014ec8bd5c
- Training data pipeline: convert, export, extract, load-to-db - Infra tooling: infra-audit, infra-gitea-link - RAG pipeline: rag-ingest, rag-query - Fine-tuning: finetune-lora, overnight-qwen3, install-unsloth - Transcripts: export-transcripts - Updated README with script index and token reduction strategy
37 lines
1.6 KiB
Markdown
37 lines
1.6 KiB
Markdown
# grace-scripts
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Grace AI helper scripts — token-efficient homelab automation.
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These scripts replace inline shell construction in AI sessions, reducing token usage and making repeated tasks reproducible and auditable.
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## Scripts
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| Script | Purpose |
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|--------|---------|
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| `convert-training-data.py` | Convert raw session exports to unsloth/axolotl-ready JSONL |
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| `export-training-data.py` | Export training data from OpenClaw sessions |
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| `export-transcripts.py` | Export session transcripts incrementally |
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| `extract-sessions.py` | Extract and parse session data |
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| `finetune-lora.py` | Run LoRA fine-tuning on local Qwen3-8B |
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| `infra-audit.py` | Audit homelab infra state and sync to MongoDB |
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| `infra-gitea-link.py` | Link infra components to Gitea issues/repos |
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| `load-transcripts-to-db.py` | Sync transcripts to PostgreSQL on DB VM |
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| `overnight-qwen3.py` | Overnight batch inference job |
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| `overnight-research.sh` | Overnight research and summarization |
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| `rag-ingest.py` | Ingest documents into Qdrant for RAG |
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| `rag-query.py` | Query Qdrant RAG index |
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| `install-unsloth.sh` | Install unsloth fine-tuning framework |
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## Credential Protocol
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All scripts pull credentials from Infisical. Never hardcode secrets.
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IPs and service URLs reference TOOLS.md / BOOTSTRAP.md in the Grace workspace.
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## Token Reduction Strategy
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Each script replaces multi-step inline shell that Grace would otherwise construct token-by-token in every session. One `exec` call to a script = ~10-50x fewer tokens than building the equivalent inline.
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## Issues & Improvements
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Use Gitea issues on this repo to track bugs, improvements, and new script requests.
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