LangChain / RAG / Vector DBs · 강의

모든 RAG 구성 요소 통합

문서 로더, 임베딩, 벡터 저장소, LLM을 하나의 일관된 LangChain RAG 애플리케이션으로 구성합니다.

모든 RAG 구성 요소 통합은(는) CoddyKit의 무료 LangChain / RAG / Vector DBs 강의입니다. 이것은 4개 중 1번째 강의입니다. 아래에서 전체 강의를 무료로 읽을 수 있으며, 내장 코드 에디터와 24/7 AI 튜터와 함께 브라우저에서 직접 실습할 수 있습니다. 이 강의는 LangChain / RAG / Vector DBs 학습 경로의 일부이며, 진행 상황이 웹과 CoddyKit 앱에 동기화됩니다. LangChain / RAG / Vector DBs 강의에는 총 4개의 강의가 포함되어 있습니다.

이 강의의 일부는 아직 번역되지 않았으며 영어로 표시됩니다.

Welcome to RAG Integration

Welcome to this lesson on integrating all the components of a Retrieval Augmented Generation (RAG) system using LangChain!

So far, you've learned about individual pieces: document loaders, text splitters, embedding models, vector stores, LLMs, and prompts. Now, it's time to bring them all together.

The RAG Pipeline Flow

A RAG application follows a clear pipeline to answer questions using external knowledge. It generally involves these steps:

Load: Ingest data from various sources.
Split: Break documents into manageable chunks.
Embed: Convert text chunks into numerical vectors.
Store: Save these vectors in a searchable database.
Retrieve: Find relevant chunks based on a user query.
Generate: Use an LLM to answer the query, referencing the retrieved context.

Essential LangChain Imports

To build our RAG application, we'll need several key classes from LangChain. These help us manage documents, create embeddings, interact with vector stores, and connect to LLMs.

We'll use components like Chroma for the vector store, OllamaEmbeddings and ChatOllama for local models, and core LangChain Expression Language (LCEL) tools.

Preparing Documents for RAG

The first step in any RAG system is preparing your knowledge base. This involves loading your data and then splitting it into smaller, manageable chunks.

Loading: Fetching content from files, databases, or web pages.
Splitting: Breaking large texts into smaller, semantically coherent pieces to fit LLM context windows and improve retrieval accuracy.

For our runnable example, we'll use simple in-memory Document objects for quick setup.

Embedding & Vector Store Setup

Once documents are split, they need to be converted into numerical representations called embeddings. These embeddings capture the semantic meaning of the text.

The embeddings are then stored in a vector store (like Chroma), which is specialized for efficient similarity search. This allows us to quickly find document chunks related to a user's query.

Setting Up the LLM & Retriever

The next crucial steps are setting up our Large Language Model (LLM) and preparing the retriever:

LLM: We'll initialize an LLM (e.g., ChatOllama) that will take the retrieved context and user question to generate an answer.
Retriever: Our vector store is converted into a retriever. This component's job is to take the user's query, embed it, search the vector store, and return the most relevant document chunks.

Crafting the Prompt Template

A well-designed prompt template is key to guiding the LLM to generate accurate and relevant answers. It instructs the LLM on how to use the provided context.

Our template will clearly define placeholders for the context (retrieved documents) and the user's question, ensuring the LLM focuses on the provided information.

LangChain Expression Language

LangChain Expression Language (LCEL) is a powerful way to compose complex chains from simple components. It uses the | operator to chain runnables together.

RunnableParallel: Allows multiple branches to run concurrently, useful for fetching context and passing the question.
RunnablePassthrough: Passes its input through, often used to keep the original question available in a parallel branch.

LCEL makes our RAG pipeline flexible and modular.

Assembling the RAG Chain

Now, let's put it all together. The RAG chain conceptually flows like this:

The user's question enters the chain.
RunnableParallel sends the question to two places: to the retriever (to get context) and also passes the original question directly.
The retrieved context and the original question are then fed into the prompt template.
The filled prompt goes to the LLM for generation.
Finally, an StrOutputParser extracts the plain text answer from the LLM's response.

