AI Tools
对文档的候选 RAG 分块方案在四个维度上评分——连贯性（干净的分句/分段边界）、覆盖率（主题聚焦 / 关键词集中度）、上下文重叠健康度以及分块大小一致性——并排对比最多三种方案并推荐最优。纯离线启发式，无模型调用。
用三种语言从你的代码中调用此工具。
curl -X POST 'https://api.elysiatools.com/zh/api/tools/ai-rag-chunk-quality-scorer' \
-H 'Content-Type: application/json' \
-d '{"document":"Retrieval-augmented generation combines a retriever and a language model. The retriever finds relevant passages in a knowledge base. Chunking strategy strongly affects retrieval quality. Smaller chunks are precise but lose context. Larger chunks carry context but dilute relevance. Overlap between chunks preserves context across boundaries. Evaluate several schemes before indexing.","unit":"tokens","method":"sentence","sizeA":256,"overlapA":32,"sizeB":512,"overlapB":64,"sizeC":1024,"overlapC":128,"renderMode":"comparison","precision":1}'以 JSON 形式 POST 提交输入参数。文件类型参数需先单独上传。
POST https://api.elysiatools.com/zh/api/tools/ai-rag-chunk-quality-scorer| 参数名 | 类型 | 必填 | 说明 |
|---|---|---|---|
| document | textarea | 是 | — |
| unit | select | 否 | — |
| method | select | 否 | — |
| sizeA | number | 是 | — |
| overlapA | number | 否 | — |
| sizeB | number | 是 | — |
| overlapB | number | 否 | — |
| sizeC | number | 是 | — |
| overlapC | number | 否 | — |
| renderMode | select | 否 | — |
| precision | number | 是 | — |
HTML 结果
{
"result": "<div>Processed HTML content</div>",
"error": "Error message (optional)",
"message": "Notification message (optional)",
"metadata": {
"key": "value"
}
}将此工具加入你的 Model Context Protocol 服务，让 AI 智能体可以列出并调用它。
将以下内容加入你的 MCP 客户端配置：
{
"mcpServers": {
"elysiatools-ai-rag-chunk-quality-scorer": {
"name": "ai-rag-chunk-quality-scorer",
"description": "对文档的候选 RAG 分块方案在四个维度上评分——连贯性（干净的分句/分段边界）、覆盖率（主题聚焦 / 关键词集中度）、上下文重叠健康度以及分块大小一致性——并排对比最多三种方案并推荐最优。纯离线启发式，无模型调用。",
"baseUrl": "https://api.elysiatools.com/mcp/sse?toolId=ai-rag-chunk-quality-scorer",
"command": "",
"args": [],
"env": {},
"isActive": true,
"type": "sse"
}
}
}连接到 SSE 端点后，列出已开放的工具：
{
"jsonrpc": "2.0",
"id": 1,
"method": "tools/list"
}通过工具 id 调用，参数由其参数表构建：
{
"jsonrpc": "2.0",
"id": 2,
"method": "tools/call",
"params": {
"name": "ai-rag-chunk-quality-scorer",
"arguments": {
"document": "Retrieval-augmented generation combines a retriever and a language model. The retriever finds relevant passages in a knowledge base. Chunking strategy strongly affects retrieval quality. Smaller chunks are precise but lose context. Larger chunks carry context but dilute relevance. Overlap between chunks preserves context across boundaries. Evaluate several schemes before indexing.",
"unit": "tokens",
"method": "sentence",
"sizeA": 256,
"overlapA": 32,
"sizeB": 512,
"overlapB": 64,
"sizeC": 1024,
"overlapC": 128,
"renderMode": "comparison",
"precision": 1
}
}
}有问题或反馈？请联系 [email protected]