Notebook

In [1]:

!pip install unstructured

Collecting unstructured
  Using cached unstructured-0.11.8-py3-none-any.whl.metadata (26 kB)
Collecting chardet (from unstructured)
  Using cached chardet-5.2.0-py3-none-any.whl.metadata (3.4 kB)
Collecting filetype (from unstructured)
  Using cached filetype-1.2.0-py2.py3-none-any.whl.metadata (6.5 kB)
Collecting python-magic (from unstructured)
  Using cached python_magic-0.4.27-py2.py3-none-any.whl.metadata (5.8 kB)
Collecting lxml (from unstructured)
  Using cached lxml-5.1.1-cp38-cp38-win_amd64.whl.metadata (3.6 kB)
Collecting nltk (from unstructured)
  Using cached nltk-3.8.1-py3-none-any.whl.metadata (2.8 kB)
Collecting tabulate (from unstructured)
  Using cached tabulate-0.9.0-py3-none-any.whl.metadata (34 kB)
Requirement already satisfied: requests in e:\anaconda3\envs\openai\lib\site-packages (from unstructured) (2.31.0)
Requirement already satisfied: beautifulsoup4 in e:\anaconda3\envs\openai\lib\site-packages (from unstructured) (4.12.3)
Collecting emoji (from unstructured)
  Using cached emoji-2.11.0-py2.py3-none-any.whl.metadata (5.3 kB)
Requirement already satisfied: dataclasses-json in e:\anaconda3\envs\openai\lib\site-packages (from unstructured) (0.6.4)
Collecting python-iso639 (from unstructured)
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Collecting langdetect (from unstructured)
  Using cached langdetect-1.0.9-py3-none-any.whl
Requirement already satisfied: numpy in e:\anaconda3\envs\openai\lib\site-packages (from unstructured) (1.24.4)
Collecting rapidfuzz (from unstructured)
  Using cached rapidfuzz-3.7.0-cp38-cp38-win_amd64.whl.metadata (11 kB)
Collecting backoff (from unstructured)
  Using cached backoff-2.2.1-py3-none-any.whl.metadata (14 kB)
Requirement already satisfied: typing-extensions in e:\anaconda3\envs\openai\lib\site-packages (from unstructured) (4.9.0)
Collecting unstructured-client (from unstructured)
  Using cached unstructured_client-0.22.0-py3-none-any.whl.metadata (7.3 kB)
Collecting wrapt (from unstructured)
  Using cached wrapt-1.16.0-cp38-cp38-win_amd64.whl.metadata (6.8 kB)
Requirement already satisfied: soupsieve>1.2 in e:\anaconda3\envs\openai\lib\site-packages (from beautifulsoup4->unstructured) (2.5)
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Collecting deepdiff>=6.0 (from unstructured-client->unstructured)
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Collecting jsonpath-python>=1.0.6 (from unstructured-client->unstructured)
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Collecting ordered-set<4.2.0,>=4.0.2 (from deepdiff>=6.0->unstructured-client->unstructured)
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Using cached unstructured-0.11.8-py3-none-any.whl (1.8 MB)
Using cached backoff-2.2.1-py3-none-any.whl (15 kB)
Using cached chardet-5.2.0-py3-none-any.whl (199 kB)
Using cached emoji-2.11.0-py2.py3-none-any.whl (433 kB)
Using cached filetype-1.2.0-py2.py3-none-any.whl (19 kB)
Using cached lxml-5.1.1-cp38-cp38-win_amd64.whl (3.9 MB)
Using cached nltk-3.8.1-py3-none-any.whl (1.5 MB)
Using cached python_iso639-2024.2.7-py3-none-any.whl (274 kB)
Using cached python_magic-0.4.27-py2.py3-none-any.whl (13 kB)
Using cached rapidfuzz-3.7.0-cp38-cp38-win_amd64.whl (1.6 MB)
Using cached tabulate-0.9.0-py3-none-any.whl (35 kB)
Using cached unstructured_client-0.22.0-py3-none-any.whl (28 kB)
Using cached wrapt-1.16.0-cp38-cp38-win_amd64.whl (37 kB)
Using cached deepdiff-6.7.1-py3-none-any.whl (76 kB)
Using cached jsonpath_python-1.0.6-py3-none-any.whl (7.6 kB)
Using cached ordered_set-4.1.0-py3-none-any.whl (7.6 kB)
Installing collected packages: filetype, wrapt, tabulate, rapidfuzz, python-magic, python-iso639, ordered-set, lxml, langdetect, jsonpath-python, emoji, chardet, backoff, nltk, deepdiff, unstructured-client, unstructured
Successfully installed backoff-2.2.1 chardet-5.2.0 deepdiff-6.7.1 emoji-2.11.0 filetype-1.2.0 jsonpath-python-1.0.6 langdetect-1.0.9 lxml-5.1.1 nltk-3.8.1 ordered-set-4.1.0 python-iso639-2024.2.7 python-magic-0.4.27 rapidfuzz-3.7.0 tabulate-0.9.0 unstructured-0.11.8 unstructured-client-0.22.0 wrapt-1.16.0

