| - After making changes, ALWAYS make sure to start up a new server so I can test it. | |
| - Always look for existing code to iterate on instead of creating new code. | |
| - Do not drastically change the patterns before trying to iterate on existing patterns. | |
| - Always kill all existing related servers that may have been created in previous testing before trying to start a new server. | |
| - Always prefer simple solutions | |
| - Avoid duplication of code whenever possible, which means checking for other areas of the codebase that might already have similar code and functionality | |
| - Write code that takes into account the different environments: dev, test, and prod | |
| - You are careful to only make changes that are requested or you are confident are well understood and related to the change being requested | |
| - When fixing an issue or bug, do not introduce a new pattern or technology without first exhausting all options for the existing implementation. And if you finally do this, make sure to remove the old implementation afterwards so we d |
| from manim import * | |
| import numpy as np | |
| class DualRingHeatTransferAnimation(Scene): | |
| def construct(self): | |
| # Configuration | |
| num_blocks = 20 | |
| num_around = 39 | |
| block_size = 0.4 | |
| outer_radius = 3.3 |
| import geopandas as gpd | |
| import matplotlib.pyplot as plt | |
| import pandas as pd | |
| import matplotlib.colors as mcolors | |
| from shapely.geometry import Polygon, MultiPolygon | |
| import shapely.ops | |
| import os.path | |
| def get_text_color(fill_color): |
This FastAPI application provides an API endpoint for uploading files and generating summaries using LlamaIndex. It supports a wide variety of file types including documents, images, audio, and video files.
| import time | |
| from datetime import timedelta | |
| from datasets import load_dataset | |
| from txtai import LLM | |
| from txtai.pipeline import Labels, HFTrainer | |
| def prompt(text): | |
| text = f""" |
| import instructor | |
| from pydantic import BaseModel, Field | |
| from typing import overload, Union, Literal, Generator | |
| from tqdm.asyncio import tqdm | |
| import asyncio | |
| import numpy as np | |
| import json | |
| import os, sys | |
| import diskcache, inspect, functools | |
| import random |
In the rapidly evolving field of artificial intelligence, the need for a comprehensive and structured repository for algorithms designed for intelligent agents has become increasingly important.
The Agent Algorithm Repository aims to address this need by providing a centralized platform for discovering, sharing, and utilizing a wide range of algorithms. This repository is designed to be language-agnostic, ensuring compatibility with various programming languages and promoting a standardized approach to algorithm description, documentation, and distribution.
The repository facilitates the following key objectives:
This document provides a comprehensive overview of five advanced algorithms, detailing their technical implementations using Python and Pydantic for data validation, as well as asynchronous programming for efficiency. Each algorithm is also explored in terms of practical applications across various domains. The algorithms covered include:
NEUMANN: Differentiable Logic Programs for Abstract Visual Reasoning - This algorithm integrates differentiable logic programming with neural networks, enabling advanced visual reasoning and logical deduction. It is particularly useful in computer vision, robotics, and medical imaging.
Scheduled Policy Optimization for Natural Language Communication - This algorithm optimizes policies for natural language communication, enhancing dialogue systems, customer support automation, and machine translation. It leverages policy gradient methods and scheduled learning to improve interaction quality and efficiency.
**LEFT: Logic-Enhanced Foundatio