feat(config): add ModelSelector for profile-aware model picking

Routes NER, embedding, and translation model choices through the
runtime profile so low-profile deployments use small fast models
and high-profile deployments use larger accurate models.

Helper functions: get_max_workers(), get_batch_size(),
get_graph_depth(), get_investigation_layers(), get_cache_ttl()

config/model_selector.py

@@ -0,0 +1,55 @@
+"""
+BharatGraph - Phase 31: Model Selector
+Picks the right AI model variant based on the runtime profile.
+LOW  -> smallest/fastest models (CPU-only, fits in 2GB RAM)
+HIGH -> larger/more accurate models (GPU or high-RAM server)
+Pure ASCII.
+"""
+from config.runtime_profile import PROFILE
+
+
+MODEL_VARIANTS = {
+    "ner": {
+        "low":    "xx_ent_wiki_sm",
+        "medium": "en_core_web_sm",
+        "high":   "en_core_web_trf",
+    },
+    "embeddings": {
+        "low":    "sentence-transformers/paraphrase-MiniLM-L3-v2",
+        "medium": "sentence-transformers/all-MiniLM-L6-v2",
+        "high":   "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2",
+    },
+    "translation": {
+        "low":    "Helsinki-NLP/opus-mt-en-hi",
+        "medium": "Helsinki-NLP/opus-mt-en-hi",
+        "high":   "Helsinki-NLP/opus-mt-en-ROMANCE",
+    },
+}
+
+
+def get_model(task: str) -> str:
+    """Return the model name for a task given the current runtime profile."""
+    profile_name = PROFILE.name
+    variants     = MODEL_VARIANTS.get(task, {})
+    model        = variants.get(profile_name, variants.get("medium", ""))
+    return model
+
+
+def get_max_workers() -> int:
+    return PROFILE["max_workers"]
+
+
+def get_batch_size() -> int:
+    return PROFILE["batch_size"]
+
+
+def get_graph_depth() -> int:
+    return PROFILE["graph_depth"]
+
+
+def get_investigation_layers() -> int:
+    return PROFILE["investigation_layers"]
+
+
+def get_cache_ttl() -> int:
+    return PROFILE["cache_ttl_seconds"]