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In [2]:

!pip install sentence-transformers

Requirement already satisfied: sentence-transformers in e:\anaconda3\envs\openai\lib\site-packages (2.6.1)
Requirement already satisfied: transformers<5.0.0,>=4.32.0 in e:\anaconda3\envs\openai\lib\site-packages (from sentence-transformers) (4.39.2)
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Requirement already satisfied: scipy in e:\anaconda3\envs\openai\lib\site-packages (from sentence-transformers) (1.10.1)
Requirement already satisfied: huggingface-hub>=0.15.1 in e:\anaconda3\envs\openai\lib\site-packages (from sentence-transformers) (0.22.2)
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In [3]:

!pip install chromadb

Collecting chromadb
  Using cached chromadb-0.4.24-py3-none-any.whl.metadata (7.3 kB)
Collecting build>=1.0.3 (from chromadb)
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Requirement already satisfied: requests>=2.28 in e:\anaconda3\envs\openai\lib\site-packages (from chromadb) (2.31.0)
Requirement already satisfied: pydantic>=1.9 in e:\anaconda3\envs\openai\lib\site-packages (from chromadb) (2.6.4)
Collecting chroma-hnswlib==0.7.3 (from chromadb)
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Collecting fastapi>=0.95.2 (from chromadb)
  Using cached fastapi-0.110.0-py3-none-any.whl.metadata (25 kB)
Collecting uvicorn>=0.18.3 (from uvicorn[standard]>=0.18.3->chromadb)
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Collecting posthog>=2.4.0 (from chromadb)
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Collecting pulsar-client>=3.1.0 (from chromadb)
  Using cached pulsar_client-3.4.0-cp38-cp38-win_amd64.whl.metadata (1.0 kB)
Collecting onnxruntime>=1.14.1 (from chromadb)
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Collecting opentelemetry-api>=1.2.0 (from chromadb)
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In [4]:

!pip install openai

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In [5]:

from langchain.document_loaders import TextLoader
documents = TextLoader("e:/data/AI.txt").load()

In [6]:

from langchain.text_splitter import RecursiveCharacterTextSplitter

# 문서를 청크로 분할
def split_docs(documents,chunk_size=1000,chunk_overlap=20):
  text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap)
  docs = text_splitter.split_documents(documents)
  return docs

# docs 변수에 분할 문서를 저장
docs = split_docs(documents)

In [7]:

from langchain.embeddings import SentenceTransformerEmbeddings
embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")

# Chromdb에 벡터 저장
from langchain.vectorstores import Chroma
db = Chroma.from_documents(docs, embeddings)

e:\anaconda3\envs\openai\lib\site-packages\tqdm\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from .autonotebook import tqdm as notebook_tqdm

In [8]:

import os #운영체제(os) 모듈을 가져올 때 사용되는 라이브러리
os.environ["OPENAI_API_KEY"] = "sk-" #openai 키 입력

from langchain.chat_models import ChatOpenAI
model_name = "gpt-3.5-turbo"  #GPT-3.5 turbo 모델 사용
llm = ChatOpenAI(model_name=model_name)

# Q&A 체인을 사용하여 쿼리에 대한 답변 얻기
from langchain.chains.question_answering import load_qa_chain
chain = load_qa_chain(llm, chain_type="stuff",verbose=True)

