- calculator: add `ln` alias for `math.log`, replace `^` with `**` before
AST parsing so caret-as-power works, convert SyntaxError → ValueError for
consistent error handling, and return `math.inf` on division by zero
(matching the documented meval behaviour) instead of raising an exception
- file_read / file_write: replace the hardcoded `str(path).startswith(str(d) + "/")
guard with `Path.is_relative_to()` so allowed-directory checks work on
Windows (and any OS whose separator is not `/`)
- git_tool: catch `NotADirectoryError` raised by `subprocess.run` on Windows
when `cwd` points to a non-existent path, returning a proper error result
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
Co-authored-by: Robby Manihani <manihani@stanford.edu>
* fix: close KnowledgeStore connections in connectors_router endpoints
KnowledgeStore opens a persistent SQLite connection in __init__ but was
never closed at the three instantiation sites in connectors_router.py,
leaking one file descriptor (plus its WAL handle) per call. The worst
offender is _connector_summary(), which is invoked per-connector on
every GET /connectors poll from the frontend — after ~100 polls the
backend hits the default macOS per-process FD limit (256) and asyncio's
accept() starts failing with OSError: [Errno 24] Too many open files.
Add __enter__/__exit__ to KnowledgeStore (close() already existed) and
wrap all three instantiation sites in `with` blocks so the connection
is released deterministically when the scope exits.
- connectors/store.py: add context manager protocol
- server/connectors_router.py: use `with KnowledgeStore() as store:`
in _connector_summary, _ingest, and _run_sync
* test: cover KnowledgeStore context manager + connectors_router close leak
- test_store.py: add two tests for the new __enter__/__exit__ protocol
verifying the connection closes on normal exit and on exceptions.
- test_connectors_router.py: add a regression test that monkeypatches
KnowledgeStore.close to count invocations and asserts every store
opened by GET /v1/connectors is paired with a close.
Also fix a pre-existing bug in the test_connectors_router fixture: the
router is created with prefix="/v1/connectors" internally (line 92 of
connectors_router.py), and the fixture was wrapping it again with
prefix="/v1", producing "/v1/v1/connectors". All 6 existing router
tests were failing with 404 before this fix. 5 of them now pass; the
remaining failure (test_trigger_sync) is an unrelated KeyError on a
missing response field and is out of scope for this PR.
* test: drop connectors_router regression test — skipped in CI anyway
The regression test added in the previous commit relies on FastAPI
being importable, but CI's dev-extra install does not include fastapi
(it lives in the `server` optional group). All tests in
test_connectors_router.py silently skip on CI with "fastapi not
installed", so the regression test would never actually execute there.
Revert test_connectors_router.py to the upstream main version. The
fixture bug I fixed (double prefix) and the connection-leak regression
test both deserve a separate PR scoped to test infrastructure — that
PR should either add fastapi to dev deps, split server tests into
their own CI job, or both.
The KnowledgeStore context manager tests in test_store.py are kept
because they have no fastapi dependency and will run in CI.
* feat(mining): add Pearl model conversion workflow
* test(mining): tolerate missing Docker device request type
* docs(mining): record Gemma and Qwen conversion evidence
* docs(mining): record Qwen local validation evidence
---------
Co-authored-by: krypticmouse <herumbshandilya123@gmail.com>
routing.types appears to have been an in-progress module that was
consolidated into routing.router before it was created. The lazy
import inside the router policy block was silently caught by the
surrounding try/except, causing the policy to never activate and
the executor to always fall back to the configured model regardless
of any router_policy setting.
Corrects the import to openjarvis.learning.routing.router where
build_routing_context is defined and exported.
The desktop app's chat-completions stream relies on a raw browser
fetch from the Tauri webview to the FastAPI backend, so it is
subject to CORS. The default allowlist only contained
`tauri://localhost`, which is the production webview origin on
macOS, Linux, and iOS. On Windows and Android, Tauri 2 serves the
app from `http://tauri.localhost` (or `https://tauri.localhost`
when `windows.useHttpsScheme` is enabled), so the preflight for
`POST /v1/chat/completions` was rejected and the streaming fetch
threw `TypeError: Failed to fetch` before any byte was read.
