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LangChain / RAG / Vector DBs · 강의

다중 쿼리 검색 전략

사용자 쿼리를 여러 관점으로 생성하고 다양한 검색 결과를 결합해 검색 재현율을 높입니다.

다중 쿼리 검색 전략은(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.

Boosting RAG with Multi-Query

Sometimes, a single search query isn't enough to find all the relevant information. Multi-query retrieval is a technique that helps your RAG system cast a wider net.

It generates several different versions of your original question. This helps ensure you don't miss important context, leading to more comprehensive answers.

Why One Query Isn't Enough

Imagine asking "What are the benefits of RAG?". A single search might only pick up documents directly matching those exact words. This can limit the context available to your LLM.

  • It might miss documents using terms like "advantages of RAG" or "why use RAG".
  • It could also miss related concepts that provide crucial background.

This limitation can lead to incomplete or less accurate answers from the LLM.

Expanding Your Search Horizon

Before advanced tools, people would manually brainstorm related queries to ensure a broader search. For example:

  • Original: "How does RAG improve LLM accuracy?"
  • Expanded: "What are RAG benefits for LLMs?", "RAG's impact on factual correctness", "How RAG reduces hallucinations".

While effective, this manual process is tedious and hard to scale for complex systems. We need an automated solution!

LLMs as Query Generators

Large Language Models (LLMs) are excellent at understanding context and generating variations of text. We can leverage an LLM to automatically create several alternative questions from an initial user query.

  • This uses the LLM's natural language understanding abilities.
  • It automates the query expansion process efficiently.

These diverse queries then provide multiple 'angles' for searching your knowledge base.

LangChain's MultiQueryRetriever

LangChain provides a powerful component called MultiQueryRetriever. This tool automates the entire multi-query process for you, making it easy to integrate into your RAG pipeline.

  • It uses an LLM to generate diverse queries from your initial input.
  • It then runs these multiple queries against your vector store.
  • Finally, it combines the results into a single, comprehensive set of documents for the LLM.

MultiQueryRetriever in Action

Let's see how to set up MultiQueryRetriever in Python. You'll need an LLM and an existing retriever (e.g., from a vector store).

This example mocks a vector store for demonstration. In a real application, your vector store would be pre-populated with your documents.

from langchain_community.chat_models import ChatOpenAI
from langchain.retrievers.multi_query import MultiQueryRetriever
from langchain_community.vectorstores import FAISS
from langchain_community.embeddings import OpenAIEmbeddings
from langchain_core.documents import Document

# This is a mock setup for demonstration
# In a real app, your vector store would be populated
embeddings = OpenAIEmbeddings()
vectorstore = FAISS.from_documents(
    [
        Document(page_content="RAG improves LLM factual accuracy."),
        Document(page_content="Retrieval Augmented Generation reduces hallucinations."),
        Document(page_content="The advantages of RAG include up-to-date information."),
        Document(page_content="RAG systems combine retrieval with generation."),
    ], embeddings
)
retriever = vectorstore.as_retriever()

# Initialize a chat model (replace with your actual LLM)
# For local testing, consider using a local LLM or mock
llm = ChatOpenAI(temperature=0) # Placeholder

# Create the MultiQueryRetriever
multi_query_retriever = MultiQueryRetriever.from_llm(
    retriever=retriever, llm=llm
)

print("MultiQueryRetriever initialized successfully!")
# Example usage would follow: multi_query_retriever.get_relevant_documents(query)

The Multi-Query Workflow

Here's a simplified breakdown of what MultiQueryRetriever does behind the scenes:

  1. Initial Query: You provide one query, e.g., "What are RAG's advantages?"
  2. Query Generation: The LLM takes your query and generates 2-4 alternative queries (e.g., "Benefits of RAG", "Why use RAG?", "How RAG improves LLMs").
  3. Parallel Retrieval: Each generated query is sent to your underlying retriever (e.g., vector store) simultaneously.
  4. Result Combination: All retrieved documents from these multiple searches are collected and often de-duplicated.
  5. Final Context: This combined set of documents is then passed to your main LLM for generating the final answer.

Merging Retrieved Documents

After multiple queries fetch documents, how do we best combine them to form the final context?

  • Unique Documents: The simplest approach is to gather all documents and remove duplicates. This ensures variety without redundancy.
  • Re-ranking: For more advanced control, you can apply a re-ranking model to the combined set. This model scores documents based on their relevance to the original query, ensuring the most important ones are prioritized (a topic for a future lesson!).

Why Use Multi-Query Retrieval?

Implementing multi-query strategies offers significant benefits for your RAG system:

  • Improved Recall: You're more likely to find all relevant pieces of information, even if they use different phrasing.
  • Richer Context: The LLM receives a broader and more diverse set of documents, leading to more comprehensive and accurate answers.
  • Reduced Hallucinations: With better context, the LLM is less likely to "make things up" due to lack of information.
  • Handles Ambiguity: Helps when the user's initial query is slightly ambiguous or could have multiple interpretations.

Multi-Query Check

Multi-query retrieval aims to improve retrieval by generating multiple perspectives of a user's query. Which of the following best describes the core problem it solves?

Multi-Query Recap

In this lesson, we explored Multi-Query Retrieval. We learned that a single query can often miss valuable context, and how Large Language Models (LLMs) can generate multiple, diverse queries to overcome this.

LangChain's MultiQueryRetriever automates this entire process, significantly improving the recall and richness of the context provided to your RAG system. This ultimately leads to more comprehensive and accurate answers.

자주 묻는 질문

“다중 쿼리 검색 전략” 강의는 무료인가요?

네 — “다중 쿼리 검색 전략” 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.

“다중 쿼리 검색 전략”에서 뭘 배우나요?

사용자 쿼리를 여러 관점으로 생성하고 다양한 검색 결과를 결합해 검색 재현율을 높입니다. 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.

“다중 쿼리 검색 전략” 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.

이 강의의 모든 강의

  1. 다중 쿼리 검색 전략
  2. LLM을 활용한 컨텍스트 압축
  3. 하이브리드 검색과 재순위화
  4. 상위 문서와 문장 창 검색
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