[GPT][PRIVATEGPT] Ollama

Ollama 는 LLM 모델을 Local 환경에서 손쉽게 수행하기 위한 프레임워크

 

https://ollama.com/

Ollama

 

1. 다운로드 페이지에서 해당 os 에 맞는 ollama를 다운받아 설치

 

2. model 페이지에 가서 원하는 모델을 선택 [현재:mistral]

3.터미널에서 ollama run mistral 실행

 

설치 완료

 

기존 문서검색 코드를 이용하여 ollama 를 이용하는 예제 

 

from langchain.prompts import ChatPromptTemplate

from langchain.document_loaders import UnstructuredFileLoader

from langchain.embeddings import CacheBackedEmbeddings, OllamaEmbeddings

from langchain.schema.runnable import RunnableLambda, RunnablePassthrough

from langchain.storage import LocalFileStore

from langchain.text_splitter import CharacterTextSplitter

from langchain.vectorstores.faiss import FAISS

from langchain.chat_models import ChatOllama

from langchain.callbacks.base import BaseCallbackHandler

import streamlit as st

st.set_page_config(

    page_title="PrivateGPT",

    page_icon="📃",

)

class ChatCallbackHandler(BaseCallbackHandler):

    message = ""

    def on_llm_start(self, *args, **kwargs):

        self.message_box = st.empty()

    def on_llm_end(self, *args, **kwargs):

        save_message(self.message, "ai")

    def on_llm_new_token(self, token, *args, **kwargs):

        self.message += token

        self.message_box.markdown(self.message)

llm = ChatOllama(

    model="mistral:latest",

    temperature=0.1,

    streaming=True,

    callbacks=[

        ChatCallbackHandler(),

    ],

)

@st.cache_data(show_spinner="Embedding file...")

def embed_file(file):

    file_content = file.read()

    file_path = f"./.cache/private_files/{file.name}"

    with open(file_path, "wb") as f:

        f.write(file_content)

    cache_dir = LocalFileStore(f"./.cache/private_embeddings/{file.name}")

    splitter = CharacterTextSplitter.from_tiktoken_encoder(

        separator="\n",

        chunk_size=600,

        chunk_overlap=100,

    )

    loader = UnstructuredFileLoader(file_path)

    docs = loader.load_and_split(text_splitter=splitter)

    embeddings = OllamaEmbeddings(model="mistral:latest")

    cached_embeddings = CacheBackedEmbeddings.from_bytes_store(embeddings, cache_dir)

    vectorstore = FAISS.from_documents(docs, cached_embeddings)

    retriever = vectorstore.as_retriever()

    return retriever

def save_message(message, role):

    st.session_state["messages"].append({"message": message, "role": role})

def send_message(message, role, save=True):

    with st.chat_message(role):

        st.markdown(message)

    if save:

        save_message(message, role)

def paint_history():

    for message in st.session_state["messages"]:

        send_message(

            message["message"],

            message["role"],

            save=False,

        )

def format_docs(docs):

    return "\n\n".join(document.page_content for document in docs)

prompt = ChatPromptTemplate.from_template(

    """Answer the question using ONLY the following context and not your training data. If you don't know the answer just say you don't know. DON'T make anything up.

    Context: {context}

    Question:{question}

    """

)

st.title("PrivateGPT")

st.markdown(

    """

Welcome!

Use this chatbot to ask questions to an AI about your files!

Upload your files on the sidebar.

"""

)

with st.sidebar:

    file = st.file_uploader(

        "Upload a .txt .pdf or .docx file",

        type=["pdf", "txt", "docx"],

    )

if file:

    retriever = embed_file(file)

    send_message("I'm ready! Ask away!", "ai", save=False)

    paint_history()

    message = st.chat_input("Ask anything about your file...")

    if message:

        send_message(message, "human")

        chain = (

            {

                "context": retriever | RunnableLambda(format_docs),

                "question": RunnablePassthrough(),

            }

            | prompt

            | llm

        )

        with st.chat_message("ai"):

            chain.invoke(message)

else:

    st.session_state["messages"] = []