Extending Retrieval Chains with Custom Logic
Build custom retrieval chains that integrate complex business logic, pre-processing steps, or specialized filtering.
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.
All lessons in this course
- Developing Custom Document Loaders
- Integrating Custom Embedding Models
- Extending Retrieval Chains with Custom Logic
- Building Custom Output Parsers