Rig Agents：高阶LLM编排框架 - 链载Ai

在LLM应用开发中，如何高效管理模型、上下文和工具，构建强大的智能体（Agent）Rig提供了一种高阶的LLM编排框架，帮助开发者轻松集成RAG（检索增强生成）、工具调用和自定义配置。本文将深入解析Rig Agents的核心概念、使用方式及最佳实践，助你构建从基础聊天机器人到复杂RAG知识问答系统的AI应用。

Rig Agents是Rig框架中针对LLM高阶封装的核心组件，它提供了一种模块化、可扩展的方式来管理AI代理的行为。一个Agent结合了模型、上下文、工具和配置，能够适应各种AI应用场景。

use rig::{providers:penai};use rig::completion:rompt;
#[tokio::main]async fn main() ->Result<(),Box<dyn std::error::Error>> {  let client = openai::Client::from_url("ollama","http://localhost:11434/v1");
  let agent = client.agent("qwen2:7b")    .preamble("you are a helpful assistant.")    .build();
  let response = agent.prompt("halo").await?;
  println!("{:#?}", response);
 Ok(())}

RAG（Retrieval-Augmented Generation，检索增强生成）可以让AI从知识库中动态检索信息，提高回答的准确性。

userig::{providers:penai,Embed};useserde::{Serialize};userig::completion:rompt;userig::embeddings::EmbeddingsBuilder;userig::vector_store::in_memory_store::InMemoryVectorStore;
#[derive(Embed, Serialize, Clone, Debug, Eq, PartialEq, Default)]struct WordDefinition {  id: String,  word: String, #[embed]  definitions: Vec<String>,}
#[tokio::main]async fn main() -> Result<(), Box<dyn std::error::Error>> {  let client = openai::Client::from_url("ollama","http://localhost:11434/v1");
  let embedding_model = client.embedding_model("qwen2:7b");
  let embeddings =EmbeddingsBuilder::new(embedding_model.clone())    .documents(vec![      WordDefinition {       id:"doc0".to_string(),       word:"flurbo".to_string(),       definitions: vec![         "1. *flurbo* (name): A flurbo is a green alien that lives on cold planets.".to_string(),         "2. *flurbo* (name): A fictional digital currency that originated in the animated series Rick and Morty.".to_string()        ]      },      WordDefinition {       id:"doc1".to_string(),       word:"glarb-glarb".to_string(),       definitions: vec![         "1. *glarb-glarb* (noun): A glarb-glarb is a ancient tool used by the ancestors of the inhabitants of planet Jiro to farm the land.".to_string(),         "2. *glarb-glarb* (noun): A fictional creature found in the distant, swampy marshlands of the planet Glibbo in the Andromeda galaxy.".to_string()        ]      },      WordDefinition {       id:"doc2".to_string(),       word:"linglingdong".to_string(),       definitions: vec![         "1. *linglingdong* (noun): A term used by inhabitants of the far side of the moon to describe humans.".to_string(),         "2. *linglingdong* (noun): A rare, mystical instrument crafted by the ancient monks of the Nebulon Mountain Ranges on the planet Quarm.".to_string()        ]      },    ])?    .build()    .await?;
  let vector_store =InMemoryVectorStore::from_documents(embeddings);
  let index = vector_store.index(embedding_model);
  let rag_agent = client.agent("qwen2:7b")    .preamble("      You are a dictionary assistant here to assist the user in understanding the meaning of words.      You will find additional non-standard word definitions that could be useful below.    ")    .dynamic_context(1, index)    .build();
  let resp = rag_agent.prompt("what does \"glarb-glarb\" mean?").await?;
  println!("{}", resp);

 Ok(())}

在这个Agent中，每次用户提问时，LLM会自动查询向量存储，检索最相关的文档，并将其与问题一起发送给模型，提高回答的专业度和上下文相关性。

⚠️注意: 在安装rig-core时需要开启derive这个特性，不要会报找不到Embed宏的错误。

除了RAG，Rig还支持工具增强（Tool-Augmented）模式，使Agent能够调用外部API 或执行计算。

Rig Agents提供了一种模块化、可扩展的方式来管理LLM应用，支持RAG、工具调用和灵活配置，使其成为构建高级AI代理的理想选择。无论是构建聊天助手、知识库问答系统，还是工具增强型智能体，Rig都能提供强大的支持。