mirror of
https://github.com/outbackdingo/optimclaw.git
synced 2026-09-01 09:09:19 +00:00
refactor: simplify config resolution and consolidate main.rs init (#287)
* refactor: simplify config resolution and consolidate main.rs init into AppBuilder - Add parse_bool_env() and parse_string_env() helpers to eliminate repetitive 5-line optional_env/parse/map_err/unwrap_or boilerplate across 12 config files - Add EmbeddingsConfig::create_provider() to centralize embeddings construction (fixes hardcoded 1536 dimensions and missing Ollama provider in app.rs) - Extract init_cli_tracing(), setup_wasm_channels(), start_tunnel(), run_memory_command(), run_worker(), run_claude_bridge() from main.rs - Replace ~600 lines of inline init in main.rs with AppBuilder::build_all() - Expose catalog_entries from AppComponents for gateway registry entries - Net reduction: ~738 lines across 15 files Co-Authored-By: Claude Opus 4.6 <[email protected]> * fix: propagate dev_loaded_tool_names from AppBuilder and add parse_option_env helper Address PR review feedback: - Capture dev_loaded_tool_names from WASM loading in init_extensions() and expose via AppComponents so bootstrap_hooks receives the actual dev tool names instead of an empty slice (fixes silent hook skip) - Add parse_option_env<T>() helper for Option<T> config fields, simplifying max_cost_per_day_cents and max_actions_per_hour in agent.rs Co-Authored-By: Claude Opus 4.6 <[email protected]> * fix: fetch real NEAR AI pricing and unify cost calculation path CostGuard was independently looking up pricing via costs::model_cost(), falling back to GPT-4o default rates when NEAR AI model names didn't match the static table — causing ~3x cost overestimates in logs. - Add pricing map to NearAiChatProvider that fetches real rates from /v1/model/list at startup (background, non-blocking) - Update cost_per_token() to check fetched pricing first, then static table, then default - Add cost_per_token parameter to CostGuard::record_llm_call() so the dispatcher passes provider-sourced rates directly Co-Authored-By: Claude Opus 4.6 <[email protected]> * chore: update default NEAR AI model to GLM-latest Replace fireworks llama4-maverick-instruct-basic with zai-org/GLM-latest as the default model in config and setup wizard. Co-Authored-By: Claude Opus 4.6 <[email protected]> * fix: align wizard default model name with config Change "zai/GLM-latest" to "zai-org/GLM-latest" in wizard.rs to match the default in config/llm.rs. Co-Authored-By: Claude Opus 4.6 <[email protected]> --------- Co-authored-by: Claude Opus 4.6 <[email protected]>
This commit is contained in:
Illia Polosukhin
co-authored by
Claude Opus 4.6
Claude Opus 4.6
parent
91b602790a
commit
c3ce26278a
+263
-6
@@ -6,12 +6,13 @@
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//! - **Session token auth**: Otherwise, uses `SessionManager` for Bearer session token
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//! with automatic renewal on 401 errors
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use std::collections::HashMap;
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use std::sync::Arc;
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use async_trait::async_trait;
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use reqwest::Client;
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use rust_decimal::Decimal;
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use rust_decimal_macros::dec;
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use rust_decimal::prelude::MathematicalOps;
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use secrecy::ExposeSecret;
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use serde::{Deserialize, Serialize};
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@@ -21,7 +22,7 @@ use crate::llm::provider::{
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ChatMessage, CompletionRequest, CompletionResponse, FinishReason, LlmProvider, Role, ToolCall,
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ToolCompletionRequest, ToolCompletionResponse,
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};
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use crate::llm::session::SessionManager;
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use crate::llm::{costs, session::SessionManager};
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/// Information about an available model from NEAR AI API.
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#[derive(Debug, Clone, Serialize, Deserialize)]
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@@ -42,6 +43,9 @@ pub struct NearAiChatProvider {
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session: Arc<SessionManager>,
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active_model: std::sync::RwLock<String>,
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flatten_tool_messages: bool,
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/// Per-model pricing fetched from the NEAR AI `/v1/model/list` endpoint.
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/// Maps model ID → (input_cost_per_token, output_cost_per_token).
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pricing: Arc<std::sync::RwLock<HashMap<String, (Decimal, Decimal)>>>,
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}
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impl NearAiChatProvider {
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@@ -72,13 +76,49 @@ impl NearAiChatProvider {
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})?;
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let active_model = std::sync::RwLock::new(config.model.clone());
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Ok(Self {
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let pricing = Arc::new(std::sync::RwLock::new(HashMap::new()));
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let provider = Self {
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client,
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config,
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session,
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active_model,
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flatten_tool_messages,
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})
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pricing,
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};
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// Fire-and-forget background pricing fetch — don't block startup.
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// Only spawns when a tokio runtime is active (skipped in sync tests).
