Reformulação de Consultas e Reclassificação
Explore técnicas para otimizar as consultas dos usuários e reclassificar os documentos recuperados, aumentando sua relevância para o LLM.
Reformulação de Consultas e Reclassificação é uma aula grátis de LLM Apps in Production (RAG + Vector DB + Caching) 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 LLM Apps in Production (RAG + Vector DB + Caching), e seu progresso é sincronizado entre a web e o app CoddyKit. O curso de LLM Apps in Production (RAG + Vector DB + Caching) inclui 4 aulas no total.
Partes desta aula ainda não foram traduzidas e aparecem em inglês.
Optimizing Queries for RAG
Welcome to advanced RAG techniques! In this lesson, we'll explore two powerful methods to make your Retrieval Augmented Generation (RAG) system even smarter: Query Rewriting and Reranking.
These techniques help ensure your LLM gets the most relevant information possible, leading to better and more accurate responses.
Why Raw Queries Fall Short
When a user asks a question, their initial query might not be perfect for searching your knowledge base. It could be:
- Too short or vague: Lacking specific keywords.
- Ambiguous: Having multiple possible meanings.
- Missing synonyms: Not using the exact terms found in your documents.
This can lead to your retriever fetching less relevant documents.
Understanding Query Rewriting
Query rewriting is the process of modifying the user's original query before it's sent to your document retriever.
The goal is to transform the query into a more effective search term that is more likely to match relevant documents in your vector database.
Techniques for Rewriting Queries
Query rewriting can involve several strategies:
- Query Expansion: Adding synonyms or related terms to broaden the search.
- Query Rephrasing: Changing the query's structure or wording to improve clarity.
- Query Decomposition: Breaking a complex, multi-part query into simpler, individual sub-queries.
Often, another LLM is used to perform these rewriting tasks.
Code: Simple Query Rewriting
Here's a conceptual Python example of how a simple query expansion might work. In a real system, an LLM would do the heavy lifting.
class QueryRewriter:
def rewrite(self, query):
# Simulate an LLM or a rule-based system
if "LLM performance" in query:
return query + " large language model efficiency optimization"
if "vector db" in query:
return query + " vector database semantic search"
return query
rewriter = QueryRewriter()
user_query_1 = "improve LLM performance"
rewritten_1 = rewriter.rewrite(user_query_1)
print(f"Original 1: {user_query_1}")
print(f"Rewritten 1: {rewritten_1}\n")
user_query_2 = "how to use vector db"
rewritten_2 = rewriter.rewrite(user_query_2)
print(f"Original 2: {user_query_2}")
print(f"Rewritten 2: {rewritten_2}")Why We Need Reranking
Even after a great initial search (perhaps with a rewritten query!), the top 'N' documents returned by your retriever might not be perfectly ordered by relevance.
The retriever's job is often to find potential matches. Reranking steps in to refine this order, ensuring the absolute best documents are at the very top.
The Reranking Process
Reranking works like this:
- Your initial retriever fetches a larger set of candidate documents (e.g., top 50).
- A specialized reranker model then takes each of these candidate documents, along with the original user query, and provides a more precise relevance score.
- The documents are then sorted again based on these new, more accurate scores.
This ensures the most relevant documents are passed to the LLM.
Specialized Reranking Models
Unlike a retriever that often uses embeddings for approximate similarity, rerankers typically use more sophisticated models, often called cross-encoders.
- Cross-encoders take both the query AND a document as input.
- They consider the interaction between the query and document terms directly.
- This allows for a much more nuanced understanding of relevance, though it's computationally more intensive, hence why it's only applied to a smaller subset of documents.
Code: Simulating Reranking
This example shows how a reranker might re-score and reorder an initially retrieved list of documents based on their relevance to the query.
class Reranker:
def rerank(self, query, documents):
# Simulate a cross-encoder model scoring documents
scores = {}
for doc in documents:
if "vector database" in doc.lower() and "fast" in query.lower():
scores[doc] = 0.95 # Highly relevant
elif "llm" in doc.lower() and "improve" in query.lower():
scores[doc] = 0.85
elif "database" in doc.lower():
scores[doc] = 0.7
else:
scores[doc] = 0.3 # Less relevant
# Sort documents by score in descending order
sorted_docs = sorted(documents, key=lambda d: scores.get(d, 0), reverse=True)
return sorted_docs
reranker = Reranker()
user_query = "How to build a fast vector database?"
initial_docs = [
"Introduction to LLMs",
"Building a scalable vector database",
"Optimizing LLM inference",
"Fast data ingestion for databases"
]
reranked_docs = reranker.rerank(user_query, initial_docs)
print(f"Original documents: {initial_docs}\n")
print("Reranked documents (most relevant first):")
for doc in reranked_docs:
print(f"- {doc}")The Power of Combination
The true power comes from combining both techniques:
- First, Query Rewriting creates a better search query.
- Then, your retriever uses this improved query to fetch a broader, more relevant set of documents.
- Finally, Reranking fine-tunes the order of these documents, ensuring the LLM receives the absolute best context to generate a response.
This multi-stage approach significantly boosts the quality and accuracy of your RAG system.
Check Your Understanding
Time to test what you've learned about optimizing RAG through query rewriting and reranking.
Recap & Next Steps
Great job! In this lesson, you learned about Query Rewriting and Reranking.
- Query Rewriting modifies the user's input to create a more effective search query.
- Reranking reorders initially retrieved documents using a more precise model to surface the most relevant ones.
Together, these techniques significantly enhance the quality and accuracy of your RAG applications. Next, we'll explore even more advanced RAG architectures!
Perguntas Frequentes
A aula “Reformulação de Consultas e Reclassificação” é grátis?
Sim — o texto completo de “Reformulação de Consultas e Reclassificação” 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 LLM Apps in Production (RAG + Vector DB + Caching), atualize para CoddyKit PRO. O curso de LLM Apps in Production (RAG + Vector DB + Caching) inclui 4 aulas no total.
O que vou aprender em “Reformulação de Consultas e Reclassificação”?
Explore técnicas para otimizar as consultas dos usuários e reclassificar os documentos recuperados, aumentando sua relevância para o LLM. Você pratica LLM Apps in Production (RAG + Vector DB + Caching) 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 LLM Apps in Production (RAG + Vector DB + Caching)?
Nenhuma experiência prévia é necessária. LLM Apps in Production (RAG + Vector DB + Caching) 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 “Reformulação de Consultas e Reclassificação”?
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 LLM Apps in Production (RAG + Vector DB + Caching)?
Sim. Cada aula de LLM Apps in Production (RAG + Vector DB + Caching) 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
- Reformulação de Consultas e Reclassificação
- Padrões de RAG em Múltiplas Etapas e RAG Agêntico
- Lidando com Estruturas Complexas de Documentos
- Autoconsulta e citações