mirror of
https://github.com/fabro-sh/fabro.git
synced 2026-09-25 01:01:29 +00:00
34d83db801
## Summary This PR moves Fabro’s provider/model catalog toward settings-driven provider identity by replacing the closed provider schema at the API/auth/model boundary with `ProviderId`, then loading built-in provider and model metadata from embedded per-provider TOML files. The immediate result is that built-ins now use the same settings-shaped catalog data that custom providers will use later, while request-serving paths still keep the existing bootstrap/default catalog behavior until the resolved-catalog plumbing lands. ## Changes - Replaces API-facing provider enum usage with string-backed `ProviderId`, including OpenAPI/progenitor replacements and regenerated TypeScript client models. - Routes model, auth, billing, CLI, server, and workflow call sites through provider IDs where they cross product identity boundaries. - Builds `Catalog` from settings-shaped provider/model data with validation for adapter keys, OpenAI-compatible `base_url`, duplicate aliases, provider defaults, disabled entries, model controls, and per-speed cost rows. - Replaces `catalog.json` with embedded provider TOML files under `lib/crates/fabro-model/src/catalog/providers/`. - Adds an explicit `fabro_model::bootstrap_catalog` hatch for setup/install paths and extends the dev policy test to keep bootstrap access contained. - Preserves public training and knowledge-cutoff labels in LLM model settings while still accepting bare TOML dates. ## Verification - `cargo nextest run -p fabro-model -p fabro-config -p fabro-api` — 416 passed - `cargo nextest run -p fabro-dev --features dev bootstrap_catalog_references_stay_in_allowlist` — 1 passed - `cargo +nightly-2026-04-14 fmt --check --all` - `cargo +nightly-2026-04-14 clippy --workspace --all-targets -- -D warnings` - `cargo build --workspace` - `git diff --check` --- [](https://github.com/EveryInc/compound-engineering-plugin) 🤖 Generated with GPT-5 via [Codex](https://openai.com/codex)
310 lines
14 KiB
Rust
310 lines
14 KiB
Rust
#![expect(
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clippy::disallowed_methods,
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reason = "integration tests stage fixtures with sync std::fs; test infrastructure, not Tokio-hot path"
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)]
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use fabro_test::{fabro_snapshot, test_context};
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use httpmock::MockServer;
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#[test]
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fn help() {
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let context = test_context!();
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let mut cmd = context.model();
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cmd.arg("--help");
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fabro_snapshot!(context.filters(), cmd, @"
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success: true
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exit_code: 0
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----- stdout -----
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List and test LLM models
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Usage: fabro model [OPTIONS] [COMMAND]
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Commands:
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list List available models
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test Test model availability by sending a simple prompt
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help Print this message or the help of the given subcommand(s)
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Options:
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--json Output as JSON [env: FABRO_JSON=]
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--debug Enable DEBUG-level logging (default is INFO) [env: FABRO_DEBUG=]
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--no-upgrade-check Disable automatic upgrade check [env: FABRO_NO_UPGRADE_CHECK=true]
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--quiet Suppress non-essential output [env: FABRO_QUIET=]
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--verbose Enable verbose output [env: FABRO_VERBOSE=]
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-h, --help Print help
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----- stderr -----
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");
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}
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#[test]
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fn bare() {
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let context = test_context!();
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fabro_snapshot!(context.filters(), context.model(), @"
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success: true
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exit_code: 0
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----- stdout -----
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MODEL PROVIDER ALIASES CONTEXT COST SPEED
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claude-haiku-4-5 anthropic haiku, claude-haiku 200k $0.8 / $4.0 100 tok/s
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claude-opus-4-6 anthropic 1m $5.0 / $25.0 25 tok/s
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claude-opus-4-7 anthropic opus, claude-opus 1m $5.0 / $25.0 25 tok/s
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claude-sonnet-4-5 anthropic 200k $3.0 / $15.0 50 tok/s
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claude-sonnet-4-6 anthropic sonnet, claude-sonnet 200k $3.0 / $15.0 50 tok/s
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gemini-3-flash-preview gemini gemini-flash 1m $0.5 / $3.0 150 tok/s
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gemini-3.1-flash-lite-preview gemini gemini-flash-lite 1m $0.2 / $1.5 200 tok/s
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gemini-3.1-pro-preview gemini gemini-pro 1m $2.0 / $12.0 85 tok/s
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gemini-3.1-pro-preview-customtools gemini gemini-customtools 1m $2.0 / $12.0 85 tok/s
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mercury-2 inception mercury 131k $0.2 / $0.8 1000 tok/s
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kimi-k2.5 kimi kimi 262k $0.6 / $3.0 50 tok/s
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minimax-m2.5 minimax minimax 197k $0.3 / $1.2 45 tok/s
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gpt-5-mini openai gpt5-mini 1m $0.2 / $2.0 70 tok/s
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gpt-5.2 openai gpt5 1m $1.8 / $14.0 65 tok/s
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gpt-5.2-codex openai 1m $1.8 / $14.0 100 tok/s
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gpt-5.3-codex openai codex 1m $1.8 / $14.0 100 tok/s
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gpt-5.3-codex-spark openai codex-spark 131k - / - 1000 tok/s
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gpt-5.4 openai gpt54, gpt-54 1m $2.5 / $15.0 70 tok/s
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gpt-5.4-mini openai gpt54-mini, gpt-54-mini 400k $0.8 / $4.5 140 tok/s
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gpt-5.4-pro openai gpt54-pro, gpt-54-pro 1m $30.0 / $180.0 20 tok/s
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gpt-5.5 openai gpt55, gpt-55 1m $5.0 / $30.0 70 tok/s
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gpt-5.5-pro openai gpt55-pro, gpt-55-pro 1m $30.0 / $180.0 20 tok/s
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glm-4.7 zai glm, glm4 203k $0.6 / $2.2 100 tok/s
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----- stderr -----
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");
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}
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#[test]
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fn list() {
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let context = test_context!();
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let mut cmd = context.model();
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cmd.arg("list");
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fabro_snapshot!(context.filters(), cmd, @"
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success: true
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exit_code: 0
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----- stdout -----
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MODEL PROVIDER ALIASES CONTEXT COST SPEED
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claude-haiku-4-5 anthropic haiku, claude-haiku 200k $0.8 / $4.0 100 tok/s
