local-rag-search

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Original

English

🇨🇳

Translation

Chinese

Local RAG Search Skill

本地RAG搜索技能

This skill enables you to effectively use the mcp-local-rag MCP server for intelligent web searches with semantic ranking. The server performs RAG-like similarity scoring to prioritize the most relevant results without requiring any external APIs.

本技能可让你借助mcp-local-rag MCP服务器，通过语义排序实现智能网页搜索。该服务器无需调用任何外部API，即可执行类RAG的相似度评分，优先返回最相关的结果。

Available Tools

可用工具

rag_search_ddgs

- DuckDuckGo Search

rag_search_ddgs

- DuckDuckGo搜索

Use this for privacy-focused, general web searches.

When to use:

User prefers privacy-focused searches
General information lookup
Default choice for most queries

Parameters:

```
query
```
: Natural language search query
```
num_results
```
: Initial results to fetch (default: 10)
```
top_k
```
: Most relevant results to return (default: 5)
```
include_urls
```
: Include source URLs (default: true)

适用于注重隐私的通用网页搜索。

适用场景：

用户偏好注重隐私的搜索方式
通用信息查询
大多数查询的默认选择

参数：

```
query
```
: 自然语言搜索查询词
```
num_results
```
: 初始获取的结果数量（默认值：10）
```
top_k
```
: 返回的最相关结果数量（默认值：5）
```
include_urls
```
: 是否包含来源URL（默认值：true）

rag_search_google

- Google Search

rag_search_google

- Google搜索

Use this for comprehensive, technical, or detailed searches.

When to use:

Technical or scientific queries
Need comprehensive coverage
Searching for specific documentation

适用于全面、技术类或精细化搜索。

适用场景：

技术或科学类查询
需要全面覆盖信息
搜索特定文档

deep_research

- Multi-Engine Deep Research

deep_research

- 多引擎深度研究

Use this for comprehensive research across multiple search engines.

When to use:

Researching complex topics requiring broad coverage
Need diverse perspectives from multiple sources
Gathering comprehensive information on a subject

Available backends:

```
duckduckgo
```
: Privacy-focused general search
```
google
```
: Comprehensive technical results
```
bing
```
: Microsoft's search engine
```
brave
```
: Privacy-first search
```
wikipedia
```
: Encyclopedia/factual content
```
yahoo
```
,
```
yandex
```
,
```
mojeek
```
,
```
grokipedia
```
: Alternative engines

Default:

["duckduckgo", "google"]

适用于跨多个搜索引擎开展全面研究。

适用场景：

研究需要广泛覆盖信息的复杂主题
需要从多来源获取多样化视角
收集某一主题的全面信息

可用后端引擎：

```
duckduckgo
```
: 注重隐私的通用搜索
```
google
```
: 全面的技术类结果
```
bing
```
: 微软搜索引擎
```
brave
```
: 隐私优先的搜索引擎
```
wikipedia
```
: 百科/事实类内容
```
yahoo
```
,
```
yandex
```
,
```
mojeek
```
,
```
grokipedia
```
: 替代引擎

默认值：

["duckduckgo", "google"]

deep_research_google

- Google-Only Deep Research

deep_research_google

- 仅Google深度研究

Shortcut for deep research using only Google.

仅使用Google进行深度研究的快捷方式。

deep_research_ddgs

- DuckDuckGo-Only Deep Research

deep_research_ddgs

- 仅DuckDuckGo深度研究

Shortcut for deep research using only DuckDuckGo.

仅使用DuckDuckGo进行深度研究的快捷方式。

Best Practices

最佳实践

Query Formulation

查询词构建

Use natural language: Write queries as questions or descriptive phrases
- Good: "latest developments in quantum computing"
- Good: "how to implement binary search in Python"
- Avoid: Single keywords like "quantum" or "Python"
Be specific: Include context and details
- Good: "React hooks best practices for 2024"
- Better: "React useEffect cleanup function best practices"

使用自然语言: 将查询词写成问题或描述性短语
- 示例："量子计算的最新发展"
- 示例："如何用Python实现二分查找"
- 避免：使用单个关键词，如“量子”或“Python”
明确具体: 包含上下文和细节
- 示例："2024年React Hooks最佳实践"
- 更优："React useEffect清理函数最佳实践"