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if let Ok(handle) = tokio::runtime::Handle::try_current() {
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let client = provider.client.clone();
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let base_url = provider.config.base_url.clone();
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let api_key = provider.config.api_key.clone();
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let session = provider.session.clone();
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let pricing = provider.pricing.clone();
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handle.spawn(async move {
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match fetch_pricing(&client, &base_url, api_key.as_ref(), &session).await {
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Ok(map) if !map.is_empty() => {
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tracing::info!("Loaded NEAR AI pricing for {} model(s)", map.len());
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match pricing.write() {
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Ok(mut guard) => *guard = map,
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Err(poisoned) => *poisoned.into_inner() = map,
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}
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}
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Ok(_) => {
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tracing::debug!("NEAR AI pricing endpoint returned no pricing data");
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}
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Err(e) => {
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tracing::debug!(
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"Could not fetch NEAR AI pricing (will use fallback): {}",
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e
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);
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}
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}
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});
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}
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Ok(provider)
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}
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fn api_url(&self, path: &str) -> String {
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@@ -500,8 +540,14 @@ impl LlmProvider for NearAiChatProvider {
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}
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fn cost_per_token(&self) -> (Decimal, Decimal) {
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// Default costs - could be model-specific in the future
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(dec!(0.000003), dec!(0.000015))
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let model = self.active_model_name();
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// Try fetched pricing first, then static lookup table, then default
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if let Ok(guard) = self.pricing.read()
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&& let Some(&rates) = guard.get(&model)
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{
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return rates;
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}
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costs::model_cost(&model).unwrap_or_else(costs::default_cost)
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}
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async fn list_models(&self) -> Result<Vec<String>, LlmError> {
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@@ -562,6 +608,143 @@ struct ChatCompletionMessage {
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tool_calls: Option<Vec<ChatCompletionToolCall>>,
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}
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// -- Pricing fetch types and logic -----------------------------------------
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/// Cost amount from the NEAR AI `/v1/model/list` response.
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///
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/// Real cost per token = `amount * 10^(-scale)`.
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#[derive(Debug, Deserialize)]
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struct ModelCost {
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amount: f64,
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#[serde(default)]
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scale: i32,
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}
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/// A single model entry from the pricing response.
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#[derive(Debug, Deserialize)]
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struct PricingModelEntry {
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#[serde(default, alias = "modelId", alias = "model_id")]
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model_id: Option<String>,
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#[serde(default, alias = "inputCostPerToken")]
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input_cost_per_token: Option<ModelCost>,
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#[serde(default, alias = "outputCostPerToken")]
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output_cost_per_token: Option<ModelCost>,
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#[serde(default)]
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metadata: Option<PricingMetadata>,
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}
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#[derive(Debug, Deserialize)]
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struct PricingMetadata {
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#[serde(default)]
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aliases: Vec<String>,
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}
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/// Wrapper for the `/v1/model/list` response body.
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#[derive(Debug, Deserialize)]
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struct PricingResponse {
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#[serde(default)]
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models: Option<Vec<PricingModelEntry>>,
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#[serde(default)]
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data: Option<Vec<PricingModelEntry>>,
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}
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/// Convert a `ModelCost` to a `Decimal` per-token price.
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fn model_cost_to_decimal(mc: &ModelCost) -> Option<Decimal> {
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if mc.amount == 0.0 {
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return Some(Decimal::ZERO);
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}
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// amount * 10^(-scale)
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let base = Decimal::try_from(mc.amount).ok()?;
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let factor = Decimal::TEN.checked_powi(-i64::from(mc.scale))?;
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base.checked_mul(factor)
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}
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/// Fetch pricing from the NEAR AI `/v1/model/list` endpoint.
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///
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/// Returns a map of model_id → (input_cost_per_token, output_cost_per_token).
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/// Errors are non-fatal; callers should fall back to the static lookup table.
