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Estratégias de recuperação com várias consultas

Melhore a recuperação gerando várias perspectivas da consulta do usuário e combinando resultados de buscas diferentes.

Estratégias de recuperação com várias consultas é uma aula grátis de LangChain / RAG / Vector DBs no CoddyKit. Esta é a aula 1 de 4. Você pode ler a aula completa abaixo gratuitamente — depois pratica ao vivo no navegador com um editor de código integrado e um tutor de IA 24/7. Faz parte do caminho de aprendizado de LangChain / RAG / Vector DBs, e seu progresso é sincronizado entre a web e o app CoddyKit. O curso de LangChain / RAG / Vector DBs inclui 4 aulas no total.

Partes desta aula ainda não foram traduzidas e aparecem em inglês.

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.

Perguntas Frequentes

A aula “Estratégias de recuperação com várias consultas” é grátis?

Sim — o texto completo de “Estratégias de recuperação com várias consultas” é grátis para ler aqui na web. Para praticá-la interativamente (um editor de código integrado e um tutor de IA 24/7) e desbloquear o restante do curso de LangChain / RAG / Vector DBs, atualize para CoddyKit PRO. O curso de LangChain / RAG / Vector DBs inclui 4 aulas no total.

O que vou aprender em “Estratégias de recuperação com várias consultas”?

Melhore a recuperação gerando várias perspectivas da consulta do usuário e combinando resultados de buscas diferentes. Você pratica LangChain / RAG / Vector DBs com código prático que executa diretamente no navegador, e um tutor de IA 24/7 responde suas dúvidas enquanto trabalha na aula.

Preciso ter experiência prévia para começar LangChain / RAG / Vector DBs?

Nenhuma experiência prévia é necessária. LangChain / RAG / Vector DBs no CoddyKit é estruturado para alunos iniciantes até avançados, então você pode começar aqui ou desde o início e aprender no seu ritmo. Esta é a aula 1 de 4.

Quanto tempo leva a aula “Estratégias de recuperação com várias consultas”?

A maioria das aulas CoddyKit leva cerca de 5–10 minutos. Cada uma é compacta e interativa, então você faz progresso constante e retoma exatamente de onde parou entre web e app.

Posso escrever e executar código nesta aula de LangChain / RAG / Vector DBs?

Sim. Cada aula de LangChain / RAG / Vector DBs inclui um editor de código integrado, então você escreve e executa código real direto no navegador e recebe feedback de IA instantaneamente — nenhuma configuração local necessária.

Todas as aulas deste curso

  1. Estratégias de recuperação com várias consultas
  2. Compressão contextual com LLMs
  3. Busca híbrida e reclassificação
  4. Recuperação de Documentos Pai e Janelas de Frases
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