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AI Agents with LangChain & Autonomous Workflows · 강의

검색을 위한 벡터 저장소

벡터 데이터베이스를 사용해 관련 문서 조각을 의미적 유사성에 따라 저장하고 효율적으로 검색하는 방법을 배웁니다(RAG).

검색을 위한 벡터 저장소은(는) CoddyKit의 무료 AI Agents with LangChain & Autonomous Workflows 강의입니다. 이것은 4개 중 3번째 강의입니다. 아래에서 전체 강의를 무료로 읽을 수 있으며, 내장 코드 에디터와 24/7 AI 튜터와 함께 브라우저에서 직접 실습할 수 있습니다. 이 강의는 AI Agents with LangChain & Autonomous Workflows 학습 경로의 일부이며, 진행 상황이 웹과 CoddyKit 앱에 동기화됩니다. AI Agents with LangChain & Autonomous Workflows 강의에는 총 4개의 강의가 포함되어 있습니다.

이 강의의 일부는 아직 번역되지 않았으며 영어로 표시됩니다.

Intro to Vector Stores

Welcome to the final lesson on Data Loading & Retrieval! Today, we'll dive into Vector Stores, a crucial component for building intelligent AI agents.

Think of vector stores as specialized databases designed to store and efficiently search through numerical representations of information, called embeddings.

Why Vector Stores for RAG?

Vector stores are the backbone of Retrieval Augmented Generation (RAG). RAG allows Large Language Models (LLMs) to access external, up-to-date information, overcoming their inherent limitations like:

  • Knowledge cutoffs: LLMs only know what they were trained on.
  • Hallucinations: Making up facts when uncertain.

Vector stores provide the relevant context for the LLM to generate accurate responses.

How Vector Stores Work

When you have text documents (which you learned to load and split in previous lessons), they are first converted into numerical embeddings.

These embeddings are then stored in a vector store. When a query comes in, it's also converted into an embedding. The vector store then finds document embeddings that are 'closest' (most similar) to the query embedding.

LangChain's VectorStore Abstraction

LangChain provides a powerful abstraction for interacting with various vector stores. This means you can swap out different vector database providers (like Chroma, Pinecone, FAISS) with minimal code changes.

Key methods include from_documents() to create a store from documents, add_documents() to add more, and similarity_search() to find relevant content.

Local Store: ChromaDB

For our examples, we'll use ChromaDB. It's an open-source, lightweight vector database that can run locally, making it perfect for development and testing without needing cloud services.

First, make sure you have the necessary packages installed:

  • pip install chromadb
  • pip install langchain-community
  • pip install sentence-transformers

Initializing Chroma & Embeddings

Let's set up ChromaDB with a local embedding model. The embedding model converts text into vectors.

This example uses SentenceTransformerEmbeddings, which runs directly on your machine.

from langchain_community.vectorstores import Chroma
from langchain_community.embeddings import SentenceTransformerEmbeddings
import os

def main():
    # Define a path for ChromaDB to store data locally
    # This creates a 'chroma_db' folder if it doesn't exist
    persist_directory = "./chroma_db"
    
    # Initialize a local embedding function
    # 'all-MiniLM-L6-v2' is a small, efficient model
    embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
    
    # Initialize ChromaDB. It will load if exists, or create new.
    vectordb = Chroma(
        persist_directory=persist_directory,
        embedding_function=embeddings
    )
    
    print("ChromaDB initialized successfully!")
    print(f"Database will persist at: {os.path.abspath(persist_directory)}")

if __name__ == "__main__":
    main()

Adding Documents to Chroma

Once initialized, we can add Document objects (which contain page_content and optional metadata) to our vector store. LangChain handles the embedding process automatically.

We'll add a few sample documents to our ChromaDB instance.

from langchain_community.vectorstores import Chroma
from langchain_community.embeddings import SentenceTransformerEmbeddings
from langchain_core.documents import Document
import os

def main():
    persist_directory = "./chroma_db"
    embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
    
    # Load the existing ChromaDB or create a new one
    vectordb = Chroma(
        persist_directory=persist_directory,
        embedding_function=embeddings
    )
    
    # Example documents to add
    documents = [
        Document(page_content="The quick brown fox jumps over the lazy dog."),
        Document(page_content="Artificial intelligence is transforming industries."),
        Document(page_content="Machine learning is a subset of AI."),
        Document(page_content="Dogs are known for their loyalty and companionship.")
    ]
    
    print(f"Adding {len(documents)} documents to ChromaDB...")
    # add_documents handles embedding and storing
    vectordb.add_documents(documents)
    print("Documents added.")

if __name__ == "__main__":
    main()

Performing Similarity Search

Now that documents are stored, we can query the vector store to find content semantically similar to our query. The similarity_search() method returns a list of relevant Document objects.

