* feat: add NVIDIA as an AI provider (integrate.api.nvidia.com)
* feat: add NVIDIA option to provider settings dropdown and aliases
* test: add NVIDIA provider detection and endpoint tests
* Add NVIDIA to _HOST_TO_CURATED and expand non-chat model filtering
- nvidia.com -> 'nvidia' curated key for proper provider routing
- _NON_CHAT_PREFIXES: bge, snowflake/arctic-embed, nvidia/nv-embed
- _NON_CHAT_CONTAINS: content-safety, -safety, -reward, nvclip,
kosmos, fuyu, deplot, vila, neva, gliner, riva, -parse,
-embedqa, -nemoretriever
* Expand non-chat model filtering for NVIDIA embedding/guard/video models
Add _NON_CHAT_PREFIXES: embed, recurrent
Add _NON_CHAT_CONTAINS: topic-control, guard, calibration,
ai-synthetic-video, cosmos-reason2
Catches remaining unfiltered non-chat models from NVIDIA catalog:
embedding (llama-nemotron-embed, embed-qa), guard (llama-guard,
nemoguard-topic-control), calibration (ising-calibration),
video (ai-synthetic-video-detector, cosmos-reason2),
recurrent (recurrentgemma-2b)
* Filter non-chat models in _probe_endpoint via _is_chat_model()
Previously _is_chat_model() was only used in the per-model probe
and _first_chat_model(), so non-chat models still appeared in the
model picker even though they were filtered in those specific paths.
Applying the filter at _probe_endpoint() return ensures non-chat
models (embeddings, safety guards, reward, calibration, video
detectors, CLIP, VLM, translation, parsing, recurrent, etc.) never
enter cached_models and never appear in the picker.
* Fix _NON_CHAT_CONTAINS to catch org-prefixed embedding models
Prefix checks (mid.startswith) miss models with org prefixes like
baai/bge-m3, nvidia/embed-qa-4, google/recurrentgemma-2b, etc.
Adding the same terms to _NON_CHAT_CONTAINS ensures they are caught
regardless of the org prefix.
Adds: embed, bge, recurrent, starcoder, gemma-2b
* fix(model-routes): drop collision-prone substrings from global non-chat filter
The NVIDIA PR added several substrings to the shared _NON_CHAT_PREFIXES
and _NON_CHAT_CONTAINS tuples. These are intended to filter out
embedding, retrieval, safety, and vision models from NVIDIA's catalog
that are not chat-completions-capable. However, four of the added
substrings collide with legitimate chat models served by other providers:
- gemma-2b matches google/gemma-2b-it (instruct chat model)
- starcoder matches bigcode/starcoder2-15b (code completion model)
- recurrent matches google/recurrentgemma-2b (language model)
- guard matches meta-llama/Llama-Guard-3-8B (safety classifier)
Removing these four from the global tuples keeps the NVIDIA-specific
filtering intact (safety, embedding, retrieval, and vision models are
still caught by other tokens such as content-safety, -safety, -reward,
embed, bge, -embedqa, -nemoretriever, nvclip, deplot, etc.) while
preventing false negatives for instruct/code models on other providers.
Tests added for gemma-2b-it, google/gemma-2b-it, and
bigcode/starcoder2-15b-instruct asserting they are recognized as chat
models.
Co-authored-by: Kenny Van de Maele <kenny@kvandemaele.be>
* fix(nvidia): remove duplicate bge/embed tokens from _NON_CHAT_CONTAINS
Tokens already present in _NON_CHAT_PREFIXES, making the CONTAINS
entries redundant since the prefix check runs first.
Co-authored-by: Kenny Van de Maele <kenny@kvandemaele.be>
* fix(nvidia): move bge to CONTAINS, add llama-guard, remove stray blanks
Co-authored-by: Kenny Van de Maele <kenny@kvandemaele.be>
* style: fix indentation of groq and xai test cases in test_provider_endpoints.py
---------
Co-authored-by: Kenny Van de Maele <kenny@kvandemaele.be>
When a lane reset fails to rewrite the recreated collection, the recovery path
re-adds the preserved rows. It read the embeddings with
`preserved.get("embeddings") or []` and gated the loop with
`if ids and docs and old_embeddings:`. chromadb returns embeddings as a numpy
ndarray, whose truth value is ambiguous, so both expressions raise ValueError
inside the except block — the restore is abandoned and every preserved row is
lost (the collection was already deleted), exactly when the code is trying to
avoid data loss.
