■ RunnablePassthrough 클래스의 assign 정적 메소드를 사용해 검색기를 체인에서 사용하는 방법을 보여준다.
※ OPENAI_API_KEY 환경 변수 값은 .env 파일에 정의한다.
▶ main.py
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from dotenv import load_dotenv from langchain_community.document_loaders import WebBaseLoader from langchain_text_splitters import RecursiveCharacterTextSplitter from langchain_openai import OpenAIEmbeddings from langchain_chroma import Chroma from langchain_core.prompts import ChatPromptTemplate from langchain_core.prompts import MessagesPlaceholder from langchain_openai import ChatOpenAI from langchain_core.runnables import RunnableBranch from langchain_core.output_parsers import StrOutputParser from langchain.chains.combine_documents import create_stuff_documents_chain from langchain_core.runnables import RunnablePassthrough from langchain_core.messages import HumanMessage from langchain_core.messages import AIMessage load_dotenv() webBaseLoader = WebBaseLoader("https://docs.smith.langchain.com/overview") documentList1 = webBaseLoader.load() recursiveCharacterTextSplitter = RecursiveCharacterTextSplitter(chunk_size = 500, chunk_overlap = 0) documentList2 = recursiveCharacterTextSplitter.split_documents(documentList1) openAIEmbeddings = OpenAIEmbeddings() chroma = Chroma.from_documents(documents = documentList2, embedding = openAIEmbeddings) vectorStoreRetriever = chroma.as_retriever(k = 4) # 위의 대화를 감안하여 대화와 관련된 정보를 얻기 위해 검색할 검색어를 생성합니다. 검색어로만 응답하고 다른 것은 응답하지 않습니다. contextChatPromptTemplate = ChatPromptTemplate.from_messages( [ MessagesPlaceholder(variable_name = "messages"), ("user", "Given the above conversation, generate a search query to look up in order to get information relevant to the conversation. Only respond with the query, nothing else.") ] ) chatOpenAI = ChatOpenAI(model = "gpt-4o", temperature = 0.2) contextRunnableBranch = RunnableBranch( (lambda x : len(x.get("messages", [])) == 1, (lambda x : x["messages"][-1].content) | vectorStoreRetriever), contextChatPromptTemplate | chatOpenAI | StrOutputParser() | vectorStoreRetriever, ) # 아래 맥락에 따라 사용자의 질문에 답하세요. # 맥락에 질문과 관련된 정보가 없다면, 무언가를 만들어내지 말고 "모르겠습니다"라고만 말하세요. answerSystemTemplateString = """ Answer the user's questions based on the below context. If the context doesn't contain any relevant information to the question, don't make something up and just say "I don't know" : <context> {context} </context> """ answerChatPromptTemplate = ChatPromptTemplate.from_messages( [ ("system", answerSystemTemplateString), MessagesPlaceholder(variable_name = "messages") ] ) answerRunnableBinding = create_stuff_documents_chain(chatOpenAI, answerChatPromptTemplate) runnableSequence = RunnablePassthrough.assign(context = contextRunnableBranch).assign(answer = answerRunnableBinding) responseDictionary1 = runnableSequence.invoke({"messages" : [HumanMessage(content = "Can LangSmith help test my LLM applications?")]