AI趋势邮件提醒器-weaviate

• •可能需要目标 API 的认证凭证
• •OpenAI API Key

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        "content": "## 1. 指定每周自动化的日期范围"
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        "content": "## 2. 从arXiv获取每周文章。"
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        "content": "## 3. 预处理数据"
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        "width": 840,
        "height": 380,
        "content": "## 6. 在现有云集群中创建新的Weaviate集合"
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        "content": "## 7. 配置嵌入组件"
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        "content": "## 8. 确认文章已上传。"
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      "position": [
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        "width": 660,
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        "content": "## 1. 将Weaviate配置为_工具_来配置AI代理节点"
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      "position": [
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        "width": 500,
        "height": 640,
        "content": "## 3. 配置Weaviate向量存储"
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        "content": "## 4. 添加模型、内存和输出解析器"
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        "content": "## 2. 后处理代理响应"
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        "content": "## 5. 将输出作为邮件发送！"
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      "position": [
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        "content": "# 第一部分：获取、清理、丰富并将arXiv摘要插入Weaviate"
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        "content": "# 第二部分：使用代理RAG识别研究趋势并通过邮件发送"
      },
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        "content": "## 4. 用主题分类和潜在影响预测丰富arXiv文章"
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      "parameters": {
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        "content": "为代理和结构化输出解析器选择您的模型（我们使用`claude-3.7-sonnet`）。"
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        "content": "## 5. 后处理丰富的数据"
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      "onError": "continueErrorOutput",
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      "parameters": {
        "text": "=Classify the following arXiv papers:\n\n```json\nTitle: {{ $json.title }}\nAbstract: {{ $json.summary }}",
        "options": {
          "systemMessage": "=You are an expert AI agent designed to classify academic research papers. Your task is to analyze the provided arXiv paper data and categorize it based on its content.\n\nInput Data Schema:\nThe input data will be a JSON object with the following structure:\n\n{\n  \"title\": \"string\",\n  \"summary\": \"string\"\n}\n\nYou MUST respond with a JSON object containing the following fields:\n\n\"primary_category\": (string) The single most relevant primary category for the paper. You MUST choose one category from the following predefined list. Do NOT use any category not on this list.\n\n\"secondary_categories\": (array of strings) Up to two additional relevant secondary categories. These are optional; if no secondary categories apply, provide an empty array []. If chosen, they MUST also be from the predefined list.\n\n\"potential_impact\": (integer) An integer score from 1 to 5, judging the paper's potential impact based on these criteria:\n\n1: Papers with no new existing information or limited results.\n\n2: Papers with minor incremental contributions or limited novelty.\n\n3: Papers with solid contributions, good results, and clear utility, but not groundbreaking.\n\n4: Papers with significant advancements, novel approaches, or strong potential to influence the field.\n\n5: Papers that are potential game-changers, representing paradigm shifts, or opening entirely new research directions.\n\nPredefined Categories and Definitions:\n\nFoundation Models: Models trained on broad data at scale, designed to be adaptable to a wide range of downstream tasks (e.g., large language models, large vision models, multi-modal models).\n\nLLM Fine-tuning: Techniques and methodologies for adapting pre-trained Large Language Models (LLMs) to specific tasks or datasets.\n\nParameter-Efficient Fine-tuning (PEFT): Methods that enable efficient adaptation of large pre-trained models to new tasks with minimal computational cost, by updating only a small subset of parameters (e.g., LoRA, Prompt Tuning).\n\nRetrieval-Augmented Generation (RAG): Architectures or systems that combine generative models (like LLMs) with information retrieval mechanisms to enhance the factual accuracy and relevance of generated outputs by referencing external knowledge bases.\n\nModel Quantization: Techniques for reducing the precision of model parameters (e.g., from float32 to int8) to decrease model size, memory footprint, and computational requirements, often for efficient deployment on edge devices.