LangChain / RAG / Vector DBs · レッスン

LLMによるコンテキスト圧縮

検索したドキュメントを動的にフィルタリング・圧縮して関連性の高い情報に絞り、LLMに渡すコンテキストを最適化します。

「LLMによるコンテキスト圧縮」はCoddyKit上の無料LangChain / RAG / Vector DBs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。

Why Compress Context?

When building RAG (Retrieval Augmented Generation) systems, Large Language Models (LLMs) have a limited context window. Feeding them too much irrelevant information can lead to several problems:

Performance issues: LLMs might get confused by noise.
Higher costs: More tokens mean higher API bills.
Slower responses: More text takes longer to process.

This is where contextual compression comes in!

What is Contextual Compression?

Contextual compression is a technique used to refine the documents retrieved by your RAG system before they are passed to the LLM. It acts as a smart filter and extractor.

Filter: Remove entire documents that are less relevant.
Extract: From the remaining documents, identify and keep only the most pertinent sentences or paragraphs related to the user's query.

Think of it like highlighting the most important parts of a long article.

The Base Retriever's Role

Contextual compression doesn't replace your initial retrieval mechanism. Instead, it works on top of it.

You still need a base retriever (e.g., a vector store retriever) to fetch an initial set of potentially relevant documents.
The compressor then takes these documents and applies its logic to narrow down or condense their content.

It's a refinement step, not a substitute for finding the initial information.

Introducing LangChain's Tools

LangChain provides excellent tools for implementing contextual compression:

The ContextualCompressionRetriever is the main component. It wraps your base retriever and a document compressor.
A document compressor (e.g., an LLM-based one) is the actual logic that filters or extracts information from the documents.

Let's see how to set up a dummy base retriever first.

Setting Up a Base Retriever

Here's a simple dummy retriever. In a real application, this would typically connect to a vector database like Pinecone or Chroma.

from langchain_core.documents import Document

class SimpleBaseRetriever:
    def get_relevant_documents(self, query: str):
        if "programming" in query.lower() or "python" in query.lower():
            return [
                Document(page_content="Python is a versatile programming language. Used in AI & web dev."),
                Document(page_content="Java is popular for enterprise apps."),
                Document(page_content="Data science uses Python for analysis & ML."),
                Document(page_content="History of programming languages dates back centuries.")
            ]
        return [
            Document(page_content="The quick brown fox jumps over the lazy dog."),
            Document(page_content="Cats enjoy napping in sunny spots, especially in the sun."),
            Document(page_content="A computer processes data very quickly and efficiently.")
        ]

base_retriever = SimpleBaseRetriever()
print("Base retriever initialized.")
# Example usage:
# docs = base_retriever.get_relevant_documents("python")
# for doc in docs: print(doc.page_content)

LLMs as Document Compressors

One of the most powerful ways to compress context is by using another LLM! An LLM can intelligently understand the query and the retrieved documents, then extract only the most relevant sentences.

This goes beyond simple keyword matching.
It leverages the LLM's understanding of semantics.

LangChain provides specific compressors that use LLMs for this task.

Using LLMChainExtractor (Code)

The LLMChainExtractor uses an LLM to extract relevant sections from documents. Here's how to set it up along with the ContextualCompressionRetriever.

from langchain_core.documents import Document
from langchain.retrievers.document_compressors import LLMChainExtractor
from langchain.retrievers import ContextualCompressionRetriever
from langchain_openai import OpenAI # pip install langchain-openai

# --- Dummy Base Retriever (for standalone runnable) ---
class SimpleBaseRetriever:
    def get_relevant_documents(self, query: str):
        if "programming" in query.lower() or "python" in query.lower():
            return [
                Document(page_content="Python is a versatile programming language. Used in AI & web dev."),
                Document(page_content="Java is popular for enterprise apps."),
                Document(page_content="Data science uses Python for analysis & ML."),
                Document(page_content="History of programming languages dates back centuries.")
            ]
        return [
            Document(page_content="The quick brown fox jumps over the lazy dog."),
            Document(page_content="Cats enjoy napping in sunny spots, especially in the sun."),
            Document(page_content="A computer processes data very quickly and efficiently.")
        ]
base_retriever = SimpleBaseRetriever()

# --- LLM for Compression ---
# NOTE: You need to set your OpenAI API key as an environment variable
# e.g., import os; os.environ["OPENAI_API_KEY"] = "YOUR_API_KEY"
# Ensure 'langchain-openai' is installed (pip install langchain-openai).
llm = OpenAI(temperature=0.1) # Low temp for focused extraction

# --- Create the Extractor and Compression Retriever ---
compressor = LLMChainExtractor.from_llm(llm)
compression_retriever = ContextualCompressionRetriever(
    base_compressor=compressor,
    base_retriever=base_retriever
)
print("LLMChainExtractor and ContextualCompressionRetriever initialized!")

