Agent-Skills.md

Agent Skills: RAG Frameworks

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

UncategorizedID: eyadsibai/ltk/rag-frameworks

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Skill Metadata

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

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