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Jon Saad-FalconandClaude Opus 4.6
323d7ff032
- Eval config: TOML-based suite configs defining models x benchmarks matrix, loaded via --config flag. Includes load_eval_config(), expand_suite(), 7 config dataclasses, 3 example configs, and 61 new tests. - Pillar-aligned config: generation params in IntelligenceConfig, nested engine/learning configs, agent objective/system_prompt/context_from_memory, structured learning sub-policies, TOML migration layer. - Documentation: evaluations user guide, evals API reference, updated mkdocs.yml navigation, updated architecture docs. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
27 lines
1.5 KiB
Markdown
27 lines
1.5 KiB
Markdown
# API Reference
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This section contains the auto-generated API reference for the OpenJarvis Python
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package. Documentation is extracted directly from the source code docstrings
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using [mkdocstrings](https://mkdocstrings.github.io/).
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Each page documents a major module of the framework, including abstract base
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classes, concrete implementations, and utility functions.
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## Modules
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| Module | Description |
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|--------|-------------|
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| [SDK](sdk.md) | High-level `Jarvis` class and `MemoryHandle` proxy |
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| [Core](core.md) | Registries, types, configuration, and event bus |
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| [Engine](engine.md) | Inference engine ABC and backends (Ollama, vLLM, llama.cpp, SGLang, Cloud) |
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| [Agents](agents.md) | Agent ABC and implementations (Simple, Orchestrator, OpenClaw, Custom) |
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| [Memory](memory.md) | Memory backend ABC and implementations (SQLite, FAISS, ColBERT, BM25, Hybrid) |
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| [Tools](tools.md) | Tool ABC, executor, and built-in tools (Calculator, Think, Retrieval, LLM, FileRead) |
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| [Intelligence](intelligence.md) | Heuristic router and model catalog |
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| [Learning](learning.md) | Router policies, reward functions, and trace-driven learning |
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| [Traces](traces.md) | Trace storage, collection, and analysis |
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| [Telemetry](telemetry.md) | Telemetry storage, aggregation, and instrumented wrappers |
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| [Benchmarks](bench.md) | Benchmark ABC, suite runner, latency and throughput benchmarks |
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| [Evals](evals.md) | Evaluation framework: backends, dataset providers, scorers, and suite runner |
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| [Server](server.md) | FastAPI application, OpenAI-compatible routes, and Pydantic models |
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