app.py

import os
import torch

import gradio as gr
from dotenv import load_dotenv
from langchain.callbacks.base import BaseCallbackHandler
from langchain.embeddings import CacheBackedEmbeddings
from langchain_community.retrievers import BM25Retriever
from langchain.retrievers import EnsembleRetriever
from langchain.storage import LocalFileStore
from langchain_anthropic import ChatAnthropic
from langchain_community.chat_models import ChatOllama
from langchain_community.document_loaders import NotebookLoader, TextLoader
from langchain_community.document_loaders.generic import GenericLoader
from langchain_community.document_loaders.parsers.language.language_parser import (
    LanguageParser,
)
from langchain_community.embeddings import HuggingFaceBgeEmbeddings
from langchain_community.vectorstores import FAISS, Chroma
from langchain_core.callbacks.manager import CallbackManager
from langchain_core.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import PromptTemplate
from langchain_core.runnables import ConfigurableField, RunnablePassthrough
from langchain_google_genai import GoogleGenerativeAI
from langchain_groq import ChatGroq
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain_text_splitters import Language, RecursiveCharacterTextSplitter

# Load environment variables
load_dotenv()

# Repository directories
repo_root_dir = "./docs/langchain"
repo_dirs = [
    "libs/core/langchain_core",
    "libs/community/langchain_community",
    "libs/experimental/langchain_experimental",
    "libs/partners",
    "libs/cookbook",
]
repo_dirs = [os.path.join(repo_root_dir, repo) for repo in repo_dirs]

# Load Python documents
py_documents = []
for path in repo_dirs:
    py_loader = GenericLoader.from_filesystem(
        path,
        glob="**/*",
        suffixes=[".py"],
        parser=LanguageParser(language=Language.PYTHON, parser_threshold=30),
    )
    py_documents.extend(py_loader.load())
print(f".py 파일의 개수: {len(py_documents)}")

# Load Markdown documents
mdx_documents = []
for dirpath, _, filenames in os.walk(repo_root_dir):
    for file in filenames:
        if file.endswith(".mdx") and "*venv/" not in dirpath:
            try:
                mdx_loader = TextLoader(os.path.join(dirpath, file), encoding="utf-8")
                mdx_documents.extend(mdx_loader.load())
            except Exception:
                pass
print(f".mdx 파일의 개수: {len(mdx_documents)}")

# Load Jupyter Notebook documents
ipynb_documents = []
for dirpath, _, filenames in os.walk(repo_root_dir):
    for file in filenames:
        if file.endswith(".ipynb") and "*venv/" not in dirpath:
            try:
                ipynb_loader = NotebookLoader(
                    os.path.join(dirpath, file),
                    include_outputs=True,
                    max_output_length=20,
                    remove_newline=True,
                )
                ipynb_documents.extend(ipynb_loader.load())
            except Exception:
                pass
print(f".ipynb 파일의 개수: {len(ipynb_documents)}")


# Split documents into chunks
def split_documents(documents, language, chunk_size=2000, chunk_overlap=200):
    splitter = RecursiveCharacterTextSplitter.from_language(
        language=language, chunk_size=chunk_size, chunk_overlap=chunk_overlap
    )
    return splitter.split_documents(documents)


py_docs = split_documents(py_documents, Language.PYTHON)
mdx_docs = split_documents(mdx_documents, Language.MARKDOWN)
ipynb_docs = split_documents(ipynb_documents, Language.PYTHON)

print(f"분할된 .py 파일의 개수: {len(py_docs)}")
print(f"분할된 .mdx 파일의 개수: {len(mdx_docs)}")
print(f"분할된 .ipynb 파일의 개수: {len(ipynb_docs)}")

combined_documents = py_docs + mdx_docs + ipynb_docs
print(f"총 도큐먼트 개수: {len(combined_documents)}")


# Define the device setting function
def get_device():
    if torch.cuda.is_available():
        return "cuda:0"
    elif torch.backends.mps.is_available():
        return "mps"
    else:
        return "cpu"


# Use the function to set the device in model_kwargs
device = get_device()

# Initialize embeddings and cache
store = LocalFileStore("~/.cache/embedding")
embeddings = HuggingFaceBgeEmbeddings(
    model_name="BAAI/bge-m3",
    model_kwargs={"device": device},
    encode_kwargs={"normalize_embeddings": True},
)
cached_embeddings = CacheBackedEmbeddings.from_bytes_store(
    embeddings, store, namespace=embeddings.model_name
)

