# Backend

The backend owns the application API and CAD generation workflow:

- `app/`: HTTP API, jobs, orchestration, and persistence adapters.
- `agent/`: AI prompts, tools, and skills used by the generation agent.
- `engine/`: CDSL compiler, sketch solver, and STEP generation runtime.
- `cdsl_library/`: Official CDSL examples, metadata, and search index.
- `tests/`: Engine, API, and end-to-end generation tests.

Expected development entrypoint: `app.main:app`, served by Uvicorn.

## Reasoning Effort

The backend uses the Chat Completions API. Configure a provider's reasoning
budget with `CDSL_<PROVIDER>_REASONING_EFFORT`; for the current OpenAI setup:

```dotenv
CDSL_OPENAI_REASONING_EFFORT=medium
```

Use `low`, `medium`, or `high` according to the latency/cost versus quality
tradeoff. The setting is sent as Chat Completions' `reasoning_effort` field to
authoring, streaming, and visual-review requests. Leave it empty to use the
provider/model default. The selected OpenAI-compatible endpoint must support
the requested value.

## Autonomous CDSL Agent Configuration

The autonomous agent writes one frozen free-form `requirements.md`, then
observes, measures, renders and appends one CDSL feature at a time. Its author
uses normal function calls; no provider strict JSON Schema capability or
complete modelling DAG is required. Candidate fragments are rebuilt in a
staging directory through `cdsl_only` before a checkpoint can be committed.

Final publication requires a separately configured vision-capable review model
and the Python OpenCascade/Pillow technical renderer. The agent may build and
inspect intermediate checkpoints without image review; a final run fails
closed if its independent review configuration is unavailable.

```dotenv
# Must name one configured provider and one model listed in that provider's
# CDSL_<PROVIDER>_VISION_MODELS setting. It is intentionally not inferred
# from the authoring model.
CDSL_REVIEW_PROVIDER=deepseek
CDSL_REVIEW_MODEL=deepseek-v4-flash-vision-exp
CDSL_DEEPSEEK_VISION_MODELS=deepseek-v4-flash-vision-exp

# Install Python rendering dependencies. The renderer reads the revision STEP
# file and creates canonical images without a browser or GPU driver.
pip install -r requirements.txt

# Limits apply to the current checkpoint head, never to total task complexity.
CDSL_AGENT_TOOL_CALLS_PER_CYCLE=12
CDSL_AGENT_CANDIDATE_ATTEMPTS_PER_HEAD=3
CDSL_AGENT_CONSECUTIVE_NO_PROGRESS_LIMIT=6
CDSL_AGENT_MAX_FEATURES_PER_FRAGMENT=6
CDSL_AGENT_CONTEXT_CHAR_LIMIT=24000
CDSL_AGENT_RENDER_CACHE=true
```

The author chooses each coherent 1-6 feature batch. Every rebuilt batch is
rendered and independently reviewed before it can become a checkpoint; only
an accepted reviewer verdict advances the working model. Every checkpoint is rebuilt from its fully materialized CDSL through the
`cdsl_only` runtime. Checkpoint GLB files are preview-only; STEP, CDSL, and
reports are available only after the task reaches `COMPLETED`.

The backend assigns feature and sketch IDs, appends causal dependencies and
expands only opaque current-snapshot selector tokens. It does not compile
geometry templates or correct workplanes, profiles, sizes, directions or
boolean semantics authored by the model. Failed candidates remain auditable
but never become revisions.