Release 0.1.1: Documentation fixes and cleanup#12
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- Add 'Why LLMKit?' section highlighting Rust benefits - Emphasize production-ready features (memory safety, no GIL, no leaks) - Add prompt caching examples with 90% cost savings messaging - Add extended thinking, model registry examples - Fix badge URLs (llmkit-python, llmkit-node) - Add production features table (smart router, circuit breaker, guardrails) - Improve code examples with cleaner imports for Node.js
- Remove internal 'Phase' development notes - Create clean, user-friendly provider documentation - Organize 100+ providers by category (Cloud, Inference, Regional, etc.) - Add environment variables and features for each provider - Rewrite model registry with practical examples - Add popular models table with pricing - Add capability query examples
- Remove docs with internal "Phase" references (moved to model_registry): - additional_providers.md - emerging_specialized_providers.md - domain_models.md - scientific_benchmarks.md - Rewrite CHANGELOG.md with standard Keep a Changelog format - Simplify RELEASE_NOTES.md to user-facing release notes - Update docs/INDEX.md to remove references to deleted files
- Updated model data from model_registry crawler (97 providers, 11,067 models) - Refreshed pricing, capabilities, and benchmark data - Synchronized with latest provider API changes
- Update Cargo.toml versions (main, python, node) - Update pyproject.toml version - Update package.json version - Update CHANGELOG.md with 0.1.1 release notes
Word and TranscribeResponse are returned from API calls and cannot be instantiated directly in tests (no #[new] constructor in PyO3 bindings).
The tests were passing - the CI failure was a Rust toolchain issue, not Python test failures.
The cargo-deny-action v2 runs in a musl container which conflicted with rust-toolchain.toml causing: error: override toolchain 'stable-x86_64-unknown-linux-musl' is not installed Installing the stable toolchain explicitly before running cargo-deny resolves the conflict.
The model alias 'gpt-4o' is shared by multiple providers (OpenAI, OpenRouter). The last provider in the registry wins the alias lookup. Using the full ID 'openai/gpt-4o' ensures the test checks the correct provider.
When multiple providers offer the same model (e.g., gpt-4o via OpenAI, Azure, OpenRouter), aliases now resolve to the native provider: - gpt-4o → openai/gpt-4o (not azure or openrouter) - claude-sonnet-4-5 → anthropic/claude-sonnet-4-5 Added is_native_provider() function to identify canonical providers for model families (OpenAI for GPT/O1/O3, Anthropic for Claude, etc.) Also fixes raw_id collision where openrouter/gpt-4o's raw_id "gpt-4o" was overwriting the OpenAI entry.
The test was checking for docs/domain_models.md and docs/scientific_benchmarks.md which were moved to ~/projects/model_registry as part of documentation cleanup. Updated to check for core docs that actually exist: - docs/INDEX.md - docs/MODELS_REGISTRY.md - docs/getting-started-rust.md
Emphasize 100+ providers and 11,000+ models across all packages.
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Summary
llmkit-python,llmkit-node)Changes
Fixed
pip install llmkit-python(was incorrectlyllmkit)npm install llmkit-node(was incorrectlyllmkit)Documentation
Test plan