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Llama Index Rag

作者 Tang Weigang · GitHub ↗ · v0.1.0 · MIT-0
cross-platform ⚠ suspicious
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在 OpenClaw 中安装
/install llama-index-rag
功能描述
LlamaIndex：把任意文档变 LLM 可查询知识的 Python 框架。4 大支柱（Index/Retriever/QueryEngine/Synthesizer）+ 52 条 anti-pattern 约束（5 fatal）。 LlamaIndex: a Python framework that tur...
使用说明 (SKILL.md)

这个 skill 适合什么用户？能做哪些任务？

概览

LlamaIndex 是把任意文档变成 LLM 可查询知识的 Python 框架（github.com/run-llama/llama_index）。四大支柱（Index / Retriever / QueryEngine / ResponseSynthesizer）配置化检索-合成循环；Ingestion pipeline 处理 Document → Node → Embedding → Index 转换，带 content-hash 缓存；workflow / agent 子模块（FunctionAgent / ReActAgent / CodeActAgent / multi-agent...

Doramagic 晶体页: https://doramagic.ai/zh/crystal/llama-index-rag

知识规模

  • 52 条约束 (5 fatal + 47 non-fatal)
  • 上游源码: run-llama/llama_index @ commit 0a6c90bf
  • 蓝图 ID: finance-bp-135

用法

Host AI（Claude Code / Cursor / OpenClaw）读 references/seed.yaml，按其中的：

  • intent_router 匹配用户意图
  • architecture 理解项目架构
  • constraints 应用 anti-pattern 约束
  • business_decisions 参考核心设计决策

FAQ 摘要

这个 skill 适合什么用户？能做哪些任务？

适合做企业知识库、文档问答、RAG 应用的工程师：从 PDF / Markdown / 网页等异构文档构建索引，结合 retrieve-then-synthesize 给 LLM 提供外部知识。覆盖 FunctionAgent / ReActAgent / CodeActAgent 等 agent 范式。访问 doramagic.ai/r/llama-index 查看完整用例。

需要准备什么环境？依赖什么？

Python 3.9+，至少一个 LLM provider（默认隐式 OpenAI gpt-3.5-turbo）和一个 embedding provider（默认隐式 OpenAI text-embedding-ada-002 → 1536 维）。默认用内存 SimpleVectorStore，持久化需安装对应集成包。

会踩哪些坑？这个 skill 怎么防护？

本 skill 内置 52 条约束（5 条 fatal）。典型踩坑：(1) ServiceContext 已硬删除（不是 deprecated），3 个入口直接 raise ValueError；(2) SentenceSplitter chunk_overlap 默认 200（与文档常引用的 constants.DEFAULT_CHUNK_OVERLAP=20 不一致）；

完整文档: 见 references/seed.yaml (v6.1 schema). 浏览页: https://doramagic.ai/zh/crystal/llama-index-rag

安全使用建议
This is an instruction-only, domain-specific LlamaIndex/RAG blueprint (finance-focused). Before installing or invoking: (1) review references/seed.yaml and human_summary to confirm it targets the workflows you want (it expects ZVT and local finance data); (2) expect the host to run the listed Python precondition checks (which probe for zvt, data directories, and write permissions) and to attempt to read local files like LATEST.yaml — run in a test environment if you are uncertain; (3) note the SKILL.md mentions default OpenAI providers but does not request API keys — be cautious about supplying credentials if the agent later asks for them; (4) if you do not want the agent to access local files or run python checks, do not enable or invoke this skill. Overall the skill is internally consistent with its stated purpose, but it will interact with local tooling and data, so review included seed.yaml and preconditions before use.
功能分析
Type: OpenClaw Skill Name: llama-index-rag Version: 0.1.0 The bundle exhibits a major functional discrepancy between its advertised purpose and its internal logic. While the metadata and SKILL.md describe a LlamaIndex RAG (Retrieval-Augmented Generation) tool, the references/seed.yaml and human_summary.md are almost entirely focused on the 'zvt' quantitative trading framework for A-shares. The seed.yaml file mandates the installation of the 'zvt' package, checks for trading data in '~/.zvt', and enforces 'semantic locks' for financial transactions (e.g., SL-01: sell before buy). This 'Frankenstein' configuration is highly irregular and could cause an AI agent to execute unintended financial trading logic or install unrelated software (zvt) when a user expects RAG functionality.
能力标签
crypto
能力评估
✓ Purpose & Capability
The skill advertises LlamaIndex RAG guidance and provides a compiled blueprint (finance-bp-135). The heavy finance/ZVT content and backtest preconditions are consistent with the included seed.yaml and human_summary (this is a domain-specific LlamaIndex/RAG crystal). There is no unexpected request for unrelated credentials or binaries.
ℹ Instruction Scope
Runtime instructions require the host AI to reload and read references/seed.yaml, run precondition checks (Python commands that verify zvt installation, data directories, and write permissions), and enforce disclosure/traceback rules. These actions involve reading skill bundle files and potentially checking the host filesystem (e.g., for LATEST.yaml or ZVT_HOME). This is in-scope for a blueprint that performs local backtests, but users should expect the skill to read local files and run local Python checks.
✓ Install Mechanism
No install spec and no code files to execute; the skill is instruction-only. This minimizes install-time risk (nothing is downloaded or written by the skill itself).
ℹ Credentials
The skill does not declare or require environment variables or credentials. SKILL.md mentions Python 3.9+ and 'an LLM provider / embedding provider (default implicit OpenAI)' as runtime expectations but does not request OpenAI keys. This is a documentation/assumption mismatch (it may expect provider access at runtime), not an overt credential exfiltration request.
✓ Persistence & Privilege
always is false and autonomous invocation is allowed (platform default). The skill does not ask to persist or modify other skills or global agent configuration. It references host workspace paths for scripts/skills/trace, which is reasonable for a blueprint that expects to write traces, but it does not itself include an install step that would create persistent binaries.
如何使用
  1. 确保已安装 OpenClaw（本地或 Docker 部署）
  2. 在对话框中输入安装命令：/install llama-index-rag
  3. 安装完成后，直接呼叫该 Skill 的名称或使用 /llama-index-rag 触发
  4. 根据 Skill 的参数说明提供必要输入，即可获得结构化输出
版本历史
v0.1.0
LlamaIndex RAG framework skill — 52 constraints / 5 fatal. ServiceContext removed, CJK chunking pitfalls.
元数据
Slug llama-index-rag
版本 0.1.0
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 1
常见问题

Llama Index Rag 是什么？

LlamaIndex：把任意文档变 LLM 可查询知识的 Python 框架。4 大支柱（Index/Retriever/QueryEngine/Synthesizer）+ 52 条 anti-pattern 约束（5 fatal）。 LlamaIndex: a Python framework that tur... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 68 次。

如何安装 Llama Index Rag？

在 OpenClaw 或 Claude Code 对话框中运行命令「/install llama-index-rag」即可一键安装，无需额外配置。

Llama Index Rag 是免费的吗？

是的，Llama Index Rag 完全免费，采用 MIT-0 许可证，可自由下载、安装和使用。

Llama Index Rag 支持哪些平台？

Llama Index Rag 跨平台运行，可在任意部署了 OpenClaw / Claude Code 的环境中使用（cross-platform）。

谁开发了 Llama Index Rag？

由 Tang Weigang（@tangweigang-jpg）开发并维护，当前版本 v0.1.0。

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