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claude-opus-4-6 anthropic 1m $5.0 / $25.0 25 tok/s
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claude-opus-4-7 anthropic opus, claude-opus 1m $5.0 / $25.0 25 tok/s
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claude-sonnet-4-5 anthropic 200k $3.0 / $15.0 50 tok/s
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claude-sonnet-4-6 anthropic sonnet, claude-sonnet 200k $3.0 / $15.0 50 tok/s
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gemini-3-flash-preview gemini gemini-flash 1m $0.5 / $3.0 150 tok/s
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gemini-3.1-flash-lite-preview gemini gemini-flash-lite 1m $0.2 / $1.5 200 tok/s
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gemini-3.1-pro-preview gemini gemini-pro 1m $2.0 / $12.0 85 tok/s
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gemini-3.1-pro-preview-customtools gemini gemini-customtools 1m $2.0 / $12.0 85 tok/s
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mercury-2 inception mercury 131k $0.2 / $0.8 1000 tok/s
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kimi-k2.5 kimi kimi 262k $0.6 / $3.0 50 tok/s
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minimax-m2.5 minimax minimax 197k $0.3 / $1.2 45 tok/s
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gpt-5-mini openai gpt5-mini 1m $0.2 / $2.0 70 tok/s
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gpt-5.2 openai gpt5 1m $1.8 / $14.0 65 tok/s
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gpt-5.2-codex openai 1m $1.8 / $14.0 100 tok/s
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gpt-5.3-codex openai codex 1m $1.8 / $14.0 100 tok/s
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gpt-5.3-codex-spark openai codex-spark 131k - / - 1000 tok/s
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gpt-5.4 openai gpt54, gpt-54 1m $2.5 / $15.0 70 tok/s
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gpt-5.4-mini openai gpt54-mini, gpt-54-mini 400k $0.8 / $4.5 140 tok/s
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gpt-5.4-pro openai gpt54-pro, gpt-54-pro 1m $30.0 / $180.0 20 tok/s
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gpt-5.5 openai gpt55, gpt-55 1m $5.0 / $30.0 70 tok/s
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gpt-5.5-pro openai gpt55-pro, gpt-55-pro 1m $30.0 / $180.0 20 tok/s
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glm-4.7 zai glm, glm4 203k $0.6 / $2.2 100 tok/s
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----- stderr -----
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");
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}
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#[test]
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fn list_provider() {
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let context = test_context!();
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let mut cmd = context.model();
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cmd.args(["list", "--provider", "anthropic"]);
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fabro_snapshot!(context.filters(), cmd, @"
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success: true
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exit_code: 0
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----- stdout -----
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MODEL PROVIDER ALIASES CONTEXT COST SPEED
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claude-haiku-4-5 anthropic haiku, claude-haiku 200k $0.8 / $4.0 100 tok/s
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claude-opus-4-6 anthropic 1m $5.0 / $25.0 25 tok/s
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claude-opus-4-7 anthropic opus, claude-opus 1m $5.0 / $25.0 25 tok/s
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claude-sonnet-4-5 anthropic 200k $3.0 / $15.0 50 tok/s
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claude-sonnet-4-6 anthropic sonnet, claude-sonnet 200k $3.0 / $15.0 50 tok/s
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----- stderr -----
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");
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}
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#[test]
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fn list_query() {
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let context = test_context!();
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let mut cmd = context.model();
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cmd.args(["list", "--query", "opus"]);
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fabro_snapshot!(context.filters(), cmd, @"
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success: true
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exit_code: 0
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----- stdout -----
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MODEL PROVIDER ALIASES CONTEXT COST SPEED
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claude-opus-4-6 anthropic 1m $5.0 / $25.0 25 tok/s
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claude-opus-4-7 anthropic opus, claude-opus 1m $5.0 / $25.0 25 tok/s
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----- stderr -----
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");
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}
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#[test]
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fn list_query_aliases() {
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let context = test_context!();
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let mut cmd = context.model();
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cmd.args(["list", "--query", "codex"]);
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fabro_snapshot!(context.filters(), cmd, @"
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success: true
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exit_code: 0
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----- stdout -----
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MODEL PROVIDER ALIASES CONTEXT COST SPEED
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gpt-5.2-codex openai 1m $1.8 / $14.0 100 tok/s
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gpt-5.3-codex openai codex 1m $1.8 / $14.0 100 tok/s
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gpt-5.3-codex-spark openai codex-spark 131k - / - 1000 tok/s
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----- stderr -----
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");
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}
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#[test]
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fn list_query_case_insensitive() {
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let context = test_context!();
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let mut cmd = context.model();
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cmd.args(["list", "--query", "OPUS"]);
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fabro_snapshot!(context.filters(), cmd, @"
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success: true
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exit_code: 0
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----- stdout -----
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MODEL PROVIDER ALIASES CONTEXT COST SPEED
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claude-opus-4-6 anthropic 1m $5.0 / $25.0 25 tok/s
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claude-opus-4-7 anthropic opus, claude-opus 1m $5.0 / $25.0 25 tok/s
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----- stderr -----
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");
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}
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#[test]
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fn list_invalid_provider_errors() {
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let context = test_context!();
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let mut cmd = context.model();
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cmd.args(["list", "--provider", "not-a-provider"]);
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fabro_snapshot!(context.filters(), cmd, @"
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success: false
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exit_code: 1
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----- stdout -----
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----- stderr -----
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× unknown provider: not-a-provider
