AI搭載ダッシュボード：ビジョンからKibanaへ

You are an Elasticsearch Saved-Object generator (Kibana 9.0).
INPUTS
=====
1. PNG screenshot of a 4-panel dashboard (attached).
2. Index mapping (below) – trimmed down to only the fields present in the screenshot.
3. Example NDJSON of *one* metric visualization (below) for reference.

TASK
====
Return **only** a valid NDJSON array that recreates the dashboard exactly:
* 2 metric panels (Visits, Unique Visitors)
* 1 pie chart (Most used OS)
* 1 vertical bar chart (State Geo Dest)
* Use index pattern `kibana_sample_data_logs`.
* Preserve roughly the same layout (2×2 grid).
* Use `panelIndex` values 1-4 and random `id` strings.
* Kibana version: 9.0

export OPENAI_API_KEY="your-openai-api-key"

{
    "agent": "Mozilla/5.0 (X11; Linux i686) AppleWebKit/534.24 (KHTML, like Gecko) Chrome/11.0.696.50 Safari/534.24",
    "bytes": 8509,
    "clientip": "70.133.115.149",
    "extension": "css",
    "geo": {
        "srcdest": "US:IT",
        "src": "US",
        "dest": "IT",
        "coordinates": {
            "lat": 38.05134111,
            "lon": -103.5106908
        }
    },
    "host": "cdn.elastic-elastic-elastic.org",
    "index": "kibana_sample_data_logs",
    "ip": "70.133.115.149",
    "machine": {
        "ram": 5368709120,
        "os": "osx"
    },
    "memory": null,
    "message": "70.133.115.149 - - [2018-08-30T23:35:31.492Z] \"GET /styles/semantic-ui.css HTTP/1.1\" 200 8509 \"-\" \"Mozilla/5.0 (X11; Linux i686) AppleWebKit/534.24 (KHTML, like Gecko) Chrome/11.0.696.50 Safari/534.24\"",
    "phpmemory": null,
    "referer": "http://twitter.com/error/john-phillips",
    "request": "/styles/semantic-ui.css",
    "response": 200,
    "tags": [
        "success",
        "info"
    ],
    "@timestamp": "2025-07-03T23:35:31.492Z",
    "url": "https://cdn.elastic-elastic-elastic.org/styles/semantic-ui.css",
    "utc_time": "2025-07-03T23:35:31.492Z",
    "event": {
        "dataset": "sample_web_logs"
    },
    "bytes_gauge": 8509,
    "bytes_counter": 51201128
}

INDEX_NAME = "kibana_sample_data_logs"

es_client = Elasticsearch(
    [os.getenv("ELASTICSEARCH_URL")],
    api_key=os.getenv("ELASTICSEARCH_API_KEY"),
)

result = es_client.indices.get_mapping(index=INDEX_NAME)
index_mappings = result[list(result.keys())[0]]["mappings"]["properties"]

pip install elasticsearch pydantic langchain langchain-openai -q

class Visualization(BaseModel):
    title: str = Field(description="The dashboard title")
    type: List[Literal["pie", "bar", "metric"]]
    field: str = Field(
        description="The field that this visualization use based on the provided mappings"
    )


class Dashboard(BaseModel):
    title: str = Field(description="The dashboard title")
    visualizations: List[Visualization]

prompt = f"""
    You are an expert in analyzing Kibana dashboards from images for the version 9.0.0 of Kibana.

    You will be given a dashboard image and an Elasticsearch index mapping.

    Below are the index mappings for the index that the dashboard is based on.
    Use this to help you understand the data and the fields that are available.

    Index Mappings:
    {index_mappings}

    Only include the fields that are relevant for each visualization, based on what is visible in the image.
    """

message = [
    {
        "role": "user",
        "content": [
            {"type": "text", "text": prompt},
            {
                "type": "image",
                "source_type": "base64",
                "data": image_base64,
                "mime_type": "image/png",
            },
        ],
    }
]


try:
    llm = init_chat_model("gpt-4.1-mini")
    llm = llm.with_structured_output(Dashboard)
    dashboard_values = llm.invoke(message)

    print("Dashboard values generated by the LLM successfully")
    print(dashboard_values)
except Exception as e:
    print(f"Failed to analyze image and match fields: {str(e)}")

"Dashboard values generated by the LLM successfully
title=""Client, Extension, OS, and Response Keyword Analysis""visualizations="[
   "Visualization(title=""Count of Client IP",
   "type="[
      "metric"
   ],
   "field=""clientip"")",
   "Visualization(title=""Extension Keyword Distribution",
   "type="[
      "pie"
   ],
   "field=""extension.keyword"")",
   "Visualization(title=""Most Used OS",
   "type="[
      "bar"
   ],
   "field=""machine.os.keyword"")",
   "Visualization(title=""Response Keyword Distribution",
   "type="[
      "bar"
   ],
   "field=""response.keyword"")"
]

def fill_template_with_analysis(
    template: Dict[str, Any],
    visualization: Visualization,
    grid_data: Dict[str, Any],
):
    template_str = json.dumps(template)
    replacements = {
	 "{visualization_id}": str(uuid.uuid4()),
        "{title}": visualization.title,
        "{x}": grid_data["x"],
        "{y}": grid_data["y"],
    }

    if visualization.field:
        replacements["{field}"] = visualization.field

    for placeholder, value in replacements.items():
        template_str = template_str.replace(placeholder, str(value))

    return json.loads(template_str)

# Filling templates fields
panels = []    
grid_data = [
    {"x": 0, "y": 0},
    {"x": 12, "y": 0},
    {"x": 0, "y": 12},
    {"x": 12, "y": 12},
]


i = 0

for vis in dashboard_values.visualizations:
    for vis_type in vis.type:
        template = templates.get(vis_type, templates.get("bar", {}))
        filled_panel = fill_template_with_analysis(template, vis, grid_data[i])
        panels.append(filled_panel)
        i += 1

try:
    dashboard_id = str(uuid.uuid4())

    # post request to create the dashboard endpoint
    url = f"{os.getenv('KIBANA_URL')}/api/dashboards/dashboard/{dashboard_id}"

    dashboard_config = {
        "attributes": {
            "title": dashboard_values.title,
            "description": "Generated by AI",
            "timeRestore": True,
            "panels": panels,  # Visualizations with the values generated by the LLM
            "timeFrom": "now-7d/d",
            "timeTo": "now",
        },
    }

    headers = {
        "Content-Type": "application/json",
        "kbn-xsrf": "true",
        "Authorization": f"ApiKey {os.getenv('ELASTICSEARCH_API_KEY')}",
    }

    requests.post(
        url,
        headers=headers,
        json=dashboard_config,
    )

    # Url to the generated dashboard
    dashboard_url = f"{os.getenv('KIBANA_URL')}/app/dashboards#/view/{dashboard_id}"

    print("Dashboard URL: ", dashboard_url)
    print("Dashboard ID: ", dashboard_id)

except Exception as e:
    print(f"Failed to create dashboard: {str(e)}")

python <file_name>.py