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Bump version to 2.0.0 and upload install-galaxy.sh in deploy script

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- Update version from 1.6.3 to 2.0.0 in app/Cargo.toml and Cargo.lock
- Add install-galaxy.sh upload step to build-and-deploy-hermes script
- Include pending AI provider and agent changes
This commit is contained in:
Ryan Ward
2026-07-15 16:17:13 -05:00
parent af5315313d
commit e9a9a4c30f
19 changed files with 523 additions and 61 deletions
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app/src/ai/llms.rs
+100 -25
View File
@@ -602,7 +602,7 @@ pub struct LLMPreferences {
#[cfg(not(target_family = "wasm"))]
openai_provider_routing: HashMap<String, super::openai::client::OpenAIClientConfig>,
/// Models fetched from the OpenAI-compatible /models endpoint at runtime.
/// Stored in memory only — not persisted to TOML.
/// Used as a short-lived fallback while the fetched list is persisted to settings.
#[cfg(not(target_family = "wasm"))]
fetched_openai_models: Vec<OpenAIModelConfig>,
}
@@ -670,10 +670,18 @@ impl LLMPreferences {
AISettingsChangedEvent::BedrockEnabled { .. }
| AISettingsChangedEvent::OpenAIEnabled { .. }
| AISettingsChangedEvent::OpenAIBaseUrl { .. }
| AISettingsChangedEvent::OpenAIApiKey { .. }
| AISettingsChangedEvent::OpenAIModels { .. }
| AISettingsChangedEvent::OpenAIProviders { .. }
) {
me.inject_bedrock_models(ctx);
me.inject_openai_models(ctx);
if matches!(event, AISettingsChangedEvent::OpenAIEnabled { .. } | AISettingsChangedEvent::OpenAIBaseUrl { .. }) {
if matches!(
event,
AISettingsChangedEvent::OpenAIEnabled { .. }
| AISettingsChangedEvent::OpenAIBaseUrl { .. }
| AISettingsChangedEvent::OpenAIApiKey { .. }
) {
me.fetch_openai_models_from_endpoint(ctx);
}
// Safety: ensure the default model is still present in choices.
@@ -961,11 +969,17 @@ impl LLMPreferences {
return;
}
// Models come exclusively from the in-memory /models endpoint fetch.
let mut provider_entries: Vec<(String, String, Option<String>, Vec<OpenAIModelConfig>)> =
Vec::new();
if !self.fetched_openai_models.is_empty() {
let configured_models = settings.openai_models.value().clone();
let single_provider_models = if configured_models.is_empty() {
self.fetched_openai_models.clone()
} else {
configured_models
};
if !single_provider_models.is_empty() {
let base_url = settings.openai_base_url.value().clone();
let api_key = {
let key = settings.openai_api_key.value().clone();
@@ -980,9 +994,27 @@ impl LLMPreferences {
} else {
"LiteLLM".to_string()
};
provider_entries.push((name, base_url, api_key, self.fetched_openai_models.clone()));
provider_entries.push((name, base_url, api_key, single_provider_models));
}
provider_entries.extend(
settings
.openai_providers
.value()
.iter()
.filter_map(|provider| {
if provider.base_url.trim().is_empty() || provider.models.is_empty() {
return None;
}
Some((
provider.name.clone(),
provider.base_url.clone(),
provider.api_key.clone(),
provider.models.clone(),
))
}),
);
if provider_entries.is_empty() {
return;
}
@@ -990,20 +1022,21 @@ impl LLMPreferences {
let mut total_injected = 0;
let mut seen_model_ids: HashSet<String> = HashSet::new();
for (provider_name, base_url, api_key, models) in provider_entries {
let client_config = OpenAIClientConfig {
base_url: base_url.clone(),
api_key: api_key.clone(),
model: None, // filled per-request from model_id
};
for model in &models {
if !seen_model_ids.insert(model.model_id.clone()) {
continue;
