diff --git a/README.md b/README.md
index e1b48a08ce..ecaf6c35d6 100644
--- a/README.md
+++ b/README.md
@@ -55,7 +55,7 @@ The initially supported `Microservices` are described in the below table. More `
| Embedding |
LangChain/LlamaIndex |
- BAAI/bge-large-en-v1.5 |
+ BAAI/bge-base-en-v1.5 |
TEI-Gaudi |
Gaudi2 |
Embedding on Gaudi2 |
@@ -76,7 +76,7 @@ The initially supported `Microservices` are described in the below table. More `
| Reranking |
LangChain/LlamaIndex |
- BAAI/bge-reranker-large |
+ BAAI/bge-reranker-base |
TEI-Gaudi |
Gaudi2 |
Reranking on Gaudi2 |
diff --git a/comps/dataprep/redis/README.md b/comps/dataprep/redis/README.md
index 440eb0d457..c9fb1deaf7 100644
--- a/comps/dataprep/redis/README.md
+++ b/comps/dataprep/redis/README.md
@@ -49,7 +49,7 @@ First, you need to start a TEI service.
```bash
your_port=6006
-model="BAAI/bge-large-en-v1.5"
+model="BAAI/bge-base-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
```
diff --git a/comps/dataprep/redis/langchain/config.py b/comps/dataprep/redis/langchain/config.py
index 75715912c9..2d722a84a6 100644
--- a/comps/dataprep/redis/langchain/config.py
+++ b/comps/dataprep/redis/langchain/config.py
@@ -5,7 +5,7 @@
# Embedding model
-EMBED_MODEL = os.getenv("EMBED_MODEL", "BAAI/bge-large-en-v1.5")
+EMBED_MODEL = os.getenv("EMBED_MODEL", "BAAI/bge-base-en-v1.5")
# Redis Connection Information
REDIS_HOST = os.getenv("REDIS_HOST", "localhost")
diff --git a/comps/embeddings/README.md b/comps/embeddings/README.md
index 8ac6dfe0ca..56dc922df3 100644
--- a/comps/embeddings/README.md
+++ b/comps/embeddings/README.md
@@ -43,7 +43,7 @@ First, you need to start a TEI service.
```bash
your_port=8090
-model="BAAI/bge-large-en-v1.5"
+model="BAAI/bge-base-en-v1.5"
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-1.5 --model-id $model
```
@@ -64,7 +64,7 @@ cd langchain
# run with llama_index
cd llama_index
export TEI_EMBEDDING_ENDPOINT="http://localhost:$yourport"
-export TEI_EMBEDDING_MODEL_NAME="BAAI/bge-large-en-v1.5"
+export TEI_EMBEDDING_MODEL_NAME="BAAI/bge-base-en-v1.5"
python embedding_tei.py
```
@@ -86,7 +86,7 @@ First, you need to start a TEI service.
```bash
your_port=8090
-model="BAAI/bge-large-en-v1.5"
+model="BAAI/bge-base-en-v1.5"
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-1.5 --model-id $model
```
@@ -103,7 +103,7 @@ Export the `TEI_EMBEDDING_ENDPOINT` for later usage:
```bash
export TEI_EMBEDDING_ENDPOINT="http://localhost:$yourport"
-export TEI_EMBEDDING_MODEL_NAME="BAAI/bge-large-en-v1.5"
+export TEI_EMBEDDING_MODEL_NAME="BAAI/bge-base-en-v1.5"
```
### 2.2 Build Docker Image
diff --git a/comps/embeddings/langchain/local_embedding.py b/comps/embeddings/langchain/local_embedding.py
index 32f8944a98..6a0a1a630f 100644
--- a/comps/embeddings/langchain/local_embedding.py
+++ b/comps/embeddings/langchain/local_embedding.py
@@ -40,5 +40,5 @@ def embedding(input: TextDoc) -> EmbedDoc:
if __name__ == "__main__":
- embeddings = HuggingFaceEmbeddings(model_name="BAAI/bge-large-en-v1.5")
+ embeddings = HuggingFaceEmbeddings(model_name="BAAI/bge-base-en-v1.5")
opea_microservices["opea_service@local_embedding"].start()
diff --git a/comps/embeddings/llama_index/embedding_tei.py b/comps/embeddings/llama_index/embedding_tei.py
index cf14f7790b..943bd75350 100644
--- a/comps/embeddings/llama_index/embedding_tei.py
+++ b/comps/embeddings/llama_index/embedding_tei.py
@@ -31,7 +31,7 @@ def embedding(input: TextDoc) -> EmbedDoc:
if __name__ == "__main__":
- tei_embedding_model_name = os.getenv("TEI_EMBEDDING_MODEL_NAME", "BAAI/bge-large-en-v1.5")
+ tei_embedding_model_name = os.getenv("TEI_EMBEDDING_MODEL_NAME", "BAAI/bge-base-en-v1.5")
tei_embedding_endpoint = os.getenv("TEI_EMBEDDING_ENDPOINT", "http://localhost:8090")
embeddings = TextEmbeddingsInference(model_name=tei_embedding_model_name, base_url=tei_embedding_endpoint)
logger.info("TEI Gaudi Embedding initialized.")
