Revert "Merge remote-tracking branch 'origin/develop-ai'"

This reverts commit e13a1ce, reversing
changes made to 21304b8.

ai/.gitignore

@@ -5,5 +5,4 @@
 *.pyc
 __pycache__
 data/
-cap30.pem
-chatbot.pem
+cap30.pem

ai/llm/llm_rag.py

@@ -3,9 +3,8 @@
 from langchain_core.runnables import RunnablePassthrough, RunnableLambda
 from langchain_openai import ChatOpenAI
 from tavily import TavilyClient
-from llm.prompt import casual_prompt, is_qna_prompt, combine_result_prompt, score_prompt
+from llm.prompt import casual_prompt, is_qna_prompt, combine_result_prompt, score_prompt, translate_prompt
 from langchain.retrievers.multi_query import MultiQueryRetriever
-import deepl
 import os
 
 class LLM_RAG:
@@ -19,9 +18,7 @@ def __init__(self, trace = False):
         self.is_qna_prompt = is_qna_prompt()
         self.combine_result_prompt = combine_result_prompt()
         self.score_prompt = score_prompt()
-        self.deepl = deepl.Translator(os.getenv("DEEPL_API_KEY"))
-        self.ko_query = None
-        self.result_lang = None
+        self.translate_prompt = translate_prompt()
         self.notice_retriever = None
         self.school_retriever = None
         self.notice_multiquery_retriever = None
@@ -97,37 +94,41 @@ def set_chain(self):
             self.score_route
         )
 
+        self.translate_chain = (
+            self.translate_prompt
+            | self.llm
+            | StrOutputParser()
+        )
 
     def qna_route(self, info):
         if "question" in info["topic"].lower():
-            self.result = self.rag_combine_chain.invoke(self.ko_query)
-            score = self.score_chain.invoke({"question" : self.ko_query, "answer": self.result})
-            self.score_invoke_chain.invoke({"score" : score, "question": self.ko_query})
+
+            self.result = self.rag_combine_chain.invoke(info["question"])
+            score = self.score_chain.invoke({"question" : self.question, "answer": self.result})
+            self.score_invoke_chain.invoke({"score" : score, "question": self.question})
 
         elif "casual" in info["topic"].lower():
-            self.result =  self.casual_answer_chain.invoke(self.question)
+            self.result =  self.casual_answer_chain.invoke(info['question'])
 
         else:
-            self.result = self.rag_combine_chain.invoke(self.question)
+            self.result = self.rag_combine_chain.invoke(info["question"])
 
 
     def score_route(self, info):
         if "good" in info["score"].lower():
-            self.result = self.deepl.translate_text(self.result, target_lang=self.result_lang).text
             return self.result
         else:
             print('-- google search --')
-            content = self.tavily.qna_search(query='국민대학교 ' + self.ko_query)
-            self.result = "I couldn't find the answer, so I searched on Google.\n\n" + content
-            self.result = self.deepl.translate_text(self.result, target_lang=self.result_lang).text
+            content = self.tavily.qna_search(query='국민대학교 ' + self.question)
+            self.result = "답을 찾을 수 없어서 구글에 검색했습니다.\n\n"
+            self.result += self.translate_chain.invoke({'content' : content, 'question':self.question})
+            return self.result
 
     def format_docs(self, docs):
     # 검색한 문서 결과를 하나의 문단으로 합쳐줍니다.
         return "\n\n".join(doc.page_content + '\nmetadata=' + str(doc.metadata) for doc in docs)
 
-    def query(self, question, result_lang):
+    def query(self, question):
         self.question = question
-        self.ko_query = self.deepl.translate_text(self.question, target_lang='ko').text
-        self.result_lang = result_lang
         self.qna_route_chain.invoke(question)
         return self.result

ai/llm/prompt.py

@@ -39,4 +39,13 @@ def score_prompt():
 
     Classification:"""
     )
+    return prompt
+
+def translate_prompt():
+    prompt = PromptTemplate.from_template("""
+    You are a translator with vast knowledge of human languages. Translate the content into the language corresponding to the question. You should only translate and never answer questions.
+        
+    question : {question}
+    content : {content}
+    result :""")
     return prompt

ai/requirements.txt

@@ -6,5 +6,4 @@ langchainhub==0.1.15
 beautifulsoup4==4.12.3
 pypdf==4.1.0
 python-dotenv==1.0.1
-langchain-text-splitters==0.0.1
-deepl
+langchain-text-splitters==0.0.1

ai/run_chatbot.py

@@ -11,7 +11,6 @@
 load_dotenv()
 
 os.environ['OPENAI_API_KEY'] = os.getenv('OPENAI_API_KEY')
-os.environ['DEEPL_API_KEY'] = os.getenv('DEEPL_API_KEY')
 
 # LangSmith 사용시 아래 주석을 해제
 # os.environ['LANGCHAIN_API_KEY'] = os.getenv('LANGCHAIN_API_KEY')
@@ -41,5 +40,5 @@
     if q == str(0):
         break
     print('AI : ', end='')
-    print(llm.query(q, 'ZH'))
+    print(llm.query(q))
     print()

ai/server.py

@@ -11,7 +11,6 @@
 
 class Query(BaseModel):
     query: str
-    target_lang: str
 
 @asynccontextmanager
 async def lifespan(app:FastAPI):
@@ -59,14 +58,18 @@ async def lifespan(app:FastAPI):
 async def initiate():
     return "안녕하세요! 국민대학교 전용 챗봇 KUKU입니다. 국민대학교에 대한 건 모든 질문해주세요!"
 
-@app.post("/api/chatbot")
+@app.post("/query")
 async def query(query: Query):
-    return {'success': 'True',
+    return {'code': '200',
             'message': 'success',
             'response': {
-                'answer': llm.query(query.query, query.target_lang)
+                'answer': llm.query(query.query)
             }}
 
+@app.post("/input")
+async def input(data: UploadFile):
+    vdb.add_content(data.file)
+    return 
 
 
 if __name__ == "__main__":