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# Introduction
Welcome to this first **Bonus Unit**, where you'll learn to **fine-tune a Large Language Model (LLM) for function calling**.
In terms of LLMs, function calling is quickly be... | agents-course/units/en/bonus-unit1/introduction.mdx/0 | {
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# Introduction to Agentic Frameworks
<img src="https://huggingface.co/datasets/agents-course/course-images/resolve/main/en/unit2/thumbnail.jpg" alt="Thumbnail"/>
Welcome to this second unit, where **we'll explore different agentic frameworks** that can be used to build powerful agentic applications.
We will study:
... | agents-course/units/en/unit2/introduction.mdx/0 | {
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# Using Tools in LlamaIndex
**Defining a clear set of Tools is crucial to performance.** As we discussed in [unit 1](../../unit1/tools), clear tool interfaces are easier for LLMs to use.
Much like a software API interface for human engineers, they can get more out of the tool if it's easy to understand how it works.
... | agents-course/units/en/unit2/llama-index/tools.mdx/0 | {
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# Agentic Retrieval Augmented Generation (RAG)
In this unit, we'll be taking a look at how we can use Agentic RAG to help Alfred prepare for the amazing gala.
<Tip>We know we've already discussed Retrieval Augmented Generation (RAG) and agentic RAG in the previous unit, so feel free to skip ahead if you're already fa... | agents-course/units/en/unit3/agentic-rag/agentic-rag.mdx/0 | {
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# Observabilidad y Evaluación de Agentes de IA
¡Bienvenido a la **Unidad Extra 2**! En este capítulo, explorarás estrategias avanzadas para observar, evaluar y, en última instancia, mejorar ... | agents-course/units/es/bonus-unit2/introduction.mdx/0 | {
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# Entendiendo los Agentes de IA a través del Ciclo Pensamiento-Acción-Observación
<img src="https://huggingface.co/datasets/agents-course/course-images/resolve/main/en/unit1/whiteboard-check-3.jpg" alt="Planificación de la Unidad 1"/>
En las secciones anteriores, aprendimos:
- **Cómo las herramientas se ponen a disp... | agents-course/units/es/unit1/agent-steps-and-structure.mdx/0 | {
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# Conclusión
¡Felicidades por terminar el módulo de `LangGraph` de esta segunda Unidad! 🥳
Ahora has dominado los fundamentos para construir flujos de trabajo estructurados con LangGraph que podrás llevar a producción.
Este módulo es solo el comienzo de tu viaje con LangGraph. Para temas más avanzados, recomendamos:... | agents-course/units/es/unit2/langgraph/conclusion.mdx/0 | {
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<CourseFloatingBanner chapter={2}
classNames="absolute z-10 right-0 top-0"
notebooks={[
{label: "Google Colab", value: "https://colab.research.google.com/#fileId=https://huggingface.co/agents-course/notebooks/blob/main/unit2/smolagents/code_agents.ipynb"},
]} />
# Construcción de Agentes que Usan Código
Los a... | agents-course/units/es/unit2/smolagents/code_agents.mdx/0 | {
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# Introducción al Caso de Uso para RAG Agéntico
En esta unidad, ayudaremos a Alfred, nuestro amigable agente que está organizando la gala, utilizando RAG Agéntico para crear una herra... | agents-course/units/es/unit3/agentic-rag/introduction.mdx/0 | {
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<CourseFloatingBanner chapter={2}
classNames="absolute z-10 right-0 top-0"
notebooks={[
{label: "Google Colab", value: "https://colab.research.google.com/#fileId=https%3A//huggingface.co/agents-course/notebooks/blob/main/fr/bonus-unit2/monitoring-and-evaluating-agents.ipynb"},
]} />
# Observer et évaluer des a... | agents-course/units/fr/bonus-unit2/monitoring-and-evaluating-agents-notebook.mdx/0 | {
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# Conclusion [[conclusion]]
Félicitations pour avoir terminé cette première Unité 🥳
Vous **maîtrisez les fondamentaux** et avez créé votre premier agent !
