■ VectorStoreRetriever 클래스 : 사용자 질문에 따라 벡터 저장소에서 문서 자동 검색하기
▶ main.py
|
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 |
from langchain_community.document_loaders import WebBaseLoader from langchain_text_splitters import RecursiveCharacterTextSplitter from langchain_huggingface import HuggingFaceEmbeddings from langchain_chroma import Chroma from langchain_community.llms import LlamaCpp from langchain import hub from langchain_core.output_parsers import StrOutputParser from langchain_core.runnables import RunnablePassthrough # 웹 문서를 로드한다. webBaseLoader = WebBaseLoader("https://lilianweng.github.io/posts/2023-06-23-agent/") documentList = webBaseLoader.load() # 문서를 분할한다. recursiveCharacterTextSplitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0) splitDocumentList = recursiveCharacterTextSplitter.split_documents(documentList) # 임베딩 생성 및 유사도 검색을 위한 벡터 저장소를 설정한다. chroma = Chroma.from_documents(documents = splitDocumentList, embedding = HuggingFaceEmbeddings()) # 벡터 저장소 검색기를 설정한다. vectorStoreRetriever = chroma.as_retriever() # LLM 모델을 설정한다. gpuLayerCount = 1 batchSize = 512 # 1에서 n_ctx 사이이어야 한다. llamaCpp = LlamaCpp( model_path = "./llama-2-13b-chat.Q4_0.gguf", # llama-2-13b-chat.Q4_0.gguf 파일 경로를 설정한다. n_gpu_layers = gpuLayerCount, n_batch = batchSize, n_ctx = 2048, f16_kv = True, # 반드시 True로 설정해야 한다. 그렇지 않으면 몇 번의 호출 후에 문제가 발생하게 된다. verbose = False ) # 프롬프트 템플리트를 설정한다. promptTemplate = hub.pull("rlm/rag-prompt") # 문서 페이지 컨텐츠를 병합하는 함수를 정의한다. def mergeDocumentPageContent(documentList): return "\n\n".join(doc.page_content for doc in documentList) # 실행 체인을 설정한다. runnableSequence = ( {"context": vectorStoreRetriever | mergeDocumentPageContent, "question" : RunnablePassthrough()} | promptTemplate | llamaCpp | StrOutputParser() ) # 질의 응답을 실행한다. resultString = runnableSequence.invoke("What are the approaches to Task Decomposition?") print(resultString) """ The approaches to task decomposition include breaking down large tasks into smaller subgoals, using task-specific instructions, and using human inputs. """ |
▶ requirements.txt
|
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 |
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 diskcache==5.6.3 dnspython==2.6.1 email_validator==2.1.1 exceptiongroup==1.2.1 fastapi==0.111.0 fastapi-cli==0.0.4 filelock==3.15.1 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 joblib==1.4.2 jsonpatch==1.33 jsonpointer==3.0.0 kubernetes==30.1.0 langchain==0.2.4 langchain-chroma==0.1.1 langchain-community==0.2.4 langchain-core==0.2.6 langchain-huggingface==0.0.3 langchain-text-splitters==0.2.1 langsmith==0.1.77 llama_cpp_python==0.2.78 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 networkx==3.3 numpy==1.26.4 nvidia-cublas-cu12==12.1.3.1 nvidia-cuda-cupti-cu12==12.1.105 nvidia-cuda-nvrtc-cu12==12.1.105 nvidia-cuda-runtime-cu12==12.1.105 nvidia-cudnn-cu12==8.9.2.26 nvidia-cufft-cu12==11.0.2.54 nvidia-curand-cu12==10.3.2.106 nvidia-cusolver-cu12==11.4.5.107 nvidia-cusparse-cu12==12.1.0.106 nvidia-nccl-cu12==2.20.5 nvidia-nvjitlink-cu12==12.5.40 nvidia-nvtx-cu12==12.1.105 oauthlib==3.2.2 onnxruntime==1.18.0 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==3.10.5 overrides==7.7.0 packaging==24.1 pillow==10.3.0 posthog==3.5.0 protobuf==4.25.3 pyasn1==0.6.0 pyasn1_modules==0.4.0 pydantic==2.7.4 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 safetensors==0.4.3 scikit-learn==1.5.0 scipy==1.13.1 sentence-transformers==3.0.1 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 threadpoolctl==3.5.0 tokenizers==0.19.1 tomli==2.0.1 torch==2.3.1 tqdm==4.66.4 transformers==4.41.2 triton==2.3.1 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 langchainhub langchain-huggingface bs4 sentence-transformers llama-cpp-python 명령을 실행했다.