Rig Agents：高阶LLM编排框架

use rig::{providers::openai};use rig::completion::Prompt;
#[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(())}

use rig::{providers::openai, Embed};use serde::{Serialize};use rig::completion::Prompt;use rig::embeddings::EmbeddingsBuilder;use rig::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(())}

use rig::{Agent, Tool};
// 创建支持工具调用的 Agentlet agent = openai.agent("gpt-4")    .preamble("You are a capable assistant with tools.")    .tool(calculator)   // 静态工具    .tool(web_search)   // 静态工具    .dynamic_tools(2, tool_index, toolset)  // 动态工具    .build();

let chat_agent = openai.agent("gpt-4")    .preamble("You are a conversational assistant.")    .temperature(0.9)    .build();

let kb_agent = openai.agent("gpt-4")    .preamble("You are a knowledge base assistant.")    .dynamic_context(5, document_store)    .temperature(0.3)    .build();

let tool_agent = openai.agent("gpt-4")    .preamble("You are a tool-using assistant.")    .tool(calculator)    .tool(web_search)    .dynamic_tools(2, tool_store, toolset)    .temperature(0.5)    .build();