}
// Register the routing entry
let client_config = OpenAIClientConfig {
base_url: base_url.clone(),
api_key: api_key.clone(),
model: None, // filled per-request from model_id
max_input_tokens: Some(openai_model_context_size(model)),
max_output_tokens: model.max_output_tokens,
};
self.openai_provider_routing
.insert(model.model_id.clone(), client_config.clone());
.insert(model.model_id.clone(), client_config);
let llm_info = LLMInfo {
id: LLMId::from(model.model_id.as_str()),
@@ -1027,7 +1060,7 @@ impl LLMPreferences {
},
)]),
discount_percentage: None,
context_window: LLMContextWindow::default(),
context_window: openai_model_context_window(model),
};
self.models_by_feature
.agent_mode
@@ -1053,10 +1086,7 @@ impl LLMPreferences {
if feature.choices.is_empty() {
return;
}
let default_exists = feature
.choices
.iter()
.any(|m| m.id == feature.default_id);
let default_exists = feature.choices.iter().any(|m| m.id == feature.default_id);
if !default_exists {
let new_default = feature.choices[0].id.clone();
log::info!(
@@ -1143,17 +1173,36 @@ impl LLMPreferences {
}
};
fn u32_from_any(value: &serde_json::Value, keys: &[&str]) -> Option<u32> {
keys.iter()
.find_map(|key| value[*key].as_u64())
.and_then(|value| u32::try_from(value).ok())
}
let models: Vec<OpenAIModelConfig> = body["data"]
.as_array()
.unwrap_or(&vec![])
.map(Vec::as_slice)
.unwrap_or_default()
.iter()
.filter_map(|m| {
let id = m["id"].as_str()?;
let context_size = m["max_model_len"]
.as_u64()
.or_else(|| m["context_window"].as_u64())
.or_else(|| m["max_input_tokens"].as_u64())
.unwrap_or(200_000) as u32;
let max_input_tokens = u32_from_any(
m,
&["max_input_tokens", "input_token_limit", "max_prompt_tokens"],
);
let context_size =
u32_from_any(m, &["max_model_len", "context_window", "token_size"])
.or(max_input_tokens)
.unwrap_or(200_000);
let max_output_tokens = u32_from_any(
m,
&[
"max_output_tokens",
"output_token_limit",
"max_completion_tokens",
"max_tokens",
],
);
let display_name = id
.split('/')
@@ -1185,8 +1234,13 @@ impl LLMPreferences {
Some(OpenAIModelConfig {
model_id: id.to_string(),
display_name,
vision_supported: m["supports_vision"].as_bool().unwrap_or(false),
vision_supported: m["supports_vision"]
.as_bool()
.or_else(|| m["vision_support"].as_bool())
.unwrap_or(false),
context_size,
max_input_tokens,
max_output_tokens,
provider,
})
})
@@ -1200,7 +1254,12 @@ impl LLMPreferences {
},
|me, models, ctx| {
if !models.is_empty() {
me.fetched_openai_models = models;
me.fetched_openai_models = models.clone();
AISettings::handle(ctx).update(ctx, |settings, ctx| {
if let Err(err) = settings.openai_models.set_value(models, ctx) {
report_error!(err.context("Failed to persist fetched OpenAI models"));
}
});
me.inject_openai_models(ctx);
ctx.emit(LLMPreferencesEvent::UpdatedAvailableLLMs);
}
@@ -2101,6 +2160,22 @@ fn get_new_agent_mode_choices(
.collect()
}
#[cfg(not(target_family = "wasm"))]
fn openai_model_context_size(model: &OpenAIModelConfig) -> u32 {
model.max_input_tokens.unwrap_or(model.context_size)
}
#[cfg(not(target_family = "wasm"))]
fn openai_model_context_window(model: &OpenAIModelConfig) -> LLMContextWindow {
let context_size = openai_model_context_size(model);
LLMContextWindow {
is_configurable: false,
min: context_size,
max: context_size,
default_max: context_size,
}
}
/// Builds synthetic [`LLMInfo`]s from the user's persisted custom endpoints.
///
/// One entry per `CustomEndpointModel`. The display label is the **alias** when present,