use async_trait::async_trait;
use toolcraft_request::{ByteStream, HeaderMap, Request};
use crate::{
error::Result,
llm::Llm,
model::{
llm::{LlmInput, LlmOutput},
ollama::{OllamaChatOptions, OllamaChatRequest, OllamaChatResponse},
},
};
pub struct OllamaLlm {
request: Request,
model: String,
}
impl OllamaLlm {
pub fn new(base_url: &str, model: &str) -> Result<Self> {
let mut request = Request::new()?;
request.set_base_url(base_url)?;
let mut headers = HeaderMap::new();
headers.insert("Content-Type", "application/json".to_string())?;
headers.insert("Accept", "application/json".to_string())?;
request.set_default_headers(headers);
Ok(Self {
request,
model: model.to_string(),
})
}
}
#[async_trait]
impl Llm for OllamaLlm {
async fn chat_once(&self, input: LlmInput) -> Result<LlmOutput> {
let options = OllamaChatOptions {
num_predict: input.max_tokens,
temperature: None,
};
let body = OllamaChatRequest {
model: self.model.clone(),
messages: input.messages,
stream: Some(false),
options: Some(options),
};
let payload = serde_json::to_value(body)?;
let response = self.request.post("chat", &payload, None).await?;
let json: OllamaChatResponse = response.json().await?;
Ok(json.into())
}
async fn chat_stream(&self, input: LlmInput) -> Result<ByteStream> {
let options = OllamaChatOptions {
num_predict: input.max_tokens,
temperature: None,
};
let body = OllamaChatRequest {
model: self.model.clone(),
messages: input.messages,
stream: Some(true),
options: Some(options),
};
let payload = serde_json::to_value(body)?;
let r = self.request.post_stream("chat", &payload, None).await?;
Ok(r)
}
}