures in vector space to rapidly re
 
 Overall, this microservice provides robust backend support for applications requiring efficient similarity searches, playing a vital role in scenarios such as recommendation systems, information retrieval, or any other context where precise measurement of document similarity is crucial.
 
-# 🚀1. Start Microservice with Python (Option 1)
+## 🚀1. Start Microservice with Python (Option 1)
 
 To start the retriever microservice, you must first install the required python packages.
 
-## 1.1 Install Requirements
+### 1.1 Install Requirements
 
 ```bash
 pip install -r requirements.txt
 ```
 
-## 1.2 Start TEI Service
+### 1.2 Start TEI Service
 
 ```bash
 export LANGCHAIN_TRACING_V2=true
@@ -28,7 +28,7 @@ volume=$PWD/data
 docker run -d -p 6060:80 -v $volume:/data -e http_proxy=$http_proxy -e https_proxy=$https_proxy --pull always ghcr.io/huggingface/text-embeddings-inference:cpu-1.2 --model-id $model --revision $revision
 ```
 
-## 1.3 Verify the TEI Service
+### 1.3 Verify the TEI Service
 
 ```bash
 curl 127.0.0.1:6060/rerank \
@@ -37,7 +37,7 @@ curl 127.0.0.1:6060/rerank \
     -H 'Content-Type: application/json'
 ```
 
-## 1.4 Setup VectorDB Service
+### 1.4 Setup VectorDB Service
 
 You need to setup your own VectorDB service (Redis in this example), and ingest your knowledge documents into the vector database.
 
@@ -48,16 +48,16 @@ Remember to ingest data into it manually.
 docker run -d --name="redis-vector-db" -p 6379:6379 -p 8001:8001 redis/redis-stack:7.2.0-v9
 ```
 
-## 1.5 Start Retriever Service
+### 1.5 Start Retriever Service
 
 ```bash
 export TEI_EMBEDDING_ENDPOINT="http://${your_ip}:6060"
 python langchain/retriever_redis.py
 ```
 
-# 🚀2. Start Microservice with Docker (Option 2)
+## 🚀2. Start Microservice with Docker (Option 2)
 
-## 2.1 Setup Environment Variables
+### 2.1 Setup Environment Variables
 
 ```bash
 export RETRIEVE_MODEL_ID="BAAI/bge-base-en-v1.5"
@@ -69,7 +69,7 @@ export LANGCHAIN_API_KEY=${your_langchain_api_key}
 export LANGCHAIN_PROJECT="opea/retrievers"
 ```
 
-## 2.2 Build Docker Image
+### 2.2 Build Docker Image
 
 ```bash
 cd ../../
@@ -83,22 +83,22 @@ To start a docker container, you have two options:
 
 You can choose one as needed.
 
-## 2.3 Run Docker with CLI (Option A)
+### 2.3 Run Docker with CLI (Option A)
 
 ```bash
 docker run -d --name="retriever-redis-server" -p 7000:7000 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e REDIS_URL=$REDIS_URL -e INDEX_NAME=$INDEX_NAME -e TEI_EMBEDDING_ENDPOINT=$TEI_EMBEDDING_ENDPOINT opea/retriever-redis:latest
 ```
 
-## 2.4 Run Docker with Docker Compose (Option B)
+### 2.4 Run Docker with Docker Compose (Option B)
 
 ```bash
 cd langchain/docker
 docker compose -f docker_compose_retriever.yaml up -d
 ```
 
-# 🚀3. Consume Retriever Service
+## 🚀3. Consume Retriever Service
 
-## 3.1 Check Service Status
+### 3.1 Check Service Status
 
 ```bash
 curl http://localhost:7000/v1/health_check \
@@ -106,7 +106,7 @@ curl http://localhost:7000/v1/health_check \
   -H 'Content-Type: application/json'
 ```
 
-## 3.2 Consume Embedding Service
+### 3.2 Consume Embedding Service
 
 To consume the Retriever Microservice, you can generate a mock embedding vector of length 768 with Python.
 
diff --git a/comps/retrievers/llamaindex/README.md b/comps/retrievers/llamaindex/README.md
index 3f6db8899..28203160c 100644
--- a/comps/retrievers/llamaindex/README.md
+++ b/comps/retrievers/llamaindex/README.md
@@ -6,17 +6,17 @@ The service primarily utilizes similarity measures in vector space to rapidly re
 
 Overall, this microservice provides robust backend support for applications requiring efficient similarity searches, playing a vital role in scenarios such as recommendation systems, information retrieval, or any other context where precise measurement of document similarity is crucial.
 
-# 🚀1. Start Microservice with Python (Option 1)
+## 🚀1. Start Microservice with Python (Option 1)
 
 To start the retriever microservice, you must first install the required python packages.
 
-## 1.1 Install Requirements
+### 1.1 Install Requirements
 
 ```bash
 pip install -r requirements.txt
 ```
 
-## 1.2 Setup VectorDB Service
+### 1.2 Setup VectorDB Service
 
 You need to setup your own VectorDB service (Redis in this example), and ingest your knowledge documents into the vector database.
 
@@ -29,15 +29,15 @@ docker run -d --name="redis-vector-db" -p 6379:6379 -p 8001:8001 redis/redis-sta
 
 And then ingest data into the Redis VectorDB using the methods described in the dataprep microservice.
 
-## 1.3 Start Retriever Service
+### 1.3 Start Retriever Service
 
 ```bash
 python retriever_redis.py
 ```
 
-# 🚀2. Start Microservice with Docker (Option 2)
+## 🚀2. Start Microservice with Docker (Option 2)
 
-## 2.1 Setup Environment Variables
+### 2.1 Setup Environment Variables
 
 ```bash
 export REDIS_URL="redis://${your_ip}:6379"
@@ -47,7 +47,7 @@ export LANGCHAIN_API_KEY=${your_langchain_api_key}
 export LANGCHAIN_PROJECT="opea/retrievers"
 ```
 
-## 2.2 Build Docker Image
+### 2.2 Build Docker Image
 
 ```bash
 cd ../../
@@ -61,22 +61,22 @@ To start a docker container, you have two options:
 
 You can choose one as needed.
 
-## 2.3 Run Docker with CLI (Option A)
+### 2.3 Run Docker with CLI (Option A)
 
 ```bash
 docker run -d --name="retriever-redis-server" -p 7000:7000 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e REDIS_URL=$REDIS_URL -e INDEX_NAME=$INDEX_NAME opea/retriever-redis:latest
 ```
 
-## 2.4 Run Docker with Docker Compose (Option B)
+### 2.4 Run Docker with Docker Compose (Option B)
 
 ```bash
 cd llamaindex/docker
 docker compose -f docker_compose_retriever.yaml up -d
 ```
 
-# 🚀3. Consume Retriever Service
+## 🚀3. Consume Retriever Service
 
-## 3.1 Check Service Status
+### 3.1 Check Service Status
 
 ```bash
 curl http://localhost:7000/v1/health_check \
@@ -84,7 +84,7 @@ curl http://localhost:7000/v1/health_check \
   -H 'Content-Type: application/json'
 ```
 
-## 3.2 Consume Retriever Service
+### 3.2 Consume Retriever Service
 
 To consume the Retriever Microservice, you can generate a mock embedding vector of length 768 with Python.
 
diff --git a/comps/tts/README.md b/comps/tts/README.md
index 52ab6c92b..5045d5870 100644
--- a/comps/tts/README.md
+++ b/comps/tts/README.md
@@ -30,13 +30,13 @@ python tts.py
 curl http://localhost:9088/v1/audio/speech -XPOST -d '{"text": "Who are you?"}' -H 'Content-Type: application/json'
 ```
 
-# 🚀2. Start Microservice with Docker (Option 2)
+## 🚀2. Start Microservice with Docker (Option 2)
 
 Alternatively, you can start the TTS microservice with Docker.
 