Use an explicit `is None` check and `len(...)`, and convert ndarray batches to
lists before re-adding.
Adds tests/test_embedding_lane_ndarray_restore.py (preserved embeddings come
back as np.ndarray); existing test_embedding_lanes.py still passes.
The DB owner-rename loop in rename_user patched every SQL column named
owner, but three non-SQL stores were left behind:
1. session_manager.sessions -- in-memory Session objects carry s.owner
set at server-boot time. get_sessions_for_user() does an exact
s.owner == username check, so the renamed user chat sidebar goes empty
until a server restart.
2. data/deep_research/*.json -- each completed research report is a
standalone JSON file with an owner field. research_routes filters
by d.get(owner) == user, making every report invisible to the
renamed user.
3. data/memory.json -- a flat JSON array; each entry carries an owner
field. memory_manager.load(owner=user) filters on it, so all memories
vanish from the memory panel.
Fix: after the SQL loop, patch all three:
- iterate sm.sessions and update owner in-place (exposed via app.state)
- walk data/deep_research/*.json and rewrite owner with atomic_write_json
- update matching entries in memory.json with atomic_write_json
All three use the same case-insensitive lower() comparison the SQL loop
already uses. Each step is independently wrapped so a single failure
does not abort the others or the rename itself.
Fixes#3362
Commit e6b1009 removed the workspace feature's entry point (deleted
routes/workspace_routes.py + static/js/workspace.js and dropped the
workspace-param parsing in chat_routes), but left the downstream backend
plumbing dangling: chat_routes passed a hardcoded workspace=None into
stream_agent_loop, which forwarded it to execute_tool_block, so the
workspace value was permanently None and every workspace-gated branch
was unreachable.
Remove the now-dead code (no behavior change, since workspace was always
None):
- src/tool_execution.py: drop _resolve_tool_path_in_workspace and the
workspace params/branches on execute_tool_block, _direct_fallback,
_call_mcp_tool, _do_edit_file, and _resolve_search_root; restore the
bash/python/bg cwd to _AGENT_WORKDIR.
- src/agent_loop.py: drop the workspace param on stream_agent_loop, the
dead 'ACTIVE WORKSPACE' system-prompt block, and the workspace forward.
- routes/chat_routes.py: drop the hardcoded workspace=None arg and var.
- tests: delete test_workspace_confine.py (tested the removed feature) and
the workspace assertion in test_tool_policy.py.
Full suite: 2903 passed, 1 skipped.
Lift the LLM/Image Type select to the left of the URL input and the Add
button to its right, so the primary action (URL + Add) sits on one row.
Scan / Ollama / Key / Test stay on the action row below.
* Fix backup import dropping a user's skill on cross-tenant title/id collision
The skills block of import_data deduped incoming skills against
skills_manager.load_all(), which returns EVERY tenant's skills. So when
a user imports their own backup, any skill whose id or title collides
with another user's skill was silently skipped — the importing user
lost their own data. This is the same cross-tenant bug already fixed
for the memories block just above (#1743); the skills block was left
with the old pattern. Filter the dedup sets to the importing user's own
skills (owner == user); the full store is still saved back, preserving
other users' skills.
* Restore sys.modules after stubbing so backup test does not break collection of later src.* test modules
* Patch backup_routes auth helpers via monkeypatch instead of sys.modules stubs so the test is import-order robust
* Give FakeSkillsManager an add_skill method matching the disk-backed skills API
* fix(cookbook): allow spaces in model directory paths
Allow POSIX external-drive paths and Windows drive paths with spaces while keeping shell metacharacters rejected.
