8
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简历工具

中级

这是一个HR, AI Summarization领域的自动化工作流,包含 15 个节点。主要使用 Code, Merge, Webhook, Aggregate, GoogleDrive 等节点。 基于Llama3.1大语言模型的AI简历筛选,集成Google云端硬盘和表格

前置要求
  • HTTP Webhook 端点(n8n 会自动生成)
  • Google Drive API 凭证
  • Google Sheets API 凭证
工作流预览
可视化展示节点连接关系,支持缩放和平移
导出工作流
复制以下 JSON 配置到 n8n 导入,即可使用此工作流
{
  "id": "7wkmOQEEUAJIwFEP",
  "meta": {
    "instanceId": "558d88703fb65b2d0e44613bc35916258b0f0bf983c5d4730c00c424b77ca36a",
    "templateCredsSetupCompleted": true
  },
  "name": "cv-tool",
  "tags": [],
  "nodes": [
    {
      "id": "0cae9383-b4da-43ec-9962-afc97d56ddad",
      "name": "遍历项目",
      "type": "n8n-nodes-base.splitInBatches",
      "position": [
        -280,
        160
      ],
      "parameters": {
        "options": {}
      },
      "executeOnce": false,
      "typeVersion": 3
    },
    {
      "id": "c027ca47-5415-4f7a-9028-a0637acc195f",
      "name": "合并",
      "type": "n8n-nodes-base.merge",
      "position": [
        380,
        360
      ],
      "parameters": {
        "mode": "chooseBranch"
      },
      "typeVersion": 3
    },
    {
      "id": "2bd255ee-e3d8-46a6-9112-14b348852eae",
      "name": "聚合",
      "type": "n8n-nodes-base.aggregate",
      "position": [
        600,
        360
      ],
      "parameters": {
        "options": {},
        "aggregate": "aggregateAllItemData"
      },
      "typeVersion": 1
    },
    {
      "id": "ae51c305-87e4-4404-816e-4f2b30b80c6d",
      "name": "Ollama 模型",
      "type": "@n8n/n8n-nodes-langchain.lmOllama",
      "position": [
        920,
        840
      ],
      "parameters": {
        "model": "llama3.1:latest",
        "options": {
          "temperature": 0.1
        }
      },
      "credentials": {
        "ollamaApi": {
          "id": "7CIt42W14b1MCgxQ",
          "name": "Ollama account"
        }
      },
      "typeVersion": 1
    },
    {
      "id": "42fd32cf-1787-45c5-a2df-08955534ae64",
      "name": "自动修复输出解析器",
      "type": "@n8n/n8n-nodes-langchain.outputParserAutofixing",
      "position": [
        1020,
        660
      ],
      "parameters": {
        "options": {}
      },
      "typeVersion": 1
    },
    {
      "id": "097e8558-2a30-4c1e-a7bd-4620bc9d22a6",
      "name": "结构化输出解析器",
      "type": "@n8n/n8n-nodes-langchain.outputParserStructured",
      "position": [
        1060,
        1100
      ],
      "parameters": {
        "schemaType": "manual",
        "inputSchema": "{\n  \"$schema\": \"https://json-schema.org/draft/2020-12/schema\",\n  \"title\": \"Evaluation Form\",\n  \"type\": \"object\",\n  \"properties\": {\n    \"candidate_file_name\": {\"$ref\": \"#/definitions/candidateFileName\"},\n    \"candidate_name\": {\"$ref\": \"#/definitions/candidateName\"},\n    \"questions\": {\"$ref\": \"#/definitions/questions\"},\n    \"overall_points\": {\"$ref\": \"#/definitions/overallPoints\"}\n  },\n  \"required\": [\"candidate_file_name\", \"candidate_name\", \"questions\", \"overall_points\"],\n  \"definitions\": {\n    \"candidateFileName\": {\n      \"$schema\": \"https://json-schema.org/draft/2020-12/schema\",\n      \"title\": \"Candidate File Name\",\n      \"type\": \"string\"\n    },\n    \"candidateName\": {\n      \"$schema\": \"https://json-schema.org/draft/2020-12/schema\",\n      \"title\": \"Candidate Name\",\n      \"type\": \"string\"\n    },\n    \"questions\": {\n      \"$schema\": \"https://json-schema.org/draft/2020-12/schema\",\n      \"title\": \"Questions\",\n      \"type\": \"array\",\n      \"items\": {\n        \"$ref\": \"#/definitions/question\"\n      }\n    },\n    \"question\": {\n      \"$schema\": \"https://json-schema.org/draft/2020-12/schema\",\n      \"title\": \"Question\",\n      \"type\": \"object\",\n      \"properties\": {\n        \"question\": {\"$ref\": \"#/definitions/questionText\"},\n        \"answer\": {\"$ref\": \"#/definitions/candidateAnswer\"}\n      },\n      \"required\": [\"question\", \"answer\"]\n    },\n    \"questionText\": {\n      \"$schema\": \"https://json-schema.org/draft/2020-12/schema\",\n      \"title\": \"Question Text\",\n      \"type\": \"string\"\n    },\n    \"candidateAnswer\": {\n      \"$schema\": \"https://json-schema.org/draft/2020-12/schema\",\n      \"title\": \"Candidate Answer\",\n      \"type\": \"string\"\n    },\n    \"overallPoints\": {\n      \"$schema\": \"https://json-schema.org/draft/2020-12/schema\",\n      \"title\": \"Overall Points\",\n      \"type\": \"string\"\n    }\n  }\n}"
