Source/ollama/pdf_rag.py

from langchain_ollama import ChatOllama
from langchain_ibm import ChatWatsonx
from langchain_core.prompts import (
    PromptTemplate,
    ChatPromptTemplate,
    MessagesPlaceholder,
)
from langchain_core.output_parsers import (
    StrOutputParser,
    JsonOutputParser,
    PydanticOutputParser,
)
from langchain_core.runnables import (
    RunnablePassthrough,
    RunnableParallel,
    RunnableLambda,
)
from langchain_core.messages import HumanMessage, SystemMessage, AIMessage
from langchain_core.chat_history import (
    InMemoryChatMessageHistory,
    BaseChatMessageHistory,
)
from langchain_core.runnables.history import RunnableWithMessageHistory
from pydantic import BaseModel, Field
from typing import Literal
from dotenv import load_dotenv
import os
import gradio as gr
from langchain_community.document_loaders import (
    PyPDFLoader,
    CSVLoader,
    WebBaseLoader,
    DirectoryLoader,
)
from youtube_transcript_api import YouTubeTranscriptApi
from langchain_core.documents import Document
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_ollama import OllamaEmbeddings
from langchain_ibm import WatsonxEmbeddings
from langchain_chroma import Chroma
from langchain_community.vectorstores import FAISS

# 모델(LLM, Embeddding)
load_dotenv()

apikey = os.getenv("WATSONX_API_KEY")
project_id = os.getenv("WATSONX_PROJECT_ID")
watsonx_ai_url = os.getenv("WATSONX_URL")

ollama_embedding = OllamaEmbeddings(model="nomic-embed-text-v2-moe")
watson_llm = ChatWatsonx(
    model_id="ibm/granite-4-h-small",
    url=f"{watsonx_ai_url}",
    api_key=f"{apikey}",
    project_id=f"{project_id}",
    max_tokens=2000,
    params={"temperature": 0},
)

# qwen_llm = ChatOllama(model="qwen3.5:4b", temperature=0)


# 1단계
def process_pdf(pdf_file):
    if pdf_file is None:
        return ("PDF 파일을 업로드 해주세요.", "", "", "", "")

    # PDF 로드
    loader = PyPDFLoader(pdf_file)
    docs = loader.load()

    # 총 페이지수
    total_pages = len(docs)

    # 첫 페이지 내용
    first_page_content = docs[0].page_content[:1000]

    splitter = RecursiveCharacterTextSplitter(chunk_size=300, chunk_overlap=30)
    chunks = splitter.split_documents(docs)
    # 총 chunk 수
    total_chunks = len(chunks)

    # 첫 번째 chunk
    first_chunk_content = chunks[0].page_content

    # 첫 번째 Chunk Metadata
    first_chunk_metadata = chunks[0].metadata

    return (
        f"총 페이지 수 : {total_pages}",
        first_page_content,
        total_chunks,
        first_chunk_content,
        first_chunk_metadata,
    )


# 2단계
def rag_chat(pdf_file, question):
    if pdf_file is None:
        return ("PDF 파일을 업로드 해주세요.", "")

    # 1. PDF 로드
    loader = PyPDFLoader(pdf_file)
    docs = loader.load()

    # 2. 분할
    splitter = RecursiveCharacterTextSplitter(chunk_size=300, chunk_overlap=30)
    split_docs = splitter.split_documents(docs)

    # 3. 임베딩
    faiss_store = FAISS.from_documents(documents=split_docs, embedding=ollama_embedding)

    # 4. 검색(retriever)
    retriever = faiss_store.as_retriever(search_kwargs={"k": 3})
    retriever_docs = retriever.invoke(question)

    # 5. Context 생성
    context = "\n\n".join([doc.page_content for doc in retriever_docs])

    ### LLM
    # 1. prompt
    message = """\
당신은 PDF 기반 RAG AI 입니다.
다음 문서를 참고해서 질문에 답변하세요.

문서:
{context}

질문:
{question}
"""

    rag_prompt = ChatPromptTemplate.from_template(message)
    # 2. chain
    chain = rag_prompt | watson_llm | StrOutputParser()

    # 3. answer
    answer = chain.invoke({"context": context, "question": question})

    # 4. 답변, rag 결과 반환
    retrieved_text = ""
    for i, doc in enumerate(retriever_docs, 1):
        retrieved_text += f"""
        [검색 문서 {i}]

        내용:
        {doc.page_content}

        metadata:
        {doc.metadata}
        {'='*50}
"""

    return retrieved_text, answer


with gr.Blocks() as demo:
    gr.Markdown("# PDF RAG 학습 앱")

    with gr.Tabs():
        with gr.Tab("1단계 - PDF & Chunk 확인"):
            # 파일 업로드 컴포넌트
            pdf_input = gr.File(label="PDF 업로드", file_types=[".pdf"])
            btn1 = gr.Button("분석 시작")
            # textbox 5개
            page_output = gr.Textbox(label="총 페이지 수")
            first_output = gr.Textbox(label="첫 페이지 내용", lines=10)
            chunk_output = gr.Textbox(label="총 chunk 수")
            first_chunk_output = gr.Textbox(label="첫 번째 chunk", lines=10)
            metadata_output = gr.Textbox(label="첫 번째 Chunk Metadata", lines=5)

            btn1.click(
                fn=process_pdf,
                inputs=[pdf_input],
                outputs=[
                    page_output,
                    first_output,
                    chunk_output,
                    first_chunk_output,
                    metadata_output,
                ],
            )
        with gr.Tab("2단계 - RAG QA"):
            # 파일 업로드 컴포넌트
            pdf_input = gr.File(label="PDF 업로드", file_types=[".pdf"])
            question_input = gr.Textbox(label="질문 입력")
            btn1 = gr.Button("질문하기")
            retrieved_output = gr.Textbox(label="검색된 chunk", lines=20)
            answer_output = gr.Textbox(label="최종 답변", lines=10)

            btn1.click(
                fn=rag_chat,
                inputs=[pdf_input, question_input],
                outputs=[retrieved_output, answer_output],
            )
demo.launch()