\n\nAgentic AI / AI Agents: Systems designed for autonomous decision-making, planning, and action in dynamic environments, often involving reasoning, memory, and tool use.\n\nMultimodality: Models capable of processing, understanding, and generating content across multiple data types or modalities (e.g., text and images, audio and video).\n\nReinforcement Learning: A paradigm where an agent learns to make decisions by performing actions in an environment to maximize a cumulative reward, often through trial and error.\n\nComputer Vision (Specific Techniques): Papers focusing on particular computer vision tasks or methodologies that are not primarily about foundation models (e.g., 3D reconstruction, object detection, image segmentation, pose estimation).\n\nNatural Language Processing (Specific Techniques): Papers focusing on particular NLP tasks or methodologies that are not primarily about foundation models or LLM fine-tuning (e.g., text summarization, machine translation, sentiment analysis, named entity recognition).\n\nEthical AI / AI Safety: Research addressing the societal implications of AI, including fairness, bias detection and mitigation, interpretability, transparency, privacy, and alignment with human values.\n\nEfficient AI / AI Optimization: Techniques aimed at improving the computational efficiency, speed, or resource usage of AI models beyond just quantization, including architecture search, inference optimization, and hardware-aware design.\n\nData-centric AI: Approaches that prioritize improving the quality, quantity, and organization of data used to train AI models, rather than solely focusing on model architecture improvements.\n\nOther: A catch-all category for articles that don't fall into one of the classes mentioned above.\n\nFocus solely on the content of the paper's title, summary, and categories to make your classification. Do NOT include any conversational text or explanations in your response, only the JSON object."
        },
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    {
      "id": "36068ba8-4e86-403c-b6a6-7fe55af58a95",
      "name": "移除字段",
      "type": "n8n-nodes-base.set",
      "position": [
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      "parameters": {
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          "assignments": [
            {
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              "name": "id",
              "type": "string",
              "value": "={{ $json.id }}"
            },
            {
              "id": "714d07a2-0c05-4d12-bba4-b37ca21c6521",
              "name": "title",
              "type": "string",
              "value": "={{ $json.title }}"
            },
            {
              "id": "ec2e4ccb-70ec-4d12-8d5b-8b5359b93315",
              "name": "summary",
              "type": "string",
              "value": "={{ $json.summary }}"
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            {
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              "name": "author",
              "type": "string",
              "value": "={{ $json.author }}"
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              "name": "published",
              "type": "string",
              "value": "={{ $json.published }}"
            },
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              "name": "category",
              "type": "string",
              "value": "={{ $json.category }}"
            },
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              "name": "primary_topic",
              "type": "string",
              "value": "={{ $json.output.primary_category }}"
            },
            {
              "id": "4f82732d-4f67-4047-bd67-6c00dd1c9a80",
              "name": "secondary_topics",
              "type": "string",
              "value": "={{ $json.output.secondary_categories }}"
            },
            {
              "id": "322c2946-8a90-451a-b484-bc1da68fb178",
              "name": "potential_impact",
              "type": "number",
              "value": "={{ parseFloat($json.output.potential_impact) }}"
            }
          ]
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      "typeVersion": 3.4
    },
    {
      "id": "af99113b-8804-47bf-a74d-2265f63b97e1",
      "name": "聚合上传的arXiv ID",
      "type": "n8n-nodes-base.aggregate",
      "position": [
        4112,
        752
      ],
      "parameters": {
        "options": {},
        "fieldsToAggregate": {
          "fieldToAggregate": [
            {
              "fieldToAggregate": "metadata.arxiv_id"
            }
          ]
        }
      },
      "typeVersion": 1
    },
    {
      "id": "92511b85-b11d-4fd7-9985-326051db472a",
      "name": "添加静态sessionId",
      "type": "n8n-nodes-base.set",
      "position": [
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      "parameters": {
        "options": {},
        "assignments": {
          "assignments": [
            {
