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//! This module contains the implementation of the [Agent] struct and its builder.
//!
//! The [Agent] struct represents an LLM agent, which combines an LLM model with a preamble (system prompt),
//! a set of context documents, and a set of tools. Note: both context documents and tools can be either
//! static (i.e.: they are always provided) or dynamic (i.e.: they are RAGged at prompt-time).
//!
//! The [Agent] struct is highly configurable, allowing the user to define anything from
//! a simple bot with a specific system prompt to a complex RAG system with a set of dynamic
//! context documents and tools.
//!
//! The [Agent] struct implements the [crate::completion::Completion] and [crate::completion::Prompt] traits,
//! allowing it to be used for generating completions responses and prompts. The [Agent] struct also
//! implements the [crate::completion::Chat] trait, which allows it to be used for generating chat completions.
//!
//! The [AgentBuilder] implements the builder pattern for creating instances of [Agent].
//! It allows configuring the model, preamble, context documents, tools, temperature, and additional parameters
//! before building the agent.
//!
//! # Example
//! ```no_run
//! use rig_core::{
//! client::{CompletionClient, ProviderClient},
//! completion::{Chat, Completion, Prompt},
//! providers::openai,
//! };
//!
//! # async fn run() -> Result<(), Box<dyn std::error::Error>> {
//! let openai = openai::Client::from_env()?;
//!
//! // Configure the agent
//! let agent = openai.agent(openai::GPT_5_2)
//! .preamble("System prompt")
//! .context("Context document 1")
//! .context("Context document 2")
//! .temperature(0.8)
//! .build();
//!
//! // Use the agent for completions and prompts
//! // Generate a chat completion response from a prompt and chat history
//! let chat_response = agent.chat("Prompt", &mut Vec::<rig_core::completion::Message>::new()).await?;
//!
//! // Generate a prompt completion response from a simple prompt
//! let prompt_response = agent.prompt("Prompt").await?;
//!
//! // Generate a completion request builder from a prompt and chat history. The builder
//! // will contain the agent's configuration (i.e.: preamble, context documents, tools,
//! // model parameters, etc.), but these can be overwritten.
//! let completion_req_builder = agent
//! .completion("Prompt", Vec::<rig_core::completion::Message>::new())
//! .await?;
//!
//! let response = completion_req_builder
//! .temperature(0.9) // Overwrite the agent's temperature
//! .send()
//! .await?;
//! # Ok(())
//! # }
//! ```
//!
//! RAG Agent example
//! ```no_run
//! use rig_core::{
//! client::{CompletionClient, EmbeddingsClient, ProviderClient},
//! completion::Prompt,
//! embeddings::EmbeddingsBuilder,
//! providers::openai,
//! vector_store::in_memory_store::InMemoryVectorStore,
//! };
//!
//! # async fn run() -> Result<(), Box<dyn std::error::Error>> {
//! // Initialize OpenAI client
//! let openai = openai::Client::from_env()?;
//!
//! // Initialize OpenAI embedding model
//! let embedding_model = openai.embedding_model(openai::TEXT_EMBEDDING_3_SMALL);
//!
//! // Create vector store, compute embeddings and load them in the store
//! let mut vector_store = InMemoryVectorStore::default();
//!
//! let embeddings = EmbeddingsBuilder::new(embedding_model.clone())
//! .documents(vec![
//! "Definition of a *flurbo*: A flurbo is a green alien that lives on cold planets",
//! "Definition of a *glarb-glarb*: A glarb-glarb is an ancient tool used by the ancestors of the inhabitants of planet Jiro to farm the land.",
//! "Definition of a *linglingdong*: A term used by inhabitants of the far side of the moon to describe humans.",
//! ])?
//! .build()
//! .await?;
//!
//! vector_store.add_documents(embeddings);
//!
//! // Create vector store index
//! let index = vector_store.index(embedding_model);
//!
//! let agent = openai.agent(openai::GPT_5_2)
//! .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();
//!
//! // Prompt the agent and print the response
//! let response = agent.prompt("What does \"glarb-glarb\" mean?").await?;
//! # Ok(())
//! # }
//! ```
pub
pub use crateText;
pub use ;
pub use Agent;
pub use ;
pub use ;
pub use ;