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499What is LangChain and Why Use It
500Installation and Basic Setup
501LLMs and Chat Models in LangChain
502Prompt Templates Basics
503Chat Prompt Templates
504Output Parsers
505Chains: The Core Abstraction
506Sequential Chains
507LCEL: LangChain Expression Language
508RunnablePassthrough and RunnableParallel
509Memory: ConversationBufferMemory
510Memory: Summary and Window Memory
511Callbacks and Debugging
512LangChain vs Raw APIs Trade-offs
513What is LlamaIndex and Core Use Cases
514Documents and Nodes: LlamaIndex Data Model
515Data Connectors and Loading Documents
516Building Your First Index
517Query Engines: The Basic Interface
518Index Types: Vector, List, Tree, and Keyword
519Response Synthesis Modes
520Customizing Embedding Models and LLMs
521Node Postprocessors and Reranking
522Chat Engines for Conversational Retrieval
523Composable Indices and Sub-Question Query
524Storage Context and Persistence
525Haystack: Document-Centric Pipelines
526Semantic Kernel: Microsoft's LLM Framework
527Guidance: Constrained Generation Framework
528LiteLLM: Unified API Across Providers
529DSPy: Programming LLM Pipelines
530Marvin: AI Engineering in Python
531SimpleAI and Instructor: Lightweight Alternatives
532Framework Interoperability Patterns
533Evaluating Framework Trade-offs
534When to Choose Alternative Frameworks
535Framework vs Raw API Trade-offs
536Abstraction Tax and Lock-in Risks
537Performance Comparison: Framework vs Raw
538Debugging Framework-Wrapped Calls
539When to Choose LangChain
540When to Choose LlamaIndex
541Building Custom Thin Wrappers
542Migration Strategies Between Approaches

499What is LangChain and Why Use It
500Installation and Basic Setup
501LLMs and Chat Models in LangChain
502Prompt Templates Basics
503Chat Prompt Templates
504Output Parsers
505Chains: The Core Abstraction
506Sequential Chains
507LCEL: LangChain Expression Language
508RunnablePassthrough and RunnableParallel
509Memory: ConversationBufferMemory
510Memory: Summary and Window Memory
511Callbacks and Debugging
512LangChain vs Raw APIs Trade-offs
513What is LlamaIndex and Core Use Cases
514Documents and Nodes: LlamaIndex Data Model
515Data Connectors and Loading Documents
516Building Your First Index
517Query Engines: The Basic Interface
518Index Types: Vector, List, Tree, and Keyword
519Response Synthesis Modes
520Customizing Embedding Models and LLMs
521Node Postprocessors and Reranking
522Chat Engines for Conversational Retrieval
523Composable Indices and Sub-Question Query
524Storage Context and Persistence
525Haystack: Document-Centric Pipelines
526Semantic Kernel: Microsoft's LLM Framework
527Guidance: Constrained Generation Framework
528LiteLLM: Unified API Across Providers
529DSPy: Programming LLM Pipelines
530Marvin: AI Engineering in Python
531SimpleAI and Instructor: Lightweight Alternatives
532Framework Interoperability Patterns
533Evaluating Framework Trade-offs
534When to Choose Alternative Frameworks
535Framework vs Raw API Trade-offs
536Abstraction Tax and Lock-in Risks
537Performance Comparison: Framework vs Raw
538Debugging Framework-Wrapped Calls
539When to Choose LangChain
540When to Choose LlamaIndex
541Building Custom Thin Wrappers
542Migration Strategies Between Approaches

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Lesson 521 of 1,886·13. LLM Orchestration FrameworksPro lesson

Node Postprocessors and Reranking

Applying filters, rerankers, and transformations to retrieved nodes before generation.

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