-## 2.1 Build Images
+### 2.1 Build Images
 
-### 2.1.1 Whisper Server Image
+#### 2.1.1 Whisper Server Image
 
 - Xeon CPU
 
@@ -52,15 +52,15 @@ cd ../..
 docker build -t opea/speecht5-gaudi:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/tts/speecht5/Dockerfile_hpu .
 ```
 
-### 2.1.2 TTS Service Image
+#### 2.1.2 TTS Service Image
 
 ```bash
 docker build -t opea/tts:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/tts/Dockerfile .
 ```
 
-## 2.2 Start SpeechT5 and TTS Service
+### 2.2 Start SpeechT5 and TTS Service
 
-### 2.2.1 Start SpeechT5 Server
+#### 2.2.1 Start SpeechT5 Server
 
 - Xeon
 
@@ -74,7 +74,7 @@ docker run -p 7055:7055 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$htt
 docker run -p 7055:7055 --runtime=habana -e HABANA_VISIBLE_DEVICES=all -e OMPI_MCA_btl_vader_single_copy_mechanism=none --cap-add=sys_nice --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy opea/speecht5-gaudi:latest
 ```
 
-### 2.2.2 Start TTS service
+#### 2.2.2 Start TTS service
 
 ```bash
 ip_address=$(hostname -I | awk '{print $1}')
@@ -82,7 +82,7 @@ ip_address=$(hostname -I | awk '{print $1}')
 docker run -p 9088:9088 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e TTS_ENDPOINT=http://$ip_address:7055 opea/tts:latest
 ```
 
-### 2.2.3 Test
+#### 2.2.3 Test
 
 ```bash
 # curl
diff --git a/comps/vectorstores/README.md b/comps/vectorstores/README.md
index 6dd35ee0b..bdccf5bc0 100644
--- a/comps/vectorstores/README.md
+++ b/comps/vectorstores/README.md
@@ -2,18 +2,18 @@
 
 The Vectorstores Microservice provides convenient way to start various vector database servers.
 
-# Vectorstores Microservice with Redis
+## Vectorstores Microservice with Redis
 
 For details, please refer to this [readme](langchain/redis/README.md)
 
-# Vectorstores Microservice with Qdrant
+## Vectorstores Microservice with Qdrant
 
 For details, please refer to this [readme](langchain/qdrant/README.md)
 
-# Vectorstores Microservice with PGVector
+## Vectorstores Microservice with PGVector
 
 For details, please refer to this [readme](langchain/pgvector/README.md)
 
-# Vectorstores Microservice with Pinecone
+## Vectorstores Microservice with Pinecone
 
 For details, please refer to this [readme](langchain/pinecone/README.md)
diff --git a/comps/vectorstores/langchain/chroma/README.md b/comps/vectorstores/langchain/chroma/README.md
index c64b094f6..155593010 100644
--- a/comps/vectorstores/langchain/chroma/README.md
+++ b/comps/vectorstores/langchain/chroma/README.md
@@ -2,9 +2,9 @@
 
 Chroma is a AI-native open-source vector database focused on developer productivity and happiness. Chroma is licensed under Apache 2.0. Chroma runs in various modes, we can deploy it as a server running your local machine or in the cloud.
 
-# Getting Started
+## Getting Started
 
-## Start Chroma Server
+### Start Chroma Server
 
 To start the Chroma server on your local machine, follow these steps:
 
@@ -14,7 +14,7 @@ cd chroma
 docker compose up -d
 ```
 
-## Start Log Output
+### Start Log Output
 
 Upon starting the server, you should see log outputs similar to the following:
 
diff --git a/comps/web_retrievers/langchain/chroma/README.md b/comps/web_retrievers/langchain/chroma/README.md
index 47a308837..563a04965 100644
--- a/comps/web_retrievers/langchain/chroma/README.md
+++ b/comps/web_retrievers/langchain/chroma/README.md
@@ -2,16 +2,16 @@
 
 The Web Retriever Microservice is designed to efficiently search web pages relevant to the prompt, save them into the VectorDB, and retrieve the matched documents with the highest similarity. The retrieved documents will be used as context in the prompt to LLMs. Different from the normal RAG process, a web retriever can leverage advanced search engines for more diverse demands, such as real-time news, verifiable sources, and diverse sources.
 
-# Start Microservice with Docker
+## Start Microservice with Docker
 
-## Build Docker Image
+### Build Docker Image
 
 ```bash
 cd ../../
 docker build -t opea/web-retriever-chroma:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/web_retrievers/langchain/chroma/docker/Dockerfile .
 ```
 
-## Start TEI Service
+### Start TEI Service
 
 ```bash
 model=BAAI/bge-base-en-v1.5
@@ -20,7 +20,7 @@ volume=$PWD/data
 docker run -d -p 6060:80 -v $volume:/data -e http_proxy=$http_proxy -e https_proxy=$https_proxy --pull always ghcr.io/huggingface/text-embeddings-inference:cpu-1.2 --model-id $model --revision $revision
 ```
 
-## Start Web Retriever Service
+### Start Web Retriever Service
 
 ```bash
 # set TEI endpoint
@@ -35,7 +35,7 @@ export GOOGLE_CSE_ID=xxx
 docker run -d --name="web-retriever-chroma-server" -p 7078:7077 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e no_proxy=$no_proxy -e TEI_EMBEDDING_ENDPOINT=$TEI_EMBEDDING_ENDPOINT opea/web-retriever-chroma:latest
 ```
 
-## Consume Web Retriever Service
+### Consume Web Retriever Service
 
 To consume the Web Retriever Microservice, you can generate a mock embedding vector of length 768 with Python.
 

From cc8cd702ae8c9d25f8f962308ad120c408e17a7c Mon Sep 17 00:00:00 2001
From: lvliang-intel <liang1.lv@intel.com>
Date: Sun, 18 Aug 2024 17:11:13 +0800
Subject: [PATCH 10/11] Remove unnecessary langsmith dependency (#369)

Signed-off-by: lvliang-intel <liang1.lv@intel.com>
---
 README.md                                     |  2 -
 comps/agent/langchain/requirements.txt        |  1 -
 comps/dataprep/milvus/prepare_doc_milvus.py   |  4 -
 comps/dataprep/milvus/requirements.txt        |  1 -
 .../langchain/prepare_doc_pgvector.py         |  4 -
 .../pgvector/langchain/requirements.txt       |  1 -
 .../dataprep/pinecone/prepare_doc_pinecone.py |  4 -
 comps/dataprep/pinecone/requirements.txt      |  1 -
 .../langchain_ray/prepare_doc_redis_on_ray.py |  3 -
 .../redis/langchain_ray/requirements.txt      |  1 -
 .../redis/llama_index/prepare_doc_redis.py    |  4 -
 .../redis/llama_index/requirements.txt        |  1 -
 .../langchain-mosec/embedding_mosec.py        |  2 -
 comps/embeddings/langchain/embedding_tei.py   |  2 -
 comps/embeddings/langchain/requirements.txt   |  1 -
 comps/embeddings/llama_index/embedding_tei.py |  2 -
 .../embeddings/llama_index/local_embedding.py |  2 -
 comps/embeddings/llama_index/requirements.txt |  1 -
 .../guardrails/llama_guard/guardrails_tgi.py  |  2 -
 comps/guardrails/llama_guard/requirements.txt |  1 -
 .../guardrails/pii_detection/pii_detection.py |  1 -
 .../guardrails/pii_detection/requirements.txt |  1 -
 .../langchain/knowledge_graph.py              |  1 -
 comps/knowledgegraphs/requirements.txt        |  1 -
 comps/llms/faq-generation/tgi/llm.py          |  3 -
 .../llms/faq-generation/tgi/requirements.txt  |  1 -
 comps/llms/summarization/tgi/llm.py           |  3 -
 comps/llms/summarization/tgi/requirements.txt |  1 -
 comps/llms/text-generation/ollama/llm.py      |  2 -
 .../text-generation/ollama/requirements.txt   |  1 -
 comps/llms/text-generation/ray_serve/llm.py   | 82 +++++++++++++++++++
 .../ray_serve/requirements.txt                | 13 +++
 comps/llms/text-generation/tgi/llm.py         |  2 -
 .../llms/text-generation/tgi/requirements.txt |  1 -
 comps/llms/text-generation/vllm-ray/llm.py    |  2 -
 .../text-generation/vllm-ray/requirements.txt |  1 -
 comps/llms/text-generation/vllm-xft/llm.py    |  2 -
 .../text-generation/vllm-xft/requirements.txt |  1 -
 comps/llms/text-generation/vllm/llm.py        |  2 -
 comps/ragas/tgi/llm.py                        |  2 -
 comps/ragas/tgi/requirements.txt              |  1 -
 comps/reranks/fastrag/local_reranking.py      |  2 -
 comps/reranks/fastrag/requirements.txt        |  1 -
 .../langchain-mosec/reranking_mosec_xeon.py   |  2 -
 comps/reranks/requirements.txt                |  1 -
 comps/reranks/tei/local_reranking.py          |  2 -
 comps/reranks/tei/reranking_tei.py            |  2 -
 .../haystack/qdrant/requirements.txt          |  1 -
 .../haystack/qdrant/retriever_qdrant.py       |  2 -
 .../langchain/milvus/retriever_milvus.py      |  4 +-
 .../langchain/pgvector/requirements.txt       |  1 -
 .../langchain/pgvector/retriever_pgvector.py  |  2 -
 .../langchain/pinecone/requirements.txt       |  1 -
 .../langchain/pinecone/retriever_pinecone.py  |  2 -
 .../langchain/redis/requirements.txt          |  1 -
 .../langchain/redis/retriever_redis.py        |  2 -
 comps/retrievers/llamaindex/requirements.txt  |  1 -
 .../retrievers/llamaindex/retriever_redis.py  |  2 -
 58 files changed, 96 insertions(+), 97 deletions(-)
 create mode 100644 comps/llms/text-generation/ray_serve/llm.py
 create mode 100644 comps/llms/text-generation/ray_serve/requirements.txt