Il est **normal que vous soyez encore un peu confus par certains éléments**. Les agents sont un sujet complexe et il est courant de mettre un certain temps à tou... | agents-course/units/fr/unit1/conclusion.mdx/0 | {
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# Graphe d'analyse de documents
Alfred à votre service. En tant que majordome de confiance de M. Wayne, j'ai pris la liberté de documenter comment j'aide M. Wayne avec ses divers besoins documentaires. Pendant qu'il s'occupe de ses... activités nocturnes, je m'assure que tous ses papiers, programmes d'entraînement et ... | agents-course/units/fr/unit2/langgraph/document_analysis_agent.mdx/0 | {
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# Conclusion
Félicitations d'avoir terminé le module `smolagents` de cette deuxième unité 🥳
Vous **maîtrisez les fondamentaux** de `smolagents` et vous avez construit votre propre agent ! A présent que vous avez des compétences sur `smolagents`, vous pouvez maintenant commencer à créer des agents qui résoudront des ... | agents-course/units/fr/unit2/smolagents/conclusion.mdx/0 | {
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# Création d'un RAG pour converser avec les invités
Alfred, votre agent de confiance, se prépare pour le gala le plus extravagant du siècle. Pour s'assurer que l'événement se déroule sans encombre, il a besoin d'un accès rapide à des informations à jour sur chaque invité. Aidons le en créant un outil RAG alimenté par ... | agents-course/units/fr/unit3/agentic-rag/invitees.mdx/0 | {
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# 결론 [[conclusion]]
축하합니다! 첫 번째 유닛을 완료하셨네요 🥳
이제 **에이전트의 기본 개념을 마스터**하고 첫 AI 에이전트를 만드셨습니다!
**아직 일부 요소가 혼란스럽게 느껴지는 것은 정상**입니다. 에이전트는 복잡한 주제이며, 모든 것을 이해하는 데 시간이 걸리는 것이 일반적입니다.
계속 진행하기 전에 **배운 내용을 제대로 이해하는 시간을 가지세요**. 재미있는 부분으로 넘어가기 전에 이러한 요소들을 숙달하고 탄탄한 기초를 다지는 것이 중요합니다.
퀴즈 테스트를 통과하셨다면, 인증서를 받는 것도 잊지 마세요 🎓 👉 [여기를 클... | agents-course/units/ko/unit1/conclusion.mdx/0 | {
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# Введение
Добро пожаловать в первый **Бонусный раздел**, в котором вы научитесь **дообучать Большую Языковую Модель (LLM) вызову функций**.
С точки зрения LLM, вызов функций быстро с... | agents-course/units/ru-RU/bonus-unit1/introduction.mdx/0 | {
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# Наблюдение: Интеграция Обратной Связи для Рефлексии и Адаптации
Наблюдения - это то, **как агент воспринимает последствия своих действий**.
Они предоставляют важную информацию, которая подпитывает мыслительный процесс агента и направляет его дальнейшие действия.
Это **сигналы из окружения** - будь то данные из API... | agents-course/units/ru-RU/unit1/observations.mdx/0 | {
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# Chào mừng bạn đến với Khóa học AI Agents 🤗 [[introduction]]
<figure>
<img src="https://huggingface.co/datasets/agents-course/course-images/resolve/main/en/unit0/thumbnail.jpg" alt="Thumbnail khóa học AI Agents" width="100%"/>
<figcaption>Phông nền của hình ảnh được tạo bằng <a href="https://scenario.com/">Scenario.... | agents-course/units/vi/unit0/introduction.mdx/0 | {
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# LLM là gì?
<img src="https://huggingface.co/datasets/agents-course/course-images/resolve/main/en/unit1/whiteboard-check-1.jpg" alt="Unit 1 planning"/>
Ở phần trước, ta đã biết mỗi Agent cần **một mô hình AI làm lõi**, và LLM là loại mô hình AI phổ biến nhất cho mục đích này.
