diff --git a/comps/dataprep/redis/README.md b/comps/dataprep/redis/README.md
index 58fe3b34d..58bb42897 100644
--- a/comps/dataprep/redis/README.md
+++ b/comps/dataprep/redis/README.md
@@ -44,6 +44,32 @@ export LANGCHAIN_PROJECT="opea/gen-ai-comps:dataprep"
 export PYTHONPATH=${path_to_comps}
 ```
 
+## 1.4 Start Embedding Service
+
+First, you need to start a TEI service.
+
+```bash
+your_port=6006
+model="BAAI/bge-large-en-v1.5"
+revision="refs/pr/5"
+docker run -p $your_port:80 -v ./data:/data --name tei_server -e http_proxy=$http_proxy -e https_proxy=$https_proxy --pull always ghcr.io/huggingface/text-embeddings-inference:cpu-1.2 --model-id $model --revision $revision
+```
+
+Then you need to test your TEI service using the following commands:
+
+```bash
+curl localhost:$your_port/embed \
+    -X POST \
+    -d '{"inputs":"What is Deep Learning?"}' \
+    -H 'Content-Type: application/json'
+```
+
+After checking that it works, set up environment variables.
+
+```bash
+export TEI_ENDPOINT="http://localhost:$your_port"
+```
+
 ## 1.4 Start Document Preparation Microservice for Redis with Python Script
 
 Start document preparation microservice for Redis with below command.
@@ -69,6 +95,10 @@ Please refer to this [readme](../../../vectorstores/langchain/redis/README.md).
 ## 2.2 Setup Environment Variables
 
 ```bash
+export EMBEDDING_MODEL_ID="BAAI/bge-base-en-v1.5"
+export TEI_ENDPOINT="http://${your_ip}:6006"
+export REDIS_HOST=${your_ip}
+export REDIS_PORT=6379
 export REDIS_URL="redis://${your_ip}:6379"
 export INDEX_NAME=${your_index_name}
 export LANGCHAIN_TRACING_V2=true
diff --git a/comps/dataprep/redis/langchain/docker/docker-compose-dataprep-redis.yaml b/comps/dataprep/redis/langchain/docker/docker-compose-dataprep-redis.yaml
index e2775972d..74e2bb78f 100644
--- a/comps/dataprep/redis/langchain/docker/docker-compose-dataprep-redis.yaml
+++ b/comps/dataprep/redis/langchain/docker/docker-compose-dataprep-redis.yaml
@@ -9,6 +9,19 @@ services:
     ports:
       - "6379:6379"
       - "8001:8001"
+  tei-embedding-service:
+    image: ghcr.io/huggingface/text-embeddings-inference:cpu-1.5
+    container_name: tei-embedding-server
+    ports:
+      - "6006:80"
+    volumes:
+      - "./data:/data"
+    shm_size: 1g
+    environment:
+      no_proxy: ${no_proxy}
+      http_proxy: ${http_proxy}
+      https_proxy: ${https_proxy}
+    command: --model-id ${EMBEDDING_MODEL_ID} --auto-truncate
   dataprep-redis:
     image: opea/dataprep-redis:latest
     container_name: dataprep-redis-server
@@ -21,6 +34,8 @@ services:
       no_proxy: ${no_proxy}
       http_proxy: ${http_proxy}
       https_proxy: ${https_proxy}
+      REDIS_HOST: ${REDIS_HOST}
+      REDIS_PORT: ${REDIS_PORT}
       REDIS_URL: ${REDIS_URL}
       INDEX_NAME: ${INDEX_NAME}
       TEI_ENDPOINT: ${TEI_ENDPOINT}
diff --git a/comps/dataprep/redis/langchain/prepare_doc_redis.py b/comps/dataprep/redis/langchain/prepare_doc_redis.py
index 2a99a11ee..73d5f6ba4 100644
--- a/comps/dataprep/redis/langchain/prepare_doc_redis.py
+++ b/comps/dataprep/redis/langchain/prepare_doc_redis.py
@@ -118,7 +118,10 @@ def ingest_data_to_redis(doc_path: DocPath):
         text_splitter = HTMLHeaderTextSplitter(headers_to_split_on=headers_to_split_on)
     else:
         text_splitter = RecursiveCharacterTextSplitter(
-            chunk_size=doc_path.chunk_size, chunk_overlap=100, add_start_index=True, separators=get_separators()
+            chunk_size=doc_path.chunk_size,
+            chunk_overlap=doc_path.chunk_overlap,
+            add_start_index=True,
+            separators=get_separators(),
         )
 
     content = document_loader(path)