# Create and save FAISS index
FAISS_DB_INDEX = "./langchain_faiss"
# faiss_db = FAISS.from_documents(
#     documents=combined_documents,
#     embedding=cached_embeddings,
# )
# faiss_db.save_local(folder_path=FAISS_DB_INDEX)

# Create and save Chroma index
CHROMA_DB_INDEX = "./langchain_chroma"
# chroma_db = Chroma.from_documents(
#     documents=combined_documents,
#     embedding=cached_embeddings,
#     persist_directory=CHROMA_DB_INDEX,
# )

# load vectorstore
faiss_db = FAISS.load_local(
    FAISS_DB_INDEX, cached_embeddings, allow_dangerous_deserialization=True
)
chroma_db = Chroma(
    embedding_function=cached_embeddings,
    persist_directory=CHROMA_DB_INDEX,
)

# Create retrievers
faiss_retriever = faiss_db.as_retriever(search_type="mmr", search_kwargs={"k": 10})
chroma_retriever = chroma_db.as_retriever(
    search_type="similarity", search_kwargs={"k": 10}
)
bm25_retriever = BM25Retriever.from_documents(combined_documents)
bm25_retriever.k = 10
ensemble_retriever = EnsembleRetriever(
    retrievers=[bm25_retriever, faiss_retriever, chroma_retriever],
    weights=[0.4, 0.3, 0.3],
)

# Create prompt template
prompt = PromptTemplate.from_template(
    """당신은 20년차 AI 개발자입니다. 당신의 임무는 주어진 질문에 대하여 최대한 문서의 정보를 활용하여 답변하는 것입니다.
문서는 Python 코드에 대한 정보를 담고 있습니다. 따라서, 답변을 작성할 때에는 Python 코드에 대한 상세한 code snippet을 포함하여 작성해주세요.
최대한 자세하게 답변하고, 한글로 답변해 주세요. 주어진 문서에서 답변을 찾을 수 없는 경우, "문서에 답변이 없습니다."라고 답변해 주세요.
답변은 출처(source)를 반드시 표기해 주세요.

#참고문서:
{context}

#질문:
{question}

#답변: 

출처:
- source1
- source2
- ...
"""
)


# Define callback handler for streaming
class StreamCallback(BaseCallbackHandler):
    def on_llm_new_token(self, token: str, **kwargs):
        print(token, end="", flush=True)


streaming = os.getenv("STREAMING", "true") == "true"
print("STREAMING", streaming)

# Initialize LLMs with configuration
llm = ChatOpenAI(
    model="gpt-4o",
    temperature=0,
    streaming=streaming,
    callbacks=[StreamCallback()],
).configurable_alternatives(
    ConfigurableField(id="llm"),
    default_key="gpt4",
    claude=ChatAnthropic(
        model="claude-3-opus-20240229",
        temperature=0,
        streaming=True,
        callbacks=[StreamCallback()],
    ),
    gpt3=ChatOpenAI(
        model="gpt-3.5-turbo",
        temperature=0,
        streaming=True,
        callbacks=[StreamCallback()],
    ),
    gemini=GoogleGenerativeAI(
        model="gemini-1.5-flash",
        temperature=0,
        streaming=True,
        callbacks=[StreamCallback()],
    ),
    llama3=ChatGroq(
        model_name="llama3-70b-8192",
        temperature=0,
        streaming=True,
        callbacks=[StreamCallback()],
    ),
    ollama=ChatOllama(
        model="EEVE-Korean-10.8B:long",
        callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]),
    ),
)

# Create retrieval-augmented generation chain
rag_chain = (
    {"context": ensemble_retriever, "question": RunnablePassthrough()}
    | prompt
    | llm
    | StrOutputParser()
)


model_key = os.getenv("MODEL_KEY", "gemini")
print("MODEL_KEY", model_key)


def respond_stream(
    message,
    history: list[tuple[str, str]],
):
    response = ""
    for chunk in rag_chain.with_config(configurable={"llm": model_key}).stream(message):
        response += chunk
        yield response


def respond(
    message,
    history: list[tuple[str, str]],
):
    return rag_chain.with_config(configurable={"llm": model_key}).invoke(message)


"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
demo = gr.ChatInterface(
    respond_stream if streaming else respond,
    title="랭체인에 대해서 물어보세요!",
    description="안녕하세요!\n저는 랭체인에 대한 인공지능 QA봇입니다. 랭체인에 대해 깊은 지식을 가지고 있어요. 랭체인 개발에 관한 도움이 필요하시면 언제든지 질문해주세요!",
)


if __name__ == "__main__":
    demo.launch()