SmolAgents' CodeAgent incorrectly processes input before invoking tools, leading to truncation of long text even when a tool is explicitly designed to handle it. This defeats the purpose of tools and prevents reliable execution in workflows requiring large text processing.
SummarizationTool) that accepts long text and chunks it internally.CodeAgent.agent.run(prompt).
This directory contains tools for handling LLM operations—most notably for document summarization—using asynchronous (async) techniques. Async Tools allow SmolAgents to process large inputs efficiently by splitting work into smaller chunks and running parallel API calls, all while keeping integration with the agent framework simple.
Large text documents can exceed the fixed context window of LLMs (e.g. GPT-4), which may result in:
| """ | |
| This script handles the creation and population of an ArangoDB collection containing error codes and messages. | |
| It downloads an error code dataset from a specified URL, parses it into JSON format, and stores it in a collection | |
| for easy reference and querying. The script is designed to be run as a standalone utility to initialize or update | |
| the error code collection in an ArangoDB database. | |
| """ | |
| import requests | |
| import csv | |
| from arango import ArangoClient |
| #!/bin/bash | |
| # arangodump_db.sh | |
| # This script dumps an ArangoDB database from a Docker container with a low memory batch size. | |
| # | |
| # Usage: ./arangodump_db.sh | |
| # Make sure arangodump is in your PATH. | |
| # This script dumps an ArangoDB database from a Docker container with a low memory batch size. | |
| # The dump path follows the structure: backups/<database_name>/<timestamp>/arangodump | |
| # | |
| # Usage: ./arangodump_db.sh |
| import re | |
| import json | |
| from loguru import logger | |
| from pathlib import Path | |
| from arango import ArangoClient | |
| from arango.database import StandardDatabase | |
| from typing import Dict, Any | |
| from search_tool.arangodb.generate_schema_for_llm.utils.arango_utils import initialize_database | |
| from search_tool.shared_utils.json_utils import save_json_to_file |
| #!/usr/bin/env python | |
| # -*- coding: utf-8 -*- | |
| """ | |
| Enhanced Unsloth GRPO with Student-Teacher Reward Mechanism | |
| ------------------------------------------------------------ | |
| This script extends Unsloth's GRPO by implementing a novel student-teacher reward | |
| mechanism for improved reasoning chains. | |
| Key Components: | |
| 1. Student Model (Unsloth GRPO): |
| #!/usr/bin/env python3 | |
| """ | |
| Dynamic and Adaptive Python Environment in a Bubblewrap Sandbox | |
| Overview: | |
| This project demonstrates a minimal, safe, and self-contained Python execution | |
| environment using bubblewrap (bwrap) and uv. The goal is to provide a lightweight | |
| alternative to Docker for running agent code—in this case, dynamically generated | |
| Python code along with its dependencies—within an isolated sandbox. |
| #!/usr/bin/env python3 | |
| """ | |
| Dynamic and Adaptive Python Environment in a Bubblewrap Sandbox | |
| Overview: | |
| This project demonstrates a minimal, safe, and self-contained Python execution | |
| environment using bubblewrap (bwrap) and uv. The goal is to provide a lightweight | |
| alternative to Docker for running agent code—in this case, dynamically generated | |
| Python code along with its dependencies—within an isolated sandbox. |
This README explains how a Latent Program Networks (LPN)-inspired approach can generate tool execution strategies for multi-tool tasks in an agent workflow. This method offers advantages over relying solely on Large Language Models (LLMs) for tool strategy via prompting. A toy Python script demonstrates the concept in action.
Latent Program Networks (LPNs), originally designed for abstract reasoning (e.g., the ARC benchmark), embed "rules" or "completions" into a searchable latent space. Here, we adapt LPNs to embed tool configurations and execution order, enabling smooth coordination of multiple tools (e.g., generate_summary and create_pie_chart) for tasks like summarization and data visualization.