프롬프트 작성을 위한 벡터 데이터베이스

벡터 데이터베이스란 무엇인가요

언어를 숫자로 표현하기: 임베딩

벡터 데이터베이스에 데이터 저장하기

유사도 검색의 마법

검색 증강 생성(RAG)에서의 VDB

VDB 채우기: 예시 코드

import numpy as np

# Imagine an embedding model function
def get_embedding(text):
    # In a real scenario, this uses an LLM API
    # or a local embedding model.
    # For illustration, let's return a dummy vector.
    return np.random.rand(128).tolist() # 128-dim vector

# Imagine a simplified VectorDB client
class SimpleVectorDB:
    def __init__(self):
        self.vectors = {} # Stores {id: {'vector': [...], 'text': '...'}}

    def add_document(self, doc_id, text_content):
        vector = get_embedding(text_content)
        self.vectors[doc_id] = {'vector': vector, 'text': text_content}
        print(f"Added '{text_content[:20]}...' (ID: {doc_id})")

# --- Main simulation --- 
db = SimpleVectorDB()
db.add_document("doc1", "The capital of France is Paris.")
db.add_document("doc2", "Eiffel Tower is a landmark in Paris.")
db.add_document("doc3", "Berlin is the capital of Germany.")

VDB 질의하기: 예시 코드

import numpy as np
from scipy.spatial.distance import cosine # For similarity

# (Re-using get_embedding and SimpleVectorDB conceptually)
def get_embedding(text):
    return np.random.rand(128).tolist()

class SimpleVectorDB:
    def __init__(self):
        self.vectors = {}

    def add_document(self, doc_id, text_content):
        vector = get_embedding(text_content)
        self.vectors[doc_id] = {'vector': vector, 'text': text_content}

    def search(self, query_text, top_k=1):
        query_vector = get_embedding(query_text)
        
        scores = []
        for doc_id, data in self.vectors.items():
            doc_vector = data['vector']
            # Cosine similarity: 1 - cosine distance
            similarity = 1 - cosine(query_vector, doc_vector)
            scores.append({'id': doc_id, 'text': data['text'], 'score': similarity})
        
        # Sort by similarity in descending order
        scores.sort(key=lambda x: x['score'], reverse=True)
        return scores[:top_k]

# --- Main simulation --- 
db = SimpleVectorDB()
db.add_document("doc1", "The capital of France is Paris.")
db.add_document("doc2", "Eiffel Tower is a landmark in Paris.")
db.add_document("doc3", "Berlin is the capital of Germany.")

user_query = "What is the capital city of France?"
results = db.search(user_query, top_k=2)

print(f"Query: '{user_query}'")
print("Top results:")
for res in results:
    print(f"- Text: '{res['text']}' (Score: {res['score']:.2f})")

VDB 사용의 주요 이점

VDB에서 고려해야 할 중요 사항

빠른 확인: RAG의 벡터 DB

복습: VDB와 LLM