rag.py

################
# PDF 파일을 로드하고 문서를 쪼개서 문서벡터화 한 후 질의하기
################
import tiktoken
tokenizer = tiktoken.get_encoding('cl100k_base')
def tiktoken_len(text):
    tokens = tokenizer.encode(text)
    return len(tokens)
    
from langchain.embeddings.sentence_transformer import SentenceTransformerEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import Chroma
from langchain.document_loaders import PyPDFLoader
from langchain.embeddings import HuggingFaceEmbeddings

## pdf 파일로드 하고 쪼개기
loader = PyPDFLoader('https://wdr.ubion.co.kr/wowpass/img/event/gsat_170823/gsat_170823.pdf')
pages = loader.load_and_split()

## chunk로 쪼개기
text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=80,length_function=tiktoken_len)
sourceDocs = text_splitter.split_documents(pages)

################
# HuggingFace 모델로 문서벡터화 후 유사도 탐색
################
from langchain.embeddings import HuggingFaceEmbeddings

model_huggingface = HuggingFaceEmbeddings(model_name = 'jhgan/ko-sroberta-multitask', 
                                          model_kwargs = {'device':'cpu'}, 
                                          encode_kwargs = {'normalize_embeddings' : True})

## Chroma 기반 pdf(docs 벡터화)
db = Chroma.from_documents(sourceDocs, model_huggingface)

## 질의하기
def searchDocs(question, k=1):
    results = db.similarity_search_with_relevance_scores(question, k = k)
    return results

################
# 찾은 문서를 프롬프트에 전달하여 LLM으로 답변 생성
################
from langchain_community.chat_models import ChatOllama
llm = ChatOllama(
    base_url='http://localhost:11434',
    # model="phi3:medium", # 너무 느려서 mini로 변경
    model="phi3:mini",
)

from langchain_core.prompts import ChatPromptTemplate

prompt = ChatPromptTemplate.from_messages([
    ("system", "Please answer the following question from the document: {document}"),
    ("user", "{question}"),
])

# print('-'*50)
chain = prompt | llm
def Response(question):
    searchedDocs = searchDocs(question)
    mergedDoc = ' '.join(searchedDocs[0][0])
    return chain.invoke({"question": question, "document": mergedDoc})