diff --git a/comps/embeddings/llama_index/local_embedding.py b/comps/embeddings/llama_index/local_embedding.py
index 143d7bb07c..17ee6e89a8 100644
--- a/comps/embeddings/llama_index/local_embedding.py
+++ b/comps/embeddings/llama_index/local_embedding.py
@@ -31,5 +31,5 @@ def embedding(input: TextDoc) -> EmbedDoc:
if __name__ == "__main__":
- embeddings = HuggingFaceInferenceAPIEmbedding(model_name="BAAI/bge-large-en-v1.5")
+ embeddings = HuggingFaceInferenceAPIEmbedding(model_name="BAAI/bge-base-en-v1.5")
opea_microservices["opea_service@local_embedding"].start()
diff --git a/comps/reranks/README.md b/comps/reranks/README.md
index 9b5dc90426..c1ea8bb7a8 100644
--- a/comps/reranks/README.md
+++ b/comps/reranks/README.md
@@ -19,7 +19,7 @@ export HF_TOKEN=${your_hf_api_token}
export LANGCHAIN_TRACING_V2=true
export LANGCHAIN_API_KEY=${your_langchain_api_key}
export LANGCHAIN_PROJECT="opea/reranks"
-export RERANK_MODEL_ID="BAAI/bge-reranker-large"
+export RERANK_MODEL_ID="BAAI/bge-reranker-base"
revision=refs/pr/4
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
diff --git a/comps/reranks/langchain-mosec/mosec-docker/Dockerfile b/comps/reranks/langchain-mosec/mosec-docker/Dockerfile
index dcf38aee5e..895b209099 100644
--- a/comps/reranks/langchain-mosec/mosec-docker/Dockerfile
+++ b/comps/reranks/langchain-mosec/mosec-docker/Dockerfile
@@ -18,7 +18,7 @@ RUN pip3 install intel-extension-for-pytorch==2.2.0
RUN pip3 install transformers sentence-transformers
RUN pip3 install llmspec mosec
-RUN cd /home/user/ && export HF_ENDPOINT=https://hf-mirror.com && huggingface-cli download --resume-download BAAI/bge-reranker-large --local-dir /home/user/bge-reranker-large
+RUN cd /home/user/ && export HF_ENDPOINT=https://hf-mirror.com && huggingface-cli download --resume-download BAAI/bge-reranker-base --local-dir /home/user/bge-reranker-large
USER user
ENV EMB_MODEL="/home/user/bge-reranker-large/"
diff --git a/comps/reranks/tei/local_reranking.py b/comps/reranks/tei/local_reranking.py
index 284cca7e6d..fca2e68e6f 100644
--- a/comps/reranks/tei/local_reranking.py
+++ b/comps/reranks/tei/local_reranking.py
@@ -41,5 +41,5 @@ def reranking(input: SearchedDoc) -> RerankedDoc:
if __name__ == "__main__":
- reranker_model = CrossEncoder(model_name="BAAI/bge-reranker-large", max_length=512)
+ reranker_model = CrossEncoder(model_name="BAAI/bge-reranker-base", max_length=512)
opea_microservices["opea_service@local_reranking"].start()
diff --git a/tests/test_embeddings_langchain-mosec.sh b/tests/test_embeddings_langchain-mosec.sh
index 95858118b0..9a30b33cee 100644
--- a/tests/test_embeddings_langchain-mosec.sh
+++ b/tests/test_embeddings_langchain-mosec.sh
@@ -33,7 +33,7 @@ function build_docker_images() {
function start_service() {
mosec_endpoint=5001
- model="BAAI/bge-large-en-v1.5"
+ model="BAAI/bge-base-en-v1.5"
unset http_proxy
docker run -d --name="test-comps-embedding-langchain-mosec-endpoint" -p $mosec_endpoint:8000 opea/embedding-langchain-mosec-endpoint:comps
export MOSEC_EMBEDDING_ENDPOINT="http://${ip_address}:${mosec_endpoint}"
diff --git a/tests/test_embeddings_langchain.sh b/tests/test_embeddings_langchain.sh
index 6c6241226c..3dbbcdcd0a 100644
--- a/tests/test_embeddings_langchain.sh
+++ b/tests/test_embeddings_langchain.sh
@@ -21,10 +21,10 @@ function build_docker_images() {
function start_service() {
tei_endpoint=5001
- model="BAAI/bge-large-en-v1.5"
+ model="BAAI/bge-base-en-v1.5"
revision="refs/pr/5"
