rag-frameworks

Original：🇺🇸 English

Translated

Use when "RAG", "retrieval augmented generation", "LangChain", "LlamaIndex", "sentence transformers", "embeddings", "document QA", "chatbot with documents", "semantic search"

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Sourceeyadsibai/ltk

Added on2026-02-14

NPX Install

npx skill4agent add eyadsibai/ltk rag-frameworks

SKILL.md Content

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RAG Frameworks

Frameworks for building retrieval-augmented generation applications.

Comparison

Framework	Best For	Learning Curve	Flexibility
LangChain	Agents, chains, tools	Steeper	Highest
LlamaIndex	Data indexing, simple RAG	Gentle	Medium
Sentence Transformers	Custom embeddings	Low	High

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.

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.

Sentence Transformers

Generate high-quality embeddings for semantic similarity.

Popular models:

Model	Dimensions	Quality	Speed
all-MiniLM-L6-v2	384	Good	Fast
all-mpnet-base-v2	768	Better	Medium
e5-large-v2	1024	Best	Slow

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

RAG Architecture Patterns

Pattern	Description	When to Use
Naive RAG	Retrieve top-k, stuff in prompt	Simple QA
Parent-Child	Retrieve chunks, return parent docs	Context preservation
Hybrid Search	Vector + keyword search	Better recall
Re-ranking	Retrieve many, re-rank with cross-encoder	Higher precision
Query Expansion	Generate variations of query	Ambiguous queries

Decision Guide

Scenario	Recommendation
Simple document QA	LlamaIndex
Complex agents/tools	LangChain
Custom embedding pipeline	Sentence Transformers
Production RAG	LangChain or custom
Quick prototype	LlamaIndex
Maximum control	Build custom with Sentence Transformers

Resources

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