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name: rag-frameworks description: Use when "RAG", "retrieval augmented generation", "LangChain", "LlamaIndex", "sentence transformers", "embeddings", "document QA", "chatbot with documents", "semantic search" version: 1.0.0

RAG Frameworks

Frameworks for building retrieval-augmented generation applications.

Comparison

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LangChain

Orchestration framework for building complex LLM applications.

Core concepts:

• Chains: Sequential operations (retrieve → prompt → generate)
• Agents: LLM decides which tools to use
• LCEL: Declarative pipeline syntax with | operator
• Retrievers: Abstract interface to vector stores

Strengths: Rich ecosystem, many integrations, agent capabilities Limitations: Abstractions can be confusing, rapid API changes

Key concept: LCEL (LangChain Expression Language) for composable pipelines.

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LlamaIndex

Data framework focused on connecting LLMs to external data.

Core concepts:

• Documents → Nodes: Automatic chunking and indexing
• Index types: Vector, keyword, tree, knowledge graph
• Query engines: Retrieve and synthesize answers
• Chat engines: Stateful conversation over data

Strengths: Simple API, great for document QA, data connectors Limitations: Less flexible for complex agent workflows

Key concept: "Load data, index it, query it" - simpler mental model than LangChain.

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Sentence Transformers

Generate high-quality embeddings for semantic similarity.

Popular models:

Key concept: Bi-encoder architecture - encode query and documents separately, compare with cosine similarity.

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RAG Architecture Patterns

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Decision Guide

Resources

• LangChain: https://python.langchain.com
• LlamaIndex: https://docs.llamaindex.ai
• Sentence Transformers: https://sbert.net