菜单
Use when "RAG", "retrieval augmented generation", "LangChain", "LlamaIndex", "sentence transformers", "embeddings", "document QA", "chatbot with documents", "semantic search"
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
| 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 |
Frameworks for building retrieval-augmented generation applications.
| 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 |
Orchestration framework for building complex LLM applications.
Core concepts:
| operatorStrengths: Rich ecosystem, many integrations, agent capabilities Limitations: Abstractions can be confusing, rapid API changes
Key concept: LCEL (LangChain Expression Language) for composable pipelines.
Data framework focused on connecting LLMs to external data.
Core concepts:
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.
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.
| 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 |
| 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 |
Use when evaluating agent performance, building test frameworks, measuring quality, or asking about "agent evaluation", "LLM-as-judge", "agent testing", "quality metrics", "evaluation rubrics", "agent benchmarks"
Use when "codex", "use gpt", "gpt-5", "openai codex", "let openai", "full-auto", "autonomous code generation"
Use when compressing agent context, implementing conversation summarization, reducing token usage in long sessions, or asking about "context compression", "conversation history", "token optimization", "context limits", "summarization strategies"
Use when diagnosing agent failures, debugging lost-in-middle issues, understanding context poisoning, or asking about "context degradation", "lost in middle", "context poisoning", "attention patterns", "context clash", "agent performance drops"
Use when optimizing agent context, reducing token costs, implementing KV-cache optimization, or asking about "context optimization", "token reduction", "context limits", "observation masking", "context budgeting", "context partitioning"
Use when "CrewAI", "multi-agent systems", "agent orchestration", "AI crews", or asking about "autonomous agents", "agent collaboration", "role-based agents", "agent workflows", "AI team coordination"