mirror of
https://github.com/outbackdingo/optimclaw.git
synced 2026-08-29 17:09:31 +00:00
fix: address code review issues in gemini-cli OAuth integration
- Add cache_read_input_tokens/cache_creation_input_tokens fields (value 0) - Implement manual Debug for OAuthCredential to redact tokens - Fix hardcoded /tmp: use GeminiOauthConfig::default_credentials_path() - Replace emoji output with plain text markers - Propagate Client::builder() errors instead of silent fallback - Use tokio::fs for all file I/O in CredentialManager (was std::fs) - Use if let Some(ref pid) to avoid consuming credential.project_id - Extract uses_cloud_code_api() helper; route by major version (gemini-2+) - Concatenate multiple system messages into systemInstruction - Include functionCall parts in assistant message conversion - Add 401 retry loop with allow_retry flag for auth failures - Remove biased from tokio::select! in OAuth callback handler - Remove hardcoded context_length 1M; vary by model family - Change GOOG_API_CLIENT from Node.js spoof to gl-rust/1.0.0 - Implement list_models() with static model list - Move create_gemini_oauth_provider() before test module (clippy) - Fix 9 additional clippy warnings (collapsible_if, map_or, needless_borrow) - Run cargo fmt
This commit is contained in:
Artem
+131
-135
@@ -982,7 +982,6 @@ impl SetupWizard {
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self.setup_openai_compatible_generic(&def.id, secret_name, display_name)
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.await?;
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}
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>>>>>>> origin/main
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}
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Ok(())
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@@ -1416,7 +1415,13 @@ impl SetupWizard {
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println!();
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let creds_path = crate::config::GeminiOauthConfig::default_credentials_path();
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let cred_manager = crate::llm::gemini_oauth::CredentialManager::new(&creds_path);
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let cred_manager =
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crate::llm::gemini_oauth::CredentialManager::new(&creds_path).map_err(|e| {
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SetupError::Config(format!(
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"Failed to initialize Gemini credential manager: {}",
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e
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))
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})?;
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match cred_manager.get_valid_credential().await {
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Ok(cred) => {
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@@ -1461,129 +1466,8 @@ impl SetupWizard {
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let backend = self.settings.llm_backend.as_deref().unwrap_or("nearai");
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let registry = crate::llm::ProviderRegistry::load();
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if backend == "nearai" {
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// NEAR AI: use existing provider list_models()
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let fetched = self.fetch_nearai_models().await;
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let default_models: Vec<(String, String)> = vec![
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(
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"zai-org/GLM-latest".into(),
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"GLM Latest (default, fast)".into(),
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),
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(
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"anthropic::claude-sonnet-4-20250514".into(),
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"Claude Sonnet 4 (best quality)".into(),
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),
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(
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"openai::gpt-5.3-codex".into(),
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"GPT-5.3 Codex (flagship)".into(),
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),
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("openai::gpt-5.2".into(), "GPT-5.2".into()),
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("openai::gpt-4o".into(), "GPT-4o".into()),
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];
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let models = if fetched.is_empty() {
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default_models
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} else {
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fetched.iter().map(|m| (m.clone(), m.clone())).collect()
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};
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self.select_from_model_list(&models)?;
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} else if let Some(def) = registry.find(backend) {
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let can_list = def
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.setup
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.as_ref()
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.map(|s| s.can_list_models())
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.unwrap_or(false);
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if can_list {
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// Try to fetch models from the provider's /v1/models endpoint
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let cached_key = self
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.llm_api_key
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.as_ref()
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.map(|k| k.expose_secret().to_string());
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let models = match backend {
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"anthropic" => fetch_anthropic_models(cached_key.as_deref()).await,
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"openai" => fetch_openai_models(cached_key.as_deref()).await,
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"ollama" => {
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let base_url = self
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.settings
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.ollama_base_url
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.as_deref()
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.or(def.default_base_url.as_deref())
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.unwrap_or("http://localhost:11434");
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let models = fetch_ollama_models(base_url).await;
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if models.is_empty() {
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print_info("No models found. Pull one first: ollama pull llama3");
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}
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models
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}
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_ => {
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// Generic OpenAI-compatible model listing
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let base_url = def.default_base_url.as_deref().unwrap_or("");
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fetch_openai_compatible_models(base_url, cached_key.as_deref()).await
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}
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};
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// Apply models_filter from setup hint (e.g., Groq "chat" filters non-chat models)
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let models =
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if let Some(filter) = def.setup.as_ref().and_then(|s| s.models_filter()) {
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let filter_lower = filter.to_lowercase();
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models
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.into_iter()
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.filter(|(id, _)| id.to_lowercase().contains(&filter_lower))
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.collect()
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} else {
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models
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};
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if models.is_empty() {
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// Fall back to manual entry
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let default = &def.default_model;
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let model_id = input(&format!("Model name (default: {default})"))
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.map_err(SetupError::Io)?;
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let model_id = if model_id.is_empty() {
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default.clone()
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} else {
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model_id
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};
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self.settings.selected_model = Some(model_id.clone());
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print_success(&format!("Selected {}", model_id));
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} else {
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self.select_from_model_list(&models)?;
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}
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} else {
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// Manual model entry
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let default = &def.default_model;
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let model_id =
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input(&format!("Model name (default: {default})")).map_err(SetupError::Io)?;
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let model_id = if model_id.is_empty() {
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default.clone()
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} else {
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model_id
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};
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self.settings.selected_model = Some(model_id.clone());
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print_success(&format!("Selected {}", model_id));
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}
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"gemini_oauth" => {
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let default_models: Vec<(String, String)> = vec![
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("gemini-3.1-pro-preview".into(), "Gemini 3.1 Pro (Latest, strongest reasoning)".into()),
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("gemini-3-flash-preview".into(), "Gemini 3 Flash (Fast preview with thinking)".into()),
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("gemini-2.5-pro".into(), "Gemini 2.5 Pro (Stable, strong reasoning)".into()),
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("gemini-2.5-flash".into(), "Gemini 2.5 Flash (Fast, good quality)".into()),
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("gemini-2.5-flash-lite".into(), "Gemini 2.5 Flash Lite (Fastest, lightweight)".into()),
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];
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self.select_from_model_list(&default_models)?;
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}
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"bedrock" => {
