Adds native support for AWS Bedrock's Mantle endpoint (`bedrock-mantle`), which serves models with no `Converse`/`Invoke` support on `bedrock-runtime`, such as GPT-5.5, GPT-5.4, and Grok 4.3 but more importantly **open-weight** models Closes #60471 ## What's changed - Renamed the existing `Model` enum in the `bedrock` crate to `ConverseModel`, and added a new `MantleModel` enum for Mantle-only models. Mantle models reuse the existing OpenAI-compatible Chat Completions/Responses request and response plumbing (`into_open_ai`/`into_open_ai_response`, `OpenAiEventMapper`/`OpenAiResponseEventMapper`) already used by the native OpenAI and OpenAI-compatible providers, rather than introducing new marshalling code. - Added a `BedrockMantleModel` language model that routes requests to the `bedrock-mantle` endpoint, dispatching to Chat Completions or the Responses API depending on the model. Mantle models appear in the model picker alongside Converse models under the same Bedrock provider. - Added region gating: `bedrock-mantle` is only available in a subset of AWS Regions, so using a Mantle model outside of them surfaces a clear error naming the current Region and the supported ones, instead of an opaque HTTP failure. - Implemented Bedrock bearer token authentication for Mantle requests: a configured Bedrock API key is used as-is, and every other auth method (IAM credentials, named profile, SSO, automatic) derives a short-term token by locally SigV4-presigning a `CallWithBearerToken` request. This requires no extra network round trip and no token caching, since re-signing locally is cheap. - Added a specific error for the 403 you get when your credentials have `bedrock:CallWithBearerToken` but not the separate `bedrock-mantle:CallWithBearerToken` permission Mantle models require, since this is the most common misconfiguration. - Added a `mantle_available_models` setting so custom models served through `bedrock-mantle` can be configured, the same way other providers support custom models via `available_models`. - Documented Mantle models and the new setting in the Amazon Bedrock section of [Use a Gateway](https://zed.dev/docs/ai/use-a-gateway#amazon-bedrock). ## Testing - Added unit tests covering: the local SigV4 bearer-token signing (including a byte-for-byte cross-check against a reference implementation), Mantle endpoint URL construction, the Mantle-supported-regions list, thinking-effort normalization, and the settings-to-model protocol mapping. - `cargo test -p bedrock -p language_models -p settings_content -p settings` passes. - `./script/clippy` passes with no new warnings. Release Notes: - Added native support for AWS Bedrock's Mantle endpoint, enabling GPT-5.5, GPT-5.4, and Grok 4.3 through the Amazon Bedrock provider.
215 lines
9.8 KiB
Rust
215 lines
9.8 KiB
Rust
use std::sync::Arc;
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use collections::HashMap;
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use settings::RegisterSetting;
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use crate::provider::{
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anthropic, anthropic::AnthropicSettings, anthropic_compatible::AnthropicCompatibleSettings,
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bedrock, bedrock::AmazonBedrockSettings, cloud::ZedDotDevSettings, deepseek::DeepSeekSettings,
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google::GoogleSettings, llama_cpp::LlamaCppSettings, lmstudio::LmStudioSettings, mistral,
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mistral::MistralSettings, ollama::OllamaSettings, open_ai::OpenAiSettings,
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open_ai_compatible::OpenAiCompatibleSettings, open_router, open_router::OpenRouterSettings,
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opencode, opencode::OpenCodeSettings, resolve_custom_headers,
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vercel_ai_gateway::VercelAiGatewaySettings, x_ai::XAiSettings,
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};
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#[derive(Debug, RegisterSetting)]
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pub struct AllLanguageModelSettings {
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pub anthropic: AnthropicSettings,
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pub anthropic_compatible: HashMap<Arc<str>, AnthropicCompatibleSettings>,
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pub bedrock: AmazonBedrockSettings,
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pub deepseek: DeepSeekSettings,
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pub google: GoogleSettings,
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pub llama_cpp: LlamaCppSettings,
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pub lmstudio: LmStudioSettings,
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pub mistral: MistralSettings,
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pub ollama: OllamaSettings,
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pub opencode: OpenCodeSettings,
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pub open_router: OpenRouterSettings,
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pub openai: OpenAiSettings,
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pub openai_compatible: HashMap<Arc<str>, OpenAiCompatibleSettings>,
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pub vercel_ai_gateway: VercelAiGatewaySettings,
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pub x_ai: XAiSettings,
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pub zed_dot_dev: ZedDotDevSettings,
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}
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fn custom_headers_from(
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provider_name: &str,
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raw: Option<HashMap<String, String>>,
