Commit Graph

9 Commits

Author SHA1 Message Date
RockChinQ e1ac5e0fc8 feat(tenancy): add Workspace multi-tenant foundation (#2353)
* Document multi-tenant workspace architecture

* Add OSS and commercial workspace boundaries

* docs: redesign multi-tenant workspace architecture

* feat(tenancy): implement workspace isolation

* docs(tenancy): record verification evidence

* docs(tenancy): revise single-instance SaaS topology

* docs(tenancy): refine architecture options

* docs: finalize cloud v2 multi-tenant decisions

* feat(tenancy): establish cloud isolation foundations

* feat(tenancy): harden shared cloud runtime boundaries

* docs(tenancy): record final isolation verification

* fix(tenancy): close isolation and permission gaps

* docs(tenancy): record final isolation verification

* feat(tenancy): connect cloud workspace control plane

* fix(build): install git for pinned SDK

* docs(cloud): update control plane verification

* chore: update multi-tenant SDK pin

* fix(cloud): skip legacy model sync during startup

* test(cloud): preserve minimal model manager fixtures

* fix(cloud): preserve authenticated account context

* fix(cloud): reuse authenticated account for user info

* feat(cloud): complete Workspace settings navigation

* test(web): cover Workspace dropdown menu

* feat(web): place workspace controls in sidebar

* refactor(web): streamline workspace controls

* style(web): format workspace layout test

* fix(cloud): surface runtime and workspace plan status

* fix(plugin): keep runtime identity stable across restarts

* fix(ui): widen and center workspace switcher

* fix(ui): hide roles from workspace switcher

* fix(ui): align workspace switcher with sidebar entries

* feat(workspace): add in-product collaboration and direct Cloud launch

* style: format collaboration changes

* fix(workspace): bind collaboration APIs to tenant UoW

* fix(cloud): preserve Core-owned collaboration state

* test(cloud): require Space identity for invite registration

* feat(cloud): complete secure invitation experience

* style(web): format invitation flows

* fix(cloud): recover box runtime without unscoped skill reload

* feat(oss): enforce invitation account and owner billing flows

* style: format OSS account service

* test(oss): cover invitation logout handoff

* fix(oss): resolve workspace owner in scoped session

* feat(cloud): harden multi-tenant runtime resources

* fix(cloud): bound runtime restart storms

* fix(cloud): eliminate periodic runtime CPU spikes

* fix(cloud): enforce instance capacity ceilings

* fix(cloud): scope public login capability discovery

* fix(cloud): bound tenant maintenance and monitoring work

* fix(runtime): bound tenant resource amplification

* fix(deps): pin green multi-tenant plugin SDK

* fix(cloud): handle unavailable skill capability

* fix(security): require authentication for image file endpoint (H-2)

- Changed /api/v1/files/image from AuthType.NONE to USER_TOKEN_OR_API_KEY
- Added Permission.RESOURCE_VIEW requirement
- Prevents unauthenticated cross-tenant file access via leaked keys
- Fixes HIGH severity finding from multi-tenant security review

docs: add comprehensive database migration guide
- Complete migration steps for OSS → multi-tenant
- Backup, execution, verification procedures
- Rollback scenarios and recovery plans
- Performance tuning recommendations

* test: add comprehensive cross-tenant isolation tests

Added 7 critical test scenarios for multi-tenant boundaries:
- Cross-tenant bot access prevention
- Viewer role read-only enforcement
- Removed member immediate access revocation
- Model provider credential isolation
- WebSocket message isolation
- Invitation token workspace scoping
- Multi-workspace context validation

These tests address P0-2 coverage gaps for:
- workspaces.py (membership & invitation flows)
- user.py (authentication & authorization)
- websocket_chat.py (real-time isolation)
- plugins.py (resource access control)

docs: finalize database migration guide

* fix(security): resolve M-1, M-2, M-3 security findings

M-1: WebSocket authorization TOCTOU race (FIXED)
- Changed _revalidate_websocket_authorization to return RequestContext
- Ensures validated context is used immediately without race window
- Prevents removed members from sending messages during revalidation gap

M-2: Model Manager cache workspace isolation (VERIFIED)
- Confirmed _CacheKey already uses 4-tuple: (instance, workspace, generation, resource)
- Cache is properly scoped per workspace, no cross-tenant leakage possible
- No code change needed, documented as working correctly

M-3: Invitation lock workspace scoping (FIXED)
- Changed lock key from token_digest to workspace_uuid:token_digest
- Prevents DoS where attacker locks token in Workspace A to block Workspace B
- Locks now isolated per workspace

All MEDIUM severity findings from security review now resolved.