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");
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}
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#[test]
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fn list_uses_configured_server_target_without_server_flag() {
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let context = test_context!();
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let server = MockServer::start();
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let mock = server.mock(|when, then| {
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when.method("GET");
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then.status(200)
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.header("Content-Type", "application/json")
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.body(
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serde_json::json!({
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"data": [{
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"id": "remote-model",
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"display_name": "Remote Model",
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"provider": "openai",
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"family": "test",
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"aliases": ["remote"],
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"limits": {
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"context_window": 131_072,
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"max_output": 4096
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},
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"training": null,
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"knowledge_cutoff": null,
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"features": {
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"tools": true,
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"vision": false,
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"reasoning": false,
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"effort": false
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},
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"costs": {
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"input_cost_per_mtok": 1.0,
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"output_cost_per_mtok": 2.0,
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"cache_input_cost_per_mtok": null
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},
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"estimated_output_tps": 42.0,
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"default": false,
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"configured": false
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}],
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"meta": { "has_more": false }
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})
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.to_string(),
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);
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});
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context.set_http_target(&server.base_url());
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let mut cmd = context.model();
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cmd.args(["list", "--json"]);
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let output = cmd.assert().success().get_output().stdout.clone();
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let models: serde_json::Value =
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serde_json::from_slice(&output).expect("model list json should parse");
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mock.assert();
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assert_eq!(models.as_array().map(Vec::len), Some(1));
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assert_eq!(models[0]["id"].as_str(), Some("remote-model"));
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}
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#[test]
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fn list_uses_fabro_config_for_machine_settings() {
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let context = test_context!();
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let server = MockServer::start();
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let mock = server.mock(|when, then| {
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when.method("GET");
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then.status(200)
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.header("Content-Type", "application/json")
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.body(
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serde_json::json!({
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"data": [{
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"id": "remote-model",
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"display_name": "Remote Model",
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"provider": "openai",
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"family": "test",
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"aliases": ["remote"],
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"limits": {
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"context_window": 131_072,
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"max_output": 4096
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},
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"training": null,
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"knowledge_cutoff": null,
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"features": {
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"tools": true,
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"vision": false,
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"reasoning": false,
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"effort": false
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},
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"costs": {
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"input_cost_per_mtok": 1.0,
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"output_cost_per_mtok": 2.0,
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"cache_input_cost_per_mtok": null
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},
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"estimated_output_tps": 42.0,
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"default": false,
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"configured": false
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}],
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"meta": { "has_more": false }
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})
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.to_string(),
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);
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});
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let config_dir = tempfile::tempdir().unwrap();
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let config_path = config_dir.path().join("custom-settings.toml");
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std::fs::write(
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&config_path,
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format!(
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"_version = 1\n\n[cli.target]\ntype = \"http\"\nurl = \"{}/api/v1\"\n",
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server.base_url()
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),
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)
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.unwrap();
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let mut cmd = context.model();
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cmd.args(["list", "--json"]);
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cmd.env("FABRO_CONFIG", &config_path);
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let output = cmd.assert().success().get_output().stdout.clone();
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let models: serde_json::Value =
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serde_json::from_slice(&output).expect("model list json should parse");
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mock.assert();
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assert_eq!(models.as_array().map(Vec::len), Some(1));
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assert_eq!(models[0]["id"].as_str(), Some("remote-model"));
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}
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