Hunter Bown
bd603a271c
Lets the model OCR a screenshot, scanned receipt, whiteboard photo, or image-only PDF the user drops into the workspace, without bouncing through `exec_shell` (which would mean an approval prompt plus the model having to remember tesseract's CLI surface). The tool spawns `tesseract <image> -` and returns the recognised text inline — no file is written. Capability is ReadOnly + parallel since OCR is a side-effect-free read. Registration is gated on `crate::dependencies::resolve_tesseract()` via the new `ToolRegistryBuilder::with_image_ocr_tools()` builder, hooked into `with_agent_tools` alongside `pandoc_convert`. When tesseract is missing the tool isn't advertised — same probe-then-decide pattern v0.8.31 introduced for Python. The execute path also late-resolves so a concurrent uninstall surfaces the install-tesseract hint rather than the raw spawn failure. `deepseek doctor`'s "Tool Dependencies" section reports tesseract status next to pandoc / node / python with platform-aware install hints. For non-default language packs or PSM modes the user can still drop into `exec_shell` with the full tesseract CLI surface. Tests check the metadata (ReadOnly + parallel, not WritesFiles), the missing-path rejection, and the happy-path OCR round-trip against `crates/tui/tests/fixtures/ocr_hello.png` — a 2 KB 300×100 grayscale PNG generated with ImageMagick rendering "HELLO OCR" in Helvetica. The happy-path test skips silently on hosts without tesseract (matching the catalog-build behaviour) and on hosts where the fixture isn't checked out (sparse / shallow clones). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
crates/tui/tests/
Integration tests for the TUI binary. Per CONTRIBUTING.md, each crate's
integration tests live in its own tests/ directory; the repository-root
tests/ directory is unused.
Mock LLM client (integration_mock_llm.rs)
crates/tui/src/llm_client/mock.rs provides a MockLlmClient that implements
the LlmClient trait by replaying queue-driven canned responses and capturing
every outgoing MessageRequest. Tests mock at the trait boundary — never
at the reqwest HTTP layer — because the trait is the durable abstraction the
runtime is meant to depend on.
Coverage today exercises the trait surface end-to-end:
- streaming turn loop
- reasoning-content replay across tool-call rounds (V4 §5.1.1, the bug that broke v0.4.9-v0.5.1)
- tool-call round-trip with chunked input JSON
- multi-tool-call ordering inside a single turn
- compaction-style non-streaming
create_message - sub-agent style independent parent/child mocks
- capacity-gate observation of a captured request before stream drain
Four full-engine tests (engine_full_*) are #[ignore]-marked. They unblock
when core::engine::Engine is refactored to take Arc<dyn LlmClient> instead
of a concrete Option<DeepSeekClient>. See the comment block at the bottom of
integration_mock_llm.rs for the exact refactor surface.
--record mode for deepseek eval
The offline deepseek eval harness now accepts --record <DIR>. When set,
each tool step appends one JSON Lines record to <DIR>/<scenario>.jsonl
(default scenario: offline-tool-loop.jsonl). Each line is a self-contained
JSON object with the schema:
{ "request": { "step": "list_dir", "kind": "List" },
"response_events": [ { "type": "ok", "output": "…" } ] }
The mock LLM client (crate::llm_client::mock) replays these fixtures by
mapping each response_events array onto a canned Vec<StreamEvent>. Drop
generated fixtures into crates/tui/tests/fixtures/ so they ride the repo and
feed the mock in CI.
Quick example:
cargo run --bin deepseek -- eval --record crates/tui/tests/fixtures
cat crates/tui/tests/fixtures/offline-tool-loop.jsonl | jq .
The scenario name is sanitized to [A-Za-z0-9_-] before forming the filename,
so unusual scenario strings stay portable across platforms.