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let model_id = input("Bedrock model ID (e.g., anthropic.claude-opus-4-6-v1)")
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.map_err(SetupError::Io)?;
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if model_id.is_empty() {
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return Err(SetupError::Config("Model ID is required".to_string()));
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}
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self.settings.selected_model = Some(model_id.clone());
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print_success(&format!("Selected {}", model_id));
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}
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_ => {
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match backend {
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"nearai" => {
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// NEAR AI: use existing provider list_models()
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let fetched = self.fetch_nearai_models().await;
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let default_models: Vec<(String, String)> = vec![
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@@ -1610,18 +1494,130 @@ impl SetupWizard {
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};
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self.select_from_model_list(&models)?;
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}
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"gemini_oauth" | "gemini-oauth" => {
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let default_models: Vec<(String, String)> = vec![
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(
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"gemini-2.0-flash".into(),
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"Gemini 2.0 Flash (Latest, fast)".into(),
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),
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(
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"gemini-2.0-flash-thinking-exp-1219".into(),
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"Gemini 2.0 Flash Thinking (Latest, reasoning)".into(),
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),
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(
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"gemini-1.5-pro".into(),
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"Gemini 1.5 Pro (Stable, strong reasoning)".into(),
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),
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(
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"gemini-1.5-flash".into(),
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"Gemini 1.5 Flash (Fastest, good quality)".into(),
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),
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];
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self.select_from_model_list(&default_models)?;
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}
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self.settings.selected_model = Some(model_id.clone());
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print_success(&format!("Selected {}", model_id));
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} else {
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// Unknown provider, manual entry
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let model_id = input("Model name (e.g., meta-llama/Llama-3-8b-chat-hf)")
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.map_err(SetupError::Io)?;
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if model_id.is_empty() {
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return Err(SetupError::Config("Model name is required".to_string()));
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"bedrock" => {
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let model_id =
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input("Bedrock model ID (e.g., anthropic.claude-v3-sonnet-20240229-v1:0)")
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.map_err(SetupError::Io)?;
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if model_id.is_empty() {
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return Err(SetupError::Config("Model ID is required".to_string()));
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}
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self.settings.selected_model = Some(model_id.clone());
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print_success(&format!("Selected {}", model_id));
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}
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_ => {
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if let Some(def) = registry.find(backend) {
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let can_list = def
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.setup
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.as_ref()
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.map(|s| s.can_list_models())
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.unwrap_or(false);
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if can_list {
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// Try to fetch models from the provider's /v1/models endpoint
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let cached_key = self
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.llm_api_key
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.as_ref()
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.map(|k| k.expose_secret().to_string());
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let models = match backend {
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"anthropic" => fetch_anthropic_models(cached_key.as_deref()).await,
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"openai" => fetch_openai_models(cached_key.as_deref()).await,
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"ollama" => {
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let base_url = self
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.settings
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.ollama_base_url
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.as_deref()
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.or(def.default_base_url.as_deref())
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.unwrap_or("http://localhost:11434");
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let models = fetch_ollama_models(base_url).await;
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if models.is_empty() {
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print_info(
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"No models found. Pull one first: ollama pull llama3",
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);
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}
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models
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}
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_ => {
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// Generic OpenAI-compatible model listing
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let base_url = def.default_base_url.as_deref().unwrap_or("");
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fetch_openai_compatible_models(base_url, cached_key.as_deref())
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.await
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}
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};
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// Apply models_filter from setup hint (e.g., Groq "chat" filters non-chat models)
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let models = if let Some(filter) =
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def.setup.as_ref().and_then(|s| s.models_filter())
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{
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let filter_lower = filter.to_lowercase();
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models
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.into_iter()
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.filter(|(id, _)| id.to_lowercase().contains(&filter_lower))
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.collect()
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} else {
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models
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};
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if models.is_empty() {
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// Fall back to manual entry
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let default = &def.default_model;
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let model_id = input(&format!("Model name (default: {default})"))
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.map_err(SetupError::Io)?;
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let model_id = if model_id.is_empty() {
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default.clone()
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} else {
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model_id
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};
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self.settings.selected_model = Some(model_id.clone());
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print_success(&format!("Selected {}", model_id));
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} else {
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self.select_from_model_list(&models)?;
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}
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} else {
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// Manual model entry
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let default = &def.default_model;
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let model_id = input(&format!("Model name (default: {default})"))
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.map_err(SetupError::Io)?;
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let model_id = if model_id.is_empty() {
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default.clone()
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} else {
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model_id
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};
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self.settings.selected_model = Some(model_id.clone());
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print_success(&format!("Selected {}", model_id));
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}
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} else {
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// Unknown provider, manual entry
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let model_id = input("Model name (e.g., meta-llama/Llama-3-8b-chat-hf)")
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.map_err(SetupError::Io)?;
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if model_id.is_empty() {
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return Err(SetupError::Config("Model name is required".to_string()));
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}
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self.settings.selected_model = Some(model_id.clone());
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print_success(&format!("Selected {}", model_id));
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}
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}
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self.settings.selected_model = Some(model_id.clone());
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print_success(&format!("Selected {}", model_id));
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}
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Ok(())
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