LangChain / RAG / Vector DBs · Ders

LLM'lerle Bağlamsal Sıkıştırma

İlgili içeriğe odaklanmak için alınan belgeleri dinamik olarak filtreleyip sıkıştırarak LLM'ye aktarılan bağlamı en iyi duruma getirin.

2. ders / 412 adım

LLM'lerle Bağlamsal Sıkıştırma, CoddyKit'te ücretsiz bir LangChain / RAG / Vector DBs dersidir. Bu, 4 dersinin 2. dersidir. Aşağıdan dersin tamamını ücretsiz okuyabilir, sonra tarayıcıda yerleşik kod editörü ve 7/24 yapay zeka koçu ile uygulamalı olarak pratik yapabilirsin. Bu, LangChain / RAG / Vector DBs öğrenme yolunun bir parçasıdır ve ilerlemeniz web ve CoddyKit uygulaması arasında senkronize olur. LangChain / RAG / Vector DBs kursu toplamda 4 dersten oluşur.

Bu dersin bazı bölümleri henüz çevrilmemiş olup İngilizce olarak gösterilmektedir.

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!

İnteraktif derste cevapla

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.

Başlamak ücretsiz

Yapay zeka eğitmeniyle LangChain / RAG / Vector DBs öğren — ücretsiz

Tarayıcında gerçek kod yaz ve çalıştır, 7/24 yapay zeka eğitmeninden anında yardım al; web'de ya da uygulamada kaldığın yerden devam et.

Kurslar: 12
Dersler: 48

Ücretsiz öğrenmeye başla Tüm kursu görüntüle

Sıkça Sorulan Sorular

“LLM'lerle Bağlamsal Sıkıştırma” dersi ücretsiz mi?

Evet — “LLM'lerle Bağlamsal Sıkıştırma” dersin tüm metni burada web'de ücretsiz olarak okunabilir. Etkileşimli olarak pratik yapmak (yerleşik kod editörü ve 7/24 yapay zeka koçu) ve LangChain / RAG / Vector DBs kursunun geri kalanını açmak için CoddyKit PRO'ya yükselt. LangChain / RAG / Vector DBs kursu toplamda 4 dersten oluşur.

“LLM'lerle Bağlamsal Sıkıştırma” dersinde ne öğreneceğim?

İlgili içeriğe odaklanmak için alınan belgeleri dinamik olarak filtreleyip sıkıştırarak LLM'ye aktarılan bağlamı en iyi duruma getirin. LangChain / RAG / Vector DBs ile uygulamalı kodu tarayıcıda doğrudan çalıştırarak pratik yaparsın ve 7/24 yapay zeka koçu dersi çalışırken sorularını yanıtlar.

LangChain / RAG / Vector DBs öğrenmeye başlamak için deneyim gerekli mi?

Önceden deneyim gerekmez. CoddyKit'te LangChain / RAG / Vector DBs, başlangıçtan ileri seviyeye kadar yapılandırıldığı için buradan başlayabilir veya başından başlayıp kendi hızında ilerleme yapabilirsin. Bu, 4 dersinin 2. dersidir.

“LLM'lerle Bağlamsal Sıkıştırma” dersi ne kadar sürer?

Çoğu CoddyKit dersi yaklaşık 5–10 dakika sürer. Her biri kısa ve etkileşimli olduğu için sabit ilerleme yaparsın ve web ile uygulama arasında tam olarak bıraktığın yerden devam edebilirsin.

Bu LangChain / RAG / Vector DBs dersinde kod yazıp çalıştırabilir miyim?

Evet. Her LangChain / RAG / Vector DBs dersi yerleşik bir kod editörü içerir, bu sayede tarayıcıda gerçek kod yazıp çalıştırabilir ve anlık yapay zeka geri bildirimi alırsın — yerel kurulum gerekli değildir.

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