■ create_retrieval_chain 함수를 사용해 RAG 애플리케이션에서 소스를 구하는 방법을 보여준다.
※ OPENAI_API_KEY 환경 변수 값은 .env 파일에 정의한다.
▶ main.py
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import bs4 from dotenv import load_dotenv 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_openai import ChatOpenAI from langchain_core.prompts import ChatPromptTemplate from langchain.chains.combine_documents import create_stuff_documents_chain from langchain.chains import create_retrieval_chain load_dotenv() 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() recursiveCharacterTextSplitter = RecursiveCharacterTextSplitter(chunk_size = 1000, chunk_overlap = 200) splitDocumentList = recursiveCharacterTextSplitter.split_documents(documentList) chroma = Chroma.from_documents(documents=splitDocumentList, embedding=OpenAIEmbeddings()) vectorStoreRetriever = chroma.as_retriever() chatOpenAI = ChatOpenAI(model = "gpt-4o-mini") chatPromptTemplate = 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}"), ("human", "{input}") ] ) runnableBinding1 = create_stuff_documents_chain(chatOpenAI, chatPromptTemplate) runnableBinding2 = create_retrieval_chain(vectorStoreRetriever, runnableBinding1) responseDictionary = runnableBinding2.invoke({"input" : "What is Task Decomposition?"}) print(responseDictionary) """ { 'input' : 'What is Task Decomposition?', 'context' : [ Document( metadata = {'source' : 'https://lilianweng.github.io/posts/2023-06-23-agent/'}, page_content = 'Fig. 1. Overview of a LLM-powered autonomous agent system.\nComponent One: Planning#\nA complicated task usually involves many steps. An agent needs to know what they are and plan ahead.\nTask Decomposition#\nChain of thought (CoT; Wei et al. 2022) has become a standard prompting technique for enhancing model performance on complex tasks. The model is instructed to “think step by step” to utilize more test-time computation to decompose hard tasks into smaller and simpler steps. CoT transforms big tasks into multiple manageable tasks and shed lights into an interpretation of the model’s thinking pree structure. The search process can be BFS (breadth-first search) or DFS (depth-first search) with each state evaluated by a classifier (via a prompt) or majority vote.\nTask decomposition can be done (1) by LLM with simple prompting like "Steps for XYZ.\\n1.", "What are the subgoals for achieving XYZ?", (2) by using task-specific instructions; e.g. "Write a story outline." for writing a novel, or (3) with human inputs.' ), Document( metadata = {'source' : 'https://lilianweng.github.io/posts/2023-06-23-agent/'}, page_content = 'Resources:\n1. Internet access for searches and information gathering.\n2. Long Term memory management.\n3. GPT-3.5 powered Agents for delegation of simple tasks.\n4. File output.\n\nPerformance Evaluation:\n1. Continuously review and analyze your actions to ensure you are performing to the best of your abilities.\n2. Constructively self-criticize your big-picture behavior constantly.\n3. Reflect on past decisions and strategies to refine your approach.\n4. Every command has a cost, so be smart and efficient. Aim to complete tasks in the least number of steps.' ), Document( metadata = {'source' : 'https://lilianweng.github.io/posts/2023-06-23-agent/'}, page_content = "(3) Task execution: Expert models execute on the specific tasks and log results.\nInstruction:\n\nWith the input and the inference results, the AI assistant needs to describe the process and results. The previous stages can be formed as - User Input: {{ User Input }}, Task Planning: {{ Tasks }}, Model Selection: {{ Model Assignment }}, Task Execution: {{ Predictions }}. You must first answer the user's request in a straightforward manner. Then describe the task process and show your analysis and model inference results to the user in the first person. If inference results contain a file path, must tell the user the complete file path." ) ], 'answer' : 'Task Decomposition is the process of breaking down a complex task into smaller, manageable steps to facilitate easier execution and understanding. Techniques like Chain of Thought (CoT) and Tree of Thoughts enhance this process by guiding models to think step-by-step and explore multiple reasoning possibilities at each stage. This approach helps in improving performance on complex tasks by simplifying them into subgoals.' } """ |
▶ requirements.txt
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aiohappyeyeballs==2.4.4 aiohttp==3.11.9 aiosignal==1.3.1 annotated-types==0.7.0 anyio==4.6.2.post1 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==0.5.20 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==0.115.5 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==0.28.0 httpx-sse==0.4.0 huggingface-hub==0.26.3 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==0.3.9 langchain-chroma==0.1.4 langchain-community==0.3.9 langchain-core==0.3.21 langchain-openai==0.2.10 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==1.56.0 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==5.29.0 pyasn1==0.6.1 pyasn1_modules==0.4.1 pydantic==2.10.2 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==1.16.0 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==0.21.0 tqdm==4.67.1 typer==0.14.0 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 langchain-community langchain-openai langchain-chroma bs4 명령을 실행했다.