diff --git a/README.md b/README.md
index 8c3af2373..e1b48a08c 100644
--- a/README.md
+++ b/README.md
@@ -173,7 +173,6 @@ A `Microservices` can be created by using the decorator `register_microservice`.
 
 ```python
 from langchain_community.embeddings import HuggingFaceHubEmbeddings
-from langsmith import traceable
 
 from comps import register_microservice, EmbedDoc, ServiceType, TextDoc
 
@@ -187,7 +186,6 @@ from comps import register_microservice, EmbedDoc, ServiceType, TextDoc
     input_datatype=TextDoc,
     output_datatype=EmbedDoc,
 )
-@traceable(run_type="embedding")
 def embedding(input: TextDoc) -> EmbedDoc:
     embed_vector = embeddings.embed_query(input.text)
     res = EmbedDoc(text=input.text, embedding=embed_vector)
diff --git a/comps/agent/langchain/requirements.txt b/comps/agent/langchain/requirements.txt
index 16859ac2c..fa1c4423b 100644
--- a/comps/agent/langchain/requirements.txt
+++ b/comps/agent/langchain/requirements.txt
@@ -11,7 +11,6 @@ langchain-openai
 langchain_community
 langchainhub
 langgraph
-langsmith
 numpy
 
 # used by cloud native
diff --git a/comps/dataprep/milvus/prepare_doc_milvus.py b/comps/dataprep/milvus/prepare_doc_milvus.py
index 143010c06..a293963c4 100644
--- a/comps/dataprep/milvus/prepare_doc_milvus.py
+++ b/comps/dataprep/milvus/prepare_doc_milvus.py
@@ -22,7 +22,6 @@
 from langchain_core.documents import Document
 from langchain_milvus.vectorstores import Milvus
 from langchain_text_splitters import HTMLHeaderTextSplitter
-from langsmith import traceable
 from pyspark import SparkConf, SparkContext
 
 from comps import DocPath, opea_microservices, register_microservice
@@ -167,7 +166,6 @@ async def ingest_link_to_milvus(link_list: List[str]):
 
 
 @register_microservice(name="opea_service@prepare_doc_milvus", endpoint="/v1/dataprep", host="0.0.0.0", port=6010)
-@traceable(run_type="tool")
 async def ingest_documents(
     files: Optional[Union[UploadFile, List[UploadFile]]] = File(None),
     link_list: Optional[str] = Form(None),
@@ -239,7 +237,6 @@ def process_files_wrapper(files):
 @register_microservice(
     name="opea_service@prepare_doc_milvus_file", endpoint="/v1/dataprep/get_file", host="0.0.0.0", port=6011
 )
-@traceable(run_type="tool")
 async def rag_get_file_structure():
     print("[ dataprep - get file ] start to get file structure")
 
@@ -270,7 +267,6 @@ def delete_by_partition_field(my_milvus, partition_field):
 @register_microservice(
     name="opea_service@prepare_doc_milvus_del", endpoint="/v1/dataprep/delete_file", host="0.0.0.0", port=6012
 )
-@traceable(run_type="tool")
 async def delete_single_file(file_path: str = Body(..., embed=True)):
     """Delete file according to `file_path`.
 
diff --git a/comps/dataprep/milvus/requirements.txt b/comps/dataprep/milvus/requirements.txt
index cf088a1c0..48b8be1d9 100644
--- a/comps/dataprep/milvus/requirements.txt
+++ b/comps/dataprep/milvus/requirements.txt
@@ -9,7 +9,6 @@ langchain
 langchain-community
 langchain-text-splitters
 langchain_milvus
-langsmith
 markdown
 numpy
 openai
diff --git a/comps/dataprep/pgvector/langchain/prepare_doc_pgvector.py b/comps/dataprep/pgvector/langchain/prepare_doc_pgvector.py
index efb394991..f46e466ba 100644
--- a/comps/dataprep/pgvector/langchain/prepare_doc_pgvector.py
+++ b/comps/dataprep/pgvector/langchain/prepare_doc_pgvector.py
@@ -13,7 +13,6 @@
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain_community.embeddings import HuggingFaceBgeEmbeddings, HuggingFaceHubEmbeddings
 from langchain_community.vectorstores import PGVector
-from langsmith import traceable
 
 from comps import DocPath, opea_microservices, register_microservice
 from comps.dataprep.utils import (
@@ -174,7 +173,6 @@ async def ingest_link_to_pgvector(link_list: List[str]):
     host="0.0.0.0",
     port=6007,
 )
-@traceable(run_type="tool")
 async def ingest_documents(
     files: Optional[Union[UploadFile, List[UploadFile]]] = File(None), link_list: Optional[str] = Form(None)
 ):
@@ -214,7 +212,6 @@ async def ingest_documents(
 @register_microservice(
     name="opea_service@prepare_doc_pgvector", endpoint="/v1/dataprep/get_file", host="0.0.0.0", port=6007
 )
-@traceable(run_type="tool")
 async def rag_get_file_structure():
     print("[ dataprep - get file ] start to get file structure")
 
@@ -229,7 +226,6 @@ async def rag_get_file_structure():
 @register_microservice(
     name="opea_service@prepare_doc_pgvector", endpoint="/v1/dataprep/delete_file", host="0.0.0.0", port=6007
 )
-@traceable(run_type="tool")
 async def delete_single_file(file_path: str = Body(..., embed=True)):
     """Delete file according to `file_path`.
 
diff --git a/comps/dataprep/pgvector/langchain/requirements.txt b/comps/dataprep/pgvector/langchain/requirements.txt
index 84fd48e52..b5b4f168b 100644
--- a/comps/dataprep/pgvector/langchain/requirements.txt
+++ b/comps/dataprep/pgvector/langchain/requirements.txt
@@ -8,7 +8,6 @@ huggingface_hub
 langchain
 langchain-community
 langchain-text-splitters
-langsmith
 markdown
 numpy
 opentelemetry-api
diff --git a/comps/dataprep/pinecone/prepare_doc_pinecone.py b/comps/dataprep/pinecone/prepare_doc_pinecone.py
index ec0e200a2..cbee8cd94 100644
--- a/comps/dataprep/pinecone/prepare_doc_pinecone.py
+++ b/comps/dataprep/pinecone/prepare_doc_pinecone.py
@@ -14,7 +14,6 @@
 from langchain_community.embeddings import HuggingFaceBgeEmbeddings, HuggingFaceEmbeddings, HuggingFaceHubEmbeddings
 from langchain_pinecone import PineconeVectorStore
 from langchain_text_splitters import HTMLHeaderTextSplitter
-from langsmith import traceable
 from pinecone import Pinecone, ServerlessSpec
 
 from comps import DocPath, opea_microservices, opea_telemetry, register_microservice
@@ -172,7 +171,6 @@ async def ingest_link_to_pinecone(link_list: List[str]):
 
 
 @register_microservice(name="opea_service@prepare_doc_pinecone", endpoint="/v1/dataprep", host="0.0.0.0", port=6007)
-@traceable(run_type="tool")
 async def ingest_documents(
     files: Optional[Union[UploadFile, List[UploadFile]]] = File(None),
     link_list: Optional[str] = Form(None),
@@ -223,7 +221,6 @@ async def ingest_documents(
 @register_microservice(
     name="opea_service@prepare_doc_pinecone_file", endpoint="/v1/dataprep/get_file", host="0.0.0.0", port=6008
 )
-@traceable(run_type="tool")
 async def rag_get_file_structure():
     print("[ dataprep - get file ] start to get file structure")
 
@@ -238,7 +235,6 @@ async def rag_get_file_structure():
 @register_microservice(
     name="opea_service@prepare_doc_pinecone_del", endpoint="/v1/dataprep/delete_file", host="0.0.0.0", port=6009
 )
-@traceable(run_type="tool")
 async def delete_all(file_path: str = Body(..., embed=True)):
     """Delete file according to `file_path`.
 
diff --git a/comps/dataprep/pinecone/requirements.txt b/comps/dataprep/pinecone/requirements.txt
index a2d5c4298..06ab12ecd 100644
--- a/comps/dataprep/pinecone/requirements.txt
+++ b/comps/dataprep/pinecone/requirements.txt
@@ -10,7 +10,6 @@ langchain-community
 langchain-openai
 langchain-pinecone
 langchain-text-splitters
-langsmith
 markdown
 numpy
 opentelemetry-api
