인프런 커뮤니티 질문&답변
작성한 질문수
4.3강의: Bad Request 오류 발생
해결된 질문
작성
·
139
0
답변 3
0
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 chunk를 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"]
def get_llm(model='solar-embedding-1-large'):
llm = ChatUpstage(model=model)여기서 모델을 넘겨주지 않는게 공식문서 가이드입니다. 만약 모델을 지정하신다면 아래 링크에 있는 모델들 중 하나를 사용하셔야해요
https://developers.upstage.ai/docs/getting-started/models#solar-llm
그리고 model 말고 model_name 을 넘겨줘야합니다! 소스코드도 같이 드릴게요~
0
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 <module> 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
0
get_llm()에서는 embedding model이 아니라 chat model을 사용하셔야 합니다!https://developers.upstage.ai/docs/getting-started/quick-start#make-an-api-request