# Multi-dimensional brand essence extraction
import torch.nn.functional as F
from transformers import AutoModel, AutoTokenizer
# Processing 847 brand touchpoints...
semantic_embeddings = model.encode([
brand_voice_corpus, # 12,847 text samples
visual_style_vectors, # 3,291 design elements
behavioral_patterns, # 8,934 interaction logs
strategic_positioning # competitive analysis
]).reshape(1024, -1)
# Registering 1024-dim vector to MCP endpoint
> mcp_server.register_tool(
name="brand_dna_oracle",
vector=brand_dna.detach().numpy(),
similarity_threshold=0.847
)
✓ Brand DNA vectorized: 1024 dimensions