diff --git a/comps/dataprep/redis/langchain_ray/prepare_doc_redis_on_ray.py b/comps/dataprep/redis/langchain_ray/prepare_doc_redis_on_ray.py
index c55165061..07ad81da8 100644
--- a/comps/dataprep/redis/langchain_ray/prepare_doc_redis_on_ray.py
+++ b/comps/dataprep/redis/langchain_ray/prepare_doc_redis_on_ray.py
@@ -26,7 +26,6 @@
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain_community.embeddings import HuggingFaceBgeEmbeddings, HuggingFaceHubEmbeddings
 from langchain_community.vectorstores import Redis
-from langsmith import traceable
 
 cur_path = pathlib.Path(__file__).parent.resolve()
 comps_path = os.path.join(cur_path, "../../../../")
@@ -322,7 +321,6 @@ async def ingest_documents(files: List[UploadFile] = File(None), link_list: str
 @register_microservice(
     name="opea_service@prepare_doc_redis_file", endpoint="/v1/dataprep/get_file", host="0.0.0.0", port=6008
 )
-@traceable(run_type="tool")
 async def rag_get_file_structure():
     print("[ get_file_structure] ")
 
@@ -337,7 +335,6 @@ async def rag_get_file_structure():
 @register_microservice(
     name="opea_service@prepare_doc_redis_del", endpoint="/v1/dataprep/delete_file", host="0.0.0.0", port=6009
 )
-@traceable(run_type="tool")
 async def delete_single_file(file_path: str = Body(..., embed=True)):
     """Delete file according to `file_path`.
 
diff --git a/comps/dataprep/redis/langchain_ray/requirements.txt b/comps/dataprep/redis/langchain_ray/requirements.txt
index 50d57b20e..a77ba5c4d 100644
--- a/comps/dataprep/redis/langchain_ray/requirements.txt
+++ b/comps/dataprep/redis/langchain_ray/requirements.txt
@@ -7,7 +7,6 @@ fastapi
 huggingface_hub
 langchain
 langchain-community
-langsmith
 numpy
 opentelemetry-api
 opentelemetry-exporter-otlp
diff --git a/comps/dataprep/redis/llama_index/prepare_doc_redis.py b/comps/dataprep/redis/llama_index/prepare_doc_redis.py
index da176555b..ae4d00461 100644
--- a/comps/dataprep/redis/llama_index/prepare_doc_redis.py
+++ b/comps/dataprep/redis/llama_index/prepare_doc_redis.py
@@ -8,7 +8,6 @@
 
 from config import EMBED_MODEL, INDEX_NAME, REDIS_URL
 from fastapi import Body, File, HTTPException, UploadFile
-from langsmith import traceable
 from llama_index.core import SimpleDirectoryReader, StorageContext, VectorStoreIndex
 from llama_index.core.settings import Settings
 from llama_index.embeddings.huggingface import HuggingFaceEmbedding
@@ -55,7 +54,6 @@ async def ingest_data_to_redis(doc_path: DocPath):
 
 
 @register_microservice(name="opea_service@prepare_doc_redis", endpoint="/v1/dataprep", host="0.0.0.0", port=6007)
-@traceable(run_type="tool")
 # llama index only support upload files now
 async def ingest_documents(files: Optional[Union[UploadFile, List[UploadFile]]] = File(None)):
     print(f"files:{files}")
@@ -81,7 +79,6 @@ async def ingest_documents(files: Optional[Union[UploadFile, List[UploadFile]]]
 @register_microservice(
     name="opea_service@prepare_doc_redis_file", endpoint="/v1/dataprep/get_file", host="0.0.0.0", port=6008
 )
-@traceable(run_type="tool")
 async def rag_get_file_structure():
     print("[ get_file_structure] ")
 
@@ -96,7 +93,6 @@ async def rag_get_file_structure():
 @register_microservice(
     name="opea_service@prepare_doc_redis_del", endpoint="/v1/dataprep/delete_file", host="0.0.0.0", port=6009
 )
-@traceable(run_type="tool")
 async def delete_single_file(file_path: str = Body(..., embed=True)):
     """Delete file according to `file_path`.
 
diff --git a/comps/dataprep/redis/llama_index/requirements.txt b/comps/dataprep/redis/llama_index/requirements.txt
index ad75869c1..2f808e534 100644
--- a/comps/dataprep/redis/llama_index/requirements.txt
+++ b/comps/dataprep/redis/llama_index/requirements.txt
@@ -1,7 +1,6 @@
 docarray[full]
 fastapi
 huggingface_hub
-langsmith
 llama-index
 llama-index-embeddings-huggingface==0.2.0
 llama-index-readers-file
diff --git a/comps/embeddings/langchain-mosec/embedding_mosec.py b/comps/embeddings/langchain-mosec/embedding_mosec.py
index f13c23eca..702b1937c 100644
--- a/comps/embeddings/langchain-mosec/embedding_mosec.py
+++ b/comps/embeddings/langchain-mosec/embedding_mosec.py
@@ -6,7 +6,6 @@
 from typing import List, Optional
 
 from langchain_community.embeddings import OpenAIEmbeddings
-from langsmith import traceable
 
 from comps import (
     EmbedDoc,
@@ -53,7 +52,6 @@ def empty_embedding() -> List[float]:
     input_datatype=TextDoc,
     output_datatype=EmbedDoc,
 )
-@traceable(run_type="embedding")
 @register_statistics(names=["opea_service@embedding_mosec"])
 def embedding(input: TextDoc) -> EmbedDoc:
     start = time.time()
diff --git a/comps/embeddings/langchain/embedding_tei.py b/comps/embeddings/langchain/embedding_tei.py
index 6a5fbc614..a318ff0bf 100644
--- a/comps/embeddings/langchain/embedding_tei.py
+++ b/comps/embeddings/langchain/embedding_tei.py
@@ -6,7 +6,6 @@
 from typing import Union
 
 from langchain_huggingface import HuggingFaceEndpointEmbeddings
-from langsmith import traceable
 
 from comps import (
     EmbedDoc,
@@ -32,7 +31,6 @@
     host="0.0.0.0",
     port=6000,
 )
-@traceable(run_type="embedding")
 @register_statistics(names=["opea_service@embedding_tei_langchain"])
 def embedding(
     input: Union[TextDoc, EmbeddingRequest, ChatCompletionRequest]
diff --git a/comps/embeddings/langchain/requirements.txt b/comps/embeddings/langchain/requirements.txt
index 8f0dd3ad4..1bfe6f44c 100644
--- a/comps/embeddings/langchain/requirements.txt
+++ b/comps/embeddings/langchain/requirements.txt
@@ -3,7 +3,6 @@ fastapi
 huggingface_hub
 langchain
 langchain_huggingface
-langsmith
 opentelemetry-api
 opentelemetry-exporter-otlp
 opentelemetry-sdk
diff --git a/comps/embeddings/llama_index/embedding_tei.py b/comps/embeddings/llama_index/embedding_tei.py
index 4f3920d32..9042a61a7 100644
--- a/comps/embeddings/llama_index/embedding_tei.py
+++ b/comps/embeddings/llama_index/embedding_tei.py
@@ -3,7 +3,6 @@
 
 import os
 
-from langsmith import traceable
 from llama_index.embeddings.text_embeddings_inference import TextEmbeddingsInference
 
 from comps import EmbedDoc, ServiceType, TextDoc, opea_microservices, register_microservice
@@ -18,7 +17,6 @@
     input_datatype=TextDoc,
     output_datatype=EmbedDoc,
 )
-@traceable(run_type="embedding")
 def embedding(input: TextDoc) -> EmbedDoc:
     embed_vector = embeddings._get_query_embedding(input.text)
     res = EmbedDoc(text=input.text, embedding=embed_vector)
diff --git a/comps/embeddings/llama_index/local_embedding.py b/comps/embeddings/llama_index/local_embedding.py
index f6a69afaf..53cc30e15 100644
--- a/comps/embeddings/llama_index/local_embedding.py
+++ b/comps/embeddings/llama_index/local_embedding.py
@@ -1,7 +1,6 @@
 # Copyright (C) 2024 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-from langsmith import traceable
 from llama_index.embeddings.huggingface_api import HuggingFaceInferenceAPIEmbedding
 
 from comps import EmbedDoc, ServiceType, TextDoc, opea_microservices, register_microservice
@@ -16,7 +15,6 @@
     input_datatype=TextDoc,
     output_datatype=EmbedDoc,
 )
-@traceable(run_type="embedding")
 def embedding(input: TextDoc) -> EmbedDoc:
     embed_vector = embeddings.get_text_embedding(input.text)