Symptom users reported: in the Logs tab the request appears to
succeed up to "Request sent", followed immediately by
"Stream error: Failed to fetch" and "Response: 22 chars" -- which
is exactly the length of the synthesized fallback string
`Error: Failed to fetch` written by InputArea.tsx when streamChat
throws.
Add `http://tauri.localhost` and `https://tauri.localhost` to both
default origin lists (`ServerConfig.cors_origins` and the
`create_app` fallback), and add a regression test that drives a
real preflight against `/v1/chat/completions` for each of the
three Tauri origin schemes.
Browser model loading kept working because it is routed through
the Rust `tauriInvoke('fetch_models')` command, which is a
server-to-server HTTP call not subject to browser CORS -- only
streaming chat goes through the webview's fetch.
Removes the pull_request trigger so the workflow only runs on explicit
@claude mentions or manual workflow_dispatch.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
server/app.py defaulted to allow_origins=["*"] combined with
allow_credentials=True when cors_origins was not passed. That
combination is invalid per the CORS spec — browsers reject it — and
when used loosely it signals that any cross-origin request with
credentials is allowed, exposing session cookies and auth headers.
The reference config at configs/openjarvis/config.toml binds to
0.0.0.0 without setting cors_origins, so this fallback fired in every
default deployment.
Default to a closed list (Vite dev origin + Tauri webview) instead of
the wildcard. Users who actually need a custom origin already pass it
via cors_origins.
Closes#222
The dashboard at /dashboard renders blank once the Rust security module
is compiled (uv run maturin develop ...). The middleware sets
"Content-Security-Policy: default-src 'self'", which blocks the inline
scripts and styles the dashboard HTML relies on.
Loosen the policy to allow 'unsafe-inline' and 'unsafe-eval' so the
dashboard works once the security middleware is active. The same string
is exported via SECURITY_HEADERS for tests, so update both call sites.
Closes#261
configs/openjarvis/config.toml is the file users copy as their starting
configuration. It explicitly set [security] enabled = false (with a
comment about eval performance) — meaning users who follow the
quickstart unknowingly run with GuardrailsEngine, InjectionScanner,
CapabilityPolicy, and rate limiting all disabled.
The SecurityConfig.enabled default in code is True, so removing the
override here lets every install ship with security on. Eval workflows
that genuinely need it disabled can opt out via their own config.
Closes#224
SQLite FTS5 BM25 scores collapse toward zero on small corpora — when
every indexed document contains the query term, IDF approaches zero so
scores return ~1e-6. The default context_min_score = 0.1 silently
filters out every result before injection, so memory is stored but
never reaches the model on a fresh install.
Lowering both defaults to 0.0 lets retrieved context flow through;
users who want strict filtering can still set [memory]
context_min_score in config.toml.
Closes#262
uv.lock pinned mlx-lm==0.29.1, which predates the qwen3_5 architecture.
Running mlx_lm.server with mlx-community/Qwen3.5-* models therefore
fails with 'Model type qwen3_5 not supported.' on every uv sync, even
when pyproject.toml's loose constraint (>=0.19) would otherwise allow
a newer release.
Tighten the inference-mlx extra to mlx-lm>=0.31.1 (the first release
with Qwen3.5 support) and regenerate uv.lock — resolves to mlx-lm
0.31.3.
The lockfile diff is large because uv produces a single cross-platform
lock and adds 'split' entries for a handful of transitive deps
(transformers, vllm, huggingface-hub, mistral-common, etc.). Every
split entry is marker-gated to:
python_full_version >= '3.14' and sys_platform != 'linux'
and sys_platform != 'win32'
i.e. macOS on Python 3.14+ — the very subset that actually pulls in
mlx-lm 0.31.x. Linux, Windows, and Python <= 3.13 paths keep the
existing pins (transformers 4.57.6, vllm 0.17.1, …).
Closes#191
Reported via twitter (issue #269): the macOS install guide and other
docs recommend a 4B model on CPU but the README and quickstart pull /
ask a much larger model (qwen3:8b) — confusing and slow for first-time
CPU-only users.
Switch the README quickstart and the docs/getting-started/quickstart.md
'Chat with Any Model' tile to qwen3.5:4b, with an inline note that GPU
users can scale up to qwen3.5:9b or larger. This matches the existing
chat-simple preset and the macOS guide's CPU recommendation.
Closes#269