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reserved: &[&str],
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) -> http_client::CustomHeaders {
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raw.as_ref()
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.filter(|map| !map.is_empty())
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.map(|map| resolve_custom_headers(provider_name, map, reserved))
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.unwrap_or_default()
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}
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impl settings::Settings for AllLanguageModelSettings {
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const PRESERVED_KEYS: Option<&'static [&'static str]> = Some(&["version"]);
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fn from_settings(content: &settings::SettingsContent) -> Self {
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let language_models = content.language_models.clone().unwrap();
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let anthropic = language_models.anthropic.unwrap();
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let anthropic_compatible = language_models.anthropic_compatible.unwrap();
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let bedrock = language_models.bedrock.unwrap();
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let deepseek = language_models.deepseek.unwrap();
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let google = language_models.google.unwrap();
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let llama_cpp = language_models.llama_cpp.unwrap();
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let lmstudio = language_models.lmstudio.unwrap();
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let mistral = language_models.mistral.unwrap();
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let ollama = language_models.ollama.unwrap();
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let opencode = language_models.opencode.unwrap();
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let open_router = language_models.open_router.unwrap();
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let openai = language_models.openai.unwrap();
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let openai_compatible = language_models.openai_compatible.unwrap();
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let vercel_ai_gateway = language_models.vercel_ai_gateway.unwrap();
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let x_ai = language_models.x_ai.unwrap();
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let zed_dot_dev = language_models.zed_dot_dev.unwrap();
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Self {
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anthropic: AnthropicSettings {
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api_url: anthropic.api_url.unwrap(),
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available_models: anthropic.available_models.unwrap_or_default(),
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custom_headers: custom_headers_from(
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"Anthropic",
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anthropic.custom_headers,
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anthropic::RESERVED_HEADER_NAMES,
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),
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},
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anthropic_compatible: anthropic_compatible
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.into_iter()
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.map(|(key, value)| {
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let provider_label = format!("Anthropic Compatible ({key})");
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(
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key,
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AnthropicCompatibleSettings {
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api_url: value.api_url,
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available_models: value.available_models,
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custom_headers: custom_headers_from(
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&provider_label,
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value.custom_headers,
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anthropic::RESERVED_HEADER_NAMES,
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),
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},
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)
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})
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.collect(),
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bedrock: AmazonBedrockSettings {
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available_models: bedrock.available_models.unwrap_or_default(),
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mantle_available_models: bedrock.mantle_available_models.unwrap_or_default(),
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custom_headers: custom_headers_from(
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"Amazon Bedrock",
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bedrock.custom_headers,
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bedrock::RESERVED_HEADER_NAMES,
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),
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region: bedrock.region,
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endpoint: bedrock.endpoint_url, // todo(should be api_url)
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profile_name: bedrock.profile,
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role_arn: None, // todo(was never a setting for this...)
