feat: add logs-to-training pipeline prototype (PII-safe ingest, SFT/DPO export, validation, split integrity)#4
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theUtkarshRaj wants to merge 3 commits into
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…te CLI) Adds logs_to_training package: Langfuse-style ingest, redaction, complexity tags, tool consistency checks, LoRA-ready SFT JSONL, DPO candidates with hard-negative templates, disjoint split export, gold_examples fixtures, and pytest coverage. Co-authored-by: Cursor <cursoragent@cursor.com>
Co-authored-by: Cursor <cursoragent@cursor.com>
Co-authored-by: Cursor <cursoragent@cursor.com>
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Built this as a full implementation-first prototype for the complete DMP midpoint slice, but with particular emphasis beyond baseline ingest/export on downstream governance layers:
My intent was to complement core prototype directions with stronger training-readiness, quality safeguards, and reviewer tooling. Happy to modularize or split advanced layers if narrower review boundaries are preferred. |
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Summary
This PR adds an implementation-first prototype for transforming production QA + agentic logs into privacy-safe, trajectory-aware training datasets.
Included
Motivation
This aligns directly with the DMP 2026 project goal of converting production logs into safe, trajectory-aware datasets suitable for SFT (LoRA) and DPO workflows, with emphasis on data quality, privacy, and downstream training readiness.
Validation
Notes
This is designed as a modular prototype aligned with midpoint milestone goals. If preferred, I’m happy to split this into smaller PRs (core pipeline / validation / DPO extensions) for easier review.