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Productize SES into an AI Data & Training Toolkit - #7
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This commit evolves SES into a comprehensive data & training toolkit:
- Rust Core (`core_rs`): Added `batch_hard_negative_mining` and `semantic_dedup` to `jas_vector_core` for highly optimized C-contiguous array computations.
- Python Module (`ses.data`): Added a complete training toolkit including exporters, hard negative miners, deduplicators, synthetic pair generation (SFT & DPO), and RAFT example generation.
- Python Core (`ses.core.llm`): Extended local LLM routing to support OpenAI-compatible endpoints alongside Ollama.
- Database (`gateway/database.py`): Extended PostgreSQL and SQLite adapters with `dataset_jobs` tables and background job management functions.
- API endpoints (`gateway/server.py`): Added `/api/v1/datasets/generate`, `/api/v1/datasets/jobs/{job_id}`, and `/api/v1/datasets/download/{job_id}` for managing async dataset generation tasks, as well as `/api/v1/tools/agent-spec`.
- CLI commands (`ses/cli.py`): Extended the CLI with `data` subcommand providing curation, deduplication, mining, and dataset generation functionalities.
- Web UI (`portal`): Created the new Training & Dataset Studio (`/studio`) interface to launch and monitor generation jobs visually.
Co-authored-by: Julio-Patron <225645035+Julio-Patron@users.noreply.github.com>
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Implementation of the SES Productization Plan. Adds Data & Training Toolkit features including dataset exporters (JSONL, Parquet), hard negative mining, semantic deduplication, synthetic pair generation (SFT/DPO), and RAFT dataset generation. Also adds CLI commands, Gateway REST API endpoints for async job monitoring, an updated Database layer, and a new Training Studio dashboard in the Next.js UI.
PR created automatically by Jules for task 2575690193616040359 started by @Julio-Patron