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class CellSignalingSimulationAgent: def run(self, df_signal: pd.DataFrame) -> AgentResult: peak_receptor = float(df_signal[“receptor_active”].max()) peak_kinase = float(df_signal[“kinase_active”].max()) peak_tf = float(df_signal[“tf_active”].max()) t_receptor = float(df_signal.loc[df_signal[“receptor_active”].idxmax(), “time”]) t_kinase = float(df_signal.loc[df_signal[“kinase_active”].idxmax(), “time”]) t_tf…

Voice assistants that engage in back-and-forth communication are something you’ve likely experienced. But a voice assistant that provides rational, uninterrupted exchanges via spoken dialogue? That’s what…

In this tutorial, we build a complete, production-style pipeline for detecting and redacting personally identifiable information using the OpenAI Privacy Filter. We begin by setting up…

Image by Editor   # Introduction  Python decorators can be incredibly useful in projects involving AI and machine learning system development. They excel at separating key logic…

(WORK_DIR / “judge.prompty”).write_text(“””— name: Judge model: api: chat configuration: type: openai connection: open_ai_connection model: gpt-4o-mini parameters: temperature: 0 max_tokens: 150 response_format: {type: json_object} inputs: question: {type:…

import random, numpy as np, torch, torch.nn as nn, torch.nn.functional as F import matplotlib.pyplot as plt from dataclasses import dataclass from typing import Tuple, Dict, List…

Enterprises building AI agents often require more than what managed foundation model (FM) services can provide. They need precise control over performance tuning, cost optimization at…

@dataclass class MemoryItem: memory_id: int topic: str entity: str slot: str value: str text: str def build_memory_bank() -> List[MemoryItem]: entities = [ { “entity”: “Astra”, “topic”:…

With Amazon Bedrock Knowledge Bases, you can give foundation models (FMs) and agents contextual information from your organization’s private data sources to deliver more relevant, accurate,…

import subprocess, sys def pip(*pkgs): subprocess.check_call([sys.executable, “-m”, “pip”, “install”, “-q”, *pkgs]) pip(“budoux”) import json, time, textwrap, html, random, re, os, tempfile from pathlib import Path import…