# 쿼리를 작성하고 유사성 검색을 수행하여 답변을 생성,따라서 txt에 있는 내용을 질의해야 합니다
query = "AI란?"
matching_docs = db.similarity_search(query)
answer =  chain.run(input_documents=matching_docs, question=query)
answer

e:\anaconda3\envs\openai\lib\site-packages\langchain_core\_api\deprecation.py:117: LangChainDeprecationWarning: The class `langchain_community.chat_models.openai.ChatOpenAI` was deprecated in langchain-community 0.0.10 and will be removed in 0.2.0. An updated version of the class exists in the langchain-openai package and should be used instead. To use it run `pip install -U langchain-openai` and import as `from langchain_openai import ChatOpenAI`.
  warn_deprecated(
Number of requested results 4 is greater than number of elements in index 3, updating n_results = 3
e:\anaconda3\envs\openai\lib\site-packages\langchain_core\_api\deprecation.py:117: LangChainDeprecationWarning: The function `run` was deprecated in LangChain 0.1.0 and will be removed in 0.2.0. Use invoke instead.
  warn_deprecated(


> Entering new StuffDocumentsChain chain...


> Entering new LLMChain chain...
Prompt after formatting:
System: Use the following pieces of context to answer the user's question. 
If you don't know the answer, just say that you don't know, don't try to make up an answer.
----------------
Artificial intelligence (AI) is the intelligence of machines or software, as opposed to the intelligence of humans or animals. It is a field of study in computer science that develops and studies intelligent machines. Such machines may be called AIs.

AI technology is widely used throughout industry, government, and science. Some high-profile applications are: advanced web search engines (e.g., Google Search), recommendation systems (used by YouTube, Amazon, and Netflix), understanding human speech (such as Google Assistant, Siri, and Alexa), self-driving cars (e.g., Waymo), generative and creative tools (ChatGPT and AI art), and superhuman play and analysis in strategy games (such as chess and Go).[1]

The various sub-fields of AI research are centered around particular goals and the use of particular tools. The traditional goals of AI research include reasoning, knowledge representation, planning, learning, natural language processing, perception, and support for robotics.[a] General intelligence (the ability to complete any task performable by a human) is among the field's long-term goals.[11]

To solve these problems, AI researchers have adapted and integrated a wide range of problem-solving techniques, including search and mathematical optimization, formal logic, artificial neural networks, and methods based on statistics, operations research, and economics.[b] AI also draws upon psychology, linguistics, philosophy, neuroscience and other fields.[12]

Alan Turing was the first person to carry out substantial research in the field that he called Machine Intelligence.[2] Artificial intelligence was founded as an academic discipline in 1956.[3] The field went through multiple cycles of optimism[4][5] followed by disappointment and loss of funding.[6][7] Funding and interest vastly increased after 2012 when deep learning surpassed all previous AI techniques,[8] and after 2017 with the transformer architecture.[9] This led to the AI spring of the 2020s, with companies, universities, and laboratories overwhelmingly based in the United States pioneering significant advances in artificial intelligence.[10]
Human: AI란?

> Finished chain.

> Finished chain.

Out[8]:

'AI는 인간이나 동물의 지능과는 달리 기계나 소프트웨어의 지능을 의미합니다. 컴퓨터 과학 분야에서 개발되고 연구되는 AI는 인공 지능이라고 불리기도 합니다. AI 기술은 산업, 정부, 과학 분야에서 널리 사용되며 구글 검색, 유튜브, 아마존, 넷플릭스의 추천 시스템, 구글 어시스턴트, 시리, 알렉사와 같은 음성 인식, 웨이모와 같은 자율 주행 자동차, ChatGPT와 AI 아트와 같은 창조적인 도구, 체스와 바둑과 같은 전략 게임에서의 초인간 수준의 플레이와 분석 등이 대표적인 응용 분야입니다. AI 연구의 여러 하위 분야는 특정한 목표와 도구의 사용을 중심으로 한다고 합니다. Alan Turing은 기계 지능이라고 부르는 분야에서 중요한 연구를 수행한 첫 번째 사람으로 인공 지능은 1956년 학문 분야로 성립되었습니다. 이후 2012년 이후 딥 러닝이 이전의 모든 AI 기술을 능가하면서 AI에 대한 흥미와 투자가 크게 증가했으며 2017년 이후 transformer 아키텍처가 등장하면서 AI 분야에서 큰 발전이 이루어졌습니다. 이로 인해 2020년대 AI 분야에서 미국을 중심으로 한 기업, 대학, 연구소가 중요한 발전을 이뤄내고 있습니다.'

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