     res = EmbedDoc(text=input.text, embedding=embed_vector)
diff --git a/comps/embeddings/llama_index/requirements.txt b/comps/embeddings/llama_index/requirements.txt
index 908c38b06..4f1457e4a 100644
--- a/comps/embeddings/llama_index/requirements.txt
+++ b/comps/embeddings/llama_index/requirements.txt
@@ -1,7 +1,6 @@
 docarray[full]
 fastapi
 huggingface_hub
-langsmith
 llama-index-embeddings-huggingface-api
 llama-index-embeddings-text-embeddings-inference
 opentelemetry-api
diff --git a/comps/guardrails/llama_guard/guardrails_tgi.py b/comps/guardrails/llama_guard/guardrails_tgi.py
index b415876ed..93b046e97 100644
--- a/comps/guardrails/llama_guard/guardrails_tgi.py
+++ b/comps/guardrails/llama_guard/guardrails_tgi.py
@@ -7,7 +7,6 @@
 from langchain_community.utilities.requests import JsonRequestsWrapper
 from langchain_huggingface import ChatHuggingFace
 from langchain_huggingface.llms import HuggingFaceEndpoint
-from langsmith import traceable
 
 from comps import GeneratedDoc, ServiceType, TextDoc, opea_microservices, register_microservice
 
@@ -63,7 +62,6 @@ def get_tgi_service_model_id(endpoint_url, default=DEFAULT_MODEL):
     input_datatype=Union[GeneratedDoc, TextDoc],
     output_datatype=TextDoc,
 )
-@traceable(run_type="llm")
 def safety_guard(input: Union[GeneratedDoc, TextDoc]) -> TextDoc:
     if isinstance(input, GeneratedDoc):
         messages = [{"role": "user", "content": input.prompt}, {"role": "assistant", "content": input.text}]
diff --git a/comps/guardrails/llama_guard/requirements.txt b/comps/guardrails/llama_guard/requirements.txt
index 5eda60170..9f35ee74d 100644
--- a/comps/guardrails/llama_guard/requirements.txt
+++ b/comps/guardrails/llama_guard/requirements.txt
@@ -4,7 +4,6 @@ fastapi
 huggingface-hub<=0.24.0
 langchain-community
 langchain-huggingface
-langsmith
 opentelemetry-api
 opentelemetry-exporter-otlp
 opentelemetry-sdk
diff --git a/comps/guardrails/pii_detection/pii_detection.py b/comps/guardrails/pii_detection/pii_detection.py
index feecf3baf..1ae0dddae 100644
--- a/comps/guardrails/pii_detection/pii_detection.py
+++ b/comps/guardrails/pii_detection/pii_detection.py
@@ -8,7 +8,6 @@
 from pathlib import Path
 
 from fastapi import File, Form, HTTPException, UploadFile
-from langsmith import traceable
 
 cur_path = pathlib.Path(__file__).parent.resolve()
 comps_path = os.path.join(cur_path, "../../../")
diff --git a/comps/guardrails/pii_detection/requirements.txt b/comps/guardrails/pii_detection/requirements.txt
index e9bb7ba66..bc95de0fd 100644
--- a/comps/guardrails/pii_detection/requirements.txt
+++ b/comps/guardrails/pii_detection/requirements.txt
@@ -8,7 +8,6 @@ gibberish-detector
 huggingface_hub
 langchain
 langchain-community
-langsmith
 numpy
 opentelemetry-api
 opentelemetry-exporter-otlp
diff --git a/comps/knowledgegraphs/langchain/knowledge_graph.py b/comps/knowledgegraphs/langchain/knowledge_graph.py
index 9ed2c5b65..01d1a5a5a 100755
--- a/comps/knowledgegraphs/langchain/knowledge_graph.py
+++ b/comps/knowledgegraphs/langchain/knowledge_graph.py
@@ -22,7 +22,6 @@
 from langchain_community.graphs import Neo4jGraph
 from langchain_community.llms import HuggingFaceEndpoint
 from langchain_community.vectorstores.neo4j_vector import Neo4jVector
-from langsmith import traceable
 
 from comps import GeneratedDoc, GraphDoc, ServiceType, opea_microservices, register_microservice
 
diff --git a/comps/knowledgegraphs/requirements.txt b/comps/knowledgegraphs/requirements.txt
index ecb5228af..39b351530 100755
--- a/comps/knowledgegraphs/requirements.txt
+++ b/comps/knowledgegraphs/requirements.txt
@@ -8,7 +8,6 @@ langchain
 langchain_community==0.2.5
 langchain_openai
 langchainhub
-langsmith
 neo4j
 numpy
 opentelemetry-api
diff --git a/comps/llms/faq-generation/tgi/llm.py b/comps/llms/faq-generation/tgi/llm.py
index af23a74af..beaa5700b 100644
--- a/comps/llms/faq-generation/tgi/llm.py
+++ b/comps/llms/faq-generation/tgi/llm.py
@@ -9,12 +9,10 @@
 from langchain.prompts import PromptTemplate
 from langchain.text_splitter import CharacterTextSplitter
 from langchain_community.llms import HuggingFaceEndpoint
-from langsmith import traceable
 
 from comps import GeneratedDoc, LLMParamsDoc, ServiceType, opea_microservices, register_microservice
 
 
-@traceable(run_type="tool")
 def post_process_text(text: str):
     if text == " ":
         return "data: @#$\n\n"
@@ -33,7 +31,6 @@ def post_process_text(text: str):
     host="0.0.0.0",
     port=9000,
 )
-@traceable(run_type="llm")
 def llm_generate(input: LLMParamsDoc):
     llm_endpoint = os.getenv("TGI_LLM_ENDPOINT", "http://localhost:8080")
     llm = HuggingFaceEndpoint(
diff --git a/comps/llms/faq-generation/tgi/requirements.txt b/comps/llms/faq-generation/tgi/requirements.txt
index 623a8f667..fbe54af95 100644
--- a/comps/llms/faq-generation/tgi/requirements.txt
+++ b/comps/llms/faq-generation/tgi/requirements.txt
@@ -3,7 +3,6 @@ fastapi
 huggingface_hub
 langchain==0.1.16
 langserve
-langsmith
 opentelemetry-api
 opentelemetry-exporter-otlp
 opentelemetry-sdk
diff --git a/comps/llms/summarization/tgi/llm.py b/comps/llms/summarization/tgi/llm.py
index a9db6248d..43a583a96 100644
--- a/comps/llms/summarization/tgi/llm.py
+++ b/comps/llms/summarization/tgi/llm.py
@@ -8,12 +8,10 @@
 from langchain.docstore.document import Document
 from langchain.text_splitter import CharacterTextSplitter
 from langchain_huggingface import HuggingFaceEndpoint
-from langsmith import traceable
 
 from comps import GeneratedDoc, LLMParamsDoc, ServiceType, opea_microservices, register_microservice
 
 
-@traceable(run_type="tool")
 def post_process_text(text: str):
     if text == " ":
         return "data: @#$\n\n"
@@ -32,7 +30,6 @@ def post_process_text(text: str):
     host="0.0.0.0",
     port=9000,
 )
-@traceable(run_type="llm")
 def llm_generate(input: LLMParamsDoc):
     llm_endpoint = os.getenv("TGI_LLM_ENDPOINT", "http://localhost:8080")
     llm = HuggingFaceEndpoint(
diff --git a/comps/llms/summarization/tgi/requirements.txt b/comps/llms/summarization/tgi/requirements.txt
index c6c151f6e..e074ba8c8 100644
--- a/comps/llms/summarization/tgi/requirements.txt
+++ b/comps/llms/summarization/tgi/requirements.txt
@@ -6,7 +6,6 @@ langchain-huggingface
 langchain-openai
 langchain_community
 langchainhub
-langsmith
 opentelemetry-api
 opentelemetry-exporter-otlp
 opentelemetry-sdk
diff --git a/comps/llms/text-generation/ollama/llm.py b/comps/llms/text-generation/ollama/llm.py
index aadb2e2fa..1f6d330c8 100644
--- a/comps/llms/text-generation/ollama/llm.py
+++ b/comps/llms/text-generation/ollama/llm.py
@@ -5,7 +5,6 @@
 
 from fastapi.responses import StreamingResponse
 from langchain_community.llms import Ollama
-from langsmith import traceable
 
 from comps import GeneratedDoc, LLMParamsDoc, ServiceType, opea_microservices, register_microservice
 
@@ -17,7 +16,6 @@
     host="0.0.0.0",
     port=9000,
 )
-@traceable(run_type="llm")
 def llm_generate(input: LLMParamsDoc):
     ollama = Ollama(
         base_url=ollama_endpoint,
diff --git a/comps/llms/text-generation/ollama/requirements.txt b/comps/llms/text-generation/ollama/requirements.txt
