53 lines
3.3 KiB
Markdown
53 lines
3.3 KiB
Markdown
# Extending the framework (extension points)
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portfolio-optimiser is a generic core with explicit **config seams** (D4/D5, 90 %-prinsippet):
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you onboard a new project, a new data source, or a new model-map **without editing the core
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`src/portfolio_optimiser/*.py`**. The three guides below name the exact seam for each.
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> **Honesty note (rent teknisk rammeverk).** The bundled reference domain
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> (`data/reference_projects.json` + `data/docs/<id>/`) is a set of **SYNTHETIC, AI-authored
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> fixtures** — fictional construction-cost projects, dummy estimates, and placeholder
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> `verdict_input` decisions. They are flagged in each file's `_note`. A production deployer
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> **replaces the data source** with their own and supplies **Layer-2 verdicts via real HITL**
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> (fageksperter), not static config. The static `verdict_input` field is a test-fixture
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> convenience that stands in for the durable HITL verdict in the offline synthetic framework.
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## Legg til eget prosjekt
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A new project is **config + docs only** — no code change (this is exercised by the SC1 test
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`test_e_new_project_flows_through_via_config_only` + the `test_f_no_hardcoded_project_ids_in_src`
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guard, which fails if any project id leaks into `src/`).
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1. Append an entry to `src/portfolio_optimiser/data/reference_projects.json` with the full key
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set: `id`, `name`, `description`, `currency`, `cost_items`, **`docs_dir`**, and
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**`verdict_input`** (`{"decision", "rationale"}`).
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- `docs_dir` is a path **relative to the package `data/` root** (e.g. `"docs/MY-PROJECT"`);
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the loader (`reference_domain.load_reference_projects`) resolves it to an absolute path.
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- `verdict_input` carries the (synthetic) Layer-2 decision/rationale; flag it in the file's
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`_note` as synthetic if it is not a real expert verdict.
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2. Create the bundled docs folder `src/portfolio_optimiser/data/docs/<id>/` with at least one
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text file whose content names the cost-saving measure/terms (so `retrieve_chunks` returns at
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least one citable chunk).
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3. Run the project: `run_portfolio(["MY-PROJECT"], "local", client_factory=...)` (or include it
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in the default fan-out by passing no `project_ids`).
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## Legg til egen datakilde
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The retriever (`retrieval.py` / `datasource.py`) reads a **local docs folder** per project,
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selected by the project's `docs_dir` in `reference_projects.json`. To point a project at your own
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data, change its `docs_dir` to your folder and drop your cost documentation there — the citation
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seam (`{file, locator, snippet, score}`) is identical on the in-process tool and MCP paths. The
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folder is boundary-checked (fail-closed) against path traversal, so keep documents inside the
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configured `docs_dir`. A real deployer swaps the bundled synthetic docs for their own source.
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## Legg til egen modell-map
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Model choice is **config, not code** (B12): `src/portfolio_optimiser/data/model_map.json` maps
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`profile -> role -> model/deployment` (`resolve_model(profile, role)`). To use your own models:
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- Edit the `local` block to your local model ids (Ollama/LM Studio), and/or
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- Edit the `azure` block to your Foundry deployment names (the placeholders
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`REPLACE-WITH-FOUNDRY-DEPLOYMENT` are tenant-specific — replace them or supply via env).
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The role keys (`proposer`, `checker`, `default`) let you assign a distinct model per debate role;
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`default` is the fallback when a role is unmapped.
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