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              "name": "sessionId",
              "type": "string",
              "value": "static_id"
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    {
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      "name": "用于趋势分析的代理RAG",
      "type": "@n8n/n8n-nodes-langchain.agent",
      "position": [
        48,
        1584
      ],
      "parameters": {
        "text": "=Please provide a summarized trend analysis using the articles stored in the Weaviate vector store.",
        "options": {
          "systemMessage": "=**CRITICAL: YOUR FINAL RESPONSE MUST CONTAIN ABSOLUTELY NO MARKDOWN CODE FENCES (```JSON) OR ANY OTHER TEXT OUTSIDE THE PURE JSON OBJECT. IMMEDIATELY PROVIDE THE JSON.**\n\nYou are an expert AI and Machine Learning research analyst, specifically tasked with synthesizing weekly trends from arXiv publications. Your goal is to generate a concise, approachable, and easy-to-read email summary for a general audience interested in the latest developments in AI and ML. You must use the Weaviate Vector Store1 and perform a proper vector search to retrieve the data. If you were unable to retrieve data, please repeatedly try again until you can. Do not hallucinate, do not make anything up, do not rely on your memory.\n\n**Input Data:**\nYou will receive an array of JSON objects, where each object represents a single AI/ML article published on arXiv in the last week. You MUST analyze this entire collection of articles to identify key trends and notable research. Each article object has the following fields:\n- `arxiv_id`: The FULL arXiv URL of the article. (e.g. \"http://arxiv.org/abs/2507.02863v1\")\n- `title`: The title of the article.\n- `main_findings`: The main findings of the paper.\n- `primary_topic`: The primary topic assigned to the article (e.g., \"Language Model Evaluation\").\n- `secondary_topics`: A list of secondary topics assigned to the article (e.g., [\"Generative AI\", \"Natural Language Processing\"]).\n- `paper_quality`: A numerical score from 1 (very poor) to 5 (excellent quality), representing the predicted research quality.\n- `potential_impact`: A numerical score from 1 (not important) to 5 (potential paradigm shift, huge game-changer), representing the predicted impact on the field.\n\nYou will also recieve an array of JSONs with article counts by primary topic. Incorporate this into your trend anaylsis by focusing on topics with the most number of publications. Use this to guide the rest of the analysis\n\n**Trend Identification and Selection Rules:**\n- **Get article counts by `primary_topic`:** First, perform an aggregate query in the Weaviate vector store to get the counts of articles published by `primary_topic`. Use these counts to inform the rest of your analysis, focusing on the most-published topics. \n- **Identify Key Trends:** Group papers by `primary_topic` and `secondary_topics`. Prioritize trends with a higher number of associated papers, or papers with higher `potential_impact` or `paper_quality` scores.\n- **Select Representative Papers for Citation:** For each trend you summarize, choose 1-2 representative papers. These should ideally be the highest `potential_impact` or `paper_quality` papers relevant to that trend from the provided input data.\n- **Infer Future Outlook:** Based on the collective summaries and topics of the provided articles, infer and summarize likely future research directions.\n\n**Output Format (Strict JSON):**\nYour entire response MUST be **only** a valid JSON object.\n- **DO NOT include any Markdown code fences (```json) or any other text before or after the JSON object.**\n- The JSON object MUST contain exactly two top-level keys: \"subject\" and \"body\".\n- The value for \"subject\" MUST be a single string. It MUST always start with \"✨ ML Weekly Update:\" and the rest should be a succinct summary of the report's top trends for the week.\n- The value for \"body\" MUST be a single string representing the full email content in Markdown format. Use `\\\\n` for newlines within the body string. All double quotes within string values MUST be escaped as `\\\"`.\n- You must cite all of your conclusions with the matching `arxiv_id` for the paper. Cite the in markdown format so that the user can click on a word related to the paper which is linked to the `arxiv_id`.\n\nHere is an example of a citation:\n...including [MOTIF](http://arxiv.org/abs/2507.02851v1) which enables modular thinking beyond context limitations\n\nUse ONLY data from the `arxiv_id` field for citations. Do NOT create an arXiv_id spontaneously.\n\nExample JSON Output (strictly adhere to this format):\n```json\n{\n  \"subject\": \"✨ ML Weekly Update: BERT gets an upgrade, new PEFT techniques, and ML for radiology\",\n  \"body\": \"Rest of email in markdown format here.\"\n}\n\nFollow this format strictly for the email, giving the body output in markdown format:\n\nHey there,\n\nHere's a quick rundown of the key trends in Machine Learning research from the past week.