■ 단순 대화형 RAG 애플리케이션을 만드는 방법을 보여준다. (채팅 기록 수동 업데이트)
▶ main.py
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import os import bs4 from langchain_openai import ChatOpenAI from langchain_core.prompts import ChatPromptTemplate from langchain_community.document_loaders import WebBaseLoader from langchain_text_splitters import RecursiveCharacterTextSplitter from langchain_chroma import Chroma from langchain_openai import OpenAIEmbeddings from langchain.chains.combine_documents import create_stuff_documents_chain from langchain.chains import create_retrieval_chain from langchain_core.prompts import MessagesPlaceholder from langchain.chains import create_history_aware_retriever from langchain_core.messages import AIMessage, HumanMessage # OPANAI API 키를 설정한다. os.environ["OPENAI_API_KEY"] = "<OPENAI_API_KEY>" # OPENAI 챗봇 모델을 설정한다. chatOpenAI = ChatOpenAI(model = "gpt-3.5-turbo-0125") # 질문-응답 채팅 프롬프트 템플리트를 설정한다. qnaChatPromptTemplate = ChatPromptTemplate.from_messages( [ ("system", "You are an assistant for question-answering tasks. Use the following pieces of retrieved context to answer the question. If you don't know the answer, say that you don't know. Use three sentences maximum and keep the answer concise.\n\n{context}"), MessagesPlaceholder("chatHistoryList"), ("human", "{input}"), ] ) # 질문-응답 체인을 생성한다. qnaRunnableBinding = create_stuff_documents_chain(chatOpenAI, qnaChatPromptTemplate) # 웹 문서를 로딩한다. webBaseLoader = WebBaseLoader( web_paths = ("https://lilianweng.github.io/posts/2023-06-23-agent/",), bs_kwargs = dict(parse_only = bs4.SoupStrainer(class_ = ("post-content", "post-title", "post-header"))) ) documentList = webBaseLoader.load() # 문서를 분할한다. recursiveCharacterTextSplitter = RecursiveCharacterTextSplitter(chunk_size = 1000, chunk_overlap = 200) splitDocumentList = recursiveCharacterTextSplitter.split_documents(documentList) # 문서 임베딩 및 검색기를 설정한다. chroma = Chroma.from_documents(documents = splitDocumentList, embedding = OpenAIEmbeddings()) vectorStoreRetriever = chroma.as_retriever() # 질문 재구성 채팅 프롬프트 템플리트를 설정한다. contextualizeQuestionChatPromptTemplate = ChatPromptTemplate.from_messages( [ ("system", "Given a chat history and the latest user question which might reference context in the chat history, formulate a standalone question which can be understood without the chat history. Do NOT answer the question, just reformulate it if needed and otherwise return it as is."), MessagesPlaceholder("chatHistoryList"), ("human", "{input}"), ] ) # 히스토리 기반 검색기 체인을 생성한다. historyAwareRetrieverRunnableBinding = create_history_aware_retriever(chatOpenAI, vectorStoreRetriever, contextualizeQuestionChatPromptTemplate) # RAG 체인을 생성한다. ragRunnableBinding = create_retrieval_chain(historyAwareRetrieverRunnableBinding, qnaRunnableBinding) # 질문-응답을 실행한다. chatHistoryList = [] question1 = "What is Task Decomposition?" print(f"질문 1 : {question1}") print() aiMessage1 = ragRunnableBinding.invoke({"input" : question1, "chatHistoryList" : chatHistoryList}) print(aiMessage1["answer"]) print() chatHistoryList.extend( [ HumanMessage(content = question1), AIMessage(content = aiMessage1["answer"]), ] ) question2 = "What are common ways of doing it?" print(f"질문 2 : {question1}") print() aiMessage2 = ragRunnableBinding.invoke({"input" : question2, "chatHistoryList" : chatHistoryList}) print(aiMessage2["answer"]) print() """ 질문 1 : What is Task Decomposition? Task decomposition involves breaking down complex tasks into smaller and simpler steps to assist an agent in planning and executing the task efficiently. Techniques like Chain of Thought (CoT) and Tree of Thoughts can be employed to decompose tasks into manageable subtasks, enhancing the model's performance on complex tasks. Task decomposition can be facilitated through simple prompting, task-specific instructions, or human inputs to guide the agent through the sequential execution of the task. 