LangChainとLlamaIndexの基礎
LangChainやLlamaIndexなどの強力なフレームワークを使い、複雑なLLMアプリケーションを簡単に構築する方法を学びます。
「LangChainとLlamaIndexの基礎」はCoddyKit上の無料Prompt Engineering & LLM Optimization for Developersレッスンです。 これはレッスン2/4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Prompt Engineering & LLM Optimization for Developers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。 Prompt Engineering & LLM Optimization for Developers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。
Why Use LLM Frameworks?
Working directly with Large Language Model (LLM) APIs can be like building with raw LEGO bricks. It's powerful, but complex for bigger projects.
Frameworks like LangChain and LlamaIndex provide pre-built tools and structures. They simplify common tasks, making it much easier to build sophisticated LLM applications.
LangChain: Orchestrating LLMs
LangChain is a framework designed to help you build applications with LLMs by "chaining" together different components.
- It provides abstractions for working with various LLMs.
- It enables complex workflows involving multiple steps.
- Think of it as the glue for your LLM-powered application logic.
Core LangChain Components
LangChain organizes functionality into key modules:
- LLMs: Wrappers for different LLM providers (e.g., OpenAI, Anthropic).
- Prompts: Templates to easily construct dynamic prompts.
- Chains: Sequences of calls to LLMs or other utilities.
- Agents: LLMs that decide which tools to use and in what order.
- Memory: For persistent state in conversational applications.
Your First LangChain Idea
Let's see how a simple prompt and a mock LLM can work together. This example shows how LangChain conceptualizes connecting a prompt template to an LLM, even with a simulated model.
from langchain_core.prompts import PromptTemplate
from langchain_core.language_models import BaseChatModel
from langchain_core.messages import BaseMessage, HumanMessage, AIMessage
# A simple mock LLM for demonstration
class MockChatModel(BaseChatModel):
def _generate(self, messages, stop=None, run_manager=None):
last_message_content = messages[-1].content
response = f"Mock response for: '{last_message_content}'"
return AIMessage(content=response)
@property
def _llm_type(self) -> str:
return "mock_chat_model"
def main():
# 1. Define a Prompt Template
template = "Tell me a fun fact about {topic}."
prompt = PromptTemplate(template=template, input_variables=["topic"])
# 2. Instantiate our Mock LLM
llm = MockChatModel()
# 3. Format the prompt with input
formatted_prompt = prompt.format(topic="penguins")
print("--- Formatted Prompt ---")
print(formatted_prompt)
# 4. Invoke the LLM (simulated)
# In a real LangChain app, you'd use a chain, but this shows the core interaction.
response_message = llm.invoke([HumanMessage(content=formatted_prompt)])
print("\n--- Mock LLM Response ---")
print(response_message.content)
if __name__ == "__main__":
main()LlamaIndex: Data & LLMs
LlamaIndex is a data framework designed to connect your custom data sources to LLMs. Its primary goal is to make it easy to build applications that can query and understand your own private or domain-specific data.
- Focuses on data ingestion, indexing, and retrieval.
- Powers Retrieval Augmented Generation (RAG) applications.
- Helps LLMs answer questions beyond their training data.
Core LlamaIndex Concepts
LlamaIndex streamlines the RAG pipeline with these components:
- Documents: Raw data inputs (e.g., text files, PDFs, database records).
- Nodes: Chunks of text derived from Documents, often with associated metadata.
- Indexes: Data structures (like vector stores) that organize Nodes for efficient retrieval.
- Query Engines: Interfaces for querying an index, often combining retrieval and LLM synthesis.