index e224aaaa5..78b323c73 100644
--- a/comps/llms/text-generation/ollama/requirements.txt
+++ b/comps/llms/text-generation/ollama/requirements.txt
@@ -2,7 +2,6 @@ docarray[full]
 fastapi
 huggingface_hub
 langchain==0.1.16
-langsmith
 opentelemetry-api
 opentelemetry-exporter-otlp
 opentelemetry-sdk
diff --git a/comps/llms/text-generation/ray_serve/llm.py b/comps/llms/text-generation/ray_serve/llm.py
new file mode 100644
index 000000000..c86025625
--- /dev/null
+++ b/comps/llms/text-generation/ray_serve/llm.py
@@ -0,0 +1,82 @@
+# Copyright (c) 2024 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+
+from fastapi.responses import StreamingResponse
+from langchain_openai import ChatOpenAI
+
+from comps import GeneratedDoc, LLMParamsDoc, ServiceType, opea_microservices, register_microservice
+
+
+def post_process_text(text: str):
+    if text == " ":
+        return "data: @#$\n\n"
+    if text == "\n":
+        return "data: <br/>\n\n"
+    if text.isspace():
+        return None
+    new_text = text.replace(" ", "@#$")
+    return f"data: {new_text}\n\n"
+
+
+@register_microservice(
+    name="opea_service@llm_ray",
+    service_type=ServiceType.LLM,
+    endpoint="/v1/chat/completions",
+    host="0.0.0.0",
+    port=9000,
+)
+def llm_generate(input: LLMParamsDoc):
+    llm_endpoint = os.getenv("RAY_Serve_ENDPOINT", "http://localhost:8080")
+    llm_model = os.getenv("LLM_MODEL", "Llama-2-7b-chat-hf")
+    if "/" in llm_model:
+        llm_model = llm_model.split("/")[-1]
+    llm = ChatOpenAI(
+        openai_api_base=llm_endpoint + "/v1",
+        model_name=llm_model,
+        openai_api_key=os.getenv("OPENAI_API_KEY", "not_needed"),
+        max_tokens=input.max_new_tokens,
+        temperature=input.temperature,
+        streaming=input.streaming,
+        request_timeout=600,
+    )
+
+    if input.streaming:
+
+        async def stream_generator():
+            chat_response = ""
+            async for text in llm.astream(input.query):
+                text = text.content
+                chat_response += text
+                processed_text = post_process_text(text)
+                if text and processed_text:
+                    if "</s>" in text:
+                        res = text.split("</s>")[0]
+                        if res != "":
+                            yield res
+                        break
+                    yield processed_text
+            print(f"[llm - chat_stream] stream response: {chat_response}")
+            yield "data: [DONE]\n\n"
+
+        return StreamingResponse(stream_generator(), media_type="text/event-stream")
+    else:
+        response = llm.invoke(input.query)
+        response = response.content
+        return GeneratedDoc(text=response, prompt=input.query)
+
+
+if __name__ == "__main__":
+    opea_microservices["opea_service@llm_ray"].start()
diff --git a/comps/llms/text-generation/ray_serve/requirements.txt b/comps/llms/text-generation/ray_serve/requirements.txt
new file mode 100644
index 000000000..d97ce32e2
--- /dev/null
+++ b/comps/llms/text-generation/ray_serve/requirements.txt
@@ -0,0 +1,13 @@
+docarray[full]
+fastapi
+huggingface_hub
+langchain==0.1.16
+langchain_openai
+openai
+opentelemetry-api
+opentelemetry-exporter-otlp
+opentelemetry-sdk
+prometheus-fastapi-instrumentator
+ray[serve]>=2.10
+shortuuid
+transformers
diff --git a/comps/llms/text-generation/tgi/llm.py b/comps/llms/text-generation/tgi/llm.py
index c202aede7..6597d5b57 100644
--- a/comps/llms/text-generation/tgi/llm.py
+++ b/comps/llms/text-generation/tgi/llm.py
@@ -8,7 +8,6 @@
 from fastapi.responses import StreamingResponse
 from huggingface_hub import AsyncInferenceClient
 from langchain_core.prompts import PromptTemplate
-from langsmith import traceable
 from openai import OpenAI
 from template import ChatTemplate
 
@@ -37,7 +36,6 @@
     host="0.0.0.0",
     port=9000,
 )
-@traceable(run_type="llm")
 @register_statistics(names=["opea_service@llm_tgi"])
 async def llm_generate(input: Union[LLMParamsDoc, ChatCompletionRequest]):
 
diff --git a/comps/llms/text-generation/tgi/requirements.txt b/comps/llms/text-generation/tgi/requirements.txt
index 9670813d6..6b6f11ee4 100644
--- a/comps/llms/text-generation/tgi/requirements.txt
+++ b/comps/llms/text-generation/tgi/requirements.txt
@@ -3,7 +3,6 @@ docarray[full]
 fastapi
 httpx
 huggingface_hub
-langsmith
 openai==1.35.13
 opentelemetry-api
 opentelemetry-exporter-otlp
diff --git a/comps/llms/text-generation/vllm-ray/llm.py b/comps/llms/text-generation/vllm-ray/llm.py
index 6d8abd028..d3de026e0 100644
--- a/comps/llms/text-generation/vllm-ray/llm.py
+++ b/comps/llms/text-generation/vllm-ray/llm.py
@@ -16,7 +16,6 @@
 
 from fastapi.responses import StreamingResponse
 from langchain_openai import ChatOpenAI
-from langsmith import traceable
 
 from comps import GeneratedDoc, LLMParamsDoc, ServiceType, opea_microservices, register_microservice
 
@@ -28,7 +27,6 @@
     host="0.0.0.0",
     port=9000,
 )
-@traceable(run_type="llm")
 def llm_generate(input: LLMParamsDoc):
     llm_endpoint = os.getenv("vLLM_RAY_ENDPOINT", "http://localhost:8006")
     llm_model = os.getenv("LLM_MODEL", "meta-llama/Llama-2-7b-chat-hf")
diff --git a/comps/llms/text-generation/vllm-ray/requirements.txt b/comps/llms/text-generation/vllm-ray/requirements.txt
index 083a2910b..1f9020583 100644
--- a/comps/llms/text-generation/vllm-ray/requirements.txt
+++ b/comps/llms/text-generation/vllm-ray/requirements.txt
@@ -4,7 +4,6 @@ huggingface_hub
 langchain==0.1.16
 langchain_openai
 langserve
-langsmith
 openai
 opentelemetry-api
 opentelemetry-exporter-otlp
diff --git a/comps/llms/text-generation/vllm-xft/llm.py b/comps/llms/text-generation/vllm-xft/llm.py
index 02446baa6..2c479d90b 100644
--- a/comps/llms/text-generation/vllm-xft/llm.py
+++ b/comps/llms/text-generation/vllm-xft/llm.py
@@ -5,7 +5,6 @@
 
 from fastapi.responses import StreamingResponse
 from langchain_community.llms import VLLMOpenAI
-from langsmith import traceable
 
 from comps import GeneratedDoc, LLMParamsDoc, ServiceType, opea_microservices, register_microservice
 
@@ -17,7 +16,6 @@
     host="0.0.0.0",
     port=9000,
 )
-@traceable(run_type="llm")
 def llm_generate(input: LLMParamsDoc):
     llm_endpoint = os.getenv("vLLM_LLM_ENDPOINT", "http://localhost:18688")
     llm = VLLMOpenAI(
diff --git a/comps/llms/text-generation/vllm-xft/requirements.txt b/comps/llms/text-generation/vllm-xft/requirements.txt
index bc9f457c4..a4accaed2 100644
--- a/comps/llms/text-generation/vllm-xft/requirements.txt
+++ b/comps/llms/text-generation/vllm-xft/requirements.txt
@@ -1,7 +1,6 @@
 docarray[full]
 fastapi
 langchain==0.1.16
-langsmith
 opentelemetry-api
 opentelemetry-exporter-otlp
 opentelemetry-sdk
diff --git a/comps/llms/text-generation/vllm/llm.py b/comps/llms/text-generation/vllm/llm.py
index ea8691f1a..61bebbe27 100644
--- a/comps/llms/text-generation/vllm/llm.py
+++ b/comps/llms/text-generation/vllm/llm.py
@@ -5,7 +5,6 @@
 
 from fastapi.responses import StreamingResponse
 from langchain_community.llms import VLLMOpenAI
-from langsmith import traceable
 