* fix(cookbook): also allow non-ASCII (Unicode) characters in model dir paths
The ASCII-only allowlist that rejected spaces also rejected Cyrillic,
accented Latin and CJK folder names (e.g. /Volumes/Модели,
D:\AI Models\Модели) with 400 Invalid local_dir. Switch the path
character class from [A-Za-z0-9._ -] to [\w. -] (\w is Unicode-aware on
Python 3 str patterns) so localized folder names validate, while shell
metacharacters (; & | ` $ quotes newlines) stay rejected.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* fix(cookbook): reject local_dir path segments starting with '-'
The local_dir allowlist includes '-', so a directory like /models/-rf
(or D:\models\-rf) could be parsed as a CLI flag by hf/etc. (option
injection) — and quoting does not stop a value from being read as an
option. Guard against it inside the validator so the safety stays fully
self-contained there rather than depending on consumers' quoting.
---------
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Drop the in-card Local/API tab strip — each is now its own admin card with
a normal h2 heading (Local on top, API below). The API key input is
always visible (no more click-to-reveal toggle), matching how cloud
providers actually work. Local keeps the optional key reveal since
local servers usually don't need one.
Dead code removed: wireModelsTabs IIFE and the adm-epApiKeyBtn toggle wire.
Move the Added Models endpoint lists out of the Add Models card into a
dedicated sidebar tab between Add Models and AI Defaults. The card now
focuses purely on adding (Local / API tabs), while the new panel owns
the existing endpoints + Probe and Clear-offline controls.
admin.js: defensive fallback so a stale 'added' value in localStorage
falls back to 'local' instead of leaving both panes hidden.
Move the Added Local + Added API lists out of the per-type tabs into
a dedicated third tab. Each Add tab is now just the form; the new tab
collects both lists together with Local / API subheadings.
Card layout:
Add Models [Probe] [Clear offline]
[Local] [API] [Added Models]
Tab content:
Local → Add Local form
API → Add API form
Added Models → Local list + API list (subheadings)
All endpoint list/form IDs preserved. Tab switcher JS is generic so
the new 'added' tab works without code changes.
Earlier split into 4 flat cards wasn't what was asked for. Restore to
a single 'Add Models' card with two tabs at the top:
Local → Add form + Added Local Models list
API → Add form + Added API Endpoints list
Probe / Clear-offline live on the card header and act on both lists.
Active tab is remembered in localStorage so the user lands back where
they were. All form/list IDs preserved (adm-epLocalUrl, adm-epList-local,
adm-epList-api, etc.) so admin.js continues to work unchanged.
Replaces the .adm-section-toggle fold-open JS with a tab-switcher; the
fold elements no longer exist so the old handler was already a no-op.
The previous 'Add Models' card had two collapsible folds (Local + API)
inside it and 'Added Models' had two inline subsections. Both folded
states added a click-to-expand step that wasn't earning its keep —
users coming to Settings to add a model don't want a fold, they want
the form.
Reshape: four flat admin-cards in the Services panel, each with its
own h2 title matching the rest of Settings:
Add Local Model (was Add Models → Local fold)
Add API (was Add Models → API fold)
Added Local Models (was Added Models → Local subsection)
Added API Endpoints (was Added Models → API subsection)
The collapsible JS hook in admin.js already guards on
'if (!head) return' so removing the .adm-section-toggle headers
turns it into a clean no-op — no breakage.
All input/list IDs preserved (adm-epLocalUrl, adm-epList-local,
adm-epList-api, etc.) so the rest of admin.js continues to work
unchanged. Probe / Clear-offline live on the Local card and act on
both lists together (existing behavior).
* fix(llm): suppress thinking for qwen3/gemma4 on Ollama /v1 compat endpoint
When using qwen3, QwQ, gemma4, or other thinking models via Ollama's
OpenAI-compatible /v1 endpoint, the model routes all output into its
<think>...</think> reasoning block. Since Odysseus strips thinking
content from round_response and only accumulates native tool_calls,
this produces a round with 0 chars, 0 native calls, 0 tool blocks —
the agent appears to silently do nothing.
Root cause: Odysseus classifies the /v1 endpoint as provider="openai"
(not "ollama"), so the payload is built as a standard OpenAI payload
without any Ollama-specific options. Ollama's /v1 endpoint accepts
"think": false as a top-level parameter to suppress extended thinking,
but this was never sent.