Tool Selection Strategy

工具选择策略

Single Topic, Quick Answer → Use

rag_search_ddgs

rag_search_google

rag_search_ddgs(
    query="What is the capital of France?",
    top_k=3
)

Technical/Scientific Query → Use

rag_search_google

rag_search_google(
    query="Docker multi-stage build optimization techniques",
    num_results=15,
    top_k=7
)

Comprehensive Research → Use

deep_research

with multiple search terms

deep_research(
    search_terms=[
        "machine learning fundamentals",
        "neural networks architecture",
        "deep learning best practices 2024"
    ],
    backends=["google", "duckduckgo"],
    top_k_per_term=5
)

Factual/Encyclopedia Content → Use

deep_research

with Wikipedia

deep_research(
    search_terms=["World War II timeline", "WWII key battles"],
    backends=["wikipedia"],
    num_results_per_term=5
)

单一主题、快速解答 → 使用

rag_search_ddgs

或

rag_search_google

rag_search_ddgs(
    query="法国的首都是哪里?",
    top_k=3
)

技术/科学类查询 → 使用

rag_search_google

rag_search_google(
    query="Docker多阶段构建优化技巧",
    num_results=15,
    top_k=7
)

全面研究 → 使用

deep_research

并搭配多个搜索词

deep_research(
    search_terms=[
        "机器学习基础",
        "神经网络架构",
        "2024年深度学习最佳实践"
    ],
    backends=["google", "duckduckgo"],
    top_k_per_term=5
)

事实/百科类内容 → 使用搭配Wikipedia的

deep_research

deep_research(
    search_terms=["第二次世界大战时间线", "二战关键战役"],
    backends=["wikipedia"],
    num_results_per_term=5
)

Parameter Tuning

参数调优

For quick answers:

```
num_results=5-10
```
,
```
top_k=3-5
```

For comprehensive research:

```
num_results=15-20
```
,
```
top_k=7-10
```

For deep research:

num_results_per_term=10-15

top_k_per_term=3-5

Use 2-5 related search terms
Use 1-3 backends (more = more comprehensive but slower)

快速解答场景：

```
num_results=5-10
```
,
```
top_k=3-5
```

全面研究场景：

```
num_results=15-20
```
,
```
top_k=7-10
```

深度研究场景：

num_results_per_term=10-15

top_k_per_term=3-5

使用2-5个相关搜索词
使用1-3个后端引擎（数量越多覆盖越全面，但速度越慢）

Workflow Examples

工作流示例

Example 1: Current Events

示例1：时事查询

Task: "What happened at the UN climate summit last week?"

1. Use rag_search_google for recent news coverage
2. Set top_k=7 for comprehensive view
3. Present findings with source URLs

任务: "上周联合国气候峰会发生了什么?"

1. 使用rag_search_google获取近期新闻报道
2. 设置top_k=7以获取全面视角
3. 附带来源URL呈现研究结果

Example 2: Technical Deep Dive

示例2：技术深度探究

Task: "How do I optimize PostgreSQL queries?"

1. Use deep_research with multiple specific terms:
   - "PostgreSQL query optimization techniques"
   - "PostgreSQL index best practices"
   - "PostgreSQL EXPLAIN ANALYZE tutorial"
2. Use backends=["google", "stackoverflow"] if available
3. Synthesize findings into actionable guide

任务: "如何优化PostgreSQL查询?"

1. 使用deep_research并搭配多个具体搜索词:
   - "PostgreSQL查询优化技巧"
   - "PostgreSQL索引最佳实践"
   - "PostgreSQL EXPLAIN ANALYZE教程"
2. 若支持，使用backends=["google", "stackoverflow"]
3. 将研究结果整合为可操作的指南

Example 3: Multi-Perspective Research

示例3：多视角研究

Task: "Research the impact of remote work on productivity"

1. Use deep_research with diverse search terms:
   - "remote work productivity statistics 2024"
   - "hybrid work model effectiveness studies"
   - "work from home challenges research"
2. Use backends=["google", "duckduckgo"] for broad coverage
3. Synthesize different perspectives and studies

任务: "研究远程办公对生产力的影响"