Running Compressed Retrieval (Code)

Now, let's run a query and observe how the ContextualCompressionRetriever (using LLMChainExtractor) processes the documents. Pay attention to the length of the document content!

from langchain_core.documents import Document
from langchain.retrievers.document_compressors import LLMChainExtractor
from langchain.retrievers import ContextualCompressionRetriever
from langchain_openai import OpenAI # pip install langchain-openai

# --- Dummy Base Retriever (re-defined for standalone runnable) ---
class SimpleBaseRetriever:
    def get_relevant_documents(self, query: str):
        if "programming" in query.lower() or "python" in query.lower():
            return [
                Document(page_content="Python is a versatile programming language. Used in AI & web dev."),
                Document(page_content="Java is popular for enterprise apps."),
                Document(page_content="Data science uses Python for analysis & ML."),
                Document(page_content="History of programming languages dates back centuries.")
            ]
        return [
            Document(page_content="The quick brown fox jumps over the lazy dog."),
            Document(page_content="Cats enjoy napping in sunny spots, especially in the sun."),
            Document(page_content="A computer processes data very quickly and efficiently.")
        ]
base_retriever = SimpleBaseRetriever()

# --- LLM for Compression (re-defined for standalone runnable) ---
# NOTE: You need to set your OpenAI API key as an environment variable
# Ensure 'langchain-openai' is installed.
llm = OpenAI(temperature=0.1)
compressor = LLMChainExtractor.from_llm(llm)
compression_retriever = ContextualCompressionRetriever(
    base_compressor=compressor,
    base_retriever=base_retriever
)

# --- Run a Query ---
query = "What is Python used for?"
print(f"Query: '{query}'\n")

print("--- Original (simulated) documents ---")
original_docs = base_retriever.get_relevant_documents(query)
for i, doc in enumerate(original_docs):
    print(f"Doc {i+1} (Chars: {len(doc.page_content)}): {doc.page_content}")
print(f"Total original documents: {len(original_docs)}\n")

print("--- Content after compression ---")
compressed_docs = compression_retriever.get_relevant_documents(query)
for i, doc in enumerate(compressed_docs):
    # The LLMChainExtractor modifies the page_content of existing docs
    print(f"Compressed Doc {i+1} (Chars: {len(doc.page_content)}): {doc.page_content}")
print(f"Total compressed documents: {len(compressed_docs)}")

Other Compression Options

LangChain offers more than just LLMChainExtractor:

LLMChainFilter: Uses an LLM to decide if an entire document is relevant enough to keep, rather than extracting parts.
EmbeddingsFilter: Filters documents based on the semantic similarity of their embeddings to the query. This is often faster but less nuanced than LLM-based filtering.
DocumentCompressorPipeline: Allows you to chain multiple compressors together for complex logic!

Why Compression Matters

Contextual compression is a powerful technique for optimizing RAG systems:

Improved Relevance: LLMs receive more focused, pertinent information.
Reduced Cost: Fewer tokens are sent to the LLM API.
Faster Responses: LLMs process less text, leading to quicker answers.
Better Accuracy: Less irrelevant noise reduces the chance of LLM hallucinations.

It helps you get more out of your LLMs while saving resources!

Compression Check

Test your understanding of contextual compression!

Contextual Compression Recap

We've explored how contextual compression refines retrieved documents for LLMs:

It's a post-retrieval step that filters and extracts key information.
LangChain's ContextualCompressionRetriever wraps a base retriever and a document compressor.
LLMChainExtractor uses an LLM to intelligently extract relevant snippets.
Benefits include improved relevance, reduced costs, faster responses, and better accuracy.

This technique is vital for optimizing RAG systems, especially with large knowledge bases.

よくある質問

「LLMによるコンテキスト圧縮」レッスンは無料ですか？

はい。「LLMによるコンテキスト圧縮」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。

「LLMによるコンテキスト圧縮」で何を学びますか？

検索したドキュメントを動的にフィルタリング・圧縮して関連性の高い情報に絞り、LLMに渡すコンテキストを最適化します。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2/4です。

「LLMによるコンテキスト圧縮」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。

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