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4f1e196 3ad32ba 4f1e196 db54566 4f1e196 db54566 4f1e196 db54566 4f1e196 ee3e8fe 4f1e196 ee3e8fe 4f1e196 db54566 4f1e196 3ad32ba | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 | from rest_framework.response import Response
from rest_framework.views import APIView
from api.exceptions import InvalidRequestError, NotFoundError
from api.services.constants import (
COINS_DATASET_META, COINS_MODELS, QUERY_STRUCTURES,
QUERY_STRUCTURE_INTERNAL, QUERY_TREE_MAPPINGS,
)
from api.services.registry import ModelRegistry
from api.utils import clean_entity_name, clean_relation_name
def _require_loader(dataset_id):
"""Validate dataset_id and ensure its Loader is available."""
if dataset_id not in COINS_DATASET_META:
raise NotFoundError(f"Dataset '{dataset_id}' not found")
registry = ModelRegistry.get()
if registry.get_loader(dataset_id) is None:
raise NotFoundError(f"Dataset '{dataset_id}' data not loaded")
return registry
class CoinsDatasetsView(APIView):
def get(self, request):
registry = ModelRegistry.get()
datasets = []
for dataset_id, meta in COINS_DATASET_META.items():
datasets.append({
"id": dataset_id,
"name": meta["name"],
"num_entities": registry.get_entity_count(dataset_id),
"num_relations": registry.get_relation_count(dataset_id),
"description": meta["description"],
})
return Response({"datasets": datasets})
class CoinsEntitiesView(APIView):
def get(self, request, dataset_id):
registry = _require_loader(dataset_id)
q = request.query_params.get("q", None)
page = int(request.query_params.get("page", 1))
page_size = int(request.query_params.get("page_size", 50))
page_size = max(1, min(200, page_size))
page_items, total = registry.search_entities(dataset_id, q, page, page_size)
return Response({
"dataset_id": dataset_id,
"total": total,
"page": page,
"page_size": page_size,
"entities": [
{"id": eid, "name": name, "label": clean_entity_name(name, dataset_id)}
for eid, name in page_items
],
})
class CoinsRelationsView(APIView):
def get(self, request, dataset_id):
registry = _require_loader(dataset_id)
q = request.query_params.get("q", None)
page = int(request.query_params.get("page", 1))
page_size = int(request.query_params.get("page_size", 50))
page_size = max(1, min(200, page_size))
page_items, total = registry.search_relations(dataset_id, q, page, page_size)
return Response({
"dataset_id": dataset_id,
"total": total,
"page": page,
"page_size": page_size,
"relations": [
{"id": rid, "name": name, "label": clean_relation_name(name, dataset_id)}
for rid, name in page_items
],
})
class CoinsSampleTriplesView(APIView):
def get(self, request, dataset_id):
registry = _require_loader(dataset_id)
count = int(request.query_params.get("count", 10))
count = max(1, min(50, count))
seed_raw = request.query_params.get("seed")
seed = seed_raw if seed_raw not in (None, "") else None
return Response({
"dataset_id": dataset_id,
"triples": registry.sample_triples(dataset_id, count, seed=seed),
})
class CoinsSampleQueryView(APIView):
def get(self, request, dataset_id):
registry = _require_loader(dataset_id)
query_structure = request.query_params.get("query_structure")
if not query_structure:
raise InvalidRequestError("Missing required parameter: query_structure")
valid_qs = {qs["id"] for qs in QUERY_STRUCTURES}
if query_structure not in valid_qs:
raise InvalidRequestError(
f"Unknown query_structure '{query_structure}'. Must be one of: {sorted(valid_qs)}"
)
count = int(request.query_params.get("count", 1))
count = max(1, min(10, count))
seed_raw = request.query_params.get("seed")
seed = seed_raw if seed_raw not in (None, "") else None
queries = registry.sample_query(dataset_id, query_structure, count, seed=seed)
return Response({
"dataset_id": dataset_id,
"query_structure": query_structure,
"queries": queries,
})
class CoinsModelsView(APIView):
def get(self, request):
registry = ModelRegistry.get()
models = []
for model in COINS_MODELS:
available_datasets = []
for dataset_id in COINS_DATASET_META:
algos = registry.coins_checkpoints_available.get(dataset_id, [])