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async fn fetch_pricing(
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client: &Client,
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base_url: &str,
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api_key: Option<&secrecy::SecretString>,
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session: &SessionManager,
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) -> Result<HashMap<String, (Decimal, Decimal)>, LlmError> {
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let base = base_url.trim_end_matches('/');
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let url = if base.ends_with("/v1") {
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format!("{}/model/list", base)
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} else {
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format!("{}/v1/model/list", base)
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};
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let token = if let Some(key) = api_key {
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key.expose_secret().to_string()
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} else {
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let tok = session.get_token().await?;
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tok.expose_secret().to_string()
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};
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let response = client
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.get(&url)
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.header("Authorization", format!("Bearer {}", token))
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.timeout(std::time::Duration::from_secs(15))
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.send()
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.await
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.map_err(|e| LlmError::RequestFailed {
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provider: "nearai_chat".to_string(),
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reason: format!("Failed to fetch pricing: {}", e),
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})?;
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if !response.status().is_success() {
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return Err(LlmError::RequestFailed {
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provider: "nearai_chat".to_string(),
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reason: format!("Pricing endpoint returned HTTP {}", response.status()),
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});
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}
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let body = response.text().await.map_err(|e| LlmError::RequestFailed {
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provider: "nearai_chat".to_string(),
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reason: format!("Failed to read pricing response: {}", e),
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})?;
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// Parse as {models: [...]} or {data: [...]} or direct array
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let entries: Vec<PricingModelEntry> =
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if let Ok(resp) = serde_json::from_str::<PricingResponse>(&body) {
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resp.models.or(resp.data).unwrap_or_default()
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} else if let Ok(arr) = serde_json::from_str::<Vec<PricingModelEntry>>(&body) {
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arr
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} else {
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return Ok(HashMap::new());
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};
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let mut map = HashMap::new();
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for entry in &entries {
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let (Some(input_mc), Some(output_mc)) =
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(&entry.input_cost_per_token, &entry.output_cost_per_token)
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else {
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continue;
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};
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let (Some(input), Some(output)) = (
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model_cost_to_decimal(input_mc),
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model_cost_to_decimal(output_mc),
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) else {
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continue;
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};
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// Insert under the primary model_id
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if let Some(ref id) = entry.model_id {
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map.insert(id.clone(), (input, output));
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}
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// Also insert under any aliases
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if let Some(ref meta) = entry.metadata {
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for alias in &meta.aliases {
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map.insert(alias.clone(), (input, output));
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}
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}
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}
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Ok(map)
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}
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/// Rewrite tool-call / tool-result messages into plain assistant/user text.
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///
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/// NEAR AI cloud-api does not support the OpenAI multi-turn tool-calling
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@@ -748,6 +931,7 @@ fn parse_usage(usage: Option<&ChatCompletionUsage>) -> (u32, u32) {
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mod tests {
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use super::*;
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use crate::llm::session::SessionConfig;
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use rust_decimal_macros::dec;
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fn test_nearai_config(base_url: &str) -> NearAiConfig {
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NearAiConfig {
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@@ -991,4 +1175,77 @@ mod tests {
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assert!(text.starts_with("Let me check that."));
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assert!(text.contains("[Called tool `search`"));
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}
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#[test]
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fn test_model_cost_to_decimal_basic() {
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// amount=3, scale=6 → 3 * 10^-6 = 0.000003
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let mc = ModelCost {
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amount: 3.0,
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scale: 6,
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};
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let result = model_cost_to_decimal(&mc).unwrap();
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assert_eq!(result, dec!(0.000003));
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}
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#[test]
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fn test_model_cost_to_decimal_zero() {
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let mc = ModelCost {
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amount: 0.0,
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scale: 6,
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};
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assert_eq!(model_cost_to_decimal(&mc), Some(Decimal::ZERO));
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}
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#[test]
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fn test_model_cost_to_decimal_larger_scale() {
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// amount=85, scale=8 → 85 * 10^-8 = 0.00000085
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let mc = ModelCost {
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amount: 85.0,
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scale: 8,
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};
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let result = model_cost_to_decimal(&mc).unwrap();
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assert_eq!(result, dec!(0.00000085));
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}
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#[test]
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fn test_cost_per_token_uses_pricing_map() {
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let cfg = test_nearai_config("http://127.0.0.1:8318");
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let provider = NearAiChatProvider::new(cfg, test_session()).expect("provider");
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// Inject pricing directly
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{
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let mut guard = provider.pricing.write().unwrap();
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guard.insert("test-model".to_string(), (dec!(0.000001), dec!(0.000005)));
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}
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let (input, output) = provider.cost_per_token();
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assert_eq!(input, dec!(0.000001));
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assert_eq!(output, dec!(0.000005));
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}
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#[test]
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fn test_cost_per_token_falls_back_to_static() {
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let mut cfg = test_nearai_config("http://127.0.0.1:8318");
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cfg.model = "gpt-4o".to_string();
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let provider = NearAiChatProvider::new(cfg, test_session()).expect("provider");
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// No pricing in map, should fall back to static costs::model_cost
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let (input, output) = provider.cost_per_token();
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let (expected_in, expected_out) = costs::model_cost("gpt-4o").unwrap();
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assert_eq!(input, expected_in);
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assert_eq!(output, expected_out);
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}
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#[test]
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fn test_cost_per_token_falls_back_to_default() {
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let mut cfg = test_nearai_config("http://127.0.0.1:8318");
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cfg.model = "some-unknown-nearai-model".to_string();
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let provider = NearAiChatProvider::new(cfg, test_session()).expect("provider");
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// No pricing in map, not in static table, should use default_cost
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let (input, output) = provider.cost_per_token();
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let (default_in, default_out) = costs::default_cost();
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assert_eq!(input, default_in);
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assert_eq!(output, default_out);
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}
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}
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@@ -614,7 +614,7 @@ Respond with a JSON plan in this format:
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let group_section = self.build_group_section();
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format!(
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r#"You are NEAR AI Agent, an autonomous assistant.
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r#"You are IronClaw Agent, a secure autonomous assistant.
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## Response Format — CRITICAL
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