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Fix: 리뷰에서 지적된 부분 보완
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‎.env.example‎

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###############################################
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############## LLM API SELECTION ##############
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###############################################
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# LLM_PROVIDER=openai
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# OPEN_AI_LLM_KEY=
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# OPEN_AI_LLM_MODEL=gpt-4o
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# LLM_PROVIDER=gemini
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# GEMINI_API_KEY=
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# GEMINI_LLM_MODEL=gemini-2.0-flash-lite
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# LLM_PROVIDER=azure
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# AZURE_OPENAI_LLM_ENDPOINT=
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# AZURE_OPENAI_LLM_KEY=
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# AZURE_OPENAI_LLM_MODEL=
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# AZURE_OPENAI_LLM_API_VERSION=
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# LLM_PROVIDER=ollama
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# OLLAMA_LLM_BASE_URL=
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# OLLAMA_LLM_MODEL=
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# LLM_PROVIDER=huggingface
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# HUGGING_FACE_LLM_REPO_ID=
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# HUGGING_FACE_LLM_ENDPOINT=
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# HUGGING_FACE_LLM_API_TOKEN=
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# LLM_PROVIDER=bedrock
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# AWS_BEDROCK_LLM_ACCESS_KEY_ID=
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# AWS_BEDROCK_LLM_SECRET_ACCESS_KEY=
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# AWS_BEDROCK_LLM_REGION=us-west-2
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# AWS_BEDROCK_LLM_ENDPOINT_URL=https://bedrock.us-west-2.amazonaws.com
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# AWS_BEDROCK_LLM_MODEL=anthropic.claude-3-5-sonnet-20241022-v2:0\
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###############################################
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########### Embedding API SElECTION ###########
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###############################################
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# Only used if you are using an LLM that does not natively support embedding (openai or Azure)
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# EMBEDDING_ENGINE='openai'
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# OPEN_AI_KEY=sk-xxxx
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# EMBEDDING_MODEL_PREF='text-embedding-ada-002'
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# EMBEDDING_ENGINE='azure'
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# AZURE_OPENAI_ENDPOINT=
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# AZURE_OPENAI_KEY=
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# EMBEDDING_MODEL_PREF='my-embedder-model' # This is the "deployment" on Azure you want to use for embeddings. Not the base model. Valid base model is text-embedding-ada-002
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# EMBEDDING_ENGINE='ollama'
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# EMBEDDING_BASE_PATH='http://host.docker.internal:11434'
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# EMBEDDING_MODEL_PREF='nomic-embed-text:latest'
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# EMBEDDING_MODEL_MAX_CHUNK_LENGTH=8192
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# EMBEDDING_ENGINE='bedrock'
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# AWS_BEDROCK_EMBEDDING_ACCESS_KEY_ID=
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# AWS_BEDROCK_EMBEDDING_ACCESS_KEY=
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# AWS_BEDROCK_EMBEDDING_REGION=us-west-2
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# AWS_BEDROCK_EMBEDDING_MODEL_PREF=amazon.embedding-embedding-ada-002:0
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# EMBEDDING_ENGINE='gemini'
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# GEMINI_EMBEDDING_API_KEY=
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# EMBEDDING_MODEL_PREF='text-embedding-004'
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# EMBEDDING_ENGINE='huggingface'
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# HUGGING_FACE_EMBEDDING_REPO_ID=
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# HUGGING_FACE_EMBEDDING_MODEL=
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# HUGGING_FACE_EMBEDDING_API_TOKEN=

‎pyproject.toml‎

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[tool.black]
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line-length = 88
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target-version = ['py311']
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include = '\.pyi?$'
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exclude = '''
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(
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/(
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\.git
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| \.venv
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| build
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| dist
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)/
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)
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'''
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[tool.poetry.group.dev.dependencies]
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pytest = "^8.3.5"
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[project]
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name = "lang2sql"
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version = "0.1.9"
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description = ""
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authors = [
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{name = "Yoonjin Hwang",email = "[email protected]"}
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]
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readme = "README.md"
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requires-python = ">=3.11, <4.0"
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dependencies = [
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"langgraph (==0.2.62)",
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"datahub (==0.999.1)",
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"langchain (==0.3.14)",
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"langchain-community (==0.3.14)",
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"openai (==1.59.8)",
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"langchain-openai (==0.3.0)",
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"streamlit (==1.41.1)",
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"python-dotenv (==1.0.1)",
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"faiss-cpu (==1.10.0)",
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"pre-commit (==4.1.0)",
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"setuptools (>=78.1.1,<79.0.0)",
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"wheel (>=0.45.1,<0.46.0)",
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"twine (>=6.1.0,<7.0.0)",
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"langchain-aws (>=0.2.21,<0.3.0)",
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"langchain-google-genai (>=2.1.3,<3.0.0)",
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"langchain-ollama (>=0.3.2,<0.4.0)",
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"langchain-huggingface (>=0.1.2,<0.2.0)"
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]
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[build-system]
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requires = ["poetry-core>=2.0.0,<3.0.0"]
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build-backend = "poetry.core.masonry.api"

‎setup.py‎

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import os
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import glob
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with open("README.md", "r", encoding="utf-8") as fh:
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with open("docs/README.md", "r", encoding="utf-8") as fh:
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long_description = fh.read()
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# 프롬프트 파일들을 찾습니다
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prompt_files = glob.glob('prompt/*.md')
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prompt_files = glob.glob("prompt/*.md")
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prompt_files = [os.path.basename(f) for f in prompt_files]
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setup(
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description="Lang2SQL - Query Generator for Data Warehouse",
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long_description=long_description,
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long_description_content_type="text/markdown",
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packages=find_packages() + ['prompt'], # prompt 패키지 직접 추가
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packages=find_packages(), # my_package를 자동으로 찾음
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packages=find_packages() + ["prompt"], # prompt 패키지 직접 추가
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package_data={
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'prompt': ['*.md', '*.py'], # prompt 디렉토리의 모든 .md 파일 포함
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"prompt": ["*.md", "*.py"], # prompt 디렉토리의 모든 .md 파일 포함
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},
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data_files=[
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('prompt', [os.path.join('prompt', f) for f in prompt_files])
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],
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data_files=[("prompt", [os.path.join("prompt", f) for f in prompt_files])],
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include_package_data=True,
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install_requires=[
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"langgraph==0.2.62",

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