The k parameter specifies how many top similar documents to retrieve.

from langchain_community.vectorstores import Chroma
from langchain_community.embeddings import SentenceTransformerEmbeddings
import os

def main():
    persist_directory = "./chroma_db"
    embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
    
    # Load the existing ChromaDB
    vectordb = Chroma(
        persist_directory=persist_directory,
        embedding_function=embeddings
    )
    
    query = "What is AI?"
    print(f"Searching for documents similar to: '{query}'")
    
    # Perform similarity search, retrieve top 2 results
    docs = vectordb.similarity_search(query, k=2)
    
    print("\nFound relevant documents:")
    for i, doc in enumerate(docs):
        print(f"--- Document {i+1} ---")
        print(doc.page_content)

if __name__ == "__main__":
    main()

VectorStore as a Retriever

In LangChain, a Retriever is an interface that returns Documents given an unstructured query. A vector store is one of the most common ways to create a retriever.

By converting your vector store into a retriever, you can easily plug it into more complex LangChain chains and agents, especially for RAG applications.

Activating the Retriever

Here's how to convert your ChromaDB instance into a retriever and use it to fetch documents based on a query. This is the final step before integrating it into a full RAG chain.

from langchain_community.vectorstores import Chroma
from langchain_community.embeddings import SentenceTransformerEmbeddings
import os

def main():
    persist_directory = "./chroma_db"
    embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
    
    # Load the existing ChromaDB
    vectordb = Chroma(
        persist_directory=persist_directory,
        embedding_function=embeddings
    )
    
    # Convert the vector store into a retriever
    # search_kwargs allows passing arguments like 'k' to the underlying search
    retriever = vectordb.as_retriever(search_kwargs={"k": 2})
    
    query = "Tell me about AI."
    print(f"Using retriever to find documents for: '{query}'")
    
    # Use the retriever to get relevant documents
    relevant_docs = retriever.get_relevant_documents(query)
    
    print("\nRelevant documents retrieved by the retriever:")
    for i, doc in enumerate(relevant_docs):
        print(f"--- Retrieved Document {i+1} ---")
        print(doc.page_content)

if __name__ == "__main__":
    main()

Quick Check: Vector Stores

Which of the following best describes the primary purpose of a vector store in the context of Retrieval Augmented Generation (RAG)?

Recap: Vector Stores for RAG

Great job! In this lesson, you learned about:

  • The role of vector stores in enhancing LLMs through RAG.
  • How vector stores store embeddings for semantic search.
  • Setting up and interacting with a local ChromaDB.
  • Adding documents and performing similarity searches.
  • Converting a vector store into a LangChain Retriever.

You now have a solid foundation for implementing data retrieval in your AI agents!

자주 묻는 질문

“검색을 위한 벡터 저장소” 강의는 무료인가요?

네 — “검색을 위한 벡터 저장소” 전체 내용을 이 웹사이트에서 무료로 읽을 수 있습니다. 인터랙티브하게 실습하려면(내장 코드 에디터와 24/7 AI 튜터), CoddyKit PRO로 업그레이드하면 AI Agents with LangChain & Autonomous Workflows 강의 전체를 잠금 해제할 수 있습니다. AI Agents with LangChain & Autonomous Workflows 강의에는 총 4개의 강의가 포함되어 있습니다.

“검색을 위한 벡터 저장소”에서 뭘 배우나요?

벡터 데이터베이스를 사용해 관련 문서 조각을 의미적 유사성에 따라 저장하고 효율적으로 검색하는 방법을 배웁니다(RAG). 브라우저에서 직접 실행하는 실습 코드로 AI Agents with LangChain & Autonomous Workflows을(를) 배우며, 24/7 AI 튜터가 강의를 진행하면서 질문에 답변해줍니다.

AI Agents with LangChain & Autonomous Workflows을(를) 시작하는 데 경험이 필요한가요?

사전 경험은 필요하지 않습니다. CoddyKit의 AI Agents with LangChain & Autonomous Workflows은(는) 초급자부터 고급 학습자까지를 위해 구성되어 있으므로, 여기서 시작하거나 처음부터 시작할 수 있으며 자신의 속도대로 진행할 수 있습니다. 이것은 4개 중 3번째 강의입니다.

“검색을 위한 벡터 저장소” 강의는 얼마나 걸리나요?

대부분의 CoddyKit 강의는 약 5~10분이 소요됩니다. 각 강의는 간결하고 인터랙티브하여 꾸준한 진행이 가능하며, 웹과 앱에서 중단한 부분부터 바로 시작할 수 있습니다.

이 AI Agents with LangChain & Autonomous Workflows 강의에서 코드를 작성하고 실행할 수 있나요?

네. 모든 AI Agents with LangChain & Autonomous Workflows 강의에는 내장 코드 에디터가 포함되어 있으므로, 브라우저에서 바로 실제 코드를 작성하고 실행한 후 즉시 AI 피드백을 받을 수 있습니다 — 로컬 설정이 필요 없습니다.

이 강의의 모든 강의

  1. 문서 로더 해설
  2. 텍스트 분할기와 임베딩
  3. 검색을 위한 벡터 저장소
  4. 검색기 및 맥락 압축
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