app.py

import os
import gradio as gr
import platform
from langchain_community.document_loaders import ObsidianLoader
from langchain_text_splitters import RecursiveCharacterTextSplitter, Language
from langchain.embeddings import CacheBackedEmbeddings
from langchain.storage import LocalFileStore
from langchain_community.embeddings import HuggingFaceBgeEmbeddings
from langchain_community.vectorstores import FAISS
from langchain_community.retrievers import BM25Retriever
from langchain.retrievers import EnsembleRetriever
from langchain_cohere import CohereRerank
from langchain.retrievers.contextual_compression import ContextualCompressionRetriever
from langchain_core.runnables import ConfigurableField, RunnablePassthrough
from langchain_core.output_parsers import StrOutputParser
from langchain_groq import ChatGroq
from langchain_google_genai import GoogleGenerativeAI

from prompt_template import PROMPT_TEMPLATE

DIRECTORIES = ["./docs/obsidian-help", "./docs/obsidian-developer"]
FAISS_DB_INDEX = "db_index"

def load_and_process_documents(directories):
    md_docs = []
    for directory in directories:
        try:
            loader = ObsidianLoader(directory, encoding="utf-8")
            md_docs.extend(loader.load())
        except Exception:
            pass

    md_splitter = RecursiveCharacterTextSplitter.from_language(
        language=Language.MARKDOWN,
        chunk_size=2000,
        chunk_overlap=200,
    )
    return md_splitter.split_documents(md_docs)

def setup_retrieval_system(splitted_docs):
    if platform.system() == "Darwin":
        model_kwargs = {"device": "mps"}
    else:
        model_kwargs = {"device": "cpu"}
    
    embeddings = HuggingFaceBgeEmbeddings(
        model_name="BAAI/bge-m3",
        model_kwargs=model_kwargs,
        encode_kwargs={"normalize_embeddings": True},
    )

    store = LocalFileStore("./.cache/")
    cached_embeddings = CacheBackedEmbeddings.from_bytes_store(
        embeddings,
        store,
        namespace=embeddings.model_name,
    )

    if os.path.exists(FAISS_DB_INDEX):
        db = FAISS.load_local(
            FAISS_DB_INDEX,
            cached_embeddings,
            allow_dangerous_deserialization=True,
        )
    else:
        db = FAISS.from_documents(splitted_docs, cached_embeddings)
        db.save_local(folder_path=FAISS_DB_INDEX)

    faiss_retriever = db.as_retriever(search_type="mmr", search_kwargs={"k": 10})
    bm25_retriever = BM25Retriever.from_documents(splitted_docs)
    bm25_retriever.k = 10

    ensemble_retriever = EnsembleRetriever(
        retrievers=[bm25_retriever, faiss_retriever],
        weights=[0.5, 0.5],
        search_type="mmr",
    )

    compressor = CohereRerank(model="rerank-multilingual-v3.0", top_n=5)
    return ContextualCompressionRetriever(
        base_compressor=compressor,
        base_retriever=ensemble_retriever,
    )

def setup_language_model():
    return ChatGroq(
        model_name="llama3-70b-8192",
        temperature=0,
    ).configurable_alternatives(
        ConfigurableField(id="llm"),
        default_key="llama3",
        gemini=GoogleGenerativeAI(
            model="gemini-pro",
            temperature=0,
        ),
    )

def format_docs(docs):
    formatted_docs = []
    for doc in docs:
        formatted_doc = f"Page Content:\n{doc.page_content}\n"
        if doc.metadata.get("source"):
            formatted_doc += f"Source: {doc.metadata['source']}\n"
        formatted_docs.append(formatted_doc)
    return "\n---\n".join(formatted_docs)

def main():
    splitted_docs = load_and_process_documents(DIRECTORIES)
    compression_retriever = setup_retrieval_system(splitted_docs)
    llm = setup_language_model()

    rag_chain = (
        {"context": compression_retriever | format_docs, "question": RunnablePassthrough()}
        | PROMPT_TEMPLATE
        | llm
        | StrOutputParser()
    )

    def predict(message, history=None):
        return rag_chain.invoke(message)

    gr.ChatInterface(
        predict,
        title="옵시디언 노트앱 및 플러그인 개발에 대해서 물어보세요!",
        description="안녕하세요!\n저는 옵시디언 노트앱과 플러그인 개발에 대한 인공지능 QA봇입니다. 옵시디언 노트앱의 사용법, 고급 기능, 플러그인 및 테마 개발에 대해 깊은 지식을 가지고 있어요. 문서 작업, 정보 정리 또는 개발에 관한 도움이 필요하시면 언제든지 질문해주세요!",
    ).launch()

if __name__ == "__main__":
    main()