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LangChain / RAG / Vector DBs · Aula

Extensão de cadeias de recuperação com lógica personalizada

Crie cadeias de recuperação personalizadas que integrem lógica empresarial complexa, etapas de pré-processamento ou filtragem especializada.

Extensão de cadeias de recuperação com lógica personalizada 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 RAG

Welcome! In this lesson, we'll dive into extending LangChain's retrieval chains. While standard Retrieval Augmented Generation (RAG) is powerful, real-world applications often need more nuanced control.

We'll learn how to inject custom logic into the retrieval process to make your RAG systems smarter and more tailored to specific needs.

Practical Customization Needs

Why would you need custom logic in a retrieval chain? Consider these common scenarios:

  • Filtering by User Permissions: Only retrieve documents accessible to the current user.
  • Prioritizing Fresh Data: Boost documents created or updated recently.
  • Removing Irrelevant Sections: Clean up retrieved text before passing it to the LLM.
  • Dynamic Query Rephrasing: Automatically improve user queries for better search results.

These needs go beyond what a basic retriever offers.

LangChain's Custom Primitives

LangChain provides flexible primitives to insert custom Python logic directly into your chains:

  • RunnableLambda: This allows you to wrap any Python function, making it a runnable component. It's perfect for applying arbitrary transformations.
  • RunnablePassthrough: This simply passes its input through to the next step. It's useful for injecting new keys into the input dictionary or for identity operations.

These are your building blocks for custom steps.

Enhancing User Queries (Pre-processing)

One powerful customization is query pre-processing. This means modifying the user's input query before it's sent to the retriever or vector store.

  • You could add specific keywords based on detected intent.
  • Expand common abbreviations or synonyms.
  • Rephrase the query to improve embedding search results.

This subtle step can significantly boost the relevance of retrieved documents.

Custom Query Transformer Code

Let's see how to implement a simple query pre-processor using RunnableLambda. This example adds a 'detailed search for' prefix to the original query.

from langchain_core.runnables import RunnableLambda
from langchain_core.promnpts import ChatPromptTemplate
from langchain_openai import ChatOpenAI

# Mock LLM for demonstration purposes
class MockLLM(ChatOpenAI):
    def invoke(self, input):
        return f"LLM processed: {input['enhanced_query']}"

def enhance_query(input_dict):
    original_query = input_dict["question"]
    return {"enhanced_query": f"detailed search for {original_query}"}

llm = MockLLM() # In a real app, use ChatOpenAI(model="gpt-4")

prompt = ChatPromptTemplate.from_template(
    "Answer based on the following search query: {enhanced_query}"
)

custom_chain = (
    {"enhanced_query": RunnableLambda(enhance_query)} # Our custom step
    | prompt
    | llm
)

result = custom_chain.invoke({"question": "latest AI trends"})
print(result)

Refining Retrieved Documents (Post-processing)

Another crucial area for custom logic is document post-processing. This occurs after documents have been retrieved but before they are passed to the LLM.

  • Filtering: Remove documents that don't meet certain criteria (e.g., outdated, wrong source).
  • Re-ranking: Reorder documents based on custom relevance scores.
  • Summarizing: Condense lengthy documents to fit context windows.

This ensures the LLM receives the most relevant and concise context, improving answer quality and reducing token usage.

Filtering Documents by Metadata

Here's an example of filtering retrieved documents based on their metadata. We'll simulate a retriever that returns documents and then filter them to only include those from a specific 'blog' source.

from langchain_core.documents import Document
from langchain_core.runnables import RunnableLambda
from langchain_core.promnpts import ChatPromptTemplate
from langchain_openai import ChatOpenAI

# Mock LLM for demonstration
class MockLLM(ChatOpenAI):
    def invoke(self, input):
        context = input.get("context", "No context provided")
        return f"LLM processed docs: {context}"

# Mock retriever returning Documents with metadata
def mock_retrieve(query):
    return [
        Document(page_content="Doc A about AI", metadata={"source": "blog"}),
        Document(page_content="Doc B about ML", metadata={"source": "research"}),
        Document(page_content="Doc C about AI ethics", metadata={"source": "blog"}),
    ]

def filter_by_source(docs, desired_source="blog"):
    # Only keep documents from the 'blog' source
    return [doc for doc in docs if doc.metadata.get("source") == desired_source]

llm = MockLLM()
prompt = ChatPromptTemplate.from_template(
    "Answer based on the following context: {context}"
)

# Build a simple chain with retrieval and custom filter
custom_retrieval_chain = (
    RunnableLambda(mock_retrieve) # Simulate retrieval
    | RunnableLambda(filter_by_source) # Apply custom filter
    | (lambda docs: {"context": "\n\n".join([d.page_content for d in docs])}) # Format for LLM
    | prompt
    | llm
)

result = custom_retrieval_chain.invoke("AI topics")
print(result)

End-to-End Custom Chain

You can combine both query pre-processing and document post-processing within a single LangChain chain. The flow would look something like this:

  1. User Query
  2. Custom Query Pre-processor
  3. Retriever (e.g., Vector Store)
  4. Custom Document Post-processor
  5. LLM for Answer Generation

This modular approach gives you fine-grained control over every step of your RAG pipeline, making it highly adaptable to complex requirements.

Advanced: Conditional Routing

For even more dynamic behavior, LangChain offers RunnableBranch. This powerful construct allows your chain to take different paths based on certain conditions.

For example, you could:

  • Use one retriever if the query is about 'code' and another for 'general knowledge'.
  • Apply different document filters based on the user's role.

RunnableBranch enables sophisticated, context-aware RAG workflows.

Check Your Understanding

Which LangChain primitive is best suited for inserting a simple Python function to modify data (e.g., filter a list of documents) within a chain?

Recap: Extending Retrieval

Great job! In this lesson, you learned how to extend LangChain retrieval chains with custom logic:

  • We explored the need for customization in real-world RAG.
  • You discovered RunnableLambda and RunnablePassthrough as key tools.
  • We saw how to pre-process queries for better retrieval.
  • You learned to post-process retrieved documents for refined context.
  • We touched on advanced concepts like RunnableBranch for conditional logic.

Experiment with these techniques to build highly customized and efficient RAG applications!

Perguntas Frequentes

A aula “Extensão de cadeias de recuperação com lógica personalizada” é grátis?

Sim — o texto completo de “Extensão de cadeias de recuperação com lógica personalizada” 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 “Extensão de cadeias de recuperação com lógica personalizada”?

Crie cadeias de recuperação personalizadas que integrem lógica empresarial complexa, etapas de pré-processamento ou filtragem especializada. 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 “Extensão de cadeias de recuperação com lógica personalizada”?

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. Desenvolvimento de carregadores de documentos personalizados
  2. Integração de modelos personalizados de embeddings
  3. Extensão de cadeias de recuperação com lógica personalizada
  4. Criando Analisadores de Saída Personalizados
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