 from comps import GeneratedDoc, LLMParamsDoc, ServiceType, opea_microservices, opea_telemetry, register_microservice
 
@@ -29,7 +28,6 @@ def post_process_text(text: str):
     host="0.0.0.0",
     port=9000,
 )
-@traceable(run_type="llm")
 def llm_generate(input: LLMParamsDoc):
     llm_endpoint = os.getenv("vLLM_ENDPOINT", "http://localhost:8008")
     model_name = os.getenv("LLM_MODEL", "meta-llama/Meta-Llama-3-8B-Instruct")
diff --git a/comps/ragas/tgi/llm.py b/comps/ragas/tgi/llm.py
index 895705703..03c214d30 100644
--- a/comps/ragas/tgi/llm.py
+++ b/comps/ragas/tgi/llm.py
@@ -11,7 +11,6 @@
     HuggingFaceInstructEmbeddings,
 )
 from langchain_community.llms import HuggingFaceEndpoint
-from langsmith import traceable
 from ragas import evaluate
 from ragas.metrics import answer_relevancy, context_precision, context_recall, faithfulness
 
@@ -30,7 +29,6 @@
     input_datatype=RAGASParams,
     output_datatype=RAGASScores,
 )
-@traceable(run_type="llm")
 def llm_generate(input: RAGASParams):
     llm_endpoint = os.getenv("TGI_LLM_ENDPOINT", "http://localhost:8080")
 
diff --git a/comps/ragas/tgi/requirements.txt b/comps/ragas/tgi/requirements.txt
index 2c8fad29f..7839ef15d 100644
--- a/comps/ragas/tgi/requirements.txt
+++ b/comps/ragas/tgi/requirements.txt
@@ -3,7 +3,6 @@ docarray[full]
 fastapi
 huggingface_hub
 langchain==0.1.16
-langsmith
 opentelemetry-api
 opentelemetry-exporter-otlp
 opentelemetry-sdk
diff --git a/comps/reranks/fastrag/local_reranking.py b/comps/reranks/fastrag/local_reranking.py
index f11ebc1f9..4548ef9a7 100644
--- a/comps/reranks/fastrag/local_reranking.py
+++ b/comps/reranks/fastrag/local_reranking.py
@@ -4,7 +4,6 @@
 from config import RANKER_MODEL
 from fastrag.rankers import IPEXBiEncoderSimilarityRanker
 from haystack import Document
-from langsmith import traceable
 
 from comps.cores.mega.micro_service import ServiceType, opea_microservices, register_microservice
 from comps.cores.proto.docarray import RerankedDoc, SearchedDoc, TextDoc
@@ -19,7 +18,6 @@
     input_datatype=SearchedDoc,
     output_datatype=RerankedDoc,
 )
-@traceable(run_type="llm")
 def reranking(input: SearchedDoc) -> RerankedDoc:
     documents = []
     for i, d in enumerate(input.retrieved_docs):
diff --git a/comps/reranks/fastrag/requirements.txt b/comps/reranks/fastrag/requirements.txt
index c4ded91c8..e8166a77a 100644
--- a/comps/reranks/fastrag/requirements.txt
+++ b/comps/reranks/fastrag/requirements.txt
@@ -2,7 +2,6 @@ docarray[full]
 fastapi
 haystack-ai
 langchain
-langsmith
 opentelemetry-api
 opentelemetry-exporter-otlp
 opentelemetry-sdk
diff --git a/comps/reranks/langchain-mosec/reranking_mosec_xeon.py b/comps/reranks/langchain-mosec/reranking_mosec_xeon.py
index 4640433af..da3d7854a 100644
--- a/comps/reranks/langchain-mosec/reranking_mosec_xeon.py
+++ b/comps/reranks/langchain-mosec/reranking_mosec_xeon.py
@@ -22,7 +22,6 @@
 
 import requests
 from langchain_core.prompts import ChatPromptTemplate
-from langsmith import traceable
 
 from comps import (
     LLMParamsDoc,
@@ -44,7 +43,6 @@
     input_datatype=SearchedDoc,
     output_datatype=LLMParamsDoc,
 )
-@traceable(run_type="llm")
 @register_statistics(names=["opea_service@reranking_mosec_xeon"])
 def reranking(input: SearchedDoc) -> LLMParamsDoc:
     print("reranking input: ", input)
diff --git a/comps/reranks/requirements.txt b/comps/reranks/requirements.txt
index 67503038f..7260862a3 100644
--- a/comps/reranks/requirements.txt
+++ b/comps/reranks/requirements.txt
@@ -2,7 +2,6 @@ aiohttp
 docarray[full]
 fastapi
 httpx
-langsmith
 opentelemetry-api
 opentelemetry-exporter-otlp
 opentelemetry-sdk
diff --git a/comps/reranks/tei/local_reranking.py b/comps/reranks/tei/local_reranking.py
index d0fa8a79c..f02a95823 100644
--- a/comps/reranks/tei/local_reranking.py
+++ b/comps/reranks/tei/local_reranking.py
@@ -1,7 +1,6 @@
 # Copyright (C) 2024 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-from langsmith import traceable
 from sentence_transformers import CrossEncoder
 
 from comps import RerankedDoc, SearchedDoc, ServiceType, TextDoc, opea_microservices, register_microservice
@@ -16,7 +15,6 @@
     input_datatype=SearchedDoc,
     output_datatype=RerankedDoc,
 )
-@traceable(run_type="llm")
 def reranking(input: SearchedDoc) -> RerankedDoc:
     query_and_docs = [(input.initial_query, doc.text) for doc in input.retrieved_docs]
     scores = reranker_model.predict(query_and_docs)
diff --git a/comps/reranks/tei/reranking_tei.py b/comps/reranks/tei/reranking_tei.py
index 2440f800a..5575aa88f 100644
--- a/comps/reranks/tei/reranking_tei.py
+++ b/comps/reranks/tei/reranking_tei.py
@@ -9,7 +9,6 @@
 from typing import Union
 
 import requests
-from langsmith import traceable
 
 from comps import (
     LLMParamsDoc,
@@ -37,7 +36,6 @@
     input_datatype=SearchedDoc,
     output_datatype=LLMParamsDoc,
 )
-@traceable(run_type="llm")
 @register_statistics(names=["opea_service@reranking_tgi_gaudi"])
 def reranking(
     input: Union[SearchedDoc, RerankingRequest, ChatCompletionRequest]
diff --git a/comps/retrievers/haystack/qdrant/requirements.txt b/comps/retrievers/haystack/qdrant/requirements.txt
index 9b99c00fb..24458a6fb 100644
--- a/comps/retrievers/haystack/qdrant/requirements.txt
+++ b/comps/retrievers/haystack/qdrant/requirements.txt
@@ -3,7 +3,6 @@ easyocr
 fastapi
 haystack-ai==2.2.4
 langchain_community
-langsmith
 opentelemetry-api
 opentelemetry-exporter-otlp
 opentelemetry-sdk
diff --git a/comps/retrievers/haystack/qdrant/retriever_qdrant.py b/comps/retrievers/haystack/qdrant/retriever_qdrant.py
index c2cc8735b..30d1bd72d 100644
--- a/comps/retrievers/haystack/qdrant/retriever_qdrant.py
+++ b/comps/retrievers/haystack/qdrant/retriever_qdrant.py
@@ -4,7 +4,6 @@
 from haystack.components.embedders import HuggingFaceTEITextEmbedder, SentenceTransformersTextEmbedder
 from haystack_integrations.components.retrievers.qdrant import QdrantEmbeddingRetriever
 from haystack_integrations.document_stores.qdrant import QdrantDocumentStore
-from langsmith import traceable
 from qdrant_config import EMBED_DIMENSION, EMBED_ENDPOINT, EMBED_MODEL, INDEX_NAME, QDRANT_HOST, QDRANT_PORT
 
 from comps import EmbedDoc, SearchedDoc, ServiceType, TextDoc, opea_microservices, register_microservice
@@ -28,7 +27,6 @@ def initialize_qdrant_retriever() -> QdrantEmbeddingRetriever:
     host="0.0.0.0",
     port=7000,
 )
-@traceable(run_type="retriever")
 def retrieve(input: EmbedDoc) -> SearchedDoc:
     search_res = retriever.run(query_embedding=input.embedding)["documents"]
     searched_docs = [TextDoc(text=r.content) for r in search_res if r.content]
diff --git a/comps/retrievers/langchain/milvus/retriever_milvus.py b/comps/retrievers/langchain/milvus/retriever_milvus.py
index 1625eed0a..0c81e76ce 100644
--- a/comps/retrievers/langchain/milvus/retriever_milvus.py
+++ b/comps/retrievers/langchain/milvus/retriever_milvus.py
@@ -15,9 +15,8 @@
     MODEL_ID,
     MOSEC_EMBEDDING_ENDPOINT,
 )
-from langchain_community.embeddings import HuggingFaceBgeEmbeddings, HuggingFaceHubEmbeddings, OpenAIEmbeddings
+from langchain_community.embeddings import HuggingFaceBgeEmbeddings, OpenAIEmbeddings
 from langchain_milvus.vectorstores import Milvus
