Yahya Alhinai
160 lines
3.3 KiB
Markdown
160 lines
3.3 KiB
Markdown
# Graph Discovery Spec
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Status: demo-backed / active
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Scope: how PodMan discovers graph nodes, edges, risk paths, and learning paths from MongoDB
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Owner: graph discovery / Team memory observatory
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## Purpose
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Graph discovery turns MongoDB memory into a legible Team memory graph. It is not
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only layout. It decides which relationships matter, which path is highlighted,
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and which evidence explains the graph.
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The graph must answer:
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1. Who is working?
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2. Which files or symbols overlap?
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3. Where is the risk?
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4. What did PodMan do?
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5. What outcome changed memory?
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## What Is Implemented Now
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- Live materializer first: build from current MongoDB records.
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- Seeded graph second: read `team_model.graph` and mirrored graph collections.
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- Demo fallback third: return a grounded demo graph when live data is empty or
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unavailable.
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- Reachability uses MongoDB `$graphLookup` over `graph_edges`.
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## What Is Intentionally Cut
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- A graph database migration.
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- Whole-history rendering as the default view.
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- Claims that seeded graph data is live learning.
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## Source Data
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Graph discovery reads:
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- `pods`
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- `engineer_states`
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- `observations`
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- `collisions`
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- `interventions`
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- `outcomes`
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- `team_model`
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- `graph_nodes`
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- `graph_edges`
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- optional `memory_vectors`
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- optional `agent_runs`
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- optional `strategy_versions`
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## UI Graph Contract
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```text
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PodGraph
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podId
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generatedAt
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nodes
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edges
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metrics
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loop?
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activity?
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```
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Node kinds:
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```text
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engineer, feature, file, collision, intervention
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```
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Edge kinds:
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```text
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owns, editing, touches, collides, warns, learned_from
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```
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## Discovery Rules
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### Engineer nodes
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Create from pod roster, recent observations, git state, or collision membership.
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### File nodes
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Create only from normalized real file paths. Reject noise such as URLs, env
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values, scratch names, and non-file strings.
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### Collision nodes
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Create from distinct collision signatures. Collapse repeats. Prioritize
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collisions referenced by accepted outcomes.
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### Intervention nodes
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Create one visible intervention per surviving collision unless whole-graph mode
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explicitly expands history.
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### Learned paths
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Create `learned_from` only when an accepted real outcome links an intervention
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to a durable memory update.
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## Path Modes
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### Risk path
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Default mode. Highlight the clearest current chain:
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```text
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engineer -> file -> collision -> intervention -> learned owner
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```
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Dim unrelated graph material.
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### Learning edges
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Highlight `learned_from`, `owns`, and the outcomes that produced them.
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### Whole graph
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Show all materialized nodes and edges with de-emphasized non-critical edges.
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## MongoDB Traversal
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Use `graph_edges` for reachability:
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```text
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source -> target -> next target
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```
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Primary traversal questions:
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- What risks does this engineer reach?
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- Which files feed this collision?
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- Which intervention came from this collision?
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- Which learned owner came from this intervention?
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## Metrics
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Minimum metrics:
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- Learned owners.
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- Open risk paths.
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- Accept rate.
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Optional metrics:
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- Observations.
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- Interventions.
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- Memory vectors.
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- Strategy versions.
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## Acceptance Criteria
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- Default graph is not a hairball.
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- Every visible learned edge has outcome evidence.
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- Every selected node can explain why it matters.
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- Activity stream matches graph events.
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- Graph can be rebuilt from MongoDB source records.
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