Kartikeya
2223b190f6
infra: Dockerfile (backend from monorepo root) + DigitalOcean App Platform spec with health check and secret env vars. database: MongoDB Atlas + Voyage vector-memory schema and the continual-learning loop notes. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
33 lines
1.6 KiB
Markdown
33 lines
1.6 KiB
Markdown
# database
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Continual-learning memory for PodMan: **MongoDB Atlas** for the team model and
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outcomes, **Voyage** embeddings for vector recall. This is what makes PodMan
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"more useful the more you use it" (the track requirement).
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## Collections
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| Collection | Holds | Notes |
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| ---------------- | ---------------------------------------------- | ----------------------------- |
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| `pods` | `Pod` docs (members, repo) | one per pod |
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| `observations` | `EngineerContext` snapshots over time | sampled, append-only |
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| `collisions` | detected `Collision`s | for replay + precision tuning |
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| `interventions` | `Intervention`s + outcome (accepted/dismissed) | drives the self-tuning policy |
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| `memory_vectors` | Voyage embeddings of file/feature notes | Atlas Vector Search index |
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Types live in [`shared/`](../shared/src). Each engineer/file/feature note is
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embedded with Voyage and stored alongside its source doc for retrieval by the
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PodMan brain.
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## The continual-learning loop
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1. **Observe** → write `observations`.
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2. **Store** → embed notes into `memory_vectors`.
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3. **Predict** → collision detector + brain decide whether to intervene.
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4. **Outcome** → update the `interventions` doc with accepted/dismissed.
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5. **Adapt** → tune thresholds + ownership attribution from outcomes.
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## Setup
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Create an Atlas cluster + a Vector Search index on `memory_vectors.embedding`,
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then set `MONGODB_URI` and `VOYAGE_API_KEY` in `.env`.
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