AI・機械学習アイコン

from drawlib.canvas import save, setup
from drawlib.icons import ai, original_ai
from drawlib.shapes import rectangle
from drawlib.styles import Styles
from drawlib.text import text

setup(width=144, height=70)

columns = [
    (
        26.0,
        "1. Official AI Brands",
        "drawlib.icons.ai (60)",
        Styles.Neutral,
        [
            ("openai", ai.openai),
            ("claude", ai.claude),
            ("gemini", ai.gemini),
            ("pytorch", ai.pytorch),
        ],
    ),
    (
        72.0,
        "2. 2D Pastel Brands",
        "original_ai Twins (60)",
        Styles.PrimaryNeutral,
        [
            ("openai", original_ai.openai),
            ("claude", original_ai.claude),
            ("gemini", original_ai.gemini),
            ("pytorch", original_ai.pytorch),
        ],
    ),
    (
        118.0,
        "3. 2D Pastel Concepts",
        "original_ai Exclusive (20)",
        Styles.SecondaryNeutral,
        [
            ("ai_agent", original_ai.ai_agent),
            ("llm_model", original_ai.llm_model),
            ("rag_KB", original_ai.rag_knowledge_base),
            ("guardrail", original_ai.prompt_guardrail),
        ],
    ),
]

for cx, title, subtitle, card_style, icons_list in columns:
    rectangle((cx, 35.0), width=42.0, height=62.0, style=card_style.patch(shape_r=2.5))
    text((cx, 60.0), title, style=Styles.DarkBold.patch(text_size=11.0))
    text((cx, 54.8), subtitle, style=Styles.Muted.patch(text_size=10.0))

    offsets = [(-9.8, 41.5), (9.8, 41.5), (-9.8, 20.5), (9.8, 20.5)]
    sub_card_style = Styles.White.patch(shape_r=1.5, line_width=0.6)
    for (dx, iy), (lbl, fn) in zip(offsets, icons_list, strict=True):
        ix = cx + dx
        rectangle((ix, iy - 1.8), width=17.6, height=17.5, style=sub_card_style)
        fn((ix, iy + 1.5), width=7.5, style=Styles.Primary)
        text((ix, iy - 6.6), lbl, style=Styles.DarkBold.patch(text_size=10.0))

save()

from drawlib.canvas import save, setup
from drawlib.icons import ai
from drawlib.shapes import rectangle
from drawlib.styles import Styles
from drawlib.text import text

setup(width=148, height=64)

row1 = [
    ("openai", "LLM Model", ai.openai, Styles.Neutral),
    ("claude", "LLM Model", ai.claude, Styles.PrimaryNeutral),
    ("gemini", "LLM Model", ai.gemini, Styles.Neutral),
    ("meta_llama", "Open Weights", ai.meta_llama, Styles.SecondaryNeutral),
    ("vllm", "Serving", ai.vllm, Styles.Neutral),
    ("nvidia", "GPU Compute", ai.nvidia, Styles.PrimaryNeutral),
]

row2 = [
    ("langchain", "Agent SDK", ai.langchain, Styles.PrimaryNeutral),
    ("llamaindex", "RAG Framework", ai.llamaindex, Styles.Neutral),
    ("mcp", "Protocol", ai.mcp, Styles.SecondaryNeutral),
    ("pytorch", "Deep Learning", ai.pytorch, Styles.Neutral),
    ("huggingface", "Model Hub", ai.huggingface, Styles.PrimaryNeutral),
    ("langfuse", "Observability", ai.langfuse, Styles.Neutral),
]

for row_idx, items in enumerate([row1, row2]):
    cy = 46.5 if row_idx == 0 else 17.5
    for col_idx, (name, cat, icon_fn, card_st) in enumerate(items):
        cx = 15.2 + col_idx * 23.5
        rectangle((cx, cy), width=21.8, height=25.0, style=card_st.patch(shape_r=2))
        icon_fn((cx, cy + 4.8), width=8.0, style=Styles.Primary)
        text((cx, cy - 3.4), name, style=Styles.DarkBold.patch(text_size=10.0))
        text((cx, cy - 8.2), cat, style=Styles.Muted.patch(text_size=10.0))

save()

from drawlib.canvas import save, setup
from drawlib.icons import original_ai
from drawlib.shapes import rectangle
from drawlib.styles import Styles
from drawlib.text import text

setup(width=140, height=88)

rows = [
    [
        ("ai_agent", "Autonomous Agent", original_ai.ai_agent, Styles.PrimaryNeutral),
        ("multi_agent", "Swarm / Crew", original_ai.multi_agent, Styles.Neutral),
        ("agent_memory", "Context & State", original_ai.agent_memory, Styles.SecondaryNeutral),
        ("function_calling", "Structured Tool Use", original_ai.function_calling, Styles.Neutral),
    ],
    [
        ("llm_model", "Foundation Model", original_ai.llm_model, Styles.Neutral),
        ("prompt_guardrail", "Safety & Policy", original_ai.prompt_guardrail, Styles.PrimaryNeutral),
        ("rag_knowledge_base", "Document Retrieval", original_ai.rag_knowledge_base, Styles.Neutral),
        ("embedding_vector", "Dense Embeddings", original_ai.embedding_vector, Styles.SecondaryNeutral),
    ],
    [
        ("vector_database", "ANN Vector Index", original_ai.vector_database, Styles.SecondaryNeutral),
        ("fine_tuning", "LoRA / SFT", original_ai.fine_tuning, Styles.Neutral),
        ("evaluation", "LLM-as-a-Judge", original_ai.evaluation, Styles.PrimaryNeutral),
        ("gpu_node", "GPU Accelerator", original_ai.gpu_node, Styles.Neutral),
    ],
]

for row_idx, items in enumerate(rows):
    cy = 71.5 - row_idx * 27.5
    for col_idx, (title, sub, icon_fn, card_st) in enumerate(items):
        cx = 20.5 + col_idx * 33.0
        rectangle((cx, cy), width=30.5, height=24.5, style=card_st.patch(shape_r=2))
        icon_fn((cx, cy + 4.6), width=8.0, style=Styles.Primary)
        text((cx, cy - 3.4), title, style=Styles.DarkBold.patch(text_size=10.0))
        text((cx, cy - 8.2), sub, style=Styles.Muted.patch(text_size=10.0))

save()