}) print(responseDictionary1["answer"]) print("-" * 50) responseDictionary2 = runnableSequence.invoke( { "messages" : [ HumanMessage(content = "Can LangSmith help test my LLM applications?"), AIMessage(content = responseDictionary1["answer"]), HumanMessage(content = "Tell me more!") ] } ) print(responseDictionary2["answer"]) print("-" * 50) """ Yes, LangSmith is a platform for building production-grade LLM applications and allows you to closely monitor and evaluate your application, which can help in testing your LLM applications. -------------------------------------------------- LangSmith is designed to help you build and manage production-grade LLM applications. It provides tools for monitoring and evaluating your application, allowing you to ship quickly and with confidence. While it can work seamlessly with LangChain's open-source frameworks like langchain and langgraph, it is also capable of functioning independently. By using LangSmith, you can access an experiments UI to explore the results of your evaluations, which can be particularly useful for testing and refining your LLM applications. -------------------------------------------------- """ |
▶ requirements.txt
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aiohappyeyeballs==2.4.4 aiohttp==3.11.10 aiosignal==1.3.1 annotated-types==0.7.0 anyio==4.7.0 asgiref==3.8.1 attrs==24.2.0 backoff==2.2.1 bcrypt==4.2.1 beautifulsoup4==4.12.3 bs4==0.0.2 build==1.2.2.post1 cachetools==5.5.0 certifi==2024.8.30 charset-normalizer==3.4.0 chroma-hnswlib==0.7.6 chromadb==0.5.23 click==8.1.7 colorama==0.4.6 coloredlogs==15.0.1 dataclasses-json==0.6.7 Deprecated==1.2.15 distro==1.9.0 durationpy==0.9 fastapi==0.115.6 filelock==3.16.1 flatbuffers==24.3.25 frozenlist==1.5.0 fsspec==2024.10.0 google-auth==2.36.0 googleapis-common-protos==1.66.0 greenlet==3.1.1 grpcio==1.68.1 h11==0.14.0 httpcore==1.0.7 httptools==0.6.4 httpx==0.28.1 httpx-sse==0.4.0 huggingface-hub==0.26.5 humanfriendly==10.0 idna==3.10 importlib_metadata==8.5.0 importlib_resources==6.4.5 jiter==0.8.0 jsonpatch==1.33 jsonpointer==3.0.0 kubernetes==31.0.0 langchain==0.3.10 langchain-chroma==0.1.4 langchain-community==0.3.10 langchain-core==0.3.22 langchain-openai==0.2.11 langchain-text-splitters==0.3.2 langsmith==0.1.147 markdown-it-py==3.0.0 marshmallow==3.23.1 mdurl==0.1.2 mmh3==5.0.1 monotonic==1.6 mpmath==1.3.0 multidict==6.1.0 mypy-extensions==1.0.0 numpy==1.26.4 oauthlib==3.2.2 onnxruntime==1.20.1 openai==1.57.0 opentelemetry-api==1.28.2 opentelemetry-exporter-otlp-proto-common==1.28.2 opentelemetry-exporter-otlp-proto-grpc==1.28.2 opentelemetry-instrumentation==0.49b2 opentelemetry-instrumentation-asgi==0.49b2 opentelemetry-instrumentation-fastapi==0.49b2 opentelemetry-proto==1.28.2 opentelemetry-sdk==1.28.2 opentelemetry-semantic-conventions==0.49b2 opentelemetry-util-http==0.49b2 orjson==3.10.12 overrides==7.7.0 packaging==24.2 posthog==3.7.4 propcache==0.2.1 protobuf==5.29.1 pyasn1==0.6.1 pyasn1_modules==0.4.1 pydantic==2.10.3 pydantic-settings==2.6.1 pydantic_core==2.27.1 Pygments==2.18.0 PyPika==0.48.9 pyproject_hooks==1.2.0 pyreadline3==3.5.4 python-dateutil==2.9.0.post0 python-dotenv==1.0.1 PyYAML==6.0.2 regex==2024.11.6 requests==2.32.3 requests-oauthlib==2.0.0 requests-toolbelt==1.0.0 rich==13.9.4 rsa==4.9 shellingham==1.5.4 six==1.17.0 sniffio==1.3.1 soupsieve==2.6 SQLAlchemy==2.0.36 starlette==0.41.3 sympy==1.13.3 tenacity==9.0.0 tiktoken==0.8.0 tokenizers==0.20.3 tqdm==4.67.1 typer==0.15.1 typing-inspect==0.9.0 typing_extensions==4.12.2 urllib3==2.2.3 uvicorn==0.32.1 watchfiles==1.0.0 websocket-client==1.8.0 websockets==14.1 wrapt==1.17.0 yarl==1.18.3 zipp==3.21.0 |
※ pip install python-dotenv langchain-community langchain-openai langchain-chroma bs4 명령을 실행했다.