diff --git a/app/chain.py b/app/chain.py
new file mode 100644
index 0000000..cd056ad
--- /dev/null
+++ b/app/chain.py
@@ -0,0 +1,51 @@
+from langchain.schema.runnable import RunnableMap
+from langchain.vectorstores import Weaviate
+from langchain.llms import HuggingFaceEndpoint
+from langchain.prompts import PromptTemplate
+from langchain.embeddings.base import Embeddings
+import httpx
+import weaviate
+import os
+
+# 환경 변수에서 세팅
+LLM_ENDPOINT = os.environ.get('LLM_ENDPOINT', '') # https://deploymodel.cheetah.svc.cluster.local
+EMBEDDING_ENDPOINT = os.environ.get('EMBEDDING_ENDPOINT', '') # https://embedding.cheetah.svc.cluster.local
+VECTOR_DATABSE_ENDPOINT = os.environ.get('VECTOR_DATABSE_ENDPOINT', '') # https://vector.cheetah.svc.cluster.local
+VECTOR_DATABSE_APIKEY = os.environ.get('VECTOR_DATABSE_APIKEY', '') # https://vector.cheetah.svc.cluster.local
+
+# 사용자 정의 임베딩 클래스 (온프레미스 embedding 서버 사용)
+class CustomRemoteEmbeddings(Embeddings):
+    def __init__(self, endpoint_url: str):
+        self.endpoint_url = endpoint_url
+
+    def embed_documents(self, texts: list[str]) -> list[list[float]]:
+        response = httpx.post(
+            self.endpoint_url,
+            json={"inputs": texts}
+        )
+        return response.json()["embeddings"]
+
+    def embed_query(self, text: str) -> list[float]:
+        return self.embed_documents([text])[0]
+
+# Triton Inference Server 기반 LLM
+llm = HuggingFaceEndpoint(
+    endpoint_url=LLM_ENDPOINT,  # Triton inference REST endpoint
+    task="text-generation",
+    model_kwargs={"temperature": 0.7, "max_new_tokens": 512}
+)
+
+# 사용자 정의 embedding 인스턴스
+embedding_model = CustomRemoteEmbeddings(endpoint_url=EMBEDDING_ENDPOINT)
+
+# Weaviate 벡터 DB
+client = weaviate.Client(VECTOR_DATABSE_ENDPOINT)
+vectorstore = Weaviate(client=client, index_name="LangChainIndex", text_key="text", embedding=embedding_model)
+
+# RAG Prompt + Chain 구성
+# 여기에 들어가는 prompt 템플릿을 어떻게 가져가야 하는지 향후 prompt에서 가져다가 쓸 수 있는 방법이 있다면 확장성 좋을 것 같음
+prompt = PromptTemplate.from_template("Answer the question based on the following context:\n{context}\nQuestion: {question}")
+chain = RunnableMap({
+    "context": lambda x: "\n".join(doc.page_content for doc in vectorstore.similarity_search(x["question"], k=3)),
+    "question": lambda x: x["question"]
+}) | prompt | llm