weaviate 도커로 설치

참조 블로그 : 

https://velog.io/@m4a1carbin4/%EB%B2%A1%ED%84%B0-DB-%EA%B8%B0%EB%A1%9D-Weaviate-%ED%83%90%EB%B0%A9%EA%B8%B0

벡터 DB 기록 : Weaviate 탐방기 1

 

RAG의 기본 개념

RAG(Retrieval-Augmented Generation)는 대규모 언어 모델(LLM)의 한계를 극복하기 위해 제안된 새로운 자연어 처리 기술입니다. LLM은 방대한 양의 텍스트 데이터를 사전 학습하여 강력한 언어 이해 및 생성 능력을 갖추고 있지만, 학습 데이터에 없는 최신 정보나 특정 도메인 지식은 제공하기 어렵다는 단점이 있습니다.

RAG는 이러한 LLM의 한계를 극복하기 위해 ‘지식 검색’과 ‘언어 생성’을 결합한 프레임워크입니다. RAG의 기본 아이디어는 질문에 답하기 위해 필요한 지식을 외부 데이터베이스에서 검색하여 활용하는 것입니다.

 

 

할루시네이션(Hallucination)'이란?

할루시네이션은 일반적으로 '환각'이라는 뜻을 지니고 있습니다. AI 분야에서 할루시네이션은 거대언어모델(LLM)과 같은 생성형 AI가 사실이 아닌 정보를 생성하거나, 자신이 학습한 데이터에 존재하지 않는 내용을 만들어내는 것을 의미하는데요.

​Weaviate

Weaviate 웹 사이트
GitHub Repo

Weaviate 설치

클라우드 시대 가라사데 처음보는 프로그램이 있으면 우선 도커 이미지 부터 찾으라 하였다.

당연히 리눅스용 바이너리 설치와, 자체 클라우스 서비스도 제공하지만.

도커가 제일 편하다 그러니 도커를 쓰도록 하자.

 docker run -p 8080:8080 -p 50051:50051 cr.weaviate.io/semitechnologies/weaviate

Python Client를 설치.

pip install -U weaviate-client

 

python을 통해 DB에 연동

import weaviate

client = weaviate.connect_to_local()

try:
	# my trash code~~
catch :
	client.close()
    

Weaviate 에서는 data object 를 콜렉션에 저장하는데 각각을 클래스라 정의한다.
각 오브젝트는 properties와 vectorizer에 의해 생성된 벡터값을 가지는데, 해당 벡터값은 기존에는 데이터 입력 시점에 자동으로 생성 저장되지만, 해당 예시에서는 사전에 변환된 벡터값을 저장할 예정이기 때문에 해당 기능을 none으로 정의한다.

import weaviate.classes as wvc

questions = client.collections.create(
        "Question",
        vectorizer_config=wvc.config.Configure.Vectorizer.none(),
        vector_index_config=wvc.config.Configure.VectorIndex.hnsw(
            distance_metric=wvc.config.VectorDistances.COSINE # select prefered distance metric
        ),
    )

해당 코드를 통해 client에서 DB에 Question 콜렉션을 생성하고 해당 콜렉션간 유사도 분석에

COSINE 방식을 사용하도록 지정한다.

VectorIndex.hnsw? :
hnsw 는 벡터 DB에서 인덱싱을 위해 활용하는 알고리즘으로
머신러닝에서 주로 가르치는 KNN 유사도 분석 방식과 같은 부류라 생각하면 된다.

[
    {
        "Category": "SCIENCE",
        "Question": "This organ removes excess glucose from the blood & stores it as glycogen",
        "Answer": "Liver",
        "Vector": [~~~~]
    },~~~~
]
               

다음과 같은 형식으로 정의된 JSON Array 데이터

    import requests

    fname = "jeopardy_tiny_with_vectors_all-OpenAI-ada-002.json"  # This file includes pre-generated vectors
    url = f"https://raw.githubusercontent.com/weaviate-tutorials/quickstart/main/data/{fname}"
    resp = requests.get(url)
    data = json.loads(resp.text)  # Load data

    question_objs = list()
    for i, d in enumerate(data):
        question_objs.append(wvc.data.DataObject(
            properties={
                "answer": d["Answer"],
                "question": d["Question"],
                "category": d["Category"],
            },
            vector=d["vector"]
        ))

    questions = client.collections.get("Question")
    questions.data.insert_many(question_objs)    # This uses batching under the hood

 

이 예제 코드는 JSON Array 데이터에서 얻은 JSON 객체를 Quesion Collection에 입력하는 예시를 보여주는 것으로 해당 과정을 통해 실제 도커로 구동중인 벡터 DB에 데이터가 저장된다.

그럼 대망의 실제 벡터 DB에서 데이터를 검색해 보자.

	questions = client.collections.get("Question")
    query_vector = 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    response = questions.query.near_vector(
        near_vector=query_vector,
        limit=2,
        return_metadata=wvc.query.MetadataQuery(certainty=True)
    )

    print(response)

해당 예시의 실행 결과는

{
    "data": {
        "Get": {
            "Question": [
                {
                    "answer": "DNA",
                    "category": "SCIENCE",
                    "question": "In 1953 Watson & Crick built a model of the molecular structure of this, the gene-carrying substance"
                },
                {
                    "answer": "Liver",
                    "category": "SCIENCE",
                    "question": "This organ removes excess glucose from the blood & stores it as glycogen"
                }
            ]
        }
    }
}

다음과 같이 실제로 query_vector와 유사한 벡터값을 가지는 데이터를 벡터 DB에서 검색하여 보여주는것을 확인할수 있다.

root@mysql8:/data# docker run -p 8080:8080 -p 50051:50051 cr.weaviate.io/semitechnologies/weaviate
Unable to find image 'cr.weaviate.io/semitechnologies/weaviate:latest' locally
latest: Pulling from semitechnologies/weaviate
da9db072f522: Pull complete
47907bbf38ee: Pull complete
6d546fc9d3d7: Pull complete
07ea4a2852c3: Pull complete
a43dadb60a89: Pull complete
36dc1648602a: Pull complete
68c11b496139: Pull complete
Digest: sha256:9d0a73e9dfb6c69f1c4453452c61ebbae866d76b023801fe82be28eddeca418b
Status: Downloaded newer image for cr.weaviate.io/semitechnologies/weaviate:latest
{"action":"startup","build_git_commit":"a6d7f16","build_go_version":"go1.22.10","build_image_tag":"v1.27.7","build_wv_version":"1.27.7","default_vectorizer_module":"none","level":"info","msg":"the default vectorizer modules is set to \"none\", as a result all new schema classes without an explicit vectorizer setting, will use this vectorizer","time":"2024-12-08T05:32:27Z"}
{"action":"startup","auto_schema_enabled":true,"build_git_commit":"a6d7f16","build_go_version":"go1.22.10","build_image_tag":"v1.27.7","build_wv_version":"1.27.7","level":"info","msg":"aut