Querying Custom Data (Simulated)
Here's a simplified look at how LlamaIndex connects data to a query. We simulate ingesting a document and then querying it, demonstrating the core retrieval idea.
class Document:
def __init__(self, text, doc_id=None):
self.text = text
self.doc_id = doc_id if doc_id else f"doc_{hash(text)}"
class MockVectorStore:
def __init__(self):
self.store = {}
self.documents = []
def add(self, docs):
for doc in docs:
self.store[doc.doc_id] = doc.text
self.documents.append(doc)
def query(self, query_text):
for doc in self.documents:
if query_text.lower() in doc.text.lower():
return [doc.text]
return []
class MockQueryEngine:
def __init__(self, vector_store):
self.vector_store = vector_store
def query(self, query_text):
retrieved_texts = self.vector_store.query(query_text)
if retrieved_texts:
return f"Based on available info, for '{query_text}': {retrieved_texts[0]}"
return f"Could not find specific information for '{query_text}'."
def main():
# 1. Simulate Documents
documents = [
Document("The quick brown fox jumps over the lazy dog."),
Document("Cats love to nap in sunny spots."),
Document("Python is a popular programming language.")
]
# 2. Simulate building a Vector Store and Index
mock_store = MockVectorStore()
mock_store.add(documents)
# 3. Create a Query Engine
query_engine = MockQueryEngine(mock_store)
# 4. Query the data
response1 = query_engine.query("What is Python?")
print("--- Query 1 Response ---")
print(response1)
response2 = query_engine.query("What about foxes?")
print("\n--- Query 2 Response ---")
print(response2)
response3 = query_engine.query("What is the capital of France?")
print("\n--- Query 3 Response ---")
print(response3)
if __name__ == "__main__":
main()LangChain vs. LlamaIndex
While both frameworks help with LLMs, their primary focus differs:
- LangChain: Best for orchestrating complex LLM workflows, building agents, conversational bots, and integrating various tools.
- LlamaIndex: Ideal for data-intensive LLM applications, especially when you need to connect LLMs to your private or domain-specific data for RAG.
Often, you'll find them complementing each other in real-world applications.
Synergy: LangChain + LlamaIndex
It's common to use LangChain and LlamaIndex together for powerful applications:
- Use LlamaIndex to ingest, index, and retrieve relevant information from your data sources.
- Pass the retrieved context to LangChain, which then uses its "chains" or "agents" to process this context with an LLM, refine answers, or interact with other tools.
This combines LlamaIndex's data prowess with LangChain's orchestration capabilities.
Framework Roles
Consider the core strengths of LangChain and LlamaIndex. Which of the following statements accurately describe their primary roles or common use cases?
Lesson Recap
In this lesson, we explored LangChain and LlamaIndex, two powerful frameworks for building LLM applications.
- LangChain helps orchestrate complex LLM workflows, chains, and agents.
- LlamaIndex specializes in connecting LLMs to your custom data for efficient retrieval and RAG.
- These frameworks often work together, with LlamaIndex providing data context to LangChain's reasoning.
Understanding their distinct roles will help you choose the right tool for your next LLM project!
よくある質問
「LangChainとLlamaIndexの基礎」レッスンは無料ですか？
はい。「LangChainとLlamaIndexの基礎」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）、Prompt Engineering & LLM Optimization for Developers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。 Prompt Engineering & LLM Optimization for Developersコースには全4レッスンが含まれています。
「LangChainとLlamaIndexの基礎」で何を学びますか？
LangChainやLlamaIndexなどの強力なフレームワークを使い、複雑なLLMアプリケーションを簡単に構築する方法を学びます。 ブラウザで直接実行するハンズオンコードでPrompt Engineering & LLM Optimization for Developers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。
Prompt Engineering & LLM Optimization for Developers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Prompt Engineering & LLM Optimization for Developers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2/4です。
「LangChainとLlamaIndexの基礎」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Prompt Engineering & LLM Optimization for Developers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Prompt Engineering & LLM Optimization for Developers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
- LLM APIとの連携（OpenAI、Anthropic）
- LangChainとLlamaIndexの基礎
- プロンプト管理とバージョン管理
- Retrieval-Augmented Generation（RAG）の基礎