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9f164a0d2e
OpenAI-compatible providers currently cannot reuse Zed's OpenAI Chat Completions transport unless they also adopt the exact OpenAI request and response types. OpenRouter therefore maintained its own copy of request construction, authentication, status handling, response reading, and server-sent event framing. This change extracts that mechanical transport into two provider-neutral functions in `open_ai`: one for streaming requests and one for non-streaming requests. They accept any serializable request envelope, preserve custom headers and provider names, return untyped JSON for provider-specific decoding, and retain typed failures for serialization, request construction, HTTP transport, response reading, and deserialization. The existing OpenAI entry points remain as compatibility wrappers, so existing callers keep the same API and behavior. The abstraction deliberately stops at the wire boundary. OpenRouter continues to own its request and response schemas, attribution headers, routing controls, cache placement, and API-specific error interpretation. It now adapts the shared framed stream into those OpenRouter types instead of implementing a second HTTP and server-sent event stack. Moving OpenRouter onto the shared path also requires the ordinary Chat Completions schema and event mapper to preserve compatible metadata that OpenRouter already emits. This includes structured reasoning details needed for replay, fragmented reasoning accumulation, prompt-cache read and write usage, and thought signatures attached to tool calls. The stream exposes `[DONE]` explicitly rather than treating it as indistinguishable from an unexpected end of the response body. OpenRouter's routing session identifier is hashed before transmission so Zed's internal thread identifier is not exposed. The diff is larger than the extracted transport alone because the shared API is additive, the compatibility metadata must be represented in the common wire types and event mapper, and the provider-specific adapter remains intentionally independent. Roughly four hundred added lines are focused transport, metadata, error-classification, attribution, caching, and privacy tests. The provider-level OpenRouter implementation becomes smaller while preserving its existing behavior. Testing performed: - `cargo test -p open_ai` - `cargo test -p open_router` - `cargo nextest run -p language_models open_router` - `cargo check -p edit_prediction -p edit_prediction_cli` - `cargo fmt --all -- --check` - `./script/clippy -p open_ai -p open_router -p language_models -p edit_prediction_cli` - `cargo machete` Release Notes: - Improved OpenRouter reasoning continuity and request privacy. --------- Co-authored-by: Eric Holk <eric@zed.dev>
81 lines
2.7 KiB
TOML
81 lines
2.7 KiB
TOML
[package]
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name = "language_models"
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version = "0.1.0"
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edition.workspace = true
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publish.workspace = true
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license = "GPL-3.0-or-later"
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[lints]
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workspace = true
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[lib]
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path = "src/language_models.rs"
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[dependencies]
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ai_onboarding.workspace = true
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async-lock.workspace = true
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anthropic = { workspace = true, features = ["schemars"] }
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anyhow.workspace = true
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aws-config = { workspace = true, features = ["behavior-version-latest"] }
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aws-credential-types = { workspace = true, features = ["hardcoded-credentials"] }
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aws-sigv4.workspace = true
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aws_http_client.workspace = true
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base64.workspace = true
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bedrock = { workspace = true, features = ["schemars"] }
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client.workspace = true
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cloud_api_client.workspace = true
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cloud_api_types.workspace = true
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collections.workspace = true
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component.workspace = true
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convert_case.workspace = true
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copilot_chat.workspace = true
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copilot_ui.workspace = true
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credentials_provider.workspace = true
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deepseek = { workspace = true, features = ["schemars"] }
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extension.workspace = true
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extension_host.workspace = true
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fs.workspace = true
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futures.workspace = true
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google_ai = { workspace = true, features = ["schemars"] }
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gpui.workspace = true
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gpui_tokio.workspace = true
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http_client.workspace = true
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language.workspace = true
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language_model.workspace = true
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language_models_cloud.workspace = true
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llama_cpp = { workspace = true, features = ["schemars"] }
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lmstudio = { workspace = true, features = ["schemars"] }
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log.workspace = true
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menu.workspace = true
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mistral = { workspace = true, features = ["schemars"] }
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ollama = { workspace = true, features = ["schemars"] }
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open_ai = { workspace = true, features = ["schemars"] }
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openai_subscribed.workspace = true
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opencode = { workspace = true, features = ["schemars"] }
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open_router = { workspace = true, features = ["schemars"] }
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rand.workspace = true
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release_channel.workspace = true
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schemars.workspace = true
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serde.workspace = true
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serde_json.workspace = true
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settings.workspace = true
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sha2.workspace = true
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strum.workspace = true
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tokio = { workspace = true, features = ["rt", "rt-multi-thread"] }
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ui.workspace = true
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ui_input.workspace = true
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util.workspace = true
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x_ai = { workspace = true, features = ["schemars"] }
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[dev-dependencies]
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client = { workspace = true, features = ["test-support"] }
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clock = { workspace = true, features = ["test-support"] }
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cloud_llm_client.workspace = true
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db = { workspace = true, features = ["test-support"] }
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feature_flags.workspace = true
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gpui = { workspace = true, features = ["test-support"] }
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http_client = { workspace = true, features = ["test-support"] }
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language_model = { workspace = true, features = ["test-support"] }
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parking_lot.workspace = true
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pretty_assertions.workspace = true
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settings = { workspace = true, features = ["test-support"] }
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