Fix:
- Add _is_ollama_openai_compat_url() to detect local Ollama /v1 URLs
- Inject "think": false in both stream_llm and llm_call_async for
thinking models (qwen3, QwQ, gemma4, DeepSeek-R1, etc.) on this
endpoint
Verified with qwen3:14b on Ollama 0.24: with think=False the model
correctly emits native tool_calls in a single streaming chunk and
the agent executes bash/file/web tools as expected.
* fix(llm): extend _is_ollama_openai_compat_url to match localhost on any port
Per reviewer feedback on PR #3228:
1. Generalize host detection to mirror _is_ollama_native_url: match any
localhost/127.0.0.1/0.0.0.0/::1 host (not just port 11434) so that
custom OLLAMA_HOST ports and container remaps are also covered.
2. Add tests/test_llm_core_ollama_thinking.py covering:
- _is_ollama_openai_compat_url for all positive/negative URL cases
including IPv6, non-default port, native /api path, and real OpenAI
- Payload injection: think:false set for Ollama /v1 thinking model,
not set for non-thinking model, not set for real OpenAI endpoint,
and set for localhost on a non-default port (the new case)
The Teacher Mode feature stays out of the default UI per the 2.0
roadmap — backend escalation is already dormant when teacher_model is
unset (its default) and we want to focus on core reliability before
surfacing escalation as a feature.
Nothing removed from the backend:
- src/teacher_escalation.py still gates on get_setting('teacher_model')
- agent_loop.py's run_teacher_inline hook is a no-op without the setting
- settings backup/restore round-trips the teacher_model key unchanged
- power users can still set it via manage_settings or the JSON backup
settings.js's initTeacherModel already early-returns when the card's
DOM ids are missing, so the JS side is clean.
To re-surface the card, revert this commit.
* fixed confusing credentials prompt
* fix(setup): return status from create_default_admin function
* fix(setup): initialize admin creation status in main function
* fix(setup): enhance admin creation feedback and status handling
* Enhance admin user login messages with conditional feedback based on creation status
* Refine admin user creation feedback messages for clarity and actionability and formatted code
* Add fallback error message for admin creation failure in setup script
* Add run script for Uvicorn with dotenv integration
* Refactor server runner to use argparse for host and port configuration
* Remove captured output print statement from server runner
* Fix server runner to ensure cross-platform compatibility and improve log handling
* removed run.py to match original repo
* Fixing custom search not working properly
* Refactor search settings event listeners for improved functionality and clarity
* Update search function signatures to use Optional for count parameter
* revert changes
* fixed broken merge issue
* Delete services/chat_data_scraper.py
added by mistake
---------
Co-authored-by: Alexandre Teixeira <111787685+alteixeira20@users.noreply.github.com>
Surface a lot of accumulated cookbook + UI work as a single non-agent
commit so the agent rework lands cleanly.
Highlights:
- Ollama as a first-class backend in the Cookbook:
* Download input accepts ollama-style names (name:tag) → backend=ollama
* /api/cookbook/ollama/library (cached scrape of ollama.com + curated
fallback so classic models like qwen2.5 stay reachable)
* "Browse Ollama library" toggle below Download with size chips
* Engine=Ollama in hwfit toolbar merges the Ollama library into the
main scan list as per-tag rows with the same Fit/Param/Quant/VRAM
columns; click → fills Download input
- API Tokens form added to Integrations panel (matching wired
loadTokens()/initTokenForm() that had no HTML)
- Serve panel polish: Advanced fold tightening (-8px nudges on vLLM
checks, Extra args, Spec row), n_cpu_moe + Split Mode controls
pulled up 8px to align with the row's checkboxes, GGUF File dropdown
exposed for Ollama backend, GPU re-render on Edit serve restore,
_forceBackend flag so saved serveState wins over backend detection,
cookbook:servers-changed CustomEvent so panels don't need refresh
- Models page redesign: Add Models row (URL + hidden API key reveal +
Type select + Scan/Ollama/Key/Test/Add icon buttons), Probe All +
Clear-offline buttons in Added Models toolbar, offline-pill removed
(opacity already conveys state), Engine dropdown gains Ollama option
- _ping_endpoint probes /v1/models then base, accepts 4xx as
reachable (vLLM returns 404 on bare /v1, fully working endpoints
were showing offline)
- Diagnosis card: × dismiss + Copy bundle buttons restored on the
serve error feedback card
- Orphan tmux sweep re-enabled behind a 60s rate-limit + background
Thread (off the main event loop) so dead serves get discovered
- cookbook_routes auto-register watchdog: drops the endpoint if the
serve session exits non-zero within the first ~3min
- ollama-rocm sidecar awareness in download wrapper (`docker exec
ollama-rocm ollama pull` when host ollama isn't installed)
- Skill extractor sets initial_status="published" when
auto_approve_skills pref is on (audit demotes later)
- Skill list / model list / cookbook scan misc polish
Move every per-route upload byte-limit into src/upload_limits.py as a
validated, env-overridable constant via read_byte_limit_env:
- Add GALLERY_UPLOAD_MAX_BYTES, GALLERY_TRANSFORM_UPLOAD_MAX_BYTES,
MEMORY_IMPORT_MAX_BYTES, PERSONAL_UPLOAD_MAX_BYTES,
EMAIL_COMPOSE_UPLOAD_MAX_BYTES, STT_MAX_AUDIO_BYTES, ICS_MAX_BYTES.
- Routes import their constant instead of defining it locally: replaces 4
raw int(os.getenv(...)) and removes 3 hardcoded literals.
- The 3 previously-hardcoded limits (email compose, STT audio, calendar
ICS) are now env-overridable with the same ODYSSEUS_*_MAX_BYTES naming.
- Defaults unchanged, so behavior is unchanged unless an env var is set;
an invalid value now fails fast with a clear message instead of a bare
int() ValueError.
- Document all env vars in .env.example and the README.
Fixes#3364
* refactor(tools): consolidate duplicated _truncate and get_mcp_manager into src/tool_utils
Move all copies of _truncate(), get_mcp_manager(), and set_mcp_manager()
into a single leaf module (src/tool_utils.py) that imports only from
src.constants. This eliminates the lazy-import hack
('from src import agent_tools' inside function bodies) in tool_execution.py
and tool_implementations.py, and fixes a latent bug: the _truncate copy in
tool_execution.py was missing the isinstance guard and would crash on None.
Also deletes mcp_servers/_common.py — it was dead code with zero callers
anywhere in the codebase, containing its own copy of truncate() and
constants that already exist in src/constants.py.
* fix(tools): route remaining get_mcp_manager imports to src.tool_utils
The maintainer's feedback flagged src/task_scheduler.py:1857 and
routes/task_routes.py:977. A project-wide search found a third call site
in src/agent_loop.py that also imported get_mcp_manager from
src.agent_tools instead of src.tool_utils.
All three are now sourced from the canonical location in src.tool_utils.
---------
Co-authored-by: mcnoliveira <mcnoliveira@gmail.com>
Add focused tests for the z.ai/api/coding path override:
- _match_provider_curated: 5 tests verifying coding vs base key
- _probe_endpoint: 3 tests verifying model preservation, curated
append on partial response, and base-zai exclusion
Rebased onto dev per reviewer request.
Fixes#2230
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Co-authored-by: Alexandre Teixeira <111787685+alteixeira20@users.noreply.github.com>
Three issues combined to make the per-user 'Allowed models' checklist
unreliable (#3032):
1. admin.js _loadModelsForUser fetched /api/models, which is backed by
cached_models — endpoints that haven't been probed yet (e.g. a
freshly-added DeepSeek API endpoint) simply didn't show up in the
checklist. Switched to /api/model-endpoints, which always reflects
every configured endpoint regardless of cache state.
2. _saveModels sent allowed_models: [] both when the admin clicked
[All] (no restriction) and [None] (block everything) — the backend
had no way to distinguish the two.
3. _enforce_chat_privileges treated an empty allowed_models list as
'no restriction' (falsy -> skip the check), so [None] had no effect.
Added an explicit block_all_models privilege flag (defaulting to False,
and forced to False for admins) that admin.js now sets when zero models
are checked. _enforce_chat_privileges checks it first and 403s
regardless of allowed_models contents.
Maruf Hasan