      },
      "typeVersion": 1.2
    },
    {
      "id": "79957386-3e4b-4650-9490-8a29f948d5f0",
      "name": "代码",
      "type": "n8n-nodes-base.code",
      "position": [
        1260,
        360
      ],
      "parameters": {
        "jsCode": "// Extract input data\nconst items = $input.all();\nlet results = [];\nlet allQuestions = new Set();\n\n// Collect all possible question fields dynamically\nitems.forEach(item => {\n    let output = item.json.output;\n    let questions = output.questions;\n    \n    Object.keys(questions).forEach(q => allQuestions.add(q));\n});\n\n// Convert to array format\nitems.forEach(item => {\n    let output = item.json.output;\n    let row = {\n        \"Candidate File Name\": output.candidate_file_name,\n        \"Candidate Name\": output.candidate_name\n    };\n\n    // Add all dynamically available question scores\n    allQuestions.forEach(question => {\n        row[question] = output.questions[question] || \"\";\n    });\n\n    // Add overall points\n    row[\"Overall Points\"] = output.overall_points;\n    results.push(row);\n});\n\nreturn results.map(row => ({ json: row }));"
      },
      "executeOnce": false,
      "typeVersion": 2
    },
    {
      "id": "b0c56e05-f929-496d-9482-ff2f61a6e544",
      "name": "下载文件",
      "type": "n8n-nodes-base.googleDrive",
      "position": [
        -100,
        380
      ],
      "parameters": {
        "fileId": {
          "__rl": true,
          "mode": "id",
          "value": "={{ $json.id }}"
        },
        "options": {
          "googleFileConversion": {
            "conversion": {
              "docsToFormat": "application/pdf",
              "slidesToFormat": "application/pdf",
              "drawingsToFormat": "application/pdf"
            }
          }
        },
        "operation": "download"
      },
      "credentials": {
        "googleDriveOAuth2Api": {
          "id": "",
          "name": "Google Drive account"
        }
      },
      "typeVersion": 3
    },
    {
      "id": "5dc07e0a-900f-407e-be7e-da4db4168e30",
      "name": "输入条件",
      "type": "n8n-nodes-base.googleSheets",
      "position": [
        20,
        180
      ],
      "parameters": {
        "options": {},
        "sheetName": {
          "__rl": true,
          "mode": "list",
          "value": 714429904,
          "cachedResultUrl": "",
          "cachedResultName": "Search_Criteria_t2"
        },
        "documentId": {
          "__rl": true,
          "mode": "list",
          "value": "1VoJbCMXbgQm8lO37h1kAp-wFo_7rC_xthL7H4PXCNxg",
          "cachedResultUrl": "",
          "cachedResultName": "Criteria-sheet"
        }
      },
      "credentials": {
        "googleSheetsOAuth2Api": {
          "id": "",
          "name": "Google Sheets account"
        }
      },
      "typeVersion": 4.5
    },
    {
      "id": "e917b4e5-ca96-4d08-b43c-bf832710b1f4",
      "name": "从 PDF 提取内容",
      "type": "n8n-nodes-base.extractFromFile",
      "position": [
        140,
        380
      ],
      "parameters": {
        "options": {},
        "operation": "pdf"
      },
      "typeVersion": 1
    },
    {
      "id": "fbee4bec-65af-4017-98b7-9074c4fd5770",
      "name": "输出表格",
      "type": "n8n-nodes-base.googleSheets",
      "position": [
        1500,
        360
      ],
      "parameters": {
        "columns": {
          "value": {},
          "schema": [
            {
              "id": "Candidate File Name",
              "type": "string",
              "display": true,
              "removed": false,
              "required": false,
              "displayName": "Candidate File Name",
              "defaultMatch": false,
              "canBeUsedToMatch": true
            },
            {
              "id": "Candidate Name",
              "type": "string",
              "display": true,
              "removed": false,
              "required": false,
              "displayName": "Candidate Name",
              "defaultMatch": false,