-from langsmith import traceable
 
 from comps import (
     EmbedDoc,
@@ -63,7 +62,6 @@ def empty_embedding() -> List[float]:
     host="0.0.0.0",
     port=7000,
 )
-@traceable(run_type="retriever")
 @register_statistics(names=["opea_service@retriever_milvus"])
 def retrieve(input: EmbedDoc) -> SearchedDoc:
     vector_db = Milvus(
diff --git a/comps/retrievers/langchain/pgvector/requirements.txt b/comps/retrievers/langchain/pgvector/requirements.txt
index 18609d361..d3d95dee9 100644
--- a/comps/retrievers/langchain/pgvector/requirements.txt
+++ b/comps/retrievers/langchain/pgvector/requirements.txt
@@ -2,7 +2,6 @@ docarray[full]
 easyocr
 fastapi
 langchain_community
-langsmith
 opentelemetry-api
 opentelemetry-exporter-otlp
 opentelemetry-sdk
diff --git a/comps/retrievers/langchain/pgvector/retriever_pgvector.py b/comps/retrievers/langchain/pgvector/retriever_pgvector.py
index 2fba1f1c0..014a616a5 100644
--- a/comps/retrievers/langchain/pgvector/retriever_pgvector.py
+++ b/comps/retrievers/langchain/pgvector/retriever_pgvector.py
@@ -7,7 +7,6 @@
 from config import EMBED_MODEL, INDEX_NAME, PG_CONNECTION_STRING, PORT
 from langchain_community.embeddings import HuggingFaceBgeEmbeddings, HuggingFaceHubEmbeddings
 from langchain_community.vectorstores import PGVector
-from langsmith import traceable
 
 from comps import (
     EmbedDoc,
@@ -30,7 +29,6 @@
     host="0.0.0.0",
     port=PORT,
 )
-@traceable(run_type="retriever")
 @register_statistics(names=["opea_service@retriever_pgvector"])
 def retrieve(input: EmbedDoc) -> SearchedDoc:
     start = time.time()
diff --git a/comps/retrievers/langchain/pinecone/requirements.txt b/comps/retrievers/langchain/pinecone/requirements.txt
index 32df7f40f..7536f591c 100644
--- a/comps/retrievers/langchain/pinecone/requirements.txt
+++ b/comps/retrievers/langchain/pinecone/requirements.txt
@@ -6,7 +6,6 @@ huggingface_hub
 langchain
 langchain-community
 langchain-pinecone
-langsmith
 numpy
 opentelemetry-api
 opentelemetry-exporter-otlp
diff --git a/comps/retrievers/langchain/pinecone/retriever_pinecone.py b/comps/retrievers/langchain/pinecone/retriever_pinecone.py
index ba8e6526f..73e77d111 100644
--- a/comps/retrievers/langchain/pinecone/retriever_pinecone.py
+++ b/comps/retrievers/langchain/pinecone/retriever_pinecone.py
@@ -7,7 +7,6 @@
 from config import EMBED_MODEL, PINECONE_API_KEY, PINECONE_INDEX_NAME
 from langchain_community.embeddings import HuggingFaceBgeEmbeddings, HuggingFaceHubEmbeddings
 from langchain_pinecone import PineconeVectorStore
-from langsmith import traceable
 from pinecone import Pinecone, ServerlessSpec
 
 from comps import (
@@ -31,7 +30,6 @@
     host="0.0.0.0",
     port=7000,
 )
-@traceable(run_type="retriever")
 @register_statistics(names=["opea_service@retriever_pinecone"])
 def retrieve(input: EmbedDoc) -> SearchedDoc:
     start = time.time()
diff --git a/comps/retrievers/langchain/redis/requirements.txt b/comps/retrievers/langchain/redis/requirements.txt
index 3720190d3..c68c3d274 100644
--- a/comps/retrievers/langchain/redis/requirements.txt
+++ b/comps/retrievers/langchain/redis/requirements.txt
@@ -2,7 +2,6 @@ docarray[full]
 easyocr
 fastapi
 langchain_community
-langsmith
 opentelemetry-api
 opentelemetry-exporter-otlp
 opentelemetry-sdk
diff --git a/comps/retrievers/langchain/redis/retriever_redis.py b/comps/retrievers/langchain/redis/retriever_redis.py
index 43f3e0c05..a4ab5dc4e 100644
--- a/comps/retrievers/langchain/redis/retriever_redis.py
+++ b/comps/retrievers/langchain/redis/retriever_redis.py
@@ -7,7 +7,6 @@
 
 from langchain_community.embeddings import HuggingFaceBgeEmbeddings, HuggingFaceHubEmbeddings
 from langchain_community.vectorstores import Redis
-from langsmith import traceable
 from redis_config import EMBED_MODEL, INDEX_NAME, REDIS_URL
 
 from comps import (
@@ -37,7 +36,6 @@
     host="0.0.0.0",
     port=7000,
 )
-@traceable(run_type="retriever")
 @register_statistics(names=["opea_service@retriever_redis"])
 def retrieve(
     input: Union[EmbedDoc, RetrievalRequest, ChatCompletionRequest]
diff --git a/comps/retrievers/llamaindex/requirements.txt b/comps/retrievers/llamaindex/requirements.txt
index 236ea9af8..83228c259 100644
--- a/comps/retrievers/llamaindex/requirements.txt
+++ b/comps/retrievers/llamaindex/requirements.txt
@@ -3,7 +3,6 @@ docarray[full]
 easyocr
 fastapi
 httpx
-langsmith
 llama-index-vector-stores-redis
 llama_index
 opentelemetry-api
diff --git a/comps/retrievers/llamaindex/retriever_redis.py b/comps/retrievers/llamaindex/retriever_redis.py
index 4999a7235..3c387010e 100644
--- a/comps/retrievers/llamaindex/retriever_redis.py
+++ b/comps/retrievers/llamaindex/retriever_redis.py
@@ -3,7 +3,6 @@
 
 import os
 
-from langsmith import traceable
 from llama_index.core.vector_stores.types import VectorStoreQuery
 from llama_index.vector_stores.redis import RedisVectorStore
 from redis_config import INDEX_NAME, REDIS_URL
@@ -20,7 +19,6 @@
     host="0.0.0.0",
     port=7000,
 )
-@traceable(run_type="retriever")
 def retrieve(input: EmbedDoc) -> SearchedDoc:
     vector_store_query = VectorStoreQuery(query_embedding=input.embedding)
     search_res = vector_store.query(query=vector_store_query)

From cd83854f93423a2327f07430fbf781e6f879ce41 Mon Sep 17 00:00:00 2001
From: "chen, suyue" <suyue.chen@intel.com>
Date: Sun, 18 Aug 2024 19:30:51 +0800
Subject: [PATCH 11/11] Fix CI test changed file detect issue (#512)

* update wf

Signed-off-by: chensuyue <suyue.chen@intel.com>

* for test

Signed-off-by: chensuyue <suyue.chen@intel.com>

* remove test code

Signed-off-by: chensuyue <suyue.chen@intel.com>

---------

Signed-off-by: chensuyue <suyue.chen@intel.com>
---
 .github/workflows/_get-test-matrix.yml | 5 ++++-
 .github/workflows/pr-examples-test.yml | 1 +
 2 files changed, 5 insertions(+), 1 deletion(-)

diff --git a/.github/workflows/_get-test-matrix.yml b/.github/workflows/_get-test-matrix.yml
index 09be978cb..deedaa949 100644
--- a/.github/workflows/_get-test-matrix.yml
+++ b/.github/workflows/_get-test-matrix.yml
@@ -38,7 +38,10 @@ jobs:
         run: |
           set -xe
           if [ "${{ github.event_name }}" == "pull_request" ] || [ "${{ github.event_name }}" == "pull_request_target" ]; then
-            base_commit=${{ github.event.pull_request.base.sha }}
+            LATEST_COMMIT_SHA=$(curl -H "Authorization: token ${{ secrets.GITHUB_TOKEN }}" \
+            "https://api.github.com/repos/opea-project/GenAIComps/commits?sha=main" | jq -r '.[0].sha')
+            echo "Latest commit SHA is $LATEST_COMMIT_SHA"
+            base_commit=$LATEST_COMMIT_SHA
           else
             base_commit=$(git rev-parse HEAD~1) # push event
           fi
diff --git a/.github/workflows/pr-examples-test.yml b/.github/workflows/pr-examples-test.yml
index 302d6f8e1..a3eb50539 100644
--- a/.github/workflows/pr-examples-test.yml
+++ b/.github/workflows/pr-examples-test.yml
@@ -16,6 +16,7 @@ on:
       - comps/llms/text-generation/tgi/**
       - comps/dataprep/redis/langchain/**
       - requirements.txt
+      - "!**.md"
 
 # If there is a new commit, the previous jobs will be canceled
 concurrency: