import { Pipeline } from '@gargantua/core';
import { SchemaRegistry } from './registry';
const pipeline = new Pipeline({
source: 'enterprise-lake',
transforms: [
normalize({ encoding: 'utf-8' }),
deduplicate({ key: 'entity_id' }),
enrich({ provider: 'knowledge-graph' }),
],
});
async function ingest(stream) {
const schema = await SchemaRegistry
.resolve(stream.metadata);
return pipeline.run(stream, { schema });
}
export const DataMastery = {
ontology: buildOntology(sources),
validate: (record) => schema.check(record),
pipeline: pipeline.connect(),
};
from transformers import AutoModel
from gargantua.cognitive import Agent
class CognitiveEngine:
def __init__(self, config):
self.model = AutoModel.from_pretrained(
config.base_model,
quantization='int8',
)
self.agent = Agent(
reasoning='chain-of-thought',
tools=config.tool_registry,
)
async def inference(self, prompt):
context = await self.agent.plan(prompt)
embeddings = self.model.encode(context)
return self.agent.execute(
embeddings,
temperature=0.7,
max_tokens=4096,
)
terraform {
required_providers {
gargantua = {
source = "gargantua/ecosystem"
version = "~> 3.0"
}
}
}
resource "ecosystem_platform" "main" {
name = "enterprise-mesh"
region = var.deployment_region
scaling = {
min_nodes = 3
max_nodes = 120
strategy = "predictive"
}
engagement_layer {
analytics = true
realtime = true
cdn = "edge-optimized"
}
}
const nexus = await connect({
endpoint: process.env.NEXUS_URL,
auth: { type: 'bearer', token },
});
await nexus.stream('telemetry', {
window: '5m',
aggregate: 'p99',
filter: (e) => e.latency > 200,
});
model = Sequential([
layers.Dense(512, activation='relu'),
layers.Dropout(0.3),
layers.Dense(256, activation='relu'),
layers.Dense(num_classes, activation='softmax'),
])
model.compile(
optimizer=Adam(lr=3e-4),
loss='categorical_crossentropy',
metrics=['accuracy', 'f1_score'],
)
