[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

Signed-off-by: XuhuiRen <xuhui.ren@intel.com>

comps/cores/proto/docarray.py

@@ -90,16 +90,17 @@ class LLMParams(BaseDoc):
     temperature: float = 0.01
     repetition_penalty: float = 1.03
     streaming: bool = True
-
+
+
 class RAGASParams(BaseDoc):
     questions: DocList[TextDoc]
     answers: DocList[TextDoc]
     docs: DocList[TextDoc]
     ground_truths: DocList[TextDoc]
-
+
+
 class RAGASScores(BaseDoc):
     answer_relevancy: float
     faithfulness: float
     context_recallL: float
     context_precision: float
-

comps/ragas/tgi/__init__.py

@@ -10,4 +10,4 @@
 # 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.
+# limitations under the License.

comps/ragas/tgi/build_docker.sh

@@ -16,4 +16,4 @@
 
 git clone https://github.com/huggingface/tgi-gaudi.git
 cd ./tgi-gaudi/
-docker build -t ghcr.io/huggingface/tgi-gaudi:1.2.1 . --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy
+docker build -t ghcr.io/huggingface/tgi-gaudi:1.2.1 . --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy

comps/ragas/tgi/docker_compose_llm.yaml

@@ -1,43 +1,43 @@
-# 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.
-
-version: "3.8"
-
-services:
-  tgi_service:
-    image: ghcr.io/huggingface/text-generation-inference:1.4
-    container_name: tgi-service
-    ports:
-      - "8008:80"
-    volumes:
-      - "./data:/data"
-    shm_size: 1g
-    command: --model-id ${LLM_MODEL_ID}
-  llm:
-    image: opea/gen-ai-comps:llm-tgi-server
-    container_name: llm-tgi-server
-    ports:
-      - "9000:9000"
-    ipc: host
-    environment:
-      http_proxy: ${http_proxy}
-      https_proxy: ${https_proxy}
-      TGI_LLM_ENDPOINT: ${TGI_LLM_ENDPOINT}
-      HUGGINGFACEHUB_API_TOKEN: ${HUGGINGFACEHUB_API_TOKEN}
-      LANGCHAIN_API_KEY: ${LANGCHAIN_API_KEY}
-    restart: unless-stopped
-
-networks:
-  default:
-    driver: bridge
+# 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.
+
+version: "3.8"
+
+services:
+  tgi_service:
+    image: ghcr.io/huggingface/text-generation-inference:1.4
+    container_name: tgi-service
+    ports:
+      - "8008:80"
+    volumes:
+      - "./data:/data"
+    shm_size: 1g
+    command: --model-id ${LLM_MODEL_ID}
+  llm:
+    image: opea/gen-ai-comps:llm-tgi-server
+    container_name: llm-tgi-server
+    ports:
+      - "9000:9000"
+    ipc: host
+    environment:
+      http_proxy: ${http_proxy}
+      https_proxy: ${https_proxy}
+      TGI_LLM_ENDPOINT: ${TGI_LLM_ENDPOINT}
+      HUGGINGFACEHUB_API_TOKEN: ${HUGGINGFACEHUB_API_TOKEN}
+      LANGCHAIN_API_KEY: ${LANGCHAIN_API_KEY}
+    restart: unless-stopped
+
+networks:
+  default:
+    driver: bridge

comps/ragas/tgi/launch_tgi_service.sh

@@ -47,4 +47,4 @@ else
 fi
 
 # Execute the Docker run command
-eval $docker_cmd
+eval $docker_cmd

comps/ragas/tgi/llm.py

@@ -14,19 +14,25 @@
 
 import os
 
-from langchain_community.llms import HuggingFaceEndpoint
-from langsmith import traceable
 from config import EMBED_MODEL
 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.embeddings import LangchainEmbeddingsWrapper   # pylint: disable=E0611
-from langchain_community.embeddings import HuggingFaceBgeEmbeddings,HuggingFaceEmbeddings, \
-    HuggingFaceInstructEmbeddings, HuggingFaceHubEmbeddings
-from comps import GeneratedDoc, RAGASParams, ServiceType, opea_microservices, register_microservice, RAGASScores
+from ragas.embeddings import LangchainEmbeddingsWrapper  # pylint: disable=E0611
+
+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")
 
+
 @traceable(run_type="tool")
 def post_process_text(text: str):
     if text == " ":
@@ -38,14 +44,14 @@ def post_process_text(text: str):
     new_text = text.replace("Answer: ", "").replace("Human: ", "").replace(" ", "@#$")
     return f"data: {new_text}\n\n"
 
+
 @register_microservice(
     name="opea_service@llm_tgi",
     service_type=ServiceType.LLM,
     endpoint="/v1/chat/completions",
     host="0.0.0.0",
     port=9000,
 )
-
 @traceable(run_type="llm")
 def llm_generate(input: RAGASParams):
     llm_endpoint = os.getenv("TGI_LLM_ENDPOINT", "http://localhost:8080")
@@ -69,29 +75,34 @@ def llm_generate(input: RAGASParams):
         streaming=input.streaming,
         timeout=600,
     )
-    
+
     data_collections = {
-        'question': input.questions,
-        'answer': input.answers,
-        'docs' : input.docs,
-        'ground_truth': input.groundtruths
+        "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)
+    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)
+    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()
+    opea_microservices["opea_service@llm_tgi"].start()

comps/ragas/tgi/requirements.txt

@@ -1,3 +1,4 @@
+datasets
 docarray[full]
 fastapi
 huggingface_hub
@@ -7,7 +8,6 @@ langsmith
 opentelemetry-api
 opentelemetry-exporter-otlp
 opentelemetry-sdk
+ragas
 shortuuid
 transformers
-ragas
-datasets