Jira offene Probleme und Kommentare in Pinecone laden

• •Möglicherweise sind Ziel-API-Anmeldedaten erforderlich
• •OpenAI API Key
• •Pinecone API Key

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              "value": "=project = CS AND issuetype = Case AND resolution = Unresolved AND created >= -365d "
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        "jsCode": "return items[0].json.issues.map(issue => {\n  const rawDate = issue.fields.customfield_10317?.ongoingCycle?.startTime?.jira ?? null;\n  const formattedDate = rawDate \n    ? new Date(rawDate).toLocaleString(\"it-IT\", {\n        year: \"numeric\",\n        month: \"2-digit\",\n        day: \"2-digit\",\n        hour: \"2-digit\",\n        minute: \"2-digit\",\n        hour12: false\n      }).replace(',', '') \n    : null;\n\n  return {\n    json: {\n      issue_key: issue.key ?? null,\n      issue_id: issue.id ?? null,\n      summary: issue.fields.summary ?? null,\n      issue_description: issue.fields?.description ?? null,\n      product: issue.fields.customfield_10402?.[0].replace(/\\s*\\(.*?\\)\\s*/g, '').trim() ?? null,\n      level: issue.fields.customfield_10408?.[0].replace(/\\s*\\(.*?\\)\\s*/g, '').trim() ?? null,\n      customer: issue.fields.customfield_10409?.[0]?.replace(/\\s*\\(.*?\\)\\s*/g, '').trim() ?? null,\n      status: issue.fields.customfield_10001?.currentStatus?.status ?? null,\n      classification: issue.fields.customfield_10322?.value ?? null,\n      registration: formattedDate\n    }\n  };\n});\n\n\n"
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        "content": "## Published also as MCP tool"
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        "content": "## Jira openissues Extraction with merged user Comments \n"
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        "content": "## Load Jira open issues with comments into Pinecone\n\nThis workflow extracts unresolved Jira tickets (with pagination), merges user comments, cleans and transforms the text, generates embeddings with OpenAI, and stores them in a Pinecone index. The index is **recreated at every iteration** so that it always reflects the current set of unresolved issues. It also exposes the data as an MCP tool for external semantic queries.\n\n### How it works\n- **Trigger**: Runs on a schedule (8, 11, 14, 17 on weekdays).\n- **Jira issues extraction with pagination**: Fetches open issues in batches of 25 until all are loaded.\n- **Transform fields**: Extracts key info (issue ID, key, summary, description, product, customer, classification, status, registration date).\n- **Merge comments**: Collects all comments, filters out irrelevant ones (images, dots, empty markdown).\n- **Clean text**: Converts HTML content into plain text.\n- **Embedding**: Generates embeddings via OpenAI (dimensions = 512).\n- **Vector storage**: Saves vectors + metadata in Pinecone (`jira` namespace, `openissues` index). The namespace is cleared at every run to ensure only unresolved tickets remain indexed.\n- **Chunking**: Splits long texts into 512-token chunks with 50 overlap.\n- **MCP publishing**: Makes the Pinecone index available as a tool for external querying.\n\n### Setup\n1. Configure Jira credentials and adjust the JQL query as needed.\n2. Provide an OpenAI API key for embeddings.\n3. Create a Pinecone index (`openissues`, 512 dimensions) and set the namespace (`jira`).\n4. Adjust the cron expression for your schedule.\n5. (Optional) Enable the MCP nodes if you want to expose the index as an MCP tool.\n\n### Requirements\n- Jira account with API access.\n- OpenAI API key.\n- Pinecone account with a 512-dimension index.\n- n8n instance with Jira, Pinecone, and OpenAI credentials configured.\n\n### Customize\n- Change the JQL query to refine which tickets are fetched.\n- Modify `maxResults` (default 25) to adjust pagination batch size.\n- Add/remove metadata fields in the \"Extract Relevant Info\" node.\n- Adjust chunk size and overlap in the Document Chunker.\n- Swap embedding/vector store providers if needed.\n- Extend with alerts, dashboards, or AI assistants powered by the indexed data.\n\n\n "
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        "content": "## Pinecone Vector Store\n\nLoads paged openissues into a Pinecone Index\n\n"
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        "promptType": "define"
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              "value": "Service Levels\n\nBelow are the service levels related to the basic Application Maintenance service (SAMB, SAMA, SAMFS) that Siav commits to respect regarding the resolution of detected and reported anomalies.\n\nService Implementation Timelines\nThe service implementation timelines are defined based on:\n\nStatus of the ticket handling\nSeverity of the problem\nLevel of Application Maintenance subscribed\nTicket Handling Process\n\nIn the process of resolving an anomaly or handling a ticket, four main phases can be identified:\n\nTicket Registration: The ticket received by the support center (phone request) is registered in the Siav information system, which generates a unique identification code.\nSeverity Code Assignment and Ticket Number Communication: The unique identifier assigned to the TDS ticket, along with the correctly assigned severity level, is communicated to the Client.\nTicket Acceptance: The ticket enters this phase when a Siav technician takes it over and starts processing it.