Complete RAG Application Example

Try running this complete LangChain RAG application. Make sure you have Ollama installed and models like nomic-embed-text and llama2 pulled (e.g., ollama pull nomic-embed-text).

from langchain_community.vectorstores import Chroma
from langchain_community.embeddings import OllamaEmbeddings
from langchain_community.chat_models import ChatOllama
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough, RunnableParallel
from langchain_core.output_parsers import StrOutputParser
from langchain_core.documents import Document

# 1. Prepare Documents (simple in-memory for runnable example)
documents = [
    Document(page_content="LangChain is a framework for developing applications powered by large language models."),
    Document(page_content="It simplifies the process of building complex LLM workflows."),
    Document(page_content="Retrieval Augmented Generation (RAG) combines LLMs with external data retrieval."),
    Document(page_content="Ollama allows running open-source LLMs locally.")
]

# 2. Generate Embeddings and Store in Vector DB
embeddings = OllamaEmbeddings(model="nomic-embed-text")
vectorstore = Chroma.from_documents(documents=documents, embedding=embeddings)
retriever = vectorstore.as_retriever()

# 3. Initialize LLM
llm = ChatOllama(model="llama2")

# 4. Define Prompt Template
prompt = ChatPromptTemplate.from_template("""
Answer the question based ONLY on the following context:
{context}

Question: {question}
""")

# 5. Assemble the RAG Chain
rag_chain = (
    RunnableParallel({"context": retriever, "question": RunnablePassthrough()})
    | prompt
    | llm
    | StrOutputParser()
)

# 6. Invoke the Chain with a question
question = "What is LangChain?"
print(f"Question: {question}")
response = rag_chain.invoke(question)
print(f"Answer: {response}")

question_2 = "What is RAG?"
print(f"\nQuestion: {question_2}")
response_2 = rag_chain.invoke(question_2)
print(f"Answer: {response_2}")

RAG Pipeline Component Check

Which of the following components are typically part of a LangChain RAG pipeline for answering user questions?

RAG Integration Recap

Fantastic work! You've successfully learned how to integrate all the core components into a cohesive LangChain RAG application.

We walked through the full RAG pipeline from loading documents to generating answers.
You saw how LangChain's modular components and LCEL allow for flexible and powerful chain construction.
You now have a runnable example demonstrating a complete RAG system.

Next, we'll explore how to query your RAG system effectively and generate high-quality answers.

자주 묻는 질문

“모든 RAG 구성 요소 통합” 강의는 무료인가요?

네 — “모든 RAG 구성 요소 통합” 전체 내용을 이 웹사이트에서 무료로 읽을 수 있습니다. 인터랙티브하게 실습하려면(내장 코드 에디터와 24/7 AI 튜터), CoddyKit PRO로 업그레이드하면 LangChain / RAG / Vector DBs 강의 전체를 잠금 해제할 수 있습니다. LangChain / RAG / Vector DBs 강의에는 총 4개의 강의가 포함되어 있습니다.

“모든 RAG 구성 요소 통합”에서 뭘 배우나요?

문서 로더, 임베딩, 벡터 저장소, LLM을 하나의 일관된 LangChain RAG 애플리케이션으로 구성합니다. 브라우저에서 직접 실행하는 실습 코드로 LangChain / RAG / Vector DBs을(를) 배우며, 24/7 AI 튜터가 강의를 진행하면서 질문에 답변해줍니다.

LangChain / RAG / Vector DBs을(를) 시작하는 데 경험이 필요한가요?

사전 경험은 필요하지 않습니다. CoddyKit의 LangChain / RAG / Vector DBs은(는) 초급자부터 고급 학습자까지를 위해 구성되어 있으므로, 여기서 시작하거나 처음부터 시작할 수 있으며 자신의 속도대로 진행할 수 있습니다. 이것은 4개 중 1번째 강의입니다.

“모든 RAG 구성 요소 통합” 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 LangChain / RAG / Vector DBs 강의에서 코드를 작성하고 실행할 수 있나요?

네. 모든 LangChain / RAG / Vector DBs 강의에는 내장 코드 에디터가 포함되어 있으므로, 브라우저에서 바로 실제 코드를 작성하고 실행한 후 즉시 AI 피드백을 받을 수 있습니다 — 로컬 설정이 필요 없습니다.

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