Browsing: build

class EverMemAgentOS: def __init__( self, workdir: str = “/content/evermem_agent_os”, db_name: str = “evermem.sqlite”, embedding_model: str = “sentence-transformers/all-MiniLM-L6-v2”, gen_model: str = “google/flan-t5-small”, stm_max_turns: int = 10, ltm_topk:…

In this tutorial, we demonstrate how to efficiently fine-tune a large language model using Unsloth and QLoRA. We focus on building a stable, end-to-end supervised fine-tuning…

Are you struggling to balance generative AI safety with accuracy, performance, and costs? Many organizations face this challenge when deploying generative AI applications to production. A…

In this tutorial, we design an end-to-end, production-style analytics and modeling pipeline using Vaex to operate efficiently on millions of rows without materializing data in memory.…

Customer service teams face a persistent challenge. Existing chat-based assistants frustrate users with rigid responses, while direct large language model (LLM) implementations lack the structure needed…

INSTANCE_I = int(np.clip(INSTANCE_I, 0, len(X_test)-1)) x = X_test.iloc[INSTANCE_I].values y_true = float(y_test.iloc[INSTANCE_I]) pred = float(model.predict([x])[0]) iv = explainer.explain(x, budget=int(BUDGET_LOCAL), random_state=0) baseline = float(getattr(iv, “baseline_value”, 0.0)) main_effects =…

def executor_agent(step: Dict[str, Any], context: Dict[str, Any]) -> StepResult: step_id = int(step.get(“id”, 0)) title = step.get(“title”, f”Step {step_id}”) tool = step.get(“tool”, “llm”) ctx_compact = { “goal”:…

def create_marker_cluster_map(): “””Create a map with marker clustering for large datasets””” np.random.seed(123) n_locations = 5000 lats = np.random.uniform(25, 49, n_locations) lons = np.random.uniform(-125, -65, n_locations) values…

def draw_ring(ring: ConsistentHashRing, dist: Dict[str, int], title: str): node_ids = sorted(ring.nodes.keys()) plt.figure(figsize=(8, 8)) ax = plt.gca() ax.set_title(title) if not node_ids: plt.text(0.5, 0.5, “Ring is empty”, ha=”center”,…

In this tutorial, we build an advanced Griptape-based customer support automation system that combines deterministic tooling with agentic reasoning to process real-world support tickets end-to-end. We…