rig — Унифицированный API для разработки AI-приложений

--- name: rig description: > Build LLM-powered applications with Rig, the Rust AI framework. Use when creating agents, RAG pipelines, tool-calling workflows, structured extraction, or streaming completions. Covers all providers with a unified API. argument-hint: "[what-to-build]" allowed-tools: - Read - Glob - Grep - Edit - Write - Bash --- # Building with Rig Rig is a Rust library for building LLM-powered applications with a provider-agnostic API. All patterns use the builder pattern and async/await via tokio. ## Quick Start ```rust use rig::completion::Prompt; use rig::providers::openai; #[tokio::main] async fn main() -> Result<(), anyhow::Error> { let client = openai::Client::from_env(); let agent = client .agent(openai::GPT_4O) .preamble("You are a helpful assistant.") .build(); let response = agent.prompt("Hello!").await?; println!("{}", response); Ok(()) } ``` ## Core Patterns ### 1. Simple Agent ```rust let agent = client.agent(openai::GPT_4O) .preamble("System prompt") .temperature(0.7) .max_tokens(2000) .build(); let response = agent.prompt("Your question").await?; ``` ### 2. Agent with Tools Define a tool by implementing the `Tool` trait, then attach it: ```rust let agent = client.agent(openai::GPT_4O) .preamble("You can use tools.") .tool(MyTool) .build(); ``` See `references/tools.md` for the full `Tool` trait signature. ### 3. RAG (Retrieval-Augmented Generation) ```rust let embedding_model = client.embedding_model(openai::TEXT_EMBEDDING_ADA_002); let index = vector_store.index(embedding_model); let agent = client.agent(openai::GPT_4O) .preamble("Answer using the provided context.") .dynamic_context(5, index) // top-5 similar docs per query .build(); ``` See `references/rag.md` for vector store setup and the `Embed` derive macro. ### 4. Streaming ```rust use futures::StreamExt; use rig::streaming::StreamedAssistantContent; use rig::agent::prompt_request::streaming::MultiTurnStreamItem; let mut stream = agent.stream_prompt("Tell me a story").await?; while let Some(chunk) = stream.next().await { match chunk? { MultiTurnStreamItem::StreamAssistantItem( StreamedAssistantContent::Text(text) ) => print!("{}", text.text), MultiTurnStreamItem::FinalResponse(resp) => { println!("n{}", resp.response()); } _ => {} } } ``` ### 5. Structured Extraction ```rust use schemars::JsonSchema; use serde::{Deserialize, Serialize}; #[derive(Deserialize, Serialize, JsonSchema)] struct Person { pub name: Option<String>, pub age: Option<u8>, } let extractor = client.extractor::<Person>(openai::GPT_4O).build(); let person = extractor.extract("John is 30 years old.").await?; ``` ### 6. Chat with History ```rust use rig::completion::Chat; let history = vec![ Message::from("Hi, I'm Alice."), // ...previous messages ]; let response = agent.chat("What's my name?", history).await?; ``` ## Agent Builder Methods | Method | Description | |--------|-------------| | `.preamble(str)` | Set system prompt | | `.context(str)` | Add static context document | | `.dynamic_context(n, index)` | Add RAG with top-n retrieval | | `.tool(impl Tool)` | Attach a callable tool | | `.tools(Vec<Box<dyn ToolDyn>>)` | Attach multiple tools | | `.temperature(f64)` | Set temperature (0.0-1.0) | | `.max_tokens(u64)` | Set max output tokens | | `.additional_params(json!{...})` | Provider-specific params | | `.tool_choice(ToolChoice)` | Control tool usage | | `.build()` | Build the agent | ## Available Providers Create a client with `ProviderName::Client::from_env()` or `ProviderName::Client::new("key")`. | Provider | Module | Example Model Constant | |----------|--------|----------------------| | OpenAI | `openai` | `GPT_4O`, `GPT_4O_MINI` | | Anthropic | `anthropic` | `CLAUDE_4_OPUS`, `CLAUDE_4_SONNET` | | Cohere | `cohere` | `COMMAND_R_PLUS` | | Mistral | `mistral` | `MISTRAL_LARGE` | | Gemini | `gemini` | model string | | Groq | `groq` | model string | | Ollama | `ollama` | model string | | DeepSeek | `deepseek` | model string | | xAI | `xai` | model string | | Together | `together` | model string | | Perplexity | `perplexity` | model string | | OpenRouter | `openrouter` | model string | | HuggingFace | `huggingface` | model string | | Azure | `azure` | deployment string | | Hyperbolic | `hyperbolic` | model string | | Galadriel | `galadriel` | model string | | Moonshot | `moonshot` | model string | | Mira | `mira` | model string | | Voyage AI | `voyageai` | embeddings only | ## Vector Store Crates | Backend | Crate | |---------|-------| | In-memory | `rig-core` (built-in) | | MongoDB | `rig-mongodb` | | LanceDB | `rig-lancedb` | | Qdrant | `rig-qdrant` | | SQLite | `rig-sqlite` | | Neo4j | `rig-neo4j` | | Milvus | `rig-milvus` | | SurrealDB | `rig-surrealdb` | ## Key Rules - All async code runs on tokio. - Use `WasmCompatSend` / `WasmCompatSync` instead of raw `Send` / `Sync` for WASM compatibility. - Use proper error types with `thiserror` — never `Result<(), String>`. - Avoid `.unwrap()` — use `?` operator. ## Further Reference Detailed API documentation (available when installed via Claude Code skills): - **tools** — Tool trait, ToolDefinition, ToolEmbedding, attachment patterns - **rag** — Vector stores, Embed derive, EmbeddingsBuilder, search requests - **providers** — Provider-specific initialization, model constants, env vars - **patterns** — Multi-agent, hooks, streaming details, chaining, extraction For the full reference, see the Rig examples at `rig-core/examples/` or https://docs.rig.rs