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authentication_method: bedrock.authentication_method.map(Into::into),
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allow_global: bedrock.allow_global,
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guardrail_identifier: bedrock.guardrail_identifier,
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guardrail_version: bedrock.guardrail_version,
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},
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deepseek: DeepSeekSettings {
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api_url: deepseek.api_url.unwrap(),
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available_models: deepseek.available_models.unwrap_or_default(),
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custom_headers: custom_headers_from("DeepSeek", deepseek.custom_headers, &[]),
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},
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google: GoogleSettings {
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api_url: google.api_url.unwrap(),
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available_models: google.available_models.unwrap_or_default(),
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custom_headers: custom_headers_from("Google AI", google.custom_headers, &[]),
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},
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llama_cpp: LlamaCppSettings {
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api_url: llama_cpp.api_url.unwrap(),
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auto_discover: llama_cpp.auto_discover.unwrap_or(true),
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available_models: llama_cpp.available_models.unwrap_or_default(),
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context_window: llama_cpp.context_window,
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custom_headers: custom_headers_from("llama.cpp", llama_cpp.custom_headers, &[]),
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},
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lmstudio: LmStudioSettings {
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api_url: lmstudio.api_url.unwrap(),
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available_models: lmstudio.available_models.unwrap_or_default(),
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custom_headers: custom_headers_from("LM Studio", lmstudio.custom_headers, &[]),
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},
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mistral: MistralSettings {
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api_url: mistral.api_url.unwrap(),
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available_models: mistral.available_models.unwrap_or_default(),
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custom_headers: custom_headers_from(
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"Mistral",
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mistral.custom_headers,
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mistral::RESERVED_HEADER_NAMES,
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),
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},
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ollama: OllamaSettings {
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api_url: ollama.api_url.unwrap(),
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auto_discover: ollama.auto_discover.unwrap_or(true),
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available_models: ollama.available_models.unwrap_or_default(),
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context_window: ollama.context_window,
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custom_headers: custom_headers_from("Ollama", ollama.custom_headers, &[]),
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},
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opencode: OpenCodeSettings {
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api_url: opencode.api_url.unwrap(),
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available_models: opencode.available_models.unwrap_or_default(),
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custom_headers: custom_headers_from(
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"OpenCode",
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opencode.custom_headers,
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opencode::RESERVED_HEADER_NAMES,
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),
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show_zen_models: opencode.show_zen_models.unwrap_or(true),
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show_go_models: opencode.show_go_models.unwrap_or(true),
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show_free_models: opencode.show_free_models.unwrap_or(true),
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},
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open_router: OpenRouterSettings {
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api_url: open_router.api_url.unwrap(),
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available_models: open_router.available_models.unwrap_or_default(),
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custom_headers: custom_headers_from(
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"OpenRouter",
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open_router.custom_headers,
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open_router::RESERVED_HEADER_NAMES,
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),
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},
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openai: OpenAiSettings {
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api_url: openai.api_url.unwrap(),
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available_models: openai.available_models.unwrap_or_default(),
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custom_headers: custom_headers_from("OpenAI", openai.custom_headers, &[]),
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},
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openai_compatible: openai_compatible
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.into_iter()
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.map(|(key, value)| {
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let provider_label = format!("OpenAI Compatible ({key})");
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(
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key,
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OpenAiCompatibleSettings {
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api_url: value.api_url,
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available_models: value.available_models,
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custom_headers: custom_headers_from(
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&provider_label,
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value.custom_headers,
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&[],
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),
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},
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)
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})
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.collect(),
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vercel_ai_gateway: VercelAiGatewaySettings {
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api_url: vercel_ai_gateway.api_url.unwrap(),
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available_models: vercel_ai_gateway.available_models.unwrap_or_default(),
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custom_headers: custom_headers_from(
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"Vercel AI Gateway",
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vercel_ai_gateway.custom_headers,
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&[],
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),
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},
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x_ai: XAiSettings {
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api_url: x_ai.api_url.unwrap(),
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available_models: x_ai.available_models.unwrap_or_default(),
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custom_headers: custom_headers_from("xAI", x_ai.custom_headers, &[]),
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},
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zed_dot_dev: ZedDotDevSettings {
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available_models: zed_dot_dev.available_models.unwrap_or_default(),
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},
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}
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}
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}
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