diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index ea9c494bb..475f5433a 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -14,6 +14,8 @@ repos:
       - id: check-json
       - id: check-yaml
       - id: debug-statements
+      - id: mixed-line-ending
+        args: [--fix=lf]
       - id: requirements-txt-fixer
       - id: trailing-whitespace
         files: (.*\.(py|rst|cmake|yaml|yml|json|ts|js|html|svelte|sh))$
diff --git a/comps/ragas/tgi/llm.py b/comps/ragas/tgi/llm.py
index f31c66657..895705703 100644
--- a/comps/ragas/tgi/llm.py
+++ b/comps/ragas/tgi/llm.py
@@ -1,86 +1,86 @@
-# Copyright (C) 2024 Intel Corporation
-# SPDX-License-Identifier: Apache-2.0
-
-import os
-
-from datasets import Dataset
-from langchain_community.embeddings import (
-    HuggingFaceBgeEmbeddings,
-    HuggingFaceEmbeddings,
-    HuggingFaceHubEmbeddings,
-    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
-
-from comps import GeneratedDoc, RAGASParams, RAGASScores, ServiceType, opea_microservices, register_microservice
-
-tei_embedding_endpoint = os.getenv("TEI_ENDPOINT")
-EMBED_MODEL = os.getenv("EMBED_MODEL", "BAAI/bge-base-en-v1.5")
-
-
-@register_microservice(
-    name="opea_service@ragas_tgi_llm",
-    service_type=ServiceType.RAGAS,
-    endpoint="/v1/ragas",
-    host="0.0.0.0",
-    port=9050,
-    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")
-
-    # Create vectorstore
-    if tei_embedding_endpoint:
-        # create embeddings using TEI endpoint service
-        embedder = HuggingFaceHubEmbeddings(model=tei_embedding_endpoint)
-    else:
-        # create embeddings using local embedding model
-        embedder = HuggingFaceBgeEmbeddings(model_name=EMBED_MODEL)
-
-    llm = HuggingFaceEndpoint(
-        endpoint_url=llm_endpoint,
-        max_new_tokens=input.max_new_tokens,
-        top_k=input.top_k,
-        top_p=input.top_p,
-        typical_p=input.typical_p,
-        temperature=input.temperature,
-        repetition_penalty=input.repetition_penalty,
-        streaming=input.streaming,
-        timeout=600,
-    )
-
-    data_collections = {
-        "question": input.questions,
-        "answer": input.answers,
-        "docs": input.docs,
-        "ground_truth": input.groundtruths,
-    }
-    dataset = Dataset.from_dict(data_collections)
-
-    score = evaluate(
-        dataset,
-        metrics=[answer_relevancy, faithfulness, context_recall, context_precision],
-        llm=llm,
-        embeddings=embedder,
-    )
-    df = score.to_pandas()
-    answer_relevancy_average = df["answer_relevancy"][:].mean()
-    faithfulness_average = df["faithfulness"][:].mean()
-    context_recall_average = df["context_recall"][:].mean()
-    context_precision_average = df["context_precision"][:].mean()
-
-    return RAGASScores(
-        answer_relevancy=answer_relevancy_average,
-        faithfulness=faithfulness_average,
-        context_recallL=context_recall_average,
-        context_precision=context_precision_average,
-    )
-
-
-if __name__ == "__main__":
-    opea_microservices["opea_service@llm_tgi"].start()
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import os
+
+from datasets import Dataset
+from langchain_community.embeddings import (
+    HuggingFaceBgeEmbeddings,
+    HuggingFaceEmbeddings,
+    HuggingFaceHubEmbeddings,
+    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
+
+from comps import GeneratedDoc, RAGASParams, RAGASScores, ServiceType, opea_microservices, register_microservice
+
+tei_embedding_endpoint = os.getenv("TEI_ENDPOINT")
+EMBED_MODEL = os.getenv("EMBED_MODEL", "BAAI/bge-base-en-v1.5")
+
+
+@register_microservice(
+    name="opea_service@ragas_tgi_llm",
+    service_type=ServiceType.RAGAS,
+    endpoint="/v1/ragas",
+    host="0.0.0.0",
+    port=9050,
+    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")
+
+    # Create vectorstore
+    if tei_embedding_endpoint:
+        # create embeddings using TEI endpoint service
+        embedder = HuggingFaceHubEmbeddings(model=tei_embedding_endpoint)
+    else:
+        # create embeddings using local embedding model
+        embedder = HuggingFaceBgeEmbeddings(model_name=EMBED_MODEL)
+
+    llm = HuggingFaceEndpoint(
+        endpoint_url=llm_endpoint,
+        max_new_tokens=input.max_new_tokens,
+        top_k=input.top_k,
+        top_p=input.top_p,
+        typical_p=input.typical_p,
+        temperature=input.temperature,
+        repetition_penalty=input.repetition_penalty,
+        streaming=input.streaming,
+        timeout=600,
+    )
+
+    data_collections = {
+        "question": input.questions,
+        "answer": input.answers,
+        "docs": input.docs,
+        "ground_truth": input.groundtruths,
+    }
+    dataset = Dataset.from_dict(data_collections)
+
+    score = evaluate(
+        dataset,
+        metrics=[answer_relevancy, faithfulness, context_recall, context_precision],
+        llm=llm,
+        embeddings=embedder,
+    )
+    df = score.to_pandas()
+    answer_relevancy_average = df["answer_relevancy"][:].mean()
+    faithfulness_average = df["faithfulness"][:].mean()
+    context_recall_average = df["context_recall"][:].mean()
+    context_precision_average = df["context_precision"][:].mean()
+
+    return RAGASScores(
+        answer_relevancy=answer_relevancy_average,
+        faithfulness=faithfulness_average,
+        context_recallL=context_recall_average,
+        context_precision=context_precision_average,
+    )
+
+
+if __name__ == "__main__":
+    opea_microservices["opea_service@llm_tgi"].start()
diff --git a/comps/ragas/tgi/requirements.txt b/comps/ragas/tgi/requirements.txt
index 3fa49150e..2c8fad29f 100644
--- a/comps/ragas/tgi/requirements.txt
+++ b/comps/ragas/tgi/requirements.txt
@@ -1,14 +1,14 @@
-datasets
-docarray[full]
-fastapi
-huggingface_hub
-langchain==0.1.16
-langsmith
-opentelemetry-api
-opentelemetry-exporter-otlp
-opentelemetry-sdk
-prometheus-fastapi-instrumentator
-ragas
-shortuuid
-transformers
-uvicorn
+datasets
+docarray[full]
+fastapi
+huggingface_hub
+langchain==0.1.16
+langsmith
+opentelemetry-api
+opentelemetry-exporter-otlp
+opentelemetry-sdk
+prometheus-fastapi-instrumentator
+ragas
+shortuuid
+transformers
+uvicorn