\n\n## 💫 Key Research Trends This Week\nA one-sentence summary of the main trends covered in the report.\n* Bullet 1: A specific trend explained in 1 sentence or less, with the corresponding `arxiv_id` from the input report.\n* Bullet 2: A specific trend explained in 1 sentence or less, with the corresponding `arxiv_id` from the input report.\n* Bullet 3: A specific trend explained in 1 sentence or less, with the corresponding `arxiv_id` from the input report.\n** Write a maximum of 3 bullet points **\n\n## 🔮 Future Research Directions\nA one-sentence summary of future research directions from the report.\n* Bullet 1: A specific prediction explained in 1 sentence or less.\n* Bullet 2: A specific prediction explained in 1 sentence or less.\n* Bullet 3: A specific prediction explained in 1 sentence or less.\n** Write a maximum of 3 bullet points **\n\nGive a one sentence summary of the email. Then follow it with some short tips on what to look for in terms of new developments over the coming week.\n\nUntil next week,\n\nArchi 🧑🏽‍🔬"
        },
        "promptType": "define"
      },
      "retryOnFail": true,
      "typeVersion": 2
    },
    {
      "id": "bb101516-818c-48e5-87d8-1a9f98d0f3a2",
      "name": "后处理数据",
      "type": "n8n-nodes-base.set",
      "position": [
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      "parameters": {
        "options": {},
        "assignments": {
          "assignments": [
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              "id": "42cf92c6-730d-4abf-a578-480f71e220da",
              "name": "subject",
              "type": "string",
              "value": "={{ $json.output.subject }}"
            },
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              "id": "c1f254e2-8333-431e-a6f1-0efa38f4fd3b",
              "name": "output.body",
              "type": "string",
              "value": "={{ $json.output.body.replace(/\\\\n/g, '\\n') }}"
            }
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      "typeVersion": 3.4
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    {
      "id": "f5f96c9f-08ad-4707-a24e-d3c518218778",
      "name": "发送邮件",
      "type": "n8n-nodes-base.emailSend",
      "position": [
        1520,
        1584
      ],
      "webhookId": "2fc3d54c-6de7-4f75-aa5d-f8cf90a4d1c4",
      "parameters": {
        "html": "={{ $json.data }}",
        "options": {},
        "subject": "={{ $json.subject }}"
      },
      "credentials": {
        "smtp": {
          "id": "isYLskFLHSByAySn",
          "name": "SMTP account"
        }
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      "typeVersion": 2.1
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    {
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      "name": "便签3",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        0,
        0
      ],
      "parameters": {
        "color": 4,
        "width": 1600,
        "height": 380,
        "content": "# 使用arXiv和Weaviate构建每周AI趋势提醒器"
      },
      "typeVersion": 1
    },
    {
      "id": "ef75d74a-233c-4928-b527-217ce7cc956f",
      "name": "OpenAI 聊天模型",
      "type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
      "position": [
        656,
        2368
      ],
      "parameters": {
        "model": {
          "__rl": true,
          "mode": "list",
          "value": "gpt-4.1-mini",
          "cachedResultName": "gpt-4.1-mini"
        },
        "options": {}
      },
      "credentials": {
        "openAiApi": {
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          "name": "OpenAi account"
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      "typeVersion": 1.2
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      "position": [
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  ],
  "active": false,
  "pinData": {},
  "settings": {
    "executionOrder": "v1"
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  "versionId": "a8db38e2-89d4-4d15-adc1-e551dfe06e16",
  "connections": {
    "Merge": {
      "main": [
        [
          {
            "node": "Remove Fields",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Markdown": {
      "main": [
        [
          {
            "node": "Send email",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Date & Time": {
      "main": [
        [
          {
            "node": "Query arXiv",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Query arXiv": {
      "main": [
        [
          {
            "node": "Convert XML to JSON",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Remove Fields": {
      "main": [
        [
          {
            "node": "Weaviate Vector Store",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Simple Memory": {
      "ai_memory": [
        [
          {
            "node": "Agentic RAG for Trend Analysis",
            "type": "ai_memory",