              "canBeUsedToMatch": true
            },
            {
              "id": "Business Analysis",
              "type": "string",
              "display": true,
              "removed": false,
              "required": false,
              "displayName": "Business Analysis",
              "defaultMatch": false,
              "canBeUsedToMatch": true
            },
            {
              "id": "Project Management (PMO)",
              "type": "string",
              "display": true,
              "removed": false,
              "required": false,
              "displayName": "Project Management (PMO)",
              "defaultMatch": false,
              "canBeUsedToMatch": true
            },
            {
              "id": "Banking",
              "type": "string",
              "display": true,
              "removed": false,
              "required": false,
              "displayName": "Banking",
              "defaultMatch": false,
              "canBeUsedToMatch": true
            },
            {
              "id": "Communication Skills",
              "type": "string",
              "display": true,
              "removed": false,
              "required": false,
              "displayName": "Communication Skills",
              "defaultMatch": false,
              "canBeUsedToMatch": true
            },
            {
              "id": "Overall Points",
              "type": "string",
              "display": true,
              "removed": false,
              "required": false,
              "displayName": "Overall Points",
              "defaultMatch": false,
              "canBeUsedToMatch": true
            }
          ],
          "mappingMode": "autoMapInputData",
          "matchingColumns": []
        },
        "options": {},
        "operation": "append",
        "sheetName": {
          "__rl": true,
          "mode": "list",
          "value": 1534129230,
          "cachedResultUrl": "",
          "cachedResultName": "Sheet2"
        },
        "documentId": {
          "__rl": true,
          "mode": "list",
          "value": "1TvYZwuCeTVImsXdd3cNh6zYf7neJbHvCkqLjNzsCk5w",
          "cachedResultUrl": "",
          "cachedResultName": "Output-sheet"
        }
      },
      "credentials": {
        "googleSheetsOAuth2Api": {
          "id": "",
          "name": "Google Sheets account"
        }
      },
      "typeVersion": 4.5
    },
    {
      "id": "1d77e98d-20fa-4301-93ea-175881cac1dc",
      "name": "Webhook",
      "type": "n8n-nodes-base.webhook",
      "position": [
        -880,
        160
      ],
      "webhookId": "6751c9a2-4bf9-416e-8e98-905498867431",
      "parameters": {
        "path": "6751c9a2-4bf9-416e-8e98-905498867431",
        "options": {},
        "httpMethod": "POST",
        "responseMode": "responseNode"
      },
      "typeVersion": 2
    },
    {
      "id": "d7644e7c-2abb-4bb2-ac81-7595cc918a9b",
      "name": "响应 Webhook",
      "type": "n8n-nodes-base.respondToWebhook",
      "position": [
        20,
        -60
      ],
      "parameters": {
        "options": {},
        "respondWith": "json",
        "responseBody": "{\n  \"Status\": \"Workflow Completed!\"\n}"
      },
      "typeVersion": 1.1,
      "alwaysOutputData": false
    },
    {
      "id": "14869643-9588-42de-a238-195f47875d78",
      "name": "文件夹搜索",
      "type": "n8n-nodes-base.googleDrive",
      "position": [
        -580,
        160
      ],
      "parameters": {
        "filter": {
          "folderId": {
            "__rl": true,
            "mode": "list",
            "value": "1z0DTQL4nfB7lQFETQw8-fnxg1YaOpgoL",
            "cachedResultUrl": "",
            "cachedResultName": "test"
          }
        },
        "options": {},
        "resource": "fileFolder"
      },
      "credentials": {
        "googleDriveOAuth2Api": {
          "id": "",
          "name": "Google Drive account"
        }
      },
      "typeVersion": 3
    },
    {
      "id": "8f920f78-86d3-45ed-b59c-4a74e6fb108b",
      "name": "基础 LLM 链",
      "type": "@n8n/n8n-nodes-langchain.chainLlm",
      "position": [
        860,
        360
      ],
      "parameters": {
