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LangChain / RAG / Vector DBs · レッスン

RAGコンポーネントの統合

ドキュメントローダー、埋め込み、ベクトルストア、LLMを組み合わせて、一貫したLangChain RAGアプリケーションを構築します。

「RAGコンポーネントの統合」はCoddyKit上の無料LangChain / RAG / Vector DBsレッスンです。 これはレッスン1/4です。 下記で完全なレッスンを無料で読むことができます。その後、ブラウザ内の組み込みコードエディタと24時間対応のAIチューターでハンズオン演習できます。 これはLangChain / RAG / Vector DBs学習パスの一部であり、ウェブとCoddyKitアプリ全体で進捗が同期されます。 LangChain / RAG / Vector DBsコースには全4レッスンが含まれています。

このレッスンの一部はまだ翻訳されておらず、英語で表示されています。

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:

  1. The user's question enters the chain.
  2. RunnableParallel sends the question to two places: to the retriever (to get context) and also passes the original question directly.
  3. The retrieved context and the original question are then fed into the prompt template.
  4. The filled prompt goes to the LLM for generation.
  5. 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.

よくある質問

「RAGコンポーネントの統合」レッスンは無料ですか？

はい。「RAGコンポーネントの統合」の完全なテキストはこのウェブで無料で読めます。インタラクティブに演習し（組み込みコードエディタと24時間対応のAIチューター）、LangChain / RAG / Vector DBsコースの残りをアンロックするには、CoddyKit PROにアップグレードしてください。 LangChain / RAG / Vector DBsコースには全4レッスンが含まれています。

「RAGコンポーネントの統合」で何を学びますか？

ドキュメントローダー、埋め込み、ベクトルストア、LLMを組み合わせて、一貫したLangChain RAGアプリケーションを構築します。 ブラウザで直接実行するハンズオンコードでLangChain / RAG / Vector DBsを演習し、24時間対応のAIチューターがレッスンを進める中での質問に答えます。

LangChain / RAG / Vector DBsを始めるのに経験は必要ですか？

事前経験は必要ありません。CoddyKitのLangChain / RAG / Vector DBsは初級者から上級者向けに構成されているため、ここから始めるか最初から始めて、自分のペースで進むことができます。 これはレッスン1/4です。

「RAGコンポーネントの統合」レッスンにはどのくらい時間がかかりますか？

ほとんどのCoddyKitレッスンは約5～10分かかります。各レッスンはコンパクトでインタラクティブなので、着実に進歩し、ウェブとアプリ全体で正確に前回の場所から再開できます。

このLangChain / RAG / Vector DBsレッスンでコードを書いて実行できますか？

はい。すべてのLangChain / RAG / Vector DBsレッスンに組み込みコードエディタが含まれているため、ブラウザでリアルコードを書いて実行し、即座のAIフィードバックを取得できます。ローカル設定は不要です。

このコースのすべてのレッスン

  1. RAGコンポーネントの統合
  2. クエリ処理と回答生成
  3. RAGシステムの性能評価
  4. RAG 用のゴールデンテストセットを作成する
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