            "index": 0
          }
        ]
      ]
    },
    "Split Results": {
      "main": [
        [
          {
            "node": "Prep Data for Weaviate",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Get Current Date": {
      "main": [
        [
          {
            "node": "Date & Time",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Schedule Trigger": {
      "main": [
        [
          {
            "node": "Get Current Date",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Embeddings OpenAI": {
      "ai_embedding": [
        [
          {
            "node": "Weaviate Vector Store",
            "type": "ai_embedding",
            "index": 0
          }
        ]
      ]
    },
    "OpenAI Chat Model": {
      "ai_languageModel": [
        [
          {
            "node": "Agentic RAG for Trend Analysis",
            "type": "ai_languageModel",
            "index": 1
          }
        ]
      ]
    },
    "Post Process Data": {
      "main": [
        [
          {
            "node": "Markdown",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Remove Duplicates": {
      "main": [
        [
          {
            "node": "Enrich Articles with Topic Classification",
            "type": "main",
            "index": 0
          },
          {
            "node": "Merge",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Embeddings OpenAI1": {
      "ai_embedding": [
        [
          {
            "node": "Weaviate Vector Store1",
            "type": "ai_embedding",
            "index": 0
          }
        ]
      ]
    },
    "OpenAI Chat Model1": {
      "ai_languageModel": [
        [
          {
            "node": "Enrich Articles with Topic Classification",
            "type": "ai_languageModel",
            "index": 1
          }
        ]
      ]
    },
    "Convert XML to JSON": {
      "main": [
        [
          {
            "node": "Split Results",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Default Data Loader": {
      "ai_document": [
        [
          {
            "node": "Weaviate Vector Store",
            "type": "ai_document",
            "index": 0
          }
        ]
      ]
    },
    "Add Static sessionId": {
      "main": [
        [
          {
            "node": "Agentic RAG for Trend Analysis",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "OpenRouter Chat Model": {
      "ai_languageModel": [
        [
          {
            "node": "Agentic RAG for Trend Analysis",
            "type": "ai_languageModel",
            "index": 0
          }
        ]
      ]
    },
    "Weaviate Vector Store": {
      "main": [
        [
          {
            "node": "Aggregate Uploaded arXiv IDs",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "OpenRouter Chat Model1": {
      "ai_languageModel": [
        [
          {
            "node": "Enrich Articles with Topic Classification",
            "type": "ai_languageModel",
            "index": 0
          }
        ]
      ]
    },
    "OpenRouter Chat Model2": {
      "ai_languageModel": [
        [
          {
            "node": "Structured Output Parser1",
            "type": "ai_languageModel",
            "index": 0
          }
        ]
      ]
    },
    "Prep Data for Weaviate": {
      "main": [
        [
          {
            "node": "Remove Duplicates",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Weaviate Vector Store1": {
      "ai_tool": [
        [
          {
            "node": "Agentic RAG for Trend Analysis",
            "type": "ai_tool",
            "index": 0
          }
        ]
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    },
    "Structured Output Parser": {
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          {
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            "type": "ai_outputParser",
            "index": 0
          }
        ]
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    },
    "Structured Output Parser1": {
      "ai_outputParser": [
        [
          {
            "node": "Enrich Articles with Topic Classification",
            "type": "ai_outputParser",
            "index": 0
          }
        ]
      ]
    },
    "Aggregate Uploaded arXiv IDs": {
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          {
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          }
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          {
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          }
        ]
      ]
    },
    "Recursive Character Text Splitter1": {
      "ai_textSplitter": [
        [
          {
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            "type": "ai_textSplitter",
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          }
        ]
      ]
    },
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            "index": 1
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}