if model["algorithm"] in algos:
available_datasets.append(dataset_id)
models.append({
"algorithm": model["algorithm"],
"name": model["name"],
"description": model["description"],
"supported_query_structures": model["supported_query_structures"],
"available_datasets": available_datasets,
})
return Response({"models": models})
class CoinsQueryStructuresView(APIView):
def get(self, request):
return Response({"query_structures": QUERY_STRUCTURES})
class CoinsPredictView(APIView):
def post(self, request):
from api.exceptions import InferenceBusy, ModelUnavailable, InferenceError
data = request.data
# --- Parse required fields ---
dataset_id = data.get("dataset_id")
algorithm = data.get("algorithm")
query_structure = data.get("query_structure")
anchors = data.get("anchors")
relations = data.get("relations")
variables = data.get("variables") or {}
top_k = int(data.get("top_k", 10))
top_k = max(1, min(10, top_k))
# --- Validate required fields ---
if not all([dataset_id, algorithm, query_structure, anchors is not None, relations is not None]):
raise InvalidRequestError(
"Missing required field(s): dataset_id, algorithm, query_structure, anchors, relations"
)
if dataset_id not in COINS_DATASET_META:
raise NotFoundError(f"Dataset '{dataset_id}' not found")
valid_algorithms = [m["algorithm"] for m in COINS_MODELS]
if algorithm not in valid_algorithms:
raise InvalidRequestError(f"Unknown algorithm '{algorithm}'")
if query_structure not in QUERY_STRUCTURE_INTERNAL:
raise InvalidRequestError(f"Unknown query structure '{query_structure}'")
# Check algorithm supports query_structure
algo_model = next(m for m in COINS_MODELS if m["algorithm"] == algorithm)
if query_structure not in algo_model["supported_query_structures"]:
raise InvalidRequestError(
f"Algorithm '{algorithm}' does not support query structure '{query_structure}'"
)
# Check checkpoint available
registry = ModelRegistry.get()
algo_available = registry.coins_checkpoints_available.get(dataset_id, [])
if algorithm not in algo_available:
raise ModelUnavailable(
f"Model for dataset '{dataset_id}' with algorithm '{algorithm}' is not loaded"
)
qs_mapping = QUERY_TREE_MAPPINGS[query_structure]
# Find anchor and variable node IDs from template
qs_template = next(qs for qs in QUERY_STRUCTURES if qs["id"] == query_structure)
anchor_node_ids = {n["id"] for n in qs_template["nodes"] if n["type"] == "anchor"}
variable_node_ids = {n["id"] for n in qs_template["nodes"] if n["type"] == "variable"}
edge_ids = {e["id"] for e in qs_template["edges"]}
if set(anchors.keys()) != anchor_node_ids:
raise InvalidRequestError(
f"anchors keys {set(anchors.keys())} must exactly match anchor nodes {anchor_node_ids}"
)
if set(relations.keys()) != edge_ids:
raise InvalidRequestError(
f"relations keys {set(relations.keys())} must exactly match edge IDs {edge_ids}"
)
if not set(variables.keys()).issubset(variable_node_ids):
raise InvalidRequestError(
f"variables keys {set(variables.keys())} must be a subset of variable nodes {variable_node_ids}"
)
# Validate entity IDs in range
num_entities = registry.get_entity_count(dataset_id)
num_rels = registry.get_relation_count(dataset_id)
for api_id, eid in {**anchors, **variables}.items():
if not (0 <= int(eid) < num_entities):
raise InvalidRequestError(
f"Entity ID {eid} at node '{api_id}' out of range [0, {num_entities})"
)
for api_id, rid in relations.items():
if not (0 <= int(rid) < num_rels):
raise InvalidRequestError(
f"Relation ID {rid} at edge '{api_id}' out of range [0, {num_rels})"
)
# Convert to int
anchors = {k: int(v) for k, v in anchors.items()}
variables = {k: int(v) for k, v in variables.items()}
relations = {k: int(v) for k, v in relations.items()}
try:
result = registry.coins_predict(
dataset_id, algorithm, query_structure,
anchors, variables, relations, top_k,
)
except InferenceBusy:
raise
except InvalidRequestError:
raise
except ModelUnavailable:
raise
except Exception as exc:
raise InferenceError(f"Inference failed: {exc}") from exc
return Response(result)
|