Giờ ta sẽ tìm hiểu LLM là gì và cách ch... | agents-course/units/vi/unit1/what-are-llms.mdx/0 | {
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<CourseFloatingBanner chapter={2}
classNames="absolute z-10 right-0 top-0"
notebooks={[
{label: "Google Colab", value: "https://colab.research.google.com/#fileId=https://huggingface.co/agents-course/notebooks/blob/main/bonus-unit2/monitoring-and-evaluating-agents.ipynb"},
]} />
# 附加单元 2：AI 智能体(AI Agent)的可观测性与评... | agents-course/units/zh-CN/bonus_unit2/monitoring-and-evaluating-agents-notebook.mdx/0 | {
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# Observe: 整合反馈以反思和调整
Observations（观察）是**智能体感知其行动结果的方式**。
它们提供关键信息，为智能体的思考过程提供燃料并指导未来行动。
这些是**来自环境的信号**——无论是 API 返回的数据、错误信息还是系统日志——它们指导着下一轮的思考循环。
在观察阶段，智能体会：
- **收集反馈**：接收数据或确认其行动是否成功
- **附加结果**：将新信息整合到现有上下文中，有效更新记忆
- **调整策略**：使用更新后的上下文来优化后续思考和行动
例如，当天气 API 返回数据*"partly cloudy, 15°C, 60% humidity"*（局部多云，15°C，60% ... | agents-course/units/zh-CN/unit1/observations.mdx/0 | {
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# 目录
此 LlamaIndex 框架大纲是课程第 2 单元的一部分。您可以在 hf.co/learn 上访问有关 LlamaIndex 的第 2 单元 👉 <a href="https://hf.co/learn/agents-course/unit2/llama-index/introduction">这里</a>
| 标题 | 描述 |
| -------------------------------- | ------... | agents-course/units/zh-CN/unit2/llama-index/README.md/0 | {
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# 小测验（不计分）[[quiz2]]
现在该测试您对*代码智能体*、*工具调用智能体*和*工具*章节的理解了。本测验为可选且不计分。
---
### Q1: 使用 `@tool` 装饰器创建工具与创建 `Tool` 的子类之间的主要区别是什么？
以下哪个陈述最能描述这两种定义工具方法的区别？
<Question
choices={[
{
text: "使用 <code>@tool</code> 装饰器是检索类工具的强制要求，而 <code>Tool</code> 的子类仅用于文本生成任务",
explain: "两种方法都适用于任何类型的工具，包括检索类和文本生成类工具。",
},
{
... | agents-course/units/zh-CN/unit2/smolagents/quiz2.mdx/0 | {
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# 动手实践
现在你已经准备好更深入地创建你的最终智能体了，让我们看看如何提交它以供评审。
## 数据集
此排行榜使用的数据集包含从 GAIA **验证**集的一级问题中所提取的 20 个问题。
这些问题是根据回答问题所需的工具和步骤数量进行筛选的。
根据 GAIA 基准目前的状况，我们认为让你尝试在一级问题中达到 30% 的准确率是一个相对好的测试。
<img src="https://huggingface.co/datasets/agents-course/course-images/resolve/main/en/unit4/leaderboard%20GAIA%2024%3A04%3A2025.png" alt... | agents-course/units/zh-CN/unit4/hands-on.mdx/0 | {
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[package]
name = "candle-book"
version.workspace = true
edition.workspace = true
description.workspace = true
repository.workspace = true
keywords.workspace = true
categories.workspace = true
license.workspace = true
readme = "README.md"
[dependencies]
accelerate-src = { workspace = true, optional = true }
candle = { ... | candle/candle-book/Cargo.toml/0 | {
"file_path": "candle/candle-book/Cargo.toml",
"repo_id": "candle",
"token_count": 459
} | 24 |
# Installation
## 1. Create a new rust app or library
```bash
cargo new myapp
cd myapp
```
## 2. Add the correct candle version
### Standard
```bash
cargo add --git https://github.com/huggingface/candle.git candle-core
```
### CUDA
First, make sure that Cuda is correctly installed.
- `nvcc --version` should prin... | candle/candle-book/src/guide/installation.md/0 | {
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# Simplified
## How its works
This program implements a neural network to predict the winner of the second round of elections based on the results of the first round.
Basic moments:
1. A multilayer perceptron with two hidden layers is used. The first hidden layer has 4 neurons, the second has 2 neurons.
2. The inpu... | candle/candle-book/src/training/simplified.md/0 | {
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"repo_id": "candle",
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#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use anyhow::Result;
use candle_core::{Device, Tensor};
fn main() -> Result<()> {
let a = Tensor::new(&[[0.0f32, 1.0, 2.0], [3.0, 4.0, 5.0]], &Device::Cpu)?;
let b = Tensor::new(&[[88.0f32], [99.0]]... | candle/candle-core/examples/basics.rs/0 | {
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} | 27 |
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