        "text": "=%Candidate_Profile%: \n{{ $('Extract from PDF').first().json.text}}\n\nAnalyze the %Candidate_Profile% against the %Questions%:{{ $json.data.map(item => item.Matching_Criteria + \" - Output Type:\" + item.Output_Type) + \"\\n\" }}. \n\nUse this question format as input to the AI-Model :  How do you rate your experiance in {{ $json.data.map(item => item.Matching_Criteria + \"Output Type:\" + item.Output_Type) + \"\\n\" }}\n\nTake candidate_file_name as {{ $('Download-file').first().json.name }} and fetch the candidate_name from the %Candidate_Profile%\n\nFor each question output format should be : {{ $json.data[0].Output_Type}}\n\nAt the end please calculate overall point(Total Point = sum({{ $json.data[0].Output_Type}} for each criteria)).\n\n\nIf he or she didn't have any experiance under specific area please mark rating as 0.\n",
        "messages": {
          "messageValues": [
            {
              "message": "Output should be a one specific format for all outputs"
            }
          ]
        },
        "promptType": "define",
        "hasOutputParser": true
      },
      "typeVersion": 1.5
    }
  ],
  "active": false,
  "pinData": {},
  "settings": {
    "executionOrder": "v1"
  },
  "versionId": "3a4961eb-24af-44a3-b727-9e11b39ee823",
  "connections": {
    "Code": {
      "main": [
        [
          {
            "node": "Output-sheet",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Merge": {
      "main": [
        [
          {
            "node": "Aggregate",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Webhook": {
      "main": [
        [
          {
            "node": "Folder-search",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Aggregate": {
      "main": [
        [
          {
            "node": "Basic LLM Chain",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Ollama Model": {
      "ai_languageModel": [
        [
          {
            "node": "Basic LLM Chain",
            "type": "ai_languageModel",
            "index": 0
          },
          {
            "node": "Auto-fixing Output Parser",
            "type": "ai_languageModel",
            "index": 0
          }
        ]
      ]
    },
    "Output-sheet": {
      "main": [
        [
          {
            "node": "Loop Over Items",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Download-file": {
      "main": [
        [
          {
            "node": "Extract from PDF",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Folder-search": {
      "main": [
        [
          {
            "node": "Loop Over Items",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Input-criteria": {
      "main": [
        [
          {
            "node": "Merge",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Basic LLM Chain": {
      "main": [
        [
          {
            "node": "Code",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Loop Over Items": {
      "main": [
        [
          {
            "node": "Respond to Webhook",
            "type": "main",
            "index": 0
          }
        ],
        [
          {
            "node": "Download-file",
            "type": "main",
            "index": 0
          },
          {
            "node": "Input-criteria",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Extract from PDF": {
      "main": [
        [
          {
            "node": "Merge",
            "type": "main",
            "index": 1
          }
        ]
      ]
    },
    "Structured Output Parser": {
      "ai_outputParser": [
        [
          {
            "node": "Auto-fixing Output Parser",
            "type": "ai_outputParser",
            "index": 0
          }
        ]
      ]
    },
    "Auto-fixing Output Parser": {
      "ai_outputParser": [
        [
          {
            "node": "Basic LLM Chain",
            "type": "ai_outputParser",
            "index": 0
          }
        ]
      ]
    }
  }
}
常见问题

如何使用这个工作流?

复制上方的 JSON 配置代码,在您的 n8n 实例中创建新工作流并选择「从 JSON 导入」,粘贴配置后根据需要修改凭证设置即可。

这个工作流适合什么场景?

中级 - 人力资源, AI 摘要总结

需要付费吗?

本工作流完全免费,您可以直接导入使用。但请注意,工作流中使用的第三方服务(如 OpenAI API)可能需要您自行付费。

工作流信息
难度等级
中级
节点数量15
分类2
节点类型13
难度说明

适合有一定经验的用户,包含 6-15 个节点的中等复杂度工作流

作者
Lakindu Siriwardana

Lakindu Siriwardana

@laki99

AI Engineer | Researcher

外部链接
在 n8n.io 查看

分享此工作流