\nTicket Closure: When the assigned personnel inform the support service that the requested assistance has been provided to the Client (resolution of the reported issue or fulfillment of the specific request), the ticket is considered closed, and the closure details are communicated to the Client contact indicated at the ticket registration.\nProblem Severity Definition\nThe severity of the problem depends on two factors: Urgency and Impact. Below are the standard criteria for their assignment and the matrix for calculating Severity and the related response times.\n\nUrgency Level\nIndicates how quickly a restoration is needed, considering the Customer's business needs (communicated at ticket opening), and is assigned based on which and how many functionalities are no longer available.\nThe defined levels are:\n\nCritical: All functions are unavailable, with certainty or possibility of data corruption; immediate restoration is required to resume the Client's blocked activities.\nHigh: Most functionalities are unavailable, or the problem concerns important functions that block the Client’s business; rapid restoration is required to meet externally imposed deadlines.\nMedium: The unavailable functionalities are not crucial and do not prevent the Customer from continuing operations; short-term restoration is needed to meet internal deadlines.\nLow: The unavailability is limited to a few or minor functions, whose restoration can occur over a medium/long term period.\n\nImpact\nRefers to the number and type of users affected by the problems or anomalies.\n\n\n...."
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          "title": "⚒️  Ticket Jira ancora aperti  ⚒️",
          "subtitle": "=🕑 Refresh automatico ogni giorno alle 8, 11, 14, 17 🕑\n",
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        },
        "initialMessages": "Come posso aiutarti oggi? 😎"
      },
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      "name": "openIssues",
      "type": "@n8n/n8n-nodes-langchain.vectorStorePinecone",
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      "parameters": {
        "mode": "retrieve-as-tool",
        "topK": 10,
        "options": {
          "pineconeNamespace": "jira"
        },
        "toolName": "openIssue",
        "pineconeIndex": {
          "__rl": true,
          "mode": "list",
          "value": "openissues",
          "cachedResultName": "openissues"
        },
        "toolDescription": "Retrieve information on open tickets for Siav's clients.\nAlways use the 'client' metadata to filter the search results."
      },
      "credentials": {
        "pineconeApi": {
          "id": "d4Df4tS2jOYakidJ",
          "name": "PineconeApi account 2"
        }
      },
      "typeVersion": 1.2
    },
    {
      "id": "352b195e-f3b1-407c-8f62-f62ba27304a4",
      "name": "Embeddings OpenAI1",
      "type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
      "position": [
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      ],
      "parameters": {
        "options": {
          "dimensions": 512
        }
      },
      "credentials": {
        "openAiApi": {
          "id": "eftSGhVCoom3z6Q4",
          "name": "OpenAi account"
        }
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    },
    {
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        "color": 3,
        "width": 1632,
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        "content": "## RAG Agent \n"
      },
      "typeVersion": 1
    },
    {
      "id": "273d0858-1dcf-44b1-b336-91eba0e03f43",
      "name": "MCP RAG",
      "type": "@n8n/n8n-nodes-langchain.mcpClientTool",
      "position": [
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      ],
      "parameters": {
        "options": {},
        "endpointUrl": "http://localhost:5678/mcp-test/jiraticket",
        "serverTransport": "httpStreamable"
      },
      "typeVersion": 1.1
    },
    {
      "id": "64e0547e-d42d-4d43-8280-67874159ab88",
      "name": "Haftnotiz5",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
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      ],
      "parameters": {
        "color": 2,
        "width": 1728,
        "height": 736,
        "content": "## You can substitute openissue tool with RAG MCP Tool to use MCP server connection"
      },
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      "parameters": {
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        "content": "## AI Chatbot for Jira open tickets with SLA insights\n\n**Flow structure**  \n1. **ChatTrigger** → starts the conversation with the sales/support team.  \n2. **AI Agent (OpenAI)** → interprets user questions.  \n3. **SLA Node** → provides SLA rules (Basic / Advanced / Full Service, optional Fast Support).  \n4. **Pinecone Query** → searches unresolved Jira tickets (`openissues` index, `jira` namespace).  \n5. **Memory** → keeps short-term context for multi-turn conversations.  \n6. **AI Response** → returns a human-friendly summary with ticket details + SLA info.  \n\n**Key data stored in Pinecone**  \n- Issue key & description  \n- Customer & product  \n- Severity level (Red / Yellow / Green / White)  \n- Status  \n- Contract level (Basic / Advanced / Full Service)  \n- SLA explanation\n"
      },
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