1. 使用deep_research并搭配多样化搜索词:
   - "2024年远程办公生产力统计数据"
   - "混合办公模式有效性研究"
   - "居家办公挑战研究"
2. 使用backends=["google", "duckduckgo"]以获取广泛覆盖
3. 整合不同视角和研究结论

Guidelines

指南

Always cite sources: When
```
include_urls=True
```
, reference the source URLs in your response
Verify recency: Check if the content appears current and relevant
Cross-reference: For important facts, use multiple search terms or engines
Respect privacy: Use DuckDuckGo for general queries unless specific needs require Google
Batch related queries: When researching a topic, create multiple related search terms for deep_research
Semantic relevance: Trust the RAG scoring - top results are semantically closest to the query
Explain your choice: Briefly mention which tool you're using and why

始终引用来源: 当
```
include_urls=True
```
时，在回复中引用来源URL
验证时效性: 检查内容是否最新且相关
交叉验证: 对于重要事实，使用多个搜索词或引擎
注重隐私: 除非有特定需求，否则优先使用DuckDuckGo进行通用查询
批量处理相关查询: 研究某一主题时，创建多个相关搜索词用于deep_research
语义相关性: 信任RAG评分——排名靠前的结果与查询词的语义最接近
说明选择理由: 简要提及你使用的工具及原因

Error Handling

错误处理

If a search returns insufficient results:

Try rephrasing the query with different keywords
Switch to a different backend
Increase
```
num_results
```
parameter
Use
```
deep_research
```
with multiple related search terms

若搜索返回结果不足：

尝试用不同关键词改写查询词
切换至其他后端引擎
增大
```
num_results
```
参数值
使用搭配多个相关搜索词的
```
deep_research
```

Privacy Considerations

隐私注意事项

DuckDuckGo: Privacy-focused, doesn't track users
Google: Most comprehensive but tracks searches
Recommend DuckDuckGo as default unless user specifically needs Google's coverage

DuckDuckGo: 注重隐私，不会追踪用户
Google: 覆盖最全面，但会追踪搜索记录
推荐优先使用DuckDuckGo，除非用户明确需要Google的覆盖范围

Performance Notes

性能说明

First search may be slower (model loading)
Subsequent searches are faster (cached models)
More backends = more comprehensive but slower
Adjust
```
num_results
```
and
```
top_k
```
based on use case

首次搜索可能较慢（模型加载中）
后续搜索速度更快（模型已缓存）
后端引擎越多，覆盖越全面，但速度越慢
根据使用场景调整
```
num_results
```
和
```
top_k
```
参数

local-rag-search

Original

Translation

Local RAG Search Skill

本地RAG搜索技能

Available Tools

可用工具

1. rag_search_ddgs - DuckDuckGo Search

1. rag_search_ddgs - DuckDuckGo搜索

2. rag_search_google - Google Search

2. rag_search_google - Google搜索

3. deep_research - Multi-Engine Deep Research

3. deep_research - 多引擎深度研究

4. deep_research_google - Google-Only Deep Research

4. deep_research_google - 仅Google深度研究

5. deep_research_ddgs - DuckDuckGo-Only Deep Research

5. deep_research_ddgs - 仅DuckDuckGo深度研究

Best Practices

最佳实践

Query Formulation

查询词构建

Tool Selection Strategy

工具选择策略

Parameter Tuning

参数调优

Workflow Examples

工作流示例

Example 1: Current Events

示例1：时事查询

Example 2: Technical Deep Dive

示例2：技术深度探究

Example 3: Multi-Perspective Research

示例3：多视角研究

Guidelines

指南

Error Handling

错误处理

Privacy Considerations

隐私注意事项

Performance Notes

性能说明

1.
`rag_search_ddgs`
- DuckDuckGo Search

1.
`rag_search_ddgs`
- DuckDuckGo搜索

2.
`rag_search_google`
- Google Search

2.
`rag_search_google`
- Google搜索

3.
`deep_research`
- Multi-Engine Deep Research

3.
`deep_research`
- 多引擎深度研究

4.
`deep_research_google`
- Google-Only Deep Research

4.
`deep_research_google`
- 仅Google深度研究

5.
`deep_research_ddgs`
- DuckDuckGo-Only Deep Research

5.
`deep_research_ddgs`
- 仅DuckDuckGo深度研究