Busca híbrida e reclassificação
Combine busca por palavras-chave e busca semântica (busca híbrida) e use modelos de reclassificação para priorizar os documentos mais relevantes.
Busca híbrida e reclassificação é uma aula grátis de LangChain / RAG / Vector DBs no CoddyKit. Esta é a aula 3 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.
Beyond Basic Retrieval
When building advanced Retrieval Augmented Generation (RAG) systems, simply finding documents isn't enough. We need to find the most relevant documents efficiently.
Traditional keyword or semantic searches, while powerful, each have limitations. To overcome these, we can combine their strengths.
Keyword Search: Specificity
Keyword search (also known as sparse retrieval, e.g., using BM25 or TF-IDF) is excellent for finding exact matches and specific terms.
- Strengths: Great for precise queries, proper nouns, and when you know the exact wording.
- Weaknesses: Struggles with synonyms, different phrasing, or understanding conceptual meaning.
For example, searching 'Python list append' works well, but 'add element to Python array' might miss results.
Semantic Search: Understanding Meaning
Semantic search (dense retrieval, using embeddings) understands the meaning and context of your query and documents.
- Strengths: Handles synonyms, rephrased questions, and conceptual searches effectively.
- Weaknesses: Can struggle with very specific, rare terms or highly technical jargon if not well-represented in its embedding space.
It can understand 'add element to Python array' is similar to 'Python list append'.
Introducing Hybrid Search
Hybrid search combines the best of both worlds: the precision of keyword search and the contextual understanding of semantic search.
By running both types of retrieval and intelligently merging their results, hybrid search can significantly improve the relevance and completeness of retrieved documents for your RAG system.
Merging Results: Reciprocal Rank Fusion
A common method to combine results from multiple retrievers in hybrid search is Reciprocal Rank Fusion (RRF).
RRF assigns a score to each document based on its rank in each individual retriever's result list. Documents that appear high in multiple lists get a boosted score, leading to a more robust final ranking.
Try running this simplified example of RRF:
def reciprocal_rank_fusion(rank_lists, k=60):
fused_scores = {}
for rank_list in rank_lists:
for rank, doc_id in enumerate(rank_list):
if doc_id not in fused_scores:
fused_scores[doc_id] = 0.0
fused_scores[doc_id] += 1.0 / (k + rank + 1)
sorted_docs = sorted(fused_scores.items(), key=lambda item: item[1], reverse=True)
return [doc_id for doc_id, score in sorted_docs]
if __name__ == "__main__":
# Simulate results from two retrievers
keyword_results = ["docA", "docC", "docB", "docE"]
semantic_results = ["docB", "docA", "docD", "docC"]
fused_order = reciprocal_rank_fusion([keyword_results, semantic_results])
print("Fused Order:", fused_order)Hybrid Search with LangChain
LangChain provides an EnsembleRetriever to easily implement hybrid search. It takes multiple retrievers (e.g., a keyword retriever and a vector store retriever) and combines their results, often using RRF by default.
This allows you to leverage both precise keyword matches and semantic understanding in one powerful retrieval step.
# from langchain.retrievers import EnsembleRetriever
# from langchain_community.retrievers import BM25Retriever
# from langchain_community.vectorstores import FAISS
# from langchain_openai import OpenAIEmbeddings
# # Assume you have a BM25 retriever and a vector store retriever
# bm25_retriever = BM25Retriever.from_documents(docs)
# vectorstore = FAISS.from_documents(docs, OpenAIEmbeddings())
# vectorstore_retriever = vectorstore.as_retriever()
# ensemble_retriever = EnsembleRetriever(retrievers=[
# bm25_retriever,
# vectorstore_retriever
# ], weights=[0.5, 0.5])
# # query = "What are the capital cities of Europe?"
# # docs = ensemble_retriever.invoke(query)The Need for Re-ranking
Even after hybrid search, the initial set of retrieved documents might contain some noise or documents that are not perfectly ordered by relevance.
Re-ranking is a crucial next step. It takes the top-k documents from the initial retrieval and re-evaluates their relevance to the query using a more sophisticated model.
How Re-ranking Works
A re-ranking model, often a cross-encoder, takes both the user query and each retrieved document as input.
Unlike embedding models that create separate embeddings, a cross-encoder jointly processes the query and document to generate a single relevance score. This allows for a more nuanced understanding of their interaction.
Integrating Re-rankers
Various re-ranking models and services are available, such as Cohere's Re-rank API or open-source cross-encoders from libraries like sentence-transformers.
Integrating a re-ranker typically involves passing the initial retrieval results and the query to the re-ranker, which then returns the documents in a new, optimized order.
# from langchain.retrievers.document_compressors import CohereRerank
# from langchain.retrievers import ContextualCompressionRetriever
# # Assume you have an existing base_retriever (e.g., your EnsembleRetriever)
# # base_retriever = ensemble_retriever
# # Initialize the Cohere Rerank compressor
# # cohere_re_ranker = CohereRerank(top_n=5, cohere_api_key="YOUR_COHERE_API_KEY")
# # Create a compression retriever that uses the re-ranker
# # compression_retriever = ContextualCompressionRetriever(
# # base_compressor=cohere_re_ranker,
# # base_retriever=base_retriever
# # )
# # query = "What is the capital of France?"
# # compressed_docs = compression_retriever.invoke(query)The Full Advanced Retrieval Pipeline
By combining hybrid search and re-ranking, you create a robust retrieval pipeline:
- User Query
- Hybrid Search: Combines keyword and semantic retrieval to get an initial set of relevant documents.
- Re-ranking: A specialized model re-orders the top documents from hybrid search for maximum relevance.
- Context for LLM: The highly relevant, re-ranked documents are passed to the LLM for generation.
This approach leads to more accurate and contextually rich answers from your RAG system.
Quick Check: Retrieval Steps
Which of the following statements accurately describe the roles of Hybrid Search and Re-ranking in a RAG system?
Recap: Advanced Retrieval
We've explored advanced retrieval techniques to boost RAG performance:
- Hybrid Search: Combines keyword and semantic approaches for comprehensive initial retrieval.
- Reciprocal Rank Fusion (RRF): A method to merge and score results from different retrievers.
- Re-ranking: Uses specialized models (like cross-encoders) to refine the relevance order of retrieved documents.
These techniques ensure your RAG system provides the most accurate and contextually relevant information to the LLM.
Perguntas Frequentes
A aula “Busca híbrida e reclassificação” é grátis?
Sim — o texto completo de “Busca híbrida 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 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 “Busca híbrida e reclassificação”?
Combine busca por palavras-chave e busca semântica (busca híbrida) e use modelos de reclassificação para priorizar os documentos mais relevantes. 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 3 de 4.
Quanto tempo leva a aula “Busca híbrida 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 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
- Estratégias de recuperação com várias consultas
- Compressão contextual com LLMs
- Busca híbrida e reclassificação
- Recuperação de Documentos Pai e Janelas de Frases