robbond
650877e5bf
Adopt executeEpisodeDone orchestration as the canonical path for
'Done for now' activity boundary: one user Done triggers exactly
prepareCompletedEpisode -> reconsiderCompletedEpisode -> applyValidatedProposal
-> Current Understanding synthesis -> leave focused workspace.
Production changes (components/scenario-form.jsx):
- Add prepareCompletedEpisode, reconsiderCompletedEpisode, applyValidatedProposal imports
- Export executeEpisodeDone({params}) with all 4 domain functions as named
parameters (defaults to module exports) for deterministic test wiring
- Rewrite handleDoneForNowPromotion(targetNodeId) as async: delegates to
executeEpisodeDone pipeline; CU synthesis installed only on success
- Add doneInProgressRef useRef(false) for exactly-once Done enforcement
- On synthesis failure: KEEP updated graph, KEEP Findings, KEEP existing CU
- Retire produceFindingInformedSummary from ScenarioForm (legacy CU writer)
- Remove legacy idempotence guard and Evidence:[] regex dedup
Test changes (tests/ui/scenario-form-episode-done.test.jsx):
- 9 tests verifying orchestration pipeline correctness:
1. Successful path order: prepare -> reconsider -> apply -> synthesis
2. Correct prepared episode input parameters
3. Structured application evidence (no answer fields in context)
4. nextGraph used for synthesis (not stale result state)
5. Reasoning failure: apply not called, CU synthesis not called
6. Application failure: CU synthesis not called, graph not replaced
7. Synthesis failure: nextGraph remains installed (no rollback)
8. Exactly-once per call for each domain function
9. Legacy Done writer retired (pipeline does not produce deterministic summary)
Confidence Engine
An experimental prototype that tests whether an LLM can build and maintain an evidence-based reconstruction of a situation over multiple turns.
Purpose
This is Milestone 1 — a technical vertical slice only. It demonstrates:
- Sending a scenario to a local Ollama model via a Next.js server route
- Receiving structured JSON output
- Validating the result with Zod schemas
- Displaying the reconstruction and diagnostic information in a plain UI
Prerequisites
- Node.js 18+ (LTS recommended)
- npm (or equivalent package manager)
- Ollama installed and running on your local network, with a model pulled (e.g.,
ollama pull llama3)
Installation
cd confidence-engine
npm install
cp .env.example .env.local
Edit .env.local and set:
OLLAMA_BASE_URL— your Ollama server address (e.g.,http://192.168.1.100:11434)OLLAMA_MODEL— the model name (e.g.,llama3)
Development Commands
npm run dev # Start development server on localhost:3000
npm run build # Production build
npm run start # Run production server
npm run lint # ESLint check
Testing Commands
npm test # Run all tests (one-shot)
npm run test:watch # Run tests in watch mode
Tests mock the Ollama network request. No real Ollama server is needed to run them.
Verifying Ollama Connectivity
- Start the dev server:
npm run dev - Open http://localhost:3000/api/health
- You should see JSON with
"reachable": trueand your model name
Current Limitations
- Single-turn only — no conversation memory or multi-turn dialogue
- No persistence — results are not saved between requests
- Ollama only — the provider abstraction exists but only Ollama is implemented
- JSON mode reliance — output quality depends on the model's ability to produce valid structured JSON
- No question generation — no follow-up questions or uncertainty resolution yet
- No real-time streaming — waits for full response before displaying results
- Plain UI — no animations, theming, or responsive polish beyond basic layout
Deliberately Not Implemented
- Authentication / user accounts
- Billing / rate limiting
- Database or file storage
- Vector databases or embeddings
- Deployment configuration (Docker, Vercel, etc.)
- External cloud LLM providers (OpenAI, Anthropic, etc.)
- Agent frameworks or tool use
- Complex state management (Zustand, Redux, etc.)
- Multi-turn conversation history
- Question generation or ranking
Architecture Notes
The application uses a provider abstraction in lib/llm/provider.js. The server routes call the provider, which hides the Ollama-specific implementation. To add a new provider, implement the same interface:
{
generateReconstruction(scenario, modelName): Promise<Reconstruction>
}
```READMEEOF