llm.py 코드도 올려드립니다 import streamlit as st from dotenv import load_dotenv#환경 변수 로드 from langchain_upstage import ChatUpstage from langchain_upstage import UpstageEmbeddings #vector 공간 활용 from langchain_pinecone import PineconeVectorStore # pinecone 데이터베이스 from pinecone import Pinecone, ServerlessSpec from langchain.chains import RetrievalQA #답변 생성을 위해 LLM에 전달 from langchain_core.output_parsers import StrOutputParser from langchain_core.prompts import ChatPromptTemplate from langchain.chains import create_history_aware_retriever from langchain_core.prompts import MessagesPlaceholder from langchain.chains import create_retrieval_chain from langchain.chains.combine_documents import create_stuff_documents_chain from langchain_community.chat_message_histories import ChatMessageHistory from langchain_core.chat_history import BaseChatMessageHistory from langchain_core.runnables.history import RunnableWithMessageHistory store = {} def get_session_history(session_id: str) -> BaseChatMessageHistory: if session_id not in store: store[session_id] = ChatMessageHistory() return store[session_id] def get_retriever(): embedding = UpstageEmbeddings(model='solar-embedding-1-large')#OpenAI에서 제공하는 Embedding Model을 활용해서 chunk 를 vector화 index_name = "tax-markdown" database = PineconeVectorStore.from_existing_index(index_name=index_name, embedding=embedding)# 이미 생성된 데이터베이스를 사용할때 #retriever = database.similarity_search(query, k=3) retriever = database.as_retriever(search_kwargs={'k':4}) return retriever def get_llm(model='solar-embedding-1-large'): llm = ChatUpstage(model=model) return llm def get_dictionary_chain(): dictionary = ["사람을 나타내는 표현 -> 거주자"] llm = get_llm() prompt = ChatPromptTemplate.from_template(f""" 사용자의 질문을 보고, 우리의 사전을 참고해서 사용자의 질문을 변경해주세요. 만약 변경할 필요가 없다고 판단된다면, 사용자의 질문을 변경하지 않아도 됩니다. 그런 경우에는 질문만 리턴해주세요 사전: {dictionary} 질문: {{question}} """) dictionary_chain = prompt | llm | StrOutputParser() return dictionary_chain def get_rag_chain(): llm=get_llm() retriever=get_retriever() contextualize_q_system_prompt = ( "Given a chat history and the latest user question " "which might reference context in the chat history, " "formulate a standalone question which can be understood " "without the chat history. Do NOT answer the question, " "just reformulate it if needed and otherwise return it as is." ) contextualize_q_prompt = ChatPromptTemplate.from_messages( [ ("system", contextualize_q_system_prompt), MessagesPlaceholder("chat_history"), ("human", "{input}"), ] ) history_aware_retriever = create_history_aware_retriever( llm, retriever, contextualize_q_prompt ) system_prompt = ( "You are an assistant for question-answering tasks. " "Use the following pieces of retrieved context to answer " "the question. If you don't know the answer, say that you " "don't know. Use three sentences maximum and keep the " "answer concise." "\n\n" "{context}" ) qa_prompt = ChatPromptTemplate.from_messages( [ ("system", system_prompt), MessagesPlaceholder("chat_history"), ("human", "{input}"), ] ) question_answer_chain = create_stuff_documents_chain(llm, qa_prompt) rag_chain = create_retrieval_chain(history_aware_retriever, question_answer_chain) conversational_rag_chain = RunnableWithMessageHistory( rag_chain, get_session_history, input_messages_key="input", history_messages_key="chat_history", output_messages_key="answer", ).pick('answer') return conversational_rag_chain def get_ai_response(user_message): dictionary_chain = get_dictionary_chain() rag_chain = get_rag_chain() tax_chain = {"input": dictionary_chain} | rag_chain ai_response = tax_chain.stream( { "question": user_message }, config={ "configurable": {"session_id": "abc123"} }, ) return ai_response #return ai_message["answer"]
BadRequestError : Bad Request Traceback: File "C:\Users\leehonggi\anaconda3\Lib\site-packages\streamlit\runtime\scriptrunner\script_runner.py", line 542, in _run_script exec(code, module.__dict__) File "C:\Users\leehonggi\pythonwin\2024\ chat.py ", line 37, in ai_message = st.write_stream(ai_response) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\leehonggi\anaconda3\Lib\site-packages\streamlit\runtime\metrics_ util.py ", line 397, in wrapped_func result = non_optional_func(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\leehonggi\anaconda3\Lib\site-packages\streamlit\elements\ write.py ", line 159, in write_stream for chunk in stream: # type: ignore File "C:\Users\leehonggi\anaconda3\Lib\site-packages\langchain_core\runnables\ base.py ", line 3262, in stream yield from self.transform(iter([input]), config, **kwargs) File "C:\Users\leehonggi\anaconda3\Lib\site-packages\langchain_core\runnables\ base.py ", line 3249, in transform yield from self._transform_stream_with_config( File "C:\Users\leehonggi\anaconda3\Lib\site-packages\langchain_core\runnables\ base.py ", line 