This tutorial walks through installing LangChain, creating an LLM (via own GPU resources or third‑party APIs), designing parameterized prompt templates, implementing a custom output parser for structured results, and running a complete Python example that generates culturally specific names.
Amap’s visual‑technology pipeline automatically generates and updates POI names by crowdsourcing street‑level images, applying deep‑learning scene‑text recognition, dual‑branch classification of text attributes, and a BERT‑plus‑graph‑attention model that selects and orders recognized text, achieving about 95 % naming accuracy.
Amap's POI naming automation uses a two-stage cascade model: Stage 1 extracts token and sentence features with POS tags and domain-adapted BERT‑POI; Stage 2 employs a Bi‑LSTM to model line relationships, achieving over 95% semantic accuracy and 3‑6% recall improvements, thereby enhancing automatic signboard‑based POI name generation.
