Create make_chain_model.py

make_chain_model.py

@@ -0,0 +1,32 @@
+from langchain_core.runnables import RunnablePassthrough
+from langchain_core.output_parsers import StrOutputParser
+from langchain_community.chat_models import ChatOllama
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_pinecone import PineconeVectorStore
+from langchain_community.embeddings import SentenceTransformerEmbeddings
+
+def make_chain_llm(retriever,llm):
+    def format_docs(docs):
+        # 검색한 문서 결과를 하나의 문단으로 합쳐줍니다.
+        return "\n\n".join(doc.page_content for doc in docs)
+
+    # LangChain이 지원하는 다른 채팅 모델을 사용합니다. 여기서는 Ollama를 사용합니다.
+    # llm = ChatOllama(model="zephyr:latest")
+
+    template = "\"```\" Below is an instruction that describes a task. Write a response that appropriately completes the request."\
+    "제시하는 context에서만 대답하고 context에 없는 내용은 생성하지마"\
+    "make answer in korean. 한국어로 대답하세요"\
+    "\n\nContext:\n{context}\n;"\
+    "Question: {question}"\
+    "\n\nAnswer:"
+
+    prompt = ChatPromptTemplate.from_template(template)
+
+    rag_chain = (
+    {"context": retriever| format_docs, "question": RunnablePassthrough()}
+    | prompt
+    | llm
+    | StrOutputParser()
+    )
+
+    return rag_chain