2054, in _transform_stream_with_config chunk: Output = context.run (next, iterator) # type: ignore ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\leehonggi\anaconda3\Lib\site-packages\langchain_core\runnables\ base.py ", line 3211, in _transform for output in final_pipeline: File "C:\Users\leehonggi\anaconda3\Lib\site-packages\langchain_core\runnables\ passthrough.py ", line 765, in transform yield from self._transform_stream_with_config( File "C:\Users\leehonggi\anaconda3\Lib\site-packages\langchain_core\runnables\ base.py ", line 2018, in _transform_stream_with_config final_input: Optional[Input] = next(input_for_tracing, None) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\leehonggi\anaconda3\Lib\site-packages\langchain_core\runnables\ base.py ", line 5301, in transform yield from self.bound.transform( File "C:\Users\leehonggi\anaconda3\Lib\site-packages\langchain_core\runnables\ base.py ", line 5301, in transform yield from self.bound.transform( File "C:\Users\leehonggi\anaconda3\Lib\site-packages\langchain_core\runnables\ base.py ", line 3249, in transform yield from self._transform_stream_with_config( File "C:\Users\leehonggi\anaconda3\Lib\site-packages\langchain_core\runnables\ base.py ", line 2018, in _transform_stream_with_config final_input: Optional[Input] = next(input_for_tracing, None) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\leehonggi\anaconda3\Lib\site-packages\langchain_core\runnables\ base.py ", line 3700, in transform yield from self._transform_stream_with_config( File "C:\Users\leehonggi\anaconda3\Lib\site-packages\langchain_core\runnables\ base.py ", line 2054, in _transform_stream_with_config chunk: Output = context.run (next, iterator) # type: ignore ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\leehonggi\anaconda3\Lib\site-packages\langchain_core\runnables\ base.py ", line 3685, in _transform chunk = AddableDict({step_name: future.result()}) ^^^^^^^^^^^^^^^ File "C:\Users\leehonggi\anaconda3\Lib\concurrent\futures\_ base.py ", line 449, in result return self.__get_result() ^^^^^^^^^^^^^^^^^^^ File "C:\Users\leehonggi\anaconda3\Lib\concurrent\futures\_ base.py ", line 401, in __get_result raise self._exception File "C:\Users\leehonggi\anaconda3\Lib\concurrent\futures\ thread.py ", line 58, in run result = self.fn(*self.args, **self.kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\leehonggi\anaconda3\Lib\site-packages\langchain_core\runnables\ base.py ", line 3249, in transform yield from self._transform_stream_with_config( File "C:\Users\leehonggi\anaconda3\Lib\site-packages\langchain_core\runnables\ base.py ", line 2054, in _transform_stream_with_config chunk: Output = context.run (next, iterator) # type: ignore ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\leehonggi\anaconda3\Lib\site-packages\langchain_core\runnables\ base.py ", line 3211, in _transform for output in final_pipeline: File "C:\Users\leehonggi\anaconda3\Lib\site-packages\langchain_core\output_parsers\ transform.py ", line 65, in transform yield from self._transform_stream_with_config( File "C:\Users\leehonggi\anaconda3\Lib\site-packages\langchain_core\runnables\ base.py ", line 2018, in _transform_stream_with_config final_input: Optional[Input] = next(input_for_tracing, None) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\leehonggi\anaconda3\Lib\site-packages\langchain_core\runnables\ base.py ", line 1290, in transform yield from self.stream (final, config, **kwargs) File "C:\Users\leehonggi\anaconda3\Lib\site-packages\langchain_core\language_models\chat_ models.py ", line 425, in stream raise e File "C:\Users\leehonggi\anaconda3\Lib\site-packages\langchain_core\language_models\chat_ models.py ", line 405, in stream for chunk in self._stream(messages, stop=stop, **kwargs): File "C:\Users\leehonggi\anaconda3\Lib\site-packages\langchain_openai\chat_models\ base.py ", line 558, in _stream response = self.client.create(**payload) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\leehonggi\anaconda3\Lib\site-packages\openai\_utils\_ utils.py ", line 274, in wrapper return func(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\leehonggi\anaconda3\Lib\site-packages\openai\resources\chat\ completions.py ", line 668, in create return self._post( ^^^^^^^^^^^ File "C:\Users\leehonggi\anaconda3\Lib\site-packages\openai\_base_ client.py ", line 1259, in post return cast(ResponseT, self.request(cast_to, opts, stream=stream, stream_cls=stream_cls)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\leehonggi\anaconda3\Lib\site-packages\openai\_base_ client.py ", line 936, in request return self._request( ^^^^^^^^^^^^^^ File "C:\Users\leehonggi\anaconda3\Lib\site-packages\openai\_base_ client.py ", line 1040, in _request raise self._make_status_error_from_response(err.response) from None