질문 2 : What is Task Decomposition? Task decomposition can be achieved through various methods, including using language models with simple prompts, task-specific instructions, or human inputs to break down complex tasks into manageable subtasks. Additionally, techniques like the Tree of Thoughts approach can explore multiple reasoning possibilities at each step, creating a tree structure of thoughts to guide the agent through the task. Different search processes, such as BFS or DFS, can be utilized to evaluate states during the task decomposition process. """ |
▶ requirements.txt
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aiohttp==3.9.5 aiosignal==1.3.1 annotated-types==0.7.0 anyio==4.4.0 asgiref==3.8.1 async-timeout==4.0.3 attrs==23.2.0 backoff==2.2.1 bcrypt==4.1.3 beautifulsoup4==4.12.3 bs4==0.0.2 build==1.2.1 cachetools==5.3.3 certifi==2024.6.2 charset-normalizer==3.3.2 chroma-hnswlib==0.7.3 chromadb==0.5.0 click==8.1.7 coloredlogs==15.0.1 dataclasses-json==0.6.7 Deprecated==1.2.14 distro==1.9.0 dnspython==2.6.1 email_validator==2.1.1 exceptiongroup==1.2.1 fastapi==0.111.0 fastapi-cli==0.0.4 filelock==3.14.0 flatbuffers==24.3.25 frozenlist==1.4.1 fsspec==2024.6.0 google-auth==2.30.0 googleapis-common-protos==1.63.1 greenlet==3.0.3 grpcio==1.64.1 h11==0.14.0 httpcore==1.0.5 httptools==0.6.1 httpx==0.27.0 huggingface-hub==0.23.3 humanfriendly==10.0 idna==3.7 importlib_metadata==7.1.0 importlib_resources==6.4.0 Jinja2==3.1.4 jsonpatch==1.33 jsonpointer==3.0.0 kubernetes==30.1.0 langchain==0.2.3 langchain-chroma==0.1.1 langchain-community==0.2.4 langchain-core==0.2.5 langchain-openai==0.1.8 langchain-text-splitters==0.2.1 langsmith==0.1.77 markdown-it-py==3.0.0 MarkupSafe==2.1.5 marshmallow==3.21.3 mdurl==0.1.2 mmh3==4.1.0 monotonic==1.6 mpmath==1.3.0 multidict==6.0.5 mypy-extensions==1.0.0 numpy==1.26.4 oauthlib==3.2.2 onnxruntime==1.18.0 openai==1.33.0 opentelemetry-api==1.25.0 opentelemetry-exporter-otlp-proto-common==1.25.0 opentelemetry-exporter-otlp-proto-grpc==1.25.0 opentelemetry-instrumentation==0.46b0 opentelemetry-instrumentation-asgi==0.46b0 opentelemetry-instrumentation-fastapi==0.46b0 opentelemetry-proto==1.25.0 opentelemetry-sdk==1.25.0 opentelemetry-semantic-conventions==0.46b0 opentelemetry-util-http==0.46b0 orjson==3.10.4 overrides==7.7.0 packaging==23.2 posthog==3.5.0 protobuf==4.25.3 pyasn1==0.6.0 pyasn1_modules==0.4.0 pydantic==2.7.3 pydantic_core==2.18.4 Pygments==2.18.0 PyPika==0.48.9 pyproject_hooks==1.1.0 python-dateutil==2.9.0.post0 python-dotenv==1.0.1 python-multipart==0.0.9 PyYAML==6.0.1 regex==2024.5.15 requests==2.32.3 requests-oauthlib==2.0.0 rich==13.7.1 rsa==4.9 shellingham==1.5.4 six==1.16.0 sniffio==1.3.1 soupsieve==2.5 SQLAlchemy==2.0.30 starlette==0.37.2 sympy==1.12.1 tenacity==8.3.0 tiktoken==0.7.0 tokenizers==0.19.1 tomli==2.0.1 tqdm==4.66.4 typer==0.12.3 typing-inspect==0.9.0 typing_extensions==4.12.2 ujson==5.10.0 urllib3==2.2.1 uvicorn==0.30.1 uvloop==0.19.0 watchfiles==0.22.0 websocket-client==1.8.0 websockets==12.0 wrapt==1.16.0 yarl==1.9.4 zipp==3.19.2 |
※ pip install langchain langchain-chroma langchain-community langchain-openai bs4 명령을 실행했다.