unset http_proxy
- docker run -d --name="test-comps-embedding-tei-endpoint" -p $tei_endpoint:80 -v ./data:/data --pull always ghcr.io/huggingface/text-embeddings-inference:cpu-1.2 --model-id $model --revision $revision
+ docker run -d --name="test-comps-embedding-tei-endpoint" -p $tei_endpoint:80 -v ./data:/data --pull always ghcr.io/huggingface/text-embeddings-inference:cpu-1.5 --model-id $model
export TEI_EMBEDDING_ENDPOINT="http://${ip_address}:${tei_endpoint}"
tei_service_port=5002
docker run -d --name="test-comps-embedding-tei-server" -e http_proxy=$http_proxy -e https_proxy=$https_proxy -p ${tei_service_port}:6000 --ipc=host -e TEI_EMBEDDING_ENDPOINT=$TEI_EMBEDDING_ENDPOINT opea/embedding-tei:comps
diff --git a/tests/test_embeddings_llama_index.sh b/tests/test_embeddings_llama_index.sh
index 81eac442ba..19eb55304a 100644
--- a/tests/test_embeddings_llama_index.sh
+++ b/tests/test_embeddings_llama_index.sh
@@ -22,9 +22,9 @@ function build_docker_images() {
function start_service() {
tei_endpoint=5001
- model="BAAI/bge-large-en-v1.5"
+ model="BAAI/bge-base-en-v1.5"
revision="refs/pr/5"
- docker run -d --name="test-comps-embedding-tei-llama-index-endpoint" -p $tei_endpoint:80 -v ./data:/data -e http_proxy=$http_proxy -e https_proxy=$https_proxy --pull always ghcr.io/huggingface/text-embeddings-inference:cpu-1.2 --model-id $model --revision $revision
+ docker run -d --name="test-comps-embedding-tei-llama-index-endpoint" -p $tei_endpoint:80 -v ./data:/data -e http_proxy=$http_proxy -e https_proxy=$https_proxy --pull always ghcr.io/huggingface/text-embeddings-inference:cpu-1.5 --model-id $model
export TEI_EMBEDDING_ENDPOINT="http://${ip_address}:${tei_endpoint}"
tei_service_port=5010
docker run -d --name="test-comps-embedding-tei-llama-index-server" -e http_proxy=$http_proxy -e https_proxy=$https_proxy -p ${tei_service_port}:6000 --ipc=host -e TEI_EMBEDDING_ENDPOINT=$TEI_EMBEDDING_ENDPOINT opea/embedding-tei-llama-index:comps
diff --git a/tests/test_reranks_langchain-mosec.sh b/tests/test_reranks_langchain-mosec.sh
index d34957a4cc..1339c0df97 100644
--- a/tests/test_reranks_langchain-mosec.sh
+++ b/tests/test_reranks_langchain-mosec.sh
@@ -33,7 +33,7 @@ function build_docker_images() {
function start_service() {
mosec_endpoint=5006
- model="BAAI/bge-reranker-large"
+ model="BAAI/bge-reranker-base"
unset http_proxy
docker run -d --name="test-comps-reranking-langchain-mosec-endpoint" -p $mosec_endpoint:8000 opea/reranking-langchain-mosec-endpoint:comps
export MOSEC_RERANKING_ENDPOINT="http://${ip_address}:${mosec_endpoint}"
diff --git a/tests/test_reranks_tei.sh b/tests/test_reranks_tei.sh
index 0b146d81e6..fa087357ce 100644
--- a/tests/test_reranks_tei.sh
+++ b/tests/test_reranks_tei.sh
@@ -21,10 +21,10 @@ function start_service() {
tei_endpoint=5006
# Remember to set HF_TOKEN before invoking this test!
export HF_TOKEN=${HF_TOKEN}
- model=BAAI/bge-reranker-large
+ model=BAAI/bge-reranker-base
revision=refs/pr/4
volume=$PWD/data
- docker run -d --name="test-comps-reranking-tei-endpoint" -p $tei_endpoint:80 -v $volume:/data --pull always ghcr.io/huggingface/text-embeddings-inference:cpu-1.2 --model-id $model --revision $revision
+ docker run -d --name="test-comps-reranking-tei-endpoint" -p $tei_endpoint:80 -v $volume:/data --pull always ghcr.io/huggingface/text-embeddings-inference:cpu-1.5 --model-id $model
export TEI_RERANKING_ENDPOINT="http://${ip_address}:${tei_endpoint}"
tei_service_port=5007