use crate::types::*;
use super::{ChatMessage, LLM, LLMOptions, LLMResult};
use serde_json::json;
use std::future::Future;
use std::pin::Pin;
#[derive(Debug, Clone)]
pub enum GoogleModel {
Gemini15Pro, Gemini15Flash, Gemini15ProVision, GeminiPro, GeminiProVision, GeminiUltra, }
impl GoogleModel {
fn as_str(&self) -> &'static str {
match self {
GoogleModel::Gemini15Pro => "gemini-1.5-pro",
GoogleModel::Gemini15Flash => "gemini-1.5-flash",
GoogleModel::Gemini15ProVision => "gemini-1.5-pro-vision",
GoogleModel::GeminiPro => "gemini-pro",
GoogleModel::GeminiProVision => "gemini-pro-vision",
GoogleModel::GeminiUltra => "gemini-ultra",
}
}
}
impl From<GoogleModel> for String {
fn from(model: GoogleModel) -> Self {
model.as_str().to_string()
}
}
pub struct GoogleAI {
api_key: String,
model: GoogleModel,
base_url: String,
client: reqwest::Client,
default_options: LLMOptions,
}
impl GoogleAI {
pub fn new(api_key: String) -> Self {
Self {
api_key,
model: GoogleModel::Gemini15Pro,
base_url: "https://generativelanguage.googleapis.com/v1beta".to_string(),
client: reqwest::Client::new(),
default_options: LLMOptions::default(),
}
}
pub fn with_model(mut self, model: GoogleModel) -> Self {
self.model = model;
self
}
pub fn gemini15_pro(self) -> Self {
self.with_model(GoogleModel::Gemini15Pro)
}
pub fn gemini15_flash(self) -> Self {
self.with_model(GoogleModel::Gemini15Flash)
}
pub fn gemini_pro(self) -> Self {
self.with_model(GoogleModel::GeminiPro)
}
pub fn with_temperature(mut self, temperature: f32) -> Self {
self.default_options.temperature = Some(temperature);
self
}
pub fn with_max_tokens(mut self, max_tokens: u32) -> Self {
self.default_options.max_tokens = Some(max_tokens);
self
}
pub fn with_top_p(mut self, top_p: f32) -> Self {
self.default_options.top_p = Some(top_p);
self
}
pub fn with_top_k(mut self, top_k: u32) -> Self {
self.default_options.top_k = Some(top_k);
self
}
pub fn with_base_url(mut self, base_url: &str) -> Self {
self.base_url = base_url.to_string();
self
}
async fn chat_completion(
&self,
messages: &[ChatMessage],
options: &LLMOptions,
) -> Result<String> {
let model_name: String = self.model.clone().into();
let contents: Vec<serde_json::Value> = messages
.iter()
.map(|m| {
json!({
"parts": [{"text": m.content}],
"role": if m.role == "user" { "user" } else { "model" },
})
})
.collect();
let mut generation_config = json!({});
if let Some(temp) = options.temperature.or(self.default_options.temperature) {
generation_config["temperature"] = json!(temp);
}
if let Some(max_tokens) = options.max_tokens.or(self.default_options.max_tokens) {
generation_config["maxOutputTokens"] = json!(max_tokens);
}
if let Some(top_p) = options.top_p.or(self.default_options.top_p) {
generation_config["topP"] = json!(top_p);
}
if let Some(top_k) = options.top_k.or(self.default_options.top_k) {
generation_config["topK"] = json!(top_k);
}
let request_body = json!({
"contents": contents,
"generationConfig": generation_config,
});
let url = format!(
"{}/models/{}:generateContent?key={}",
self.base_url, model_name, self.api_key
);
let response = self
.client
.post(&url)
.header("Content-Type", "application/json")
.json(&request_body)
.send()
.await
.map_err(|e| LangHubError::LLMError(format!("Google request error: {}", e)))?;
if !response.status().is_success() {
let status = response.status();
let error_text = response.text().await.unwrap_or_default();
return Err(LangHubError::LLMError(format!(
"Google API error ({}): {}",
status, error_text
)));
}
let json: serde_json::Value = response
.json()
.await
.map_err(|e| LangHubError::LLMError(format!("JSON parse error: {}", e)))?;
let text = json["candidates"][0]["content"]["parts"][0]["text"]
.as_str()
.ok_or_else(|| {
LangHubError::ParseError("Missing 'text' field in response".to_string())
})?
.to_string();
Ok(text)
}
}
impl LLM for GoogleAI {
fn generate(
&self,
prompt: &str,
) -> Pin<Box<dyn Future<Output = Result<LLMResult>> + Send + '_>> {
let prompt = prompt.to_string();
let options = self.default_options.clone();
Box::pin(async move {
let messages = vec![ChatMessage::user(&prompt)];
let text = self.chat_completion(&messages, &options).await?;
Ok(LLMResult {
text,
metadata: None,
})
})
}
fn generate_with_options(
&self,
prompt: &str,
options: LLMOptions,
) -> Pin<Box<dyn Future<Output = Result<LLMResult>> + Send + '_>> {
let prompt = prompt.to_string();
Box::pin(async move {
let messages = vec![ChatMessage::user(&prompt)];
let text = self.chat_completion(&messages, &options).await?;
Ok(LLMResult {
text,
metadata: None,
})
})
}
fn chat(
&self,
messages: Vec<ChatMessage>,
) -> Pin<Box<dyn Future<Output = Result<LLMResult>> + Send + '_>> {
Box::pin(async move {
let text = self
.chat_completion(&messages, &LLMOptions::default())
.await?;
Ok(LLMResult {
text,
metadata: None,
})
})
}
fn get_model_name(&self) -> &str {
match self.model {
GoogleModel::Gemini15Pro => "gemini-1.5-pro",
GoogleModel::Gemini15Flash => "gemini-1.5-flash",
GoogleModel::Gemini15ProVision => "gemini-1.5-pro-vision",
GoogleModel::GeminiPro => "gemini-pro",
GoogleModel::GeminiProVision => "gemini-pro-vision",
GoogleModel::GeminiUltra => "gemini-ultra",
}
}
fn get_provider_name(&self) -> &str {
match self.model {
GoogleModel::Gemini15Pro => "Google-Gemini1.5-Pro",
GoogleModel::Gemini15Flash => "Google-Gemini1.5-Flash",
GoogleModel::Gemini15ProVision => "Google-Gemini1.5-Pro-Vision",
GoogleModel::GeminiPro => "Google-Gemini-Pro",
GoogleModel::GeminiProVision => "Google-Gemini-Pro-Vision",
GoogleModel::GeminiUltra => "Google-Gemini-Ultra",
}
}
fn get_provider_enum(&self) -> ModelProvider {
ModelProvider::Google
}
fn supports_function_calling(&self) -> bool {
true
}
fn supports_json_mode(&self) -> bool {
true
}
fn max_context_length(&self) -> Option<usize> {
match self.model {
GoogleModel::Gemini15Pro => Some(2_000_000),
GoogleModel::Gemini15Flash => Some(1_000_000),
GoogleModel::GeminiPro => Some(32768),
GoogleModel::GeminiUltra => Some(32768),
_ => Some(32768),
}
}
}