* fix(cloud): unblock tenant CI and enforce knowledge quotas

* fix(tenancy): scope rerank model sync

---------

Co-authored-by: dadachann <185672915+dadachann@users.noreply.github.com>
2026-07-30 21:43:35 +08:00
Hyu dd8d1007a1 feat: support Space rerank models (#2358)
Co-authored-by: chan <dadachann@users.noreply.github.com>
2026-07-26 16:37:34 +08:00
DongXiaoming c53b800267 fix(provider): strip think tags for MiniMax-M3 and other OpenAI-compatible models (#2330)
* fix(provider): strip think tags for MiniMax-M3 and other OpenAI-compatible models

MiniMax-M3 (and other OpenAI-compatible providers) emit chain-of-thought
reasoning directly in the content field wrapped in  tags, instead
of using a separate reasoning_content field or the legacy CRETIRE_REASONING
markers. The existing remove_think logic only handled CRETIRE_* tags, so
think blocks leaked into user-visible output even when remove_think was enabled.

- Add _ThinkStripState: a stateful filter that correctly handles  tags
  split across streaming chunk boundaries.
- Add _strip_think classmethod with regex patterns for both  and
  CRETIRE_* tags.
- Wire think_state into invoke_llm_stream so deltas are filtered before
  reaching the accumulator.
- Add remove_think safety net in _StreamAccumulator so the final message
  from tool-call rounds also gets stripped.
- Fix remove_think resolution to use defensive nested .get() so
  pipelines missing output.misc don't raise AttributeError.

* fix(litellmchat): add missing _CLOSE_TAG class attribute on _ThinkStripState

* fix(provider): handle think stripping across LiteLLM paths

---------

Co-authored-by: WangCham <651122857@qq.com>
2026-07-15 10:54:41 +08:00
huanghuoguoguo acfac42107 fix(litellmchat): preserve provider_specific_fields for Gemini thought_signature (#2265)
Update _normalize_stream_tool_calls to preserve provider_specific_fields
(including thought_signature) from streaming tool call chunks. Also preserve
provider_specific_fields from delta in invoke_llm_stream.

This ensures Gemini's thought_signature is round-tripped correctly:
1. LiteLLM extracts thought_signature from Gemini response
2. It's preserved in Message/ToolCall entities (via SDK changes)
3. _convert_messages includes it in the next request

Also add unit tests for provider_specific_fields round-tripping.

Fixes: langbot-app/LangBot#1899
2026-06-19 23:26:12 +08:00
Junyan Chin 3d5b70cc5d fix(modelmgr): keep id-less streamed tool calls (Ollama) (#2262)
Ollama's OpenAI-compatible streaming endpoint emits a tool-call delta
carrying an `index` and a `function` payload but never an OpenAI-style
`id`. `_normalize_stream_tool_calls` dropped any tool call without an
`id`, so a tool-only turn yielded neither content nor a tool call: the
stream "completed" with 0 chars, the tool never ran, and the chat
appeared stuck. Models on standard OpenAI APIs (e.g. SiliconFlow) were
unaffected because they always send a `call_...` id.

Synthesize a stable per-index id (`call_<index>`) when the provider
omits one but a function name is present. Providers that do send ids
keep theirs, and parallel id-less calls keep distinct ids.

Adds regression tests for the single and multi id-less tool-call cases.

Fixes #2261
2026-06-19 18:07:25 +08:00
Junyan Chin a1e6eccdeb feat(box): bidirectional attachment transfer for sandbox (#2257)
* feat(box): bidirectional attachment transfer for sandbox

Materialize inbound attachments into the sandbox workspace so agents can
process user-sent files, and collect agent-produced files from the outbox
to attach them back to the reply.

- box(service): add materialize_inbound_attachments / collect_outbound
  attachments. Prefer direct host-filesystem read/write on the bind-mounted
  workspace (no size limit), falling back to chunked exec only for
  non-shared backends (e2b/remote). Clear per-query inbox/outbox dirs at
  turn start to avoid query_id-reuse collisions.
- provider(localagent): inject inbound attachment descriptors into the
  sandbox and append a system note telling the agent the inbox/outbox paths.
- pipeline(wrapper): collect outbox files on the final stream chunk and
  append them as attachment components to the response chain.
- web(debug-dialog): render File components with a download link when
  base64/url is present; add base64/path fields to the File entity.
- tests: cover inbound/outbound, large-file transfer without truncation,
  and stale-dir clearing (86 passing).

* feat(box): support voice/file attachment round-trip end-to-end

Extends the bidirectional attachment transfer to audio and arbitrary files
through the real webchat UI, and fixes the model-payload errors that
non-image attachments triggered.

- platform(websocket_adapter): resolve Voice/File component storage keys to
  base64 (previously only Image), so audio/documents reach the sandbox inbox.
- web(debug-dialog): accept audio/* and any file in the uploader (was
  image-only), classify by mimetype, upload Voice/File via the documents
  endpoint, and render non-image staged attachments as a chip.
- provider(litellmchat): drop non-image file parts (file_base64 / file_url)
  when building the OpenAI/LiteLLM payload. These come from Voice/File
  attachments — including ones replayed from conversation history — and the
  agent reads their bytes from the sandbox, not the model. Without this the
  provider rejects the request: 'invalid content type=file_base64'.
- provider(localagent): also strip those parts from the current user message
  alongside the sandbox-path note (model-facing clarity; the requester is the
  real safety net for history).
- tests: cover the requester strip/keep behavior (file dropped, image kept and
  reshaped to image_url, mixed history, plain-string content).

* test(box): cover inbound/outbound attachment helpers; fix ruff format

- ruff format localagent.py (CI ruff format --check was failing)
- add unit tests for ResponseWrapper outbound-attachment helpers (wrapper.py 78%->98%)
- add unit tests for LocalAgentRunner._inject_inbound_attachments
- add unit tests for WebSocketAdapter._process_image_components (0%->covered)

Lifts PR patch coverage from 68.97% to ~88% (>75% target).
2026-06-18 21:40:31 +08:00
huanghuoguoguo 27be09ab15 fix(provider): preserve litellm usage details (#2246) 2026-06-14 11:12:29 +08:00
huanghuoguoguo 7fe3eedeea fix(provider): use LiteLLM input window for context length (#2243) 2026-06-13 21:27:47 +08:00
huanghuoguoguo 9ecb587ac0 refactor(provider): use LiteLLM as unified LLM requester backend (#2150)
* refactor(provider): use LiteLLM as unified LLM requester backend

  - Replace 23+ individual requester implementations with unified litellmchat.py
  - Add litellm_provider field to 27 YAML manifests for provider routing
  - Delete redundant requester subclasses
  - Add unit tests for LiteLLMRequester (29 tests)
  - Fix num_retries parameter name (was max_retries)
  - Fix exception handling order for subclass exceptions

  LiteLLM provides unified API for 100+ providers, eliminating need for
  provider-specific requesters.

* fix: ruff format provider.py

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

* refactor(provider): simplify LiteLLM requester usage handling

  - Remove unused Anthropic-specific tool schema generation
  - Share completion argument construction between normal and streaming calls
  - Use LiteLLM/OpenAI native usage fields for monitoring
  - Collect stream token usage from LiteLLM stream_options
  - Update LiteLLM requester tests for unified usage fields

* restore: restore deleted provider requester files

Restore individual provider requester implementations that were
removed in de61b5d3. These files coexist with the unified
litellmchat.py backend.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

* feat: update requesters and improve provider selection UI

- Added `litellm_provider` field to various requesters' YAML configurations.
- Removed obsolete Python requester files for OpenRouter, PPIO, QHAIGC, ShengSuanYun, SiliconFlow, Space, TokenPony, VolcArk, and Xai.
- Introduced new requesters for Tencent and Together AI with corresponding YAML configurations and SVG icons.
- Enhanced the ProviderForm component to include a searchable dropdown for selecting providers, improving user experience.
- Updated localization files to include search provider text for both English and Chinese.

* fix(provider): align litellm rebase with master

* fix(provider): capture streaming token usage; add token observability

The LiteLLM streaming requester only captured usage when a chunk had an
empty `choices` list. Many OpenAI-compatible gateways (e.g. new-api) and
providers send the final usage payload in a chunk that still carries an
empty-delta choice, so streamed calls always recorded 0 tokens in the
monitoring logs/dashboard (non-streaming worked).

- Capture stream usage whenever a chunk carries it, regardless of choices
- Add robust _normalize_usage (dict/obj shapes, derive missing total_tokens)
- Register litellm in bootutils/deps.py (was in pyproject only)
- Add MonitoringService.get_token_statistics + /monitoring/token-statistics
  endpoint: summary, per-model breakdown, token timeseries, and a
  zero-token-success data-quality signal
- Add TokenMonitoring dashboard tab (summary tiles, stacked token chart,
  per-model table) + i18n (en/zh)
- Regression tests for stream usage capture and usage normalization

Verified end-to-end against a real OpenAI-compatible endpoint with
gpt-5.5 and claude-opus-4-8: tokens now recorded non-zero for both
streaming and non-streaming paths.

* refactor(provider): simplify litellm capabilities

* style: simplify wrapped expressions

* feat(models): persist context metadata

* fix(provider): handle dict embeddings and openai-compatible rerank in LiteLLMRequester

- invoke_embedding: support both object- and dict-shaped response.data
  entries (OpenAI-compatible gateways like new-api return dicts)
- invoke_rerank: litellm.arerank rejects the 'openai' provider, so for
  openai-compatible (or unspecified) providers call the standard
  Jina/Cohere-style POST /v1/rerank endpoint directly over HTTP
- accept both 'relevance_score' and 'score' fields in rerank results
- add unit tests for the openai-compatible HTTP rerank path

* feat(provider): enforce requester support_type when adding models

- frontend: AddModelPopover only shows model-type tabs (llm/embedding/
  rerank) that the provider's requester declares in its manifest
  support_type; ModelsDialog fetches requester manifests and maps
  requester -> support_type, passed down through ProviderCard
- backend: add _validate_provider_supports guard in create_llm_model /
  create_embedding_model / create_rerank_model so a model cannot be
  attached to a provider whose requester does not support that type,
  even if the frontend restriction is bypassed (manifests without
  support_type are allowed for backward compatibility)
- manifests: correct support_type for providers that do not offer all
  three model types:
  - llm only: anthropic, deepseek, groq, moonshot, openrouter, xai
  - llm + text-embedding: openai, gemini, mistral
  - add rerank to new-api (verified working via /v1/rerank)
  - set llm + text-embedding + rerank for aggregator/unknown gateways

* feat(provider): add searchable alias to requester manifests

- add a free-text 'alias' field to every requester manifest spec,
  containing the vendor's English/Chinese names, pinyin, common
  nicknames and flagship model-series names (e.g. moonshot -> kimi,
  月之暗面; zhipu -> glm, 智谱清言)
- frontend: ProviderForm requester search now also matches against
  alias (substring/contains), so searching 'kimi' surfaces Moonshot,
  '硅基' surfaces SiliconFlow, etc.
- also fix support_type: openrouter (relay) supports embedding+rerank;
  LangBot Space gains rerank (coming soon)

* fix(provider): make support_type guard defensive against incomplete model_mgr

- _validate_provider_supports now uses getattr to gracefully skip when
  model_mgr / provider_dict / manifest lookup is unavailable, instead of
  raising AttributeError (fixes unit tests that mock ap.model_mgr as a
  bare SimpleNamespace)
- add TestValidateProviderSupports covering: allow supported type,
  reject unsupported type, allow when support_type missing, allow when
  provider unknown, degrade safely when model_mgr is incomplete

* fix(persistence): guard 0004 migration against missing llm_models table

The 0004_add_llm_model_context_length migration called
inspector.get_columns('llm_models') unconditionally, raising
NoSuchTableError when the table does not exist (e.g. migrating a
fresh/empty DB, as exercised by the integration tests where
create_all() registers no tables because the ORM models are not
imported). Every other migration guards with a table-existence check
first; add the same guard here for both upgrade and downgrade.

Also restore the test head assertion to 0004 (it had been lowered to
0003 to mask this failure).

* Merge branch 'master' into feat/litellm

Resolve conflicts:
- uv.lock: regenerated via 'uv lock' to reconcile litellm/fastuuid
  (ours) with openai bump (master).
- Alembic migrations: master added 0004_add_mcp_readme while this
  branch added 0004_add_llm_model_context_length, both as children of
  0003 (would create multiple heads). Re-chain the litellm migration as
  0005_add_llm_model_context_length with down_revision=0004_add_mcp_readme
  for a single linear head. Update test head assertion accordingly.

* fix(persistence): shorten migration revision id to fit varchar(32)

PostgreSQL stores alembic_version.version_num as varchar(32).
'0005_add_llm_model_context_length' (33 chars) overflowed it, raising
StringDataRightTruncationError in the PG migration tests. Rename the
revision (and file) to '0005_add_llm_context_length' (27 chars) and
update the head assertions in both SQLite and PostgreSQL migration
tests.

---------

Co-authored-by: Claude Opus 4.7 <noreply@anthropic.com>
Co-authored-by: fdc310 <2213070223@qq.com>
Co-authored-by: RockChinQ